Distributed Selfish Coaching

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

FutureGRID: A Program for long-term research into GRID systems architecture

Proceedings of the 2003 UK e-Science All Hands Meeting, 31st August - 3rd September, Nottingham UKThis is a project to carry out research into long-term GRID architecture, in the University of Cambridge Computer Laboratory and the Cambridge eScience Center, with support from the Microsoft Research Laboratory, Cambridge. It is part of a larger vision for future systems architectures for public computing platforms, including both scientitic GRID and commodity level computing such as games, peer2peer computing and storage services and so forth, based on work in the laboratories in recent years into massively scaleable distributed systems for storage, computation, content distribution and collaboration[26]

A Pragmatic Approach to DHT Adoption

Despite the peer-to-peer community's obvious wish to have its systems adopted, specific mechanisms to facilitate incremental adoption have not yet received the same level of attention as the many other practical concerns associated with these systems. This paper argues that ease of adoption should be elevated to a first-class concern and accordingly presents HOLD, a front-end to existing DHTs that is optimized for incremental adoption. Specifically, HOLD is backwards-compatible: it leverages DNS to provide a key-based routing service to existing Internet hosts without requiring them to install any software. This paper also presents applications that could benefit from HOLD as well as the trade-offs that accompany HOLD. Early implementation experience suggests that HOLD is practical

LERC: Coordinated Cache Management for Data-Parallel Systems

Memory caches are being aggressively used in today's data-parallel frameworks such as Spark, Tez and Storm. By caching input and intermediate data in memory, compute tasks can witness speedup by orders of magnitude. To maximize the chance of in-memory data access, existing cache algorithms, be it recency- or frequency-based, settle on cache hit ratio as the optimization objective. However, unlike the conventional belief, we show in this paper that simply pursuing a higher cache hit ratio of individual data blocks does not necessarily translate into faster task completion in data-parallel environments. A data-parallel task typically depends on multiple input data blocks. Unless all of these blocks are cached in memory, no speedup will result. To capture this all-or-nothing property, we propose a more relevant metric, called effective cache hit ratio. Specifically, a cache hit of a data block is said to be effective if it can speed up a compute task. In order to optimize the effective cache hit ratio, we propose the Least Effective Reference Count (LERC) policy that persists the dependent blocks of a compute task as a whole in memory. We have implemented the LERC policy as a memory manager in Spark and evaluated its performance through Amazon EC2 deployment. Evaluation results demonstrate that LERC helps speed up data-parallel jobs by up to 37% compared with the widely employed least-recently-used (LRU) policy

Enabling Social Applications via Decentralized Social Data Management

An unprecedented information wealth produced by online social networks, further augmented by location/collocation data, is currently fragmented across different proprietary services. Combined, it can accurately represent the social world and enable novel socially-aware applications. We present Prometheus, a socially-aware peer-to-peer service that collects social information from multiple sources into a multigraph managed in a decentralized fashion on user-contributed nodes, and exposes it through an interface implementing non-trivial social inferences while complying with user-defined access policies. Simulations and experiments on PlanetLab with emulated application workloads show the system exhibits good end-to-end response time, low communication overhead and resilience to malicious attacks.Comment: 27 pages, single ACM column, 9 figures, accepted in Special Issue of Foundations of Social Computing, ACM Transactions on Internet Technolog