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Replication Strategies in Unstructured Peer-to-Peer Networks

Edith Cohen

AT&T Labs–Research 180 Park Avenue Florham Park, NJ 07932, USA

Scott Shenker

ICSI Berkeley, CA 94704 USA

edith@research.att.com ABSTRACT

The Peer-to-Peer (P2P) architectures that are most prevalent in today’s Internet are decentralized and unstructured. Search is blind in that it is independent of the query and is thus not more e?ective than probing randomly chosen peers. One technique to improve the e?ectiveness of blind search is to proactively replicate data. We evaluate and compare di?erent replication strategies and reveal interesting structure: Two very common but very di?erent replication strategies – uniform and proportional – yield the same average performance on successful queries, and are in fact worse than any replication strategy which lies between them. The optimal strategy lies between the two and can be achieved by simple distributed algorithms. These fundamental results o?er a new understanding of replication and show that currently deployed replication strategies are far from optimal and that optimal replication is attainable by protocols that resemble existing ones in simplicity and operation.

shenker@icsi.berkeley.edu

many other P2P systems that are continuing their meteoric growth. Despite their growing importance, the performance of P2P systems is not yet well understood. P2P systems were classi?ed by [9] into three di?erent categories. Some P2P systems, such as Napster [6], are centralized in that they have a central directory server to which users can submit queries (or searches). Other P2P systems are decentralized and have no central server; the hosts form an ad hoc network among themselves and send their queries to their peers. Of these decentralized designs, some are structured in that they have close coupling between the P2P network topology and the location of data; see [12, 10, 11, 15, 2] for a sampling of these designs. Other decentralized P2P systems, such as Gnutella [3] and FastTrack [13]based Morpheus [5] and KaZaA [4], are unstructured with no coupling between topology and data location. Each of these design styles – centralized, decentralized structured, and decentralized unstructured – have their advantages and disadvantages and it is not our intent to advocate for a particular choice among them. However, the decentralized unstructured systems are the most commonly used in today’s Internet. They also raise an important performance issue – how to replicate data in such systems – and that performance issue is the subject of this paper. In these decentralized unstructured P2P systems, the hosts form a P2P overlay network; each host has a set of “neighbors” that are chosen when it joins the network. A host sends its query (e.g., searching for a particular ?le) to other hosts in the network; Gnutella uses a ?ooding algorithm to propagate the query, but many other query-propagation approaches are possible. The fundamental point is that in these unstructured systems, because the P2P network topology is unrelated to the location of data, the set of nodes receiving a particular query is unrelated to the content of the query. A host doesn’t have any information about which other hosts may best be able to resolve the query. Thus, these “blind” search probes can not be on average more e?ective than probing random nodes. Indeed, simulations in [9] suggest that random probing is a reasonable model for search performance in these decentralized unstructured P2P systems. To improve system performance, one wants to minimize the number of hosts that have to be probed before the query is resolved. One way to do this is to replicate the data on several hosts.1 That is, either when the data is originally

1 For the basic questions we address, it does not matter if the actual data is replicated or if only pointers to the data

Categories and Subject Descriptors

C.2 [Communication Networks]; H.3 [Information Storage and Retrieval]; F.2 [Analysis of Algorithms]

General Terms

Algorithms

Keywords

replication; peer-to-peer; random search

1.

INTRODUCTION

Peer-to-peer (P2P) systems, almost unheard of three years ago, are now one of the most popular Internet applications and a very signi?cant source of Internet tra?c. While Napster’s recent legal troubles may lead to its demise, there are

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are replicated. The actual use depends on the architecture and is orthogonal to the scope of this paper. Thus, in the sequel, “copies” refers to actual copies or pointers. 2 Our analysis assumes adaptive termination mechanism, where the search is stopped once the query is resolved. See [7] for a discussion of a di?erent question related to replication in P2P networks without adaptive termination.

Later on in this section we explain how to extend the definitions, results, and metrics to the case where nodes have heterogeneous capacities.

3

?

?

stored or when it is the subject of a later search, the data can be proactively replicated on other hosts. Gnutella does not support proactive replication, but at least part of the current success of FastTrack-based P2P networks can be attributed to replication: FastTrack designates high-bandwidth nodes as search-hubs (super-nodes). Each supernode replicates the index of several other peers. As a result, each FastTrack search probe emulates several Gnutella probes and thus is much more e?ective. It is clear that blind search is more e?ective when a larger index can be viewed per probe, but even though FastTrack increased per-probe capacity, it basically uses the same replication strategy as Gnutella: The relative index capacity dedicated to each item is proportional to the number of peers that have copies. Thus, current approaches to replication are largely implicit. We consider the case where one designs an explicit replication strategy. The fundamental question we address in our paper is: given ?xed constraints on per-probe capacity, what is the optimal way to replicate data?2 We de?ne replication strategies that, given a query frequency distribution, specify for each item the number of copies made. The main metric we consider is the performance on successful queries, which we measure by the resulting expected (or average) search size A. We also consider performance on insoluble queries, which is captured by the maximum search size allowed by the system. Our analysis reveals several surprising fundamental results. We ?rst consider two natural but very di?erent replication strategies: Uniform and Proportional. The Uniform strategy, replicating everything equally, appears naive, whereas the Proportional strategy, where more popular items are more replicated, is designed to perform better. However, we show that the two replication strategies have the same expected search size on successful queries. Furthermore, we show that the Uniform and Proportional strategies constitute two extreme points of a large family of strategies which lie “between” the two and that any other strategy in this family has better expected search size. We then show that one of the strategies in this family, Square-root replication, minimizes the expected search size on successful queries. Proportional and Uniform replications, however, are not equivalent. Proportional makes popular items easier to ?nd and less popular items harder to ?nd. In particular, Proportional replication requires a much higher limit on the maximum search size while Uniform minimizes this limit and, thus, minimizes resources consumed on processing insoluble queries. We show that the maximum search size with Square-root strategy is in-between the two (though closer to Uniform), and de?ne a range of optimal strategies that balance the processing of soluble and insoluble queries. Interestingly, these optimal strategies lie “between” Uniform and Square-root and are “far” from Proportional. Last, we address how to implement these replication strategies in the distributed setting of an unstructured P2P network. It is easy to implement the Uniform and Propor-

tional replication strategies; for Uniform the system creates a ?xed number of copies when the item ?rst enters the system, for Proportional the system creates a ?xed number of copies every time the item is queried. What isn’t clear is how to implement the Square-root replication policy in a distributed fashion. We present several simple distributed algorithms which produce optimal replication. Surprisingly, one of these algorithms, path replication, is implemented, in a somewhat di?erent form, in the Freenet P2P system. These results are particularly intriguing given that Proportional replication is close to what is used by current P2P networks: With Gnutella there is no proactive replication; if we assume that all copies are the result of previous queries, then the number of copies is proportional to the query rate for that item. Even FastTrack, which uses proactive replication, replicates the complete index of each node at a search hub so the relative representation of each item remains the same. Our results suggest that Proportional, although intuitively appealing, is far from optimal and a simple distributed algorithm which is consistent in spirit with FastTrack replication is able to obtain optimal replication. In the next section we introduce our model and metrics and present a precise statement of the problem. In Section 3 we examine the Uniform and Proportional replication strategies. In Section 4 we show that Square-root replication minimizes the expected search size on soluble queries and provide some comparisons to Proportional and Uniform replication. Section 5 de?nes the optimal policy parameterized by varying cost of insoluble queries. In Section 6 we present distributed algorithms that yields Square-root replication and present simulation results of its performance.

2. MODEL AND PROBLEM STATEMENT

The network consists of n nodes, each with capacity ρ which is the number of copies/keys that the node can hold. 3 Let R = nρ denote the total capacity of the system. There are m distinct data items in the system. The normalized vector of query rates takes the form q = q1 ≥ q2 ≥ · · · ≥ qm with qi = 1. The query rate qi is the fraction of all queries that are issued for the ith item. An allocation is a mapping of items to the number of copies of that item (where we assume there is no more than one copy per node). We let ri denote the number of copies of the i’th item (ri counts all copies, including the original one), and let pi ≡ ri /R be the fraction of the total system m capacity allotted to item i: i=1 ri = R. The allocation is represented by the vector p = (r1 /R, r2 /R, . . . , rm /R). A replication or allocation strategy is a mapping from the query rate distribution q to the allocation p. We assume R ≥ m ≥ ρ because outside of this region the problem is either trivial (if m ≤ ρ the optimal allocation is to have copies of all items on all nodes) or insoluble (if m > R there is no allocation with all items having at least one copy). Our analysis is geared for large values of n (and R = ρn) with our results generally stated in terms of p and ρ with n factored out. Thus, we do not concern ourselves with integrality restrictions on the ri ’s. However, we do care about the bounds on the quantities pi . Since ri ≥ 1, we

The set of legal allocations P is a polyhedron de?ned by the (m?1)-dimensional simplex of distributions on m items, intersected with an m-dimensional hypercube, which de?nes upper and lower bounds on the number of copies of each item:

m

pi

?

=

i=1

≤ pi

The legal allocation that minimizes the expected search size is the solution to the optimization problem

m

?

Minimize

i=1

qi /pi such that p ∈ P .

One obvious property of the optimal solution is monotonicity u ≥ p 1 ≥ p2 ≥ · · · ≥ p m ≥ (4)

(If p is not monotone then consider two items i, j with qi > qj and pi < pj . The allocation with pi and pj swapped is legal, if p was legal, and has a lower ESS.) In the next section we explore the performance of two common replication strategies, Uniform and Proportional. However, we ?rst discuss re?nements of our basic model.

2.1 Bounded search size and insoluble queries

So far we have assumed that searches continue until the item is found, but we now discuss applying these results in a more realistic setting when searches are truncated when some maximal search size L is reached. We say an item is locatable if a search for it is successful with high probability. Evidently, an item is locatable if the fraction of nodes with a copy ρpi is su?ciently large with respect to L. Clearly, truncated searches have some likelihood of failing altogether. The probability of failure is at most 2?C if pi ≥ C/(ρL).

?

?

Aq (p) = 1/ρ(

qi /pi ) .

i

(1)

1 ≤u.

(2) (3)

2.2 Heterogeneous capacities and bandwidth

So far we consider a homogeneous setting, where all copies have the same size and all nodes have the same storage and the same likelihood of getting probed. In reality, hosts have di?erent capacities and bandwidth and, in fact, the most recent wave of unstructured P2P networks [5, 4] exploits this asymmetry. For simplicity of presentation we will keep using the homogeneous setting in the sequel, but we note that all out results can be generalized in a fairly straightforward way to heterogeneous systems. Suppose nodes have capacities ρi and visitation weight vi (in the homogeneous case vi = 1, in general vi is the factor in which the visitation rate di?ers from the average). It is not hard to see that the average capacity seen per probe is ρ = i vi ρi . The quantity ρ simply replaces ρ in Equation 1 and in particular, the ESS of all allocations are a?ected by the same factor; thus all our ?ndings on the relative performance of di?erent allocations also apply for heterogeneous capacities and visitation rates. An issue that arises when the replication is of copies (rather than pointers) is that items often have very di?erent sizes. Our subsequent analysis can be extended to this case by treating the ith item as a group of ci items with the same

?

1 have pi ≥ where = R . Since there is no reason to have more than one copy of an item on a single node, we have n ri ≤ n and so pi ≤ u where u = R = ρ?1 . Later in this section we will discuss reasons why these bounds may be made more strict. We argued in the introduction that performance of blind search is captured well by random probes. This abstraction allows us to evaluate performance without having to consider the speci?cs of the overlay structure. Simulations in [9] show that random probes are a reasonable model for several conceivable designs including Gnutella-like overlays. Specifically, the search mechanism we consider is random search: The search repeatedly draws a node uniformly at random and asks for a copy of the item; the search is stopped when the item is found. The search size is the number of nodes drawn until an answer is found. With the random search mechanism, search sizes are random variables drawn from a Geometric distribution with expectation ρ/pi . Thus, performance is determined by how many nodes have copies of any particular item. For a query distribution q and an allocation p, we de?ne the expected search size (ESS) Aq (p) to be the expected number of nodes one needs to visit until an answer to the query is found, averaged over all items. It is not hard to see that

Thus, if C = O(log L) then the failure probability would be polynomially small in L. In order to compare two allocations under truncated search, we set the relation between L and such that the same base set of items is locatable. We now argue that when all m items are locatable, Expression (1) constitutes a close approximation of the expected truncated search size. The distribution of search sizes under truncated random searches is a Geometric distribution with parameter pi , where values greater by L are truncated by L. Thus, Expression (1), which models untruncated search, constitutes an upper bound on the truncated expected search size. The error, however, is at most mini j≥1 (1 ? ρpi )L+j = mini (1 ? ρpi )L /(ρpi ) ≤ exp(?C)/(ρ ) = (L/C) exp(?C). In the sequel, we assume that is such that > ln L/(ρL) and use the untruncated approximation (1) for the expected search size (it is within an additive term of exp(? ρL/ ln L) < 1). Also note that the likelihood for an unsuccessful search for a locatable item is at most exp(? ρL) < 1/L. Thus, even though each locatable item can have some failed searches, these searches constitute a very small fraction of total searches for the item. The maximum search size parameter L is also important for analyzing the cost of insoluble queries; that is, queries made to items that are not locatable. In actual systems, some fraction of queries are insoluble, and search performed on such queries would continue until the maximum search size is exceeded. The cost of these queries is not captured by the ESS metric, but is proportional to L and to the fraction of queries that are insoluble. When comparing replication strategies on the same set of locatable items, we need to consider both the ESS, which captures performance on soluble queries and L, which captures performance on insoluble queries. In this situation we assume that the respective L(p) is the solution of mini pi = ln L/(ρL). Thus, if fs is the fraction of queries which are soluble and (1 ? fs ) is the fraction of insoluble queries, the performance of the allocation p is fs Aq (p) + (1 ? fs )L(p) . (5)

query rate, where ci is the size of item i. As a result we obtain altered de?nitions of the three basic allocations we consider: ? Proportional has pi = ci qi / query rate and size). ? Uniform has pi = ci / size).

? ?

Lemma 3.1. Proportional and Uniform allocations have the same expected search size A = m/ρ, which is independent of the query distribution. We note that Lemma 3.1 easily generalizes to all allocations that are a mix of Proportional and Uniform, in which each item i has pi ∈ {1/m, qi }. We next characterize the space of allocations.

j

cj qj (proportional to

j

cj (proportional to item’s

?

√ √ ? Square-root has pi = ci qi / j cj qj (proportional to size and to the square-root of the query rate).

3.2 Characterizing allocations

As a warmup, we consider the space of allocations for two items (m = 2). We shall see that Proportional and Uniform constitute two points in this space with anything “between” them achieving better performance, and anything “outside” having worse performance. Consider a pair of items with qi ≥ qj . The range of allocations is de?ned by a single parameter 0 < x < 1, with pi /(pi + pj ) = x and pj /(pi + pj ) = (1 ? x). Proportional corresponds to x = qi /(qi + qj ), and Uniform to x = 0.5. The range 0.5 ≤ x ≤ qi /(qi + qj ) captures allocations “between” Uniform and Proportional. The “outside” range 0 < x < 0.5 contains non-monotone allocations where the less-popular item obtains a larger allocation. The “outside” range 1 > x > qi /(qi + qj ) contains allocations where the relative allocation of the more popular item is larger than its relative query rate. These di?erent allocations are visualized in Figure 1(A), which plots pi /pj as a function of qi /qj . The ESS for these two items is proportional to qi /x + qj /(1 ? x). This function has equal value on x = 0.5 and x = qi /(qi + qj ) and is convex. The minimum is obtained at some middle point in the “between” range. By taking the ?rst derivative, equating it to zero, and solving the resulting quadratic equation, we obtain that the minimum is obtained √ √ √ at x = qi /( qi + qj ). Figure 1(B) shows the expected search size when using these allocations on two items (m = 2 and ρ = 1). In this case, the maximum gain factor by using the optimal allocation over Uniform or Proportional is 2. In the sequel we extend the observations made here to arbitrary number of items (m ≥ 2). In particular, we develop a notion of an allocation being “between” Uniform and Proportional and show that these allocations have better ESS, and that “outside” allocations have worse ESS. We also de?ne the policy that minimizes the ESS, and we bound the maximum gain as a function on m, u, and .

3.

ALLOCATION STRATEGIES

3.1 Uniform and Proportional

We now address the performance of two replication strategies. The Uniform replication strategy is where all items are equally replicated: Definition 3.1. Uniform allocation is de?ned when 1/m ≤ u and has pi = 1/m for all i = 1, . . . , m. ≤

This is a very primitive replication strategy, where all items are treated identically even though some items are more popular than others. One wouldn’t, initially, think that such a strategy would produce good results. When there are restriction on the search size (and thus on ), Uniform allocation has the property that it is de?ned for all q for which some legal allocation exists: Any allocation p other than Uniform must have i where pi > 1/m and j where pj < 1/m; If Uniform results in allocations outside the interval [ , u], then either 1/m > u (and thus pi > u) or 1/m < (and thus pj < ). An important appeal of the Uniform allocation is that it minimizes the required maximum search size, and thus, minimizes system resources spent on insoluble queries. It follows from Equation (5) that when a large fraction of queries are insoluble, Uniform is (close to) optimal. Another natural replication strategy is to have ri be proportional to the query rate. Definition 3.2. Proportional allocation is de?ned when ≤ qi ≤ u. The Proportional solution has pi = qi for all i. Some of the intuitive appeal of Proportional allocation is that it minimizes the maximum utilization rate [9]. This metric is relevant when the replication is of copies rather than of pointers; that is, when a successful probe is much more expensive to process than an unsuccessful probe. The utilization rate of a copy is the average rate of requests it serves. Under random search, all copies of the same item i have the same utilization rate qi /pi ; when there are more copies, each individual copy has a lower utilization. To avoid hot-spots, it is desirable to have low values for the maximum utilization rate of a copy maxi qi /pi . The average utilization over all copies, m pi qi /pi = 1, is independent of the alloi=1 cation p. The proportional allocation has qi /pi = 1 for all i, and so it clearly minimizes the maximal value maxi qi /pi . When compared to Uniform, Proportional improves the most common searches at the expense of the rare ones, which presumably would improve overall performance. We now analyze some of the properties of these two strategies. A straightforward calculation reveals the following surprising result:

?

3.3 Between Uniform and Proportional

For m = 2, we noticed that all allocations that lie “between” Uniform and Proportional have smaller ESS. For general m we ?rst de?ne a precise notion of being between Uniform and Proportional: Definition 3.3. An allocation p lies between Uniform and Proportional if for any pair of items i < j we have qi /qj ≥ pi /pj ≥ 1; that is, the ratio of allocations pi /pj is between 1 (“Uniform”) and the ratio of their query rates qi /qj (“Proportional”). Note that this family includes Uniform and Proportional and that all allocations in this family are monotone. We now establish that all allocations in this family other than Uniform and Proportional have a strictly better expected search size: Theorem 3.1. Consider an allocation p between Uniform and Proportional. Then p has an expected search size of at

Relative allocations for two items 1.2 worse ESS than Proportional/Uniform 1 0.8 p2/p1 0.6 0.4 0.2 0 0 0.2 0.4 q2/q1 (A) 0.6 0.8 1 Uniform Square-Root better ESS than Proportional/Uniform worse ESS than Proportional Proportional/Uniform 2 1.8 ESS 1.6 1.4 1.2 2.2

Expected Search Size for m=2; rho=1; q_2=1-q_1

Uniform/Proportional Square-Root 1 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 q1 (B)

Figure 1: (A) The space of allocations on two items (B) The average query cost for two items most m/ρ. Moreover, if p is di?erent from Uniform or Proportional then its expected search size is strictly less than m/ρ. Proof. The limits qi /qj ≥ pi /pj ≥ 1 hold if and only if they hold for any consecutive pair of items (that is, for j = i + 1). Thus, the set of “between” allocations is characterized by the m ? 1-dimensional polyhedron obtained by intersecting the m ? 1-dimensional simplex (the constraints pi = 1 pi > 0 de?ning the space of all allocations) with the additional constraints pi ≥ pi+1 and pi+1 ≤ pi qi+1 /qi . Observe that the expected search size function F (p1 , . . . , pm ) = m qi /pi is convex. Thus, its maximum i=1 value(s) must be obtained on vertices of this polyhedron. The vertices are allocations where for any 1 ≤ i < m, either pi = pj or pi = pj qi /qj . We refer to these allocations as “vertex allocations.” It remains to show that the maximum of the expected search size function over all vertex allocations is obtained on the Uniform or Proportional allocations. We show that if we are at a vertex other than Proportional or Uniform, we can get to Uniform or Proportional by a series of moves, where each move is to a vertex with a larger ESS than the one we are currently at. Consider a “vertex” allocation p which is di?erent than Proportional and Uniform. Let 1 < k < m be the minimum such that the ratios p2 /p1 , . . . , pk+1 /pk are not all 1 or not all equal to the respective ratios qi+1 /qi . Note that by de?nition we must have that qi+1 < qi for at least one i = 1, . . . , k ? 1; and qk+1 < qk . (Otherwise, if q1 = · · · = qk then any vertex allocation is such that p1 , . . . , pk+1 are consistent with Uniform or with Proportional; if qk = qk+1 then minimality of k is contradicted). There are now two possibilities for the structure of the (k + 1)-pre?x of p: 1. We have pi+1 /pi = 1 for i = 1, . . . , k?1 and pk+1 /pk = qk /qk+1 . 2. We have pi+1 /pi = qi+1 /qi for i = 1, . . . , k ? 1 and pk+1 /pk = 1. Claim: at least one of the two “candidate” vertices characterized by

? ?

? p : pi+1 /pi = 1 for i = 1, . . . , k and pi+1 /pi = pi+1 /pi for (i > k), or ? p : pi+1 /pi = qi+1 /qi for i = 1, . . . , k and pi+1 /pi = pi+1 /pi for (i > k). has strictly worse ESS than p. Note that this claim concludes the proof: We can iteratively apply this move, each time selecting a “worse” candidate. After each move, the pre?x of the allocation which is consistent with either Uniform or Proportional is longer. Thus, after at most m ? 1 such moves, the algorithm reaches the Uniform or the Proportional allocation. The proof of the claim is fairly technical and is deferred to the Appendix. We next consider the two families of “outside” allocations and show that they perform worse than Uniform and Proportional. Lemma 3.2. Allocations such that one of the following holds ? ?j, pj < pj+1 (less popular item gets larger allocation) or ? ?j, pj /pj+1 > qj /qj+1 (between any two items, the more popular item get more than its share according to query rates) perform strictly worse than Uniform and Proportional. Proof. The arguments here are simpler, but follow the lines of the proof of Theorem 3.1. We start with the ?rst family of “outside” allocations. Consider the intersection of the allocation simplex with the halfspace constraints pi ≤ pi+1 . These constraints constitute a cone with one vertex which is the Uniform allocation. The resulting polyhedron obtained from the intersection with the simplex has additional vertices, but note that all these vertices lie on the boundary of the simplex (and thus, the ESS function is in?nite on them). We now need to show that the minimum of the ESS function over this polyhedron is obtained on the Uniform allocation. Recall that the function is convex,

and its global minimum is obtained outside this polyhedron. Thus, the minimum over the polyhedron must be obtained on a vertex, but the only vertex with bounded ESS value is the Uniform allocation. Thus, the minimum must be obtained on the Uniform allocation. Similar arguments apply for the second family. We consider the halfspace constraints pi+1 ≥ pi qi+1 /qi . The resulting polyhedron includes the Proportional allocation as the only vertex with ?nite ESS value. The following observation will become useful when bounds are imposed on allocation sizes of items. Lemma 3.3. All allocations in the “between” family result in a narrower range of possible item allocation values than Proportional, that is, we have p1 ≤ q1 and pm ≥ qm . Proof. Consider such an allocation p. We have that m m pi ≥ (qi /q1 )p1 . Thus 1 ≥ i=1 pi ≥ ( i=1 qi )p1 /q1 = p1 /q1 . Hence, p1 ≤ q1 . Similarly pi ≤ (qi /qm )pm , and we obtain that pm ≥ qm .

and depends on the query distribution. An interesting question is the potential gain of applying Square-root rather than Uniform or Proportional allocations. We refer to the ratio of the ESS under Uniform/Proportional to the ESS under Square-root as the gain factor. We ?rst bound the gain factor by m, u, and (proof is in the Appendix): Lemma 4.2. Let ASR be the expected search size using Square-root allocation. Let Auniform be the expected search size using Proportional or Uniform allocation. Then Auniform /ASR ≤ m(u + ? m u) . Moreover, this is tight for some distributions. Note that if = 1/m or u = 1/m then the only legal allocation is 1/m on all items, and indeed the gain factor is 1. If 1/m, the gain factor is roughly mu.

1000

4.

THE SQUARE-ROOT ALLOCATION

ESS Gain Factor

We consider an allocation where for any two items, the ratio of allocations is the square root of the ratio of query rates. Note that this allocation lies “between” Uniform and Proportional in the sense of De?nition 3.3. We show that this allocation minimizes the ESS. Definition 4.1. Square-root allocation is de?ned when √ √ √ √ ≤ qi / i qi ≤ u and has pi = qi / i qi for all i. Lemma 4.1. Square-root allocation, when de?ned, minimizes the expected search size. Proof. The goal is to minimize m qi /pi . This natural i=1 simple optimization problem had arisen in di?erent contexts, e.g, the capacity assignment problem [8] and scheduling data broadcast [14]. We include a proof for the sake of completeness. m?1 Substituting pm = 1 ? i=1 pi we have

m?1

? ? ? ? ?

F (p1 , . . . , pm?1 ) =

i=1

qi /pi + qm /(1 ?

m?1 We are looking for the minimum of F when i=1 pi < 1 and pi > 0. The value of F approaches ∞ when we get closer to the boundary of the simplex. Thus, the minimum must be obtained at an interior point. By solving dF/dpi = 0 we obtain that m?1

? ? ?

pi = (1 ?

pj )

qi /qm = pm

qi /qm .

j=1

4.1 How much can we gain?

Recall that for all q, the expected search size under both Uniform and Proportional allocations is m/ρ. The expected search size under Square-Root allocation is

?

(

qi

1/2 2

) /ρ ,

?

?

?

w=0.5 w=1 w=1.5 w=2

100

10

1 1 10 100 1000 10000 100000 1e+06 Number of items (m)

Figure 2: The ratio of the ESS under Uniform/Proportional to the ESS under square root allocation, for truncated Zipf-like query distributions on m items. The query rate of the ith most popular item is proportional to i?w . In [9] we considered truncated Zipf-like query distributions, where there are m items and the query rate of the ith most popular item is proportional to i?w . A simple calculation (see Table 1) shows that the gain factor dramatically grows with the skew. In particular, the gain factor is a conw w<1 w=1 1<w<2 w=2 w>2 ρASR ≈

1?w (1?w/2)2

?

m?1

pi ) .

i=1

Gain factor m m2?w

(1?w/2)2 1?w 1 ln m 4 (1?w/2)2 w?1 2 (w/2?1)2 w?1

4m/ ln m ln m

w?1 (1?w/2)2 2 w?1 (w/2?1)2

mw?1 m

m/ ln m

Table 1: Asymptotic dependence on m of the gain factor and ESS under Square-root allocation for truncated m-items Zipf distribution with power w. stant fraction for w < 1, logarithmic in m for w = 1, and (fractional) polynomial in m for w > 1; for w > 2 the ESS

Furthermore, this allocation minimizes the ESS. The proof is deferred to the Appendix, and provides a simple iterative procedure to compute the Square-root? allocation for arbitrary qi ’s. The Square-root? allocation as a function of varies be√ √ tween ≤ qm / i qi (where Square-root? coincides with the Square-root allocation) and = 1/m (where Squareroot? coincides with the Uniform allocation). On intermediate values of , a su?x of the items is assigned the minimum allocation value , but the remaining items still have allocations proportional to the square-root of their query rate. √ √ In the range [ qm / i qi , 1/m], the ESS as a function of is a piecewise hyperbolic increasing function that is √ √ minimized at = qm / i qi . The breakpoints of this function correspond to a su?x of the items that have minimum allocation. Basic algebraic manipulations show that the breakpoints are n = sn /(1 ? m si + sn (m ? n + 1)), i=n √ √ where si = qi / j qj is the allocation of the ith item under Square-root allocation. Note that the extremes of this range are indeed m = sm (Square-root allocation) and 1 = 1/m (Uniform allocation). The ESS for ∈ [ n , n?1 ) is given by

i=n

?

qi / + (

?

qi /si )

i=1

si 1 ? l(m ? n + 1)

5.

SQUARE-ROOT? AND PROPORTIONAL? ALLOCATIONS

Suppose now that we ?x the set of locatable items and the bound on the maximum search size (that is, we ?x the lower bound on the allocation of any one item). Phrased di?erently, this is like ?xing the resources consumed on insoluble queries. With (or u) ?xed, Square-root allocation may not be de?ned - since the smallest (largest) allocation may be below (above) the bound. We now ask what is the replication strategy which minimizes the ESS under these constraints ? We de?ne a natural extension of Square-root, Squareroot? , which always results in a legal allocation (when one exists, that is, when ≤ 1/m ≤ u). Square-root? allocation lies between Square-root and Uniform and we show that Square-root? minimizes the ESS. Since Square-root? minimizes the ESS while ?xing the maximum search size, by sweeping we obtain a range of optimal strategies for any given ratio of soluble and insoluble queries. The extremes of these range are Uniform allocation, which is optimal if insoluble queries completely dominate and Square-root allocation which is optimal if there are relatively few insoluble queries.

and is increasing with . The maximum search size needed to support the allocation is approximately 1/ and is decreasing with . The overall search size (as de?ned in Equation 5) is a convex combination of the ESS and the MSS and is minimized inside the interval [sm , 1/m]. Figure 5 illustrates the combined expected search size for di?erent mixes of soluble and insoluble queries as a function of . When all queries are soluble (fs = 1), the minimum is obtained at = sm , where Square-root? coincides with Square-root (which minimizes the ESS). At the other extreme, when all queries are insoluble (fs = 0), the minimum is obtained at = 1/m, where Square-root? coincides with Uniform (which minimizes the MSS).

5.2

Proportional? allocation

Similarly, with (or u) ?xed, the Proportional allocation may not be de?ned. We similarly de?ne Proportional? allocation to be the legal allocation that minimizes the maximum utilization rate. Proportional? allocation is de?ned whenever there exists a legal allocation, that is, when ≤ 1/m ≤ u. We de?ne Proportional? allocation as the unique allocation de?ned by the following conditions: ? if u > pi , pj > then pi /pj = qi /qj .

5.1 Square-root allocation

Lemma 5.1. Consider a query distribution q where q1 ≥ · · · ≥ qm and ≤ 1/m ≤ u. There is a unique monotone allocation p ∈ P for which the following conditions apply.

?

?

1. if u > pi , pj >

then pi /pj =

(qi /qj ).

When we have ≤ qi ≤ u for all i then Proportional? is the same as Proportional. It follows from Theorem 3.1 that Proportional? allocation has ESS no higher than Uniform allocation and when Proportional? is di?erent than Proportional (that is, there are qi < or qi > u) then Proportional? has a lower ESS than Uniform. Proportional? allocation lies “between” Proportional and Uniform (in the sense of Theorem 3.3).

? if pj = ≤ pi or if pj ≤ u = pi , then pi /pj ≤ qi /qj .

?

m

n?1

?

?

?

?

?

?

under Square-root is constant and the gain factor is thus linear in m. Figure 2 plots the gain factor as a function of the number of items for representative values of the power parameter w. We next consider two “natural” query distribution obtained from Web proxy logs (the Boeing logs described in [1]). We looked at the top m Urls, with “query rates” proportional to the number of requests, and top m hostnames (Web sites), with “query rates” proportional to the number of users that issued a request to the site. Figure 3 shows the gain-factor and the ESS under Square-root allocation and Proportional allocation as a function of the number of items for these two distributions. For larger values of m, the ESS of Square-root allocation is 30%-50% of that of Proportional or Uniform allocations. Although these gaps are not as dramatic as for highly-skewed Zipf distributions, they are substantial. The Uniform, Proportional, and Square-root allocations result in di?erent values of pm (the allocation assigned to the locatable item with smallest query rate); in turn, this corresponds to di?erent required minimum values of the maximum search size, which determines the cost of insoluble queries. Figure 4 shows that for the hostname distribution, the maximum search size under Square-root is within a factor of 2 of the smallest possible (Uniform allocation) whereas Proportional requires a much larger maximum search size. In the next section we develop the optimal policy, which minimizes the combined resource consumption on soluble and insoluble queries.

2. if pj = ≤ pi or if pj ≤ u = pi , then pi /pj ≤ (qi /qj ).

n?1 i=1

30000 25000 20000 ESS 15000 10000 5000 0 0

Uniform,Proportional Square-root (hostname) Square-root (url)

3.5

hostname(number of users) url(number of requests)

3 Gain Factor 5000 10000 15000 20000 25000 30000

2.5

2

1.5

1 1 10 100 1000 10000 100000 1e+06 Number of Locatable Items Number of Locatable Items

(A)

(B)

Figure 3: (A) The ESS of the di?erent strategies versus the number of locatable items m (the x axis), for the hostname and Url distributions. (B) The gain factor as a function of m for the two distributions.

Max Search Size vs Number of Items (hostname dist.) 600000 500000 Maximum Search Size 400000 300000 200000 100000 0 0 5000 10000 15000 20000 25000 30000 Number of Locatable Items Proportional Square-root Uniform Maximum Search Size relative to Uniform Ratio of Max Search Size to Opt vs Number of Items (hostname dist.) 20 Proportional/Uniform 18 Square-root/Uniform 16 14 12 10 8 6 4 2 0 0 5000 10000 15000 20000 25000 30000 Number of Locatable Items

Figure 4: Maximum search size as a function of number of locatable items, and ratio of maximum search size to the minimum possible (Uniform allocation).

6.

DISTRIBUTED REPLICATION

and deleted: ? Creation: New copies can be created after each query. After a successful random search, the requesting node creates some number of copies, call it C, at randomly-selected nodes. This number C can only depend on quantities locally observable to the requesting node. ? Deletion: Copies do not remain in the system forever; they can be deleted through many di?erent mechanisms. All we assume here of copies is that their lifetimes are independent of the identity of the item and the survival probability of a copy is non-increasing with its age: if two copies were generated at times t1 and t2 > t1 then the second copy is more likely to still exist at time t3 > t2 . This creation and deletion processes are consistent with our search and replication model and with the operation of unstructured networks. In our model, and perhaps in reality, the network would perform copy creation by visiting

Our previous results identi?ed the optimal replication strategy. In this section we show that it can be achieved, via simple distributed protocols, in a decentralized unstructured P2P network. At least conceptually, The Uniform allocation can be obtained via a simple scheme which replicates each item in a ?xed number of locations when it ?rst enters the system. We had seen that the optimal allocation is a hybrid of Squareroot and Uniform allocations. We ?rst develop algorithms that obtain Square-root allocation, and then discuss how they can be slightly modi?ed to optimally balance the cost of soluble and insoluble queries. In order to consider replication algorithms4 aimed at queryrate-dependent allocations (like Proportional and Squareroot), we model a dynamic setting where copies are created

4 A note on terminology: a replication strategy is a mapping between q and p whereas a replication algorithm is a distributed algorithm that realizes the desired replication strategy.

2000 Search size over all queries 1800 1600 1400 1200 1000 800 600 0.0005

(all insoluble) MSS (fs=0%) 0.25*ESS+0.75*MSS (fs= 25%) 0.5*ESS+0.5*MSS (fs= 50%) 0.75*ESS+0.25*MSS (fs= 75%) (all soluble) ESS (fs=100%)

0.0006

0.0007

0.0008

0.0009

0.001

Minimum allocation (l)

We propose three algorithms that achieve the above property while obeying our general guidelines. The algorithms di?er in the amount and nature of bookkeeping, and so which algorithm is more desirable in practice will depend on the details of the deployment setting. The ?rst algorithm, path replication, uses no additional bookkeeping; the second, replication with sibling-number memory, records with each copy the value C (number of sibling copies) used in its creation; the third probe memory has every node record a summary on each item it sees a probe for. We show that under some reasonable conditions, sibling-number and probememory have Ci close to its desired value and path replication has Ci converge over time to its desired value.

Figure 5: The search size over all queries (soluble and insoluble), as a function of , when Squareroot? allocation is used (Hostname distribution and m=1000). The search size is shown for di?erent mixes of soluble and insoluble queries, The point that minimizes the search size depends on the mix. nodes in a similar fashion to how it performs search.5 Our copy deletion process is consistent with the expectation that in deployed networks, copy deletion occurs either by a node going o?ine or by some replacement procedure internal to a node. We expect node crashes to be unrelated to the content (part of the index) they possess. The replacement procedure within each node should not discriminate copies based on access patterns. Two common policies, Least Recently Used (LRU) or Least Frequently Used (LFU) are inconsistent with our assumption, but other natural policies such as First In First Out (FIFO), ?xed lifetime durations, or random deletions (when a new copy is created remove a cached copy selected at random), are consistent with it. Let Ci be the average value of C that is used for creating copies of item i ( Ci may change over time). We say that the system is in steady state when the lifetime distribution of copies does not change over time. We use the following property of this deletion process in our distributed replication algorithm: Claim 6.1. If the ratio Ci / Cj remains ?xed over time and Ci , Cj are bounded from below by some constant, then pi /pj → qi Ci /(qj Cj ) Thus, Proportional allocation is obtained if we use the same value of C for all items. A more challenging task is designing algorithms that result in Square-root allocation. The challenge in achieving this is that no individual node issues or sees enough queries to estimate the query rate qi , and we would like to determine C without using additional inter-host communication on top of what is required for the search and replication. We can use the above claim to obtain the following condition for Square-root allocation

5 This process automatically adjust to designs where search is performed on a fraction of nodes, or when nodes are not evenly utilized, such as with FastTrack, since hosts receive copies in the same rate that they receive search probes.

6.1 Path replication

At any given time, the expected search size for item i, Ai , is inversely proportional to the allocation pi . If Ci is steady then pi ∝ qi Ci and thus Ai ∝ 1/(qi Ci ). The Path replication algorithm sets the number of new copies C to be the size of the search (the number of nodes probed). At the ?xed point, when Ai and Ci are steady and equal, they √ √ are proportional to 1/ qi ; and pi is proportional to qi . We show that in steady state, under reasonable conditions, Ai and Ci converge to this ?xed point. Let Ai be the value of Ai at the ?xed point. At a given point in time, the rate of generating new copies is Ai /Ai times the optimal. This means that the rate is higher (respectively, lower) than at the ?xed point when the number of copies is lower (respectively, higher) than at the ?xed point. If the time between queries is at least of the order of the time between a search and subsequent copy generation then path replication will converge to its ?xed points. A possible disadvantage of path replication is that the current Ci “overshoots” or “undershoots” the ?xed point by a large factor (Ai /Ai ). Thus, if queries arrive in large bursts or if the time between search and subsequent copy generation is large compared to the query rate then the number of copies can ?uctuate from too-few to too-many and never reach the ?xed point. Note that this convergence issue may occur even for a large number of nodes. We next consider di?erent algorithms that circumvents this issue by using Ci that is close to this ?xed-point value.

6.2 Replication with sibling-number memory

Observe that the search size alone is not su?cient for estimating the query rate qi at any point in time (path replication only reached the appropriate estimates at the ?xed √ point). In order to have Ci ∝ 1/ qi we must use some additional bookkeeping. Under replication with sibling-number memory (SNM), with each new copy we record the number of “sibling copies” that were generated when it was generated, and its generation time. The algorithm assumes that each node known the historic lifetime distribution as a function of the age of the copy (thus, according to our assumptions, the “age” of the copy provides the sibling survival rate). Consider some past query for the item, aj , let dj > 0 be the number of sibling copies generated after aj and let λj be the expected fraction of surviving copies at the current time.

?

2200

√ Corollary 6.1. If Ci ∝ 1/ qi then pi /pj → qi /qj (the proportion factor may vary with time but should be the same for all items).

v∈V

v∈V v∈V

?

Since qi = ri /Ai , we can use ( to estimate qi . We thus can use

? ?

qi ∝ 1/(λc dc ) pi ∝ 1/(λc dc ) (1/Ai ) Hence, it su?ces to choose Ci with expected value Ci ∝ (1/ 1/(λc dc ) )0.5 A0.5 . i

v∈V

v∈V

6.3 Replication with probe memory

We now consider a scheme where each node records a summary of all “recent”6 probes it had received: For each item it had seen at least one probe for, the node records the total number of probes it received and the combined size of the searches which these probes where part of (in fact, it is su?cient to use the search size up to the point that the recording node is probed). Interpret qi as the rate per node in which queries for item i are generated. The probe rate of item i, which is the rate in which a host in the network receives search probes for item i, is then ri = qi Ai , where Ai = 1/(ρpi ) is the size of the expected search size for i. Thus, intuitively, the query rate qi can be obtained (estimated) from (estimates on) Ai and ri . In order to estimate ri , which could be very low, with reasonable con?dence, we can aggregate it across multiple nodes. A natural choice for the size for this group, that allows the replication strategy to be integrated with the search process, is to aggregate along nodes on the search path. Consider now the aggregated rate in which nodes on a search path for i received search probes for i. The aggregated rate for a group of nodes of size Ai is qi A2 = qi /(ρpi )2 . Observe i that for Square-root allocation, this aggregated rate is ?xed √ for all items and is equal to ( i qi )2 /ρ2 times the rate in which nodes generate queries. This suggests a simple replication algorithm: when searching for item i, count the total number of recent probes for i seen on nodes in the search path. The count being lower than the threshold suggests that the item is over-replicated (with respect to Square-root allocation). Otherwise, it is under-replicated. More generally, by carefully combining information from nodes on the search path, we can obtain high con?dence estimates on Ai and ri , that can lead to better estimates on qi . Let v be a node and let kv be the number of recent probes it had received for item i. Let sj (j = 1, . . . , kv ) be the search size of the jth probe seen by v for item i. Let Sv = kv sj ,7 j=1 Suppose that each node stores (kv , Sv ) for each item. Let V be a set of nodes (say, the nodes encountered on a search for item i.). We can estimate the probe rate by

6 We use the informal term “recent” for the a time duration in which the allocation pi did not signi?cantly change. Given this constraint, we would typically want the duration to be as long as possible. 7 Note that the expected value of sj is Ai /2, since it is a Geometrically distributed random variable, we expect Sv /kv to rapidly converge to Ai /2 as kv grows.

? ?

√ as a (biased) estimator for 1/ qi . The accuracy of these estimates grows with v∈V kv . We earlier argued that when the allocation is Square-root, the expectation of v∈V kv (when V are nodes on the search path for i) is ?xed across items. This implies that it is desirable to aggregate statistics on probes for an item over number of nodes that is proportional to the search size of the item. The con?dence level in the estimates can be increased by increasing the proportion factor. If an item is over-allocated with respect to Square root, then the probe rate over the search path is low, in which case the estimates are not as accurate, but this only implies that the item should be replicated at a higher rate. If an item is under-allocated, then the statistics collects more probes and the estimates obtained have even larger con?dence.

6.4 Obtaining the optimal allocation

The algorithms presented above converge to Square-root allocation, but now we discuss how they can be modi?ed so that we obtain the optimal allocation. For the purposes of the discussion, assume that we ?x the set of locatable items, that is, items for which we want queries to be soluble. The optimal algorithm is a hybrid of a Uniform and a Square-root algorithms: For each locatable item, the “Uniform” component of the replication algorithm assigns a number of “permanent” copies (e.g., by nominating nodes that never delete it and are replaced when they go o?ine). The “Square-root” component, which can be based on any of the speci?c algorithms proposed above, generates “transient copies” as a followup on searches. The maximum search size is set together with the number of permanent copies so that items with this minimum number of copies would be locatable. The value of these two dependent parameters is then tuned according to the mix of soluble and insoluble queries to obtain the optimal balance between the cost of soluble and insoluble queries.

6.5 Simulations

We argued that all the distributed replication algorithms discussed here do indeed achieve the Square-root allocation, though with di?erent rates of convergence and degrees of stability. We illustrate these convergence issued by simulating two of the proposed algorithms: path replication, and replication with sibling-number memory. The simulations track the fraction of nodes containing copies of a single item in a network with 10K nodes. In our simulations, copies had a ?xed lifetime duration and queries are issued in ?xed intervals. Each search process continues until k copies are found (k ∈ {1, 5}). The number of new copies generated following each search is the search size for path replication, and the estimators discussed above for sibling-number memory. Some of the simulations included delay between the time a search is performed and

?

?

2|V |

Sv /

?

?

?

Suppose that every copy stores (d, λ) as above. Let T be such that copies generated T time units ago have a positive probability of survival. Let PT be the set of copies with age at most T . Then, c∈PT 1/(λc dc ) is an unbiased estimator for the number of requests for the item in the past T time units. Denote by 1/(λc dc ) the expectation of 1/(dλ) over copies. We thus obtain that

?

ri = ?

?

?

v∈V

kv /|V |, and the expected search size by ? Ai = 2 Sv / kv . kv )2 /(2|V | kv Sv )

v∈V

7.

CONCLUSION

thus

k

?

ψ>

=

qk (1/p1 ? k) kp1 qk+1

We now express s in terms of p1 and ψ.

k

?

s = We have

qi /pi + ψ = kq1 /p1 + ψT /T

i=1

k

? ?

k

kp1 + T = and

pi + T = p 1

i=1

qi /q1 + T = 1

i=1

T /T = pk+1 /pk+1 = p1 (qk+1 /qk )/(p1 qk+1 /q1 ) = (q1 /qk )p1 /p1 . (14) From Equations (13) and (14) we obtain

k

?

8.

APPENDIX

1/p1 = and

Substituting (15) and (16) in (12) we obtain

k

? ?

s =k

i=1

qi ? k2 qk + kqk /p1 + ψ(1 ? kp1 + p1

k

?

Thus,

k

? ?

s = 1/p1 For s we obtain

k

qi + ψ

i=1

s ?s = (

k

?

i=1

qi ? kqk )(k ? 1/p1 ) + ψp1 (

The sum of allocations must be 1, thus kp1 + T = kp1 + T = 1 (7)

T /T = pk+1 /pk+1 = p1 (qk+1 /qk )/p1 .

(8)

qk (1/p1 ? k) kp1 qk+1

?

Since the relative allocations on the tail are the same for p, p and p we obtain

?

s = (1/p1 )

qi + ψ = (1/p1 )

i=1

qi + ψT /T

i=1

(6)

The second term is always nonnegative. The ?rst term is nonnegative if k > 1/p1 . If s ? s < 0 (p has a shorter ESS than p) we obtain ψ < qk (1/p1 ? k)/p1 . (17)

If both s < s and s < s we obtain from (11) and (17) that qi < ψ < qk (1/p1 ? k)/p1 .

?

Proof (Theorem 3.1). We conclude the proof by proving the remaining claim. We start with the ?rst case (p has the form 1). We use the shorthand T = m i=k+1 pi for the m total allocation to the “tail” items and ψ = i=k+1 qi /pi for the contribution of the tail items to the ESS. Similarly, we use T = m i=k+1 pi and similarly de?ne T , ψ , and ψ . Since the relative allocation to items in the “tail” is the same for the allocations p, p , and p , we have ψ = ψT /T and ψ = ψT /T . We now express the respective expected search sizes s, s , and s , as a function of p1 and ψ. For the allocation p we obtain:

? ? ?

i=1

qi /q1 ? kqk /q1 + qk /(q1 p1 )

k

T /T = 1 ? kp1 + p1

qi /qk .

i=1

k

qi /qk ) .

i=1

k

i=1

qi /qk ? k) .

?

(qk /qk+1 ? 1)(1/p1 ? k) p1 k(1 ? qk+1 /qk )

?

This paper asks a simple question: how should one replicate data in an unstructured peer-to-peer network? To answer this question, we formulated a simple model that allows for evaluating and comparing di?erent replication strategies while abstracting away the particular structure of the network and the speci?c query-propagation method used. We found that the two most obvious replication strategies, Uniform and Proportional, yielded identical performance on soluble queries and in fact are outperformed by all strategies that lie between them. We then identi?ed the optimal replication policy (which when restricted to soluble queries, is Square-root replication). As opposed to the Uniform and Proportional allocations, the Square-root allocation is inherently nonlinear and nonlocal. Somewhat to our surprise, we were able to ?nd simple distributed replication algorithms that achieve this allocation. The distributed algorithms we proposed all result in the optimal allocation, but they involve di?erent amounts of bookkeeping, rates of convergence, and degrees of stability. An important open issue is how these algorithms would function in more realistic settings.

s ? s = (qk /qk+1 ? 1)(1/p1 ? k)

i=1

If s ? s < 0 (p is strictly better than p) we obtain that

k i=1

?

the time the followup copies are generated. Figures 6 and 7. show how the fraction of nodes with copies evolves over time. The ?gures illustrate the convergence issues discussed above: The sibling-memory algorithm arrives more quickly to Square-root allocation and also is not sensitive to delayed creation of followup copies. In related work, [9] presents simulation results from the path replication algorithm (two di?erent variants thereof). These simulations included much more general load models, and true Gnutella-like search dynamics (but with random walks rather than ?ooding for query propagation).

From Equations (7) and (8) we obtain that 1/p1 = k + qk /(qk+1 p1 ) ? kqk /qk1 . and T /T = (qk+1 /qk )p1 /p1 = (1 ? p1 k(1 ? qk+1 /qk ))

k

(9)

(10)

By substituting Equations (9) and (10) in (6) we obtain s = (k+qk /(p1 qk+1 )?kqk /qk+1 ) qi +ψ(1?p1 k(1?qk+1 /qk ))

i=1

qi ? ψ(p1 k(1 ? qk+1 /qk ))

qi

qi (11)

(12)

(13)

(15)

(16)

Delay=0; Inter-request time=2; Rep=1 0.1 0.09 fraction with copy 0.08 0.07 0.06 0.05 0.04 0.03 100 150 200 250 300 350 400 450 500 550 600 time 1 sample Sibling-Mem Path-replication fraction with copy Delay=25; Inter-request time=2; Rep=1 0.7 0.6 fraction with copy 0.5 0.4 0.3 0.2 0.1 0 100 150 200 250 300 350 400 450 500 550 600 time 1 sample Sibling-Mem Path-replication fraction with copy

Delay=0; Inter-request time=2; Rep=5 0.11 Sibling-Mem 0.1 Path-replication 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 100 150 200 250 300 350 400 450 500 550 600 time 5 samples

Figure 6: Simulating performance of Path Replication and Sibling-Memory algorithms. In these simulations there is no delay in copy creation; the copy lifetime is 100 time units; and the inter-request-time is 2 time units

Delay=25; Inter-request time=2; Rep=5 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 100 150 200 250 300 350 400 450 500 550 600 time 5 samples Sibling-Mem Path-replication

Figure 7: Performance of Path Replication and Sibling-Memory replication algorithms. In these simulation there is delay of 25 time units in copy creation; the copy lifetime is 100 time units; and the inter-request time is 2.

k Thus, qk+1 < i=1 qi /k, which is a contradiction (recall that qk+1 ≤ qi when i < k + 1). Note that strict equality occurs only when s = s = s and is only possible when q1 = · · · = qk+1 , which contradicts our assumption that qk+1 /qk was inconsistent with previous relations. We now apply similar arguments to handle the second case (where p has the form 2). In this case we have

?

From Equations (19) and (20) we obtain

k

?

?1 (p1 )?1 = q1 /(p1 qk ) ? qk

qi + k

i=1

(21)

and

k

?

k

?

T /T = 1 ? (p1 /q1 )

qi + kp1 qk /q1

i=1

(22)

s=

qi /pi + ψ = kq1 /p1 + ψ .

i=1

(18)

Substituting (21) and (22) in (6) we obtain

s = qi ?

i=1

k

?

p1 /q1 We have

qi + T = kp1 + T = 1 .

i=1

(19)

Thus,

k i=1

?

k i=1

?

T /T = pk+1 /pk+1 = qk p1 /(q1 p1 )

(20)

qi ? k)

(q1 /p1 ?

?

?

?1 s ? s = (qk

qi ) ? ψp1 qk /qi

.

?

1?

?

?

?

?

The sum of allocations in p and p must satisfy

q1 p1 q k

k

1 ( q i )2 + ψ qk i=1

k

p1 qk ?1 (qk qi ? k) q1 i=1

k

If s ?s < 0 we obtain that (note that since qi are monotone k ?1 non-increasing and they are not all equal we have qk i=1 qi > k) ψ> q1 q1 ( ? p 1 qk p 1

?

k

qi ) .

i=1

(23)

Repeating the same steps for p we have T+ and pk+1 p1 qk /q1 p 1 qk T = = = T pk+1 p1 qk+1 /q1 p1 qk+1 From Equations (24) and (25) we obtain (p1 )?1 = and

?

either at u or at . 8 Solving x + (m ? x)u = 1 we obtain that x = (um ? 1)/(u ? ) items have allocation and the remaining m?x = (1?m )/(u? ) items have allocations u. Let q (respectively, qu ) be the query rate of items obtaining allocation (respectively, u). We have xq + (m ? x)qu = 1 and √ √ √ q /(x q + (m ? x) qu ) = . Substituting x and solving the above we obtain

2

i=1

T p1 (qk ? qk+1 ) =1+ T q1 qk+1

Substituting Equations (21) and (27) in (12) we get

s = qi +

i=1

i=1

thus, s ?s = (1? qk+1 p 1 qk kq1 )(k )+ψ qi ? qk p1 q1 qk+1 i=1

? ?

k

k

qi (1?

i=1

qk+1 ). qk

Hence, if s ? s < 0 we obtain (recall that qk+1 < qk and q thus (1 ? k+1 ) > 0.) q

k

? ? ?

ψ<

and, since ψ is nonnegative, we have ( q1 ? p1

?

k

Assume to the contrary that s < s and s < s. We obtain a contradiction from Equations (23),(28),(29). Proof (Lemma 4.2). The expected search size with Uniform allocation is m/ρ for any choice of q. The search size with optimal allocation is at most

m q| ≤ qi / qi ≤u

?

√ max √

?

1/ρ(

√ 2 qi ) .

?

There can be one item which lies in between these two limits (when x is not integral), but this adds a small term which we ignore.

8

?

√ qi over the We are now interested in the minimum of (m ? 1)-simplex intersected with the cube ≤ qi ≤ u. The function is concave and maximized at an interior point. Minima are obtained at vertices, which are de?ned by intersection of m?1 dimensional faces of the cube with the simplex. Algebraically, the function is minimized when allocations are

?

i=1

?

i=1

qi ) ≥ 0 .

?

kq1 qk+1 p 1 qk k qi i=1

q1 ? p1

k

qi

i=1

(28)

(29)

?

1+

?

?

?

k(qk ? qk+1 ) qk

k

kq1 qk+1 +ψ p1 q k

?

qk ? qk+1 q1 qk

qi +

i=1

qk+1 p 1 qk

(26)

k

qi

i=1

(27)

p1 (qk ? qk+1 ) q1 qk+1

k

qi

Proof (Lemma 5.1). Consider a monotone allocation p for which one of these conditions does not apply. A simple case analysis establishes that a legal allocation with smaller ESS can be obtained by “locally” reallocating pi + pj among pi and pj (increasing one and decreasing the other by the same amount). Thus, these conditions are necessary for an optimal allocation. We now focus on the set of legal allocations for which condition 1 hold. These allocations have a (possibly empty) pre?x of u’s and (possibly empty) su?x of ’s and the middle part is square-root allocated and has values between u and . As argued above, the optimal allocation must be of this form. We ?rst argue that there must be at least one allocation in this set by explicitly de?ning it: Let x = (1 ? )m/(u ? ) be the number of u’s and m ? x ? 1 be the number of ’s in the su?x. There is at most one item in the middle part, and it has allocation between u and . We next show that any allocation of this form for which condition 2 does not hold, has a neighbor allocation of that form with a lower ESS. By neighbor allocation we refer to one where the su?x or pre?x lengths di?er by at most one. To prove this, consider the case where pj = ≤ pi and pi / > qi /qj (the other case is similar). If this is true for any such j and i then it is true for j being the minimal for which pj = and i = j ? 1. If we add j to the “middle part” and recalculate square-root allocation then it is not hard to see that we get a better allocation with values between u and . If pj?1 = u (there was no middle part) we place both pj?1 and pj in a newly-created middle part. It remains to show that there is only one local minima to this process of moving to a better neighbor. Consider an allocation. Let j be the last index for which pj = u. Let k + 1 be the ?rst index for which pk+1 = . The optimality conditions imply that u qj+1 /qj ≤ pj+1 < u and qk /qk+1 ≥ pk > . We show that if there are two di?erent solutions that ful?ll the optimality conditions then we get a contradiction. Let j, k and j , k be the corresponding

?

k

?

?

p1 q1

k

qi ) = T +

p1 q1

k

qi ) = 1

i=1

?

(24)

q =

u+ ?m u u2 u+ ?m u

(25)

qu =

Thus, at the minimum point, √ √ √ √ qi = x q + (m ? x) qu = 1/ u + ? m u .

indexes and p, p the corresponding allocations. We ?rst consider the case where j = j and k < k . We have pk+1 , . . . , pk > = pk+1 , . . . , pk . As allocations sum to 1 we have pj < pj . We must have pj /pk+1 = qj /qk+1 . On qj /qk+1 . the other hand, we also must have pj /pk+1 ≤ Thus, pj /pk+1 ≥ pj /pk+1 , which is a contradiction. The claims for other cases are similar: If j < j < k < k we consider the items j and k . We have pj < u and pk > thus we must have pj /pk = qj /qk < u/ . On the other hand, the assumed optimality of the other allocation implies that qj /qk > u/ . Another case is j < j < k < k. We consider the total allocation to items j, . . . , k . If it is larger in p we get a contradiction with the allocation of pi (i < j). If it is larger we get a contradiction by looking at pi (i ≥ k + 1).

9.

REFERENCES

[1] Boeing proxy logs. http://www.web-caching.com/traces-logs.html. [2] Open Source Community. The free network project rewiring the internet. In http://freenet.sourceforge.net/, 2001. [3] Open Source Community. Gnutella. In http://gnutella.wego.com/, 2001. [4] KaZaA ?le sharing network. KaZaA. In http://www.kazaa.com/, 2002. [5] Morpheus ?le sharing system. Morpheus. In http://www.musiccity.com/, 2002. [6] Napster Inc. The napster homepage. In http://www.napster.com/, 2001.

[7] J. Kangasharju, K. W. Ross, and D. A. Turner. Optimal content replication in P2P communities. Manuscript, 2002. [8] L. Kleinrock. Queueing Systems, Volume II: Computer Applications. Wiley-Interscience, New York, 1976. [9] Q. Lv, P. Cao, E. Cohen, K. Li, and S. Shenker. Search and replication in unstructured peer-to-peer networks. In Proceedings of the 16th annual ACM International Conference on supercomputing, 2002. [10] S. Ratnassamy, P. Francis, M. Handley, R. Karp, and S. Shenker. A scalable content-addressable network. In Proceedings of the ACM SIGCOMM’01 Conference, 2001. [11] A. Rowstron and P. Druschel. Storage management and caching in past, a large-scale, persistent peer-to-peer storage utility. In Proceedings of SOSP’01, 2001. [12] I. Stoica, R. Morris, D. Karger, M. F. Kaashoek, and H. Balakrishnana. Chord: A scalable peer-to-peer lookup service for internet applications. In Proceedings of the ACM SIGCOMM’01 Conference, 2001. [13] FastTrack Peer-to-Peer technology company. FastTrack. In http://www.fasttrack.nu/, 2001. [14] N. H. Vaidya and S. Hameed. Scheduling data broadcast in asymmetric communication environments. ACM/Baltzer Wireless Networks, 5, 1999. [15] B. Y. Zhao, J. Kubiatowicz, and A. Joseph. Tapestry: An infrastructure for fault-tolerant wide-area location and routing. Technical Report UCB/CSD-01-1141, University of California at Berkeley, Computer Science Department, 2001.

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