Abstract — Although cooperation generally increases the amount of resources available to a community of nodes, thus improving individual and collective performance, it also allows for the appearance of potential mistreatment problems through the exposition of one node’s resources to others. We study such concerns by considering a group of independent, rational, self-aware nodes that cooperate using on-line caching algorithms, where the exposed resource is the storage of each node. Motivated by content networking applications – including web caching, CDNs, and P2P – this paper extends our previous work on the off-line version of the problem, which was limited to object replication and was conducted under a game-theoretic framework. We identify and investigate two causes of mistreatment: (1) cache state interactions (due to the cooperative servicing of requests) and (2) the adoption of a common scheme for cache replacement/redirection/admission policies. Using analytic models, numerical solutions of these models, as well as simulation experiments, we show that online cooperation schemes using caching are fairly robust to mistreatment caused by state interactions. When this becomes possible, the interaction through the exchange of miss-streams has to be very intense, making it feasible for the mistreated nodes to detect and react to the exploitation. This robustness ceases to exist when nodes fetch and store objects in response to remote requests, i.e., when they operate as Level-2 caches (or proxies) for other nodes. Regarding mistreatment due to a common scheme, we show that this can easily take place when the “outlier ” characteristics of some of the nodes get overlooked. This finding underscores the importance of allowing cooperative caching nodes the flexibility of choosing from a diverse set of schemes to fit the peculiarities of individual nodes. To that end, we outline an emulation-based framework for the development of mistreatment-resilient distributed selfish caching schemes.

Service placement is a key problem in communication networks as it determines how efficiently the user service demands are supported. This problem has been traditionally approached through the formulation and resolution of large optimization problems requiring global knowledge and a continuous recalculation of the solution in case of network changes. Such approaches are not suitable for large-scale and dynamic network environments. In this paper, the problem of determining the optimal location of a service facility is revisited and addressed in a way that is both scalable and deals inherently with network dynamicity. In particular, service migration which enables service facilities to move between neighbor nodes towards more communication cost-effective positions, is based on local information. The migration policies proposed in this work are analytically shown to be capable of moving a service facility between neighbor nodes in a way that the cost of service provision is reduced and – under certain conditions – the service facility reaches the optimal (cost minimizing) location, and locks in there as long as the environment does not change; as network conditions change, the migration process is automatically resumed, thus, naturally responding to network dynamicity under certain conditions. The analytical findings of this work are also supported by simulation results that shed some additional light on the behavior and effectiveness of the proposed policies.

The Replica Placement Problem (RPP) aims at selecting the nodes for duplicating data objects in order to optimize their access. Even though a lot of work already exists on RPP, the issue of implementing the resulting allocation scheme is typically overlooked. In this paper we introduce the Replica Transfer Scheduling Problem (RTSP), briefly stated as: given a network of servers with limited storage capacity, a set of data objects and two replication schemes old new X and X, find a schedule of object transfers and new old deletions for implementing X based on X with minimum communication cost. Given that this problem is NP-complete, we introduce several different heuristics to solve it, and evaluate them via simulations.

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Abstract. We consider distributed collaborative caching groups where individual members are autonomous and self-aware. Such groups have been emerging in many new overlay and peer-to-peer applications. In a recent work of ours, we considered distributed caching protocols where group members (nodes) cooperate to satisfy requests for information objects either locally or remotely from the group, or otherwise from the origin server. In such setting, we identified the problem of a node being mistreated, i.e., its access cost for fetching information objects becoming worse with cooperation than without. We identified two causes of mistreatment: (1) the use of a common caching scheme which controls whether a node should not rely on other nodes in the group by keeping its own local copy of the object once retrieved from the group; and (2) the state interaction that can take place when the miss-request streams from other nodes in the group are allowed to affect the state of the local replacement algorithm. We also showed that both these issues can be

The distributed partitioning of autonomous, self-aware nodes into cooperative groups, within which scarce resources could be effectively shared for the benefit of the group, is increasingly emerging as a hallmark of many newly-proposed overlay and peer-topeer applications. Distributed caching protocols in which group members cooperate to satisfy local requests for objects is a canonical example of such applications. In recent work of ours we identified mistreatment as a potentially serious problem for nodes participating in such cooperative caching arrangements. Mistreatment materializes when a node’s access cost for fetching objects worsens as a result of cooperation. To that end, we outlined an emulation-based framework for the development of mistreatment-resilient distributed selfish caching schemes. Under this framework, a node opts to participate in the group only if its individual access cost is less than the one achieved while in isolation. In this paper, we argue against the use of such static “all or nothing ” approaches which force an individual node to either join or not join a cooperative group. Instead, we advocate the use of a smoother approach, whereby the level of cooperation is tied to the benefit that a node begets from joining a group. To that end, we propose a distributed and easily deployable feedback-control scheme which mitigates mistreatment. Under our proposed adaptive scheme, a node independently emulates its performance as if it were acting in a greedy local manner and then adapts its caching policy in the direction of reducing its measured access cost below its emulated greedy local cost. Using control-theoretic analysis, we show that our proposed scheme converges to the minimal access cost, and indeed outperforms any static scheme. We also show that our scheme results in insignificant degradation to the performance of the caching group under typical operating scenaria.

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