Bloom's Filters : Their Types and Analysis

Bloom filtrelerini ve çeşitlerini inceleyen bir çalışmanın özetidir. Bloom filtresi sorgulama üyeliklerini desteklemek amacıyla setleri temsil eden rasgele bir veri yapısıdır. 1970’lerde daha çok veri tabanı optimizasyonlarında kullanılmıştır. Bu yakınlarda bilgisayar ağları ile ilgili çalışma yapanlar daha sık kullanmaya başlamıştır. Bu çalışmada filtrelerin çeşitleri analiz edilecektir.In this paper we discuss Bloom filter in its original form and the varieties of its extensions. A Bloom filter is a randomized data-structure for concisely representing a set in order to support approximate membership queries. Although it was devised in 1970 for the purpose of spell checking, it was seldom used except in database optimization. In recent years, it has been rediscovered by the networking community, and has become a key component in many networking systems applications. In this paper, we will examine and analyse the different types of this filter

Bloom's Filters : Their Types and Analysis

Bloom filtrelerini ve çeşitlerini inceleyen bir çalışmanın özetidir. Bloom filtresi sorgulama üyeliklerini desteklemek amacıyla setleri temsil eden rasgele bir veri yapısıdır. 1970’lerde daha çok veri tabanı optimizasyonlarında kullanılmıştır. Bu yakınlarda bilgisayar ağları ile ilgili çalışma yapanlar daha sık kullanmaya başlamıştır. Bu çalışmada filtrelerin çeşitleri analiz edilecektir.In this paper we discuss Bloom filter in its original form and the varieties of its extensions. A Bloom filter is a randomized data-structure for concisely representing a set in order to support approximate membership queries. Although it was devised in 1970 for the purpose of spell checking, it was seldom used except in database optimization. In recent years, it has been rediscovered by the networking community, and has become a key component in many networking systems applications. In this paper, we will examine and analyse the different types of this filter

Adaptive Bloom filter

A Bloom filter is a simple randomized data structure that answers membership query with no false negative and a small false positive probability. It is an elegant data compression technique for membership information, and has broad applications. In this paper, we generalize the traditional Bloom filter to Adaptive Bloom Filter, which incorporates the information on the query frequencies and the membership likelihood of the elements into its optimal design. It has been widely observed that in many applications, some popular elements are queried much more often than the others. The traditional Bloom filter for data sets with irregular query patterns and non-uniform membership likelihood can be further optimized. We derive the optimal configuration of the Bloom filter with query-frequency and membership-likelihood information, and show that the adapted Bloom filter always outperforms the traditional Bloom filter. Under reasonable frequency models such as the step distribution or the Zipf's distribution, the improvement of the false positive probability of the adaptive Bloom filter over that of the traditional Bloom filter is usually of orders of magnitude

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

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

Dynamic organization schemes for cooperative proxy caching

In a generic cooperative caching architecture, web proxies form a mesh network. When a proxy cannot satisfy a request, it forwards the request to the other nodes of the mesh. Since a local cache cannot fulfill the majority of the arriving requests (typical values of the local hit ratio are about 30-50%), the volume of queries diverted to neighboring nodes can substantially grow and may consume considerable amount of system resources. A proxy does not need to cooperate with every node of the mesh due to the following reasons: (i) the traffic characteristics may be highly diverse; (ii) the contents of some nodes may extensively overlap; (iii) the inter-node distance might be too large. Furthermore, organizing N proxies in a mesh topology introduces scalability problems, since the number of queries is of the order of N/sup 2/. Therefore, restricting the number of neighbors for each proxy to k < N - 1 will likely lead to a balanced trade-off between query overhead and hit ratio, provided cooperation is done among useful neighbors. For a number of reasons the selection of useful neighbors is not efficient. An obvious reason is that web access patterns change dynamically. Furthermore, availability of proxies is not always globally known. This paper proposes a set of algorithms that enable proxies to independently explore the network and choose the k most beneficial (according to local criteria) neighbors in a dynamic fashion. The simulation experiments illustrate that the proposed dynamic neighbor reconfiguration schemes significantly reduce the overhead incurred by the mesh topology while yielding higher hit ratios compared to the static approach.published_or_final_versio