26 research outputs found

    Distributed Selfish Coaching

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    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 at each node. Motivated by content networking applications -- including web caching, CDNs, and P2P -- this paper extends our previous work on the on-line version of the problem, which was conducted under a game-theoretic framework, and limited to object replication. 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 management policies. Using analytic models, numerical solutions of these models, as well as simulation experiments, we show that on-line cooperation schemes using caching are fairly robust to mistreatment caused by state interactions. To appear in a substantial manner, 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 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. Our framework utilizes a simple control-theoretic approach to dynamically parameterize the cache management scheme. We show performance evaluation results that quantify the benefits from instantiating such a framework, which could be substantial under skewed demand profiles.National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 0202067); EU IST (CASCADAS and E-NEXT); Marie Curie Outgoing International Fellowship of the EU (MOIF-CT-2005-007230

    Optimal Data Placement on Networks With Constant Number of Clients

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    We introduce optimal algorithms for the problems of data placement (DP) and page placement (PP) in networks with a constant number of clients each of which has limited storage availability and issues requests for data objects. The objective for both problems is to efficiently utilize each client's storage (deciding where to place replicas of objects) so that the total incurred access and installation cost over all clients is minimized. In the PP problem an extra constraint on the maximum number of clients served by a single client must be satisfied. Our algorithms solve both problems optimally when all objects have uniform lengths. When objects lengths are non-uniform we also find the optimal solution, albeit a small, asymptotically tight violation of each client's storage size by Ï”\epsilonlmax where lmax is the maximum length of the objects and Ï”\epsilon some arbitrarily small positive constant. We make no assumption on the underlying topology of the network (metric, ultrametric etc.), thus obtaining the first non-trivial results for non-metric data placement problems

    Managing the Future Internet through Intelligent In-Network Substrates

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    The current Internet has been founded on the architectural premise of a simple network service used to interconnect relatively intelligent end systems. While this simplicity allowed it to reach an impressive scale, the predictive manner in which ISP networks are currently planned and configured through external management systems and the uniform treatment of all traffic are hampering its use as a unifying multi-service network. The future Internet will need to be more intelligent and adaptive, optimizing continuously the use of its resources and recovering from transient problems, faults and attacks without any impact on the demanding services and applications running over it. This article describes an architecture that allows intelligence to be introduced within the network to support sophisticated self-management functionality in a coordinated and controllable manner. The presented approach, based on intelligent substrates, can potentially make the Internet more adaptable, agile, sustainable, and dependable given the requirements of emerging services with highly demanding traffic and rapidly changing locations. We discuss how the proposed framework can be applied to three representative emerging scenarios: dynamic traffic engineering (load balancing across multiple paths); energy efficiency in ISP network infrastructures; and cache management in content-centric networks

    A Feedback Control Approach to Mitigating Mistreatment

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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 addressed by introducing two simple additional parameters that affect the caching behavior (the reliance and the interaction parameters). In this paper, we argue against a static rule-of-thumb policy of setting these parameters since the performance, in terms of average object access cost, depends on a multitude of system parameters (namely, group size, cache sizes, demand skewness, and distances). We then propose a feedback control approach to mitigating mistreatment in distributed caching groups. In our approach, a node independently emulates its performance as if it were acting selfishly and then adapts its reliance and interaction parameters in the direction of reducing its measured access cost below its emulated selfish cost. To ensure good convergence and stability properties, we use a (Proportional-Integral-Differential) PID-style controller. Our simulation results show that our controller adapts to the minimal access cost and outperforms static-parameter schemes
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