Neural Distributed Autoassociative Memories: A Survey

Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension. The purpose of this paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons). Scope. The survey is focused mainly on the networks of Hopfield, Willshaw and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections and networks with a bipartite graph structure for non-binary data with linear constraints. Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors.Comment: 31 page

Distributed Collaborative Monitoring in Software Defined Networks

We propose a Distributed and Collaborative Monitoring system, DCM, with the following properties. First, DCM allow switches to collaboratively achieve flow monitoring tasks and balance measurement load. Second, DCM is able to perform per-flow monitoring, by which different groups of flows are monitored using different actions. Third, DCM is a memory-efficient solution for switch data plane and guarantees system scalability. DCM uses a novel two-stage Bloom filters to represent monitoring rules using small memory space. It utilizes the centralized SDN control to install, update, and reconstruct the two-stage Bloom filters in the switch data plane. We study how DCM performs two representative monitoring tasks, namely flow size counting and packet sampling, and evaluate its performance. Experiments using real data center and ISP traffic data on real network topologies show that DCM achieves highest measurement accuracy among existing solutions given the same memory budget of switches

Scalable cooperative caching algorithm based on bloom filters

This thesis presents the design, implementation and evaluation of a novel cooperative caching algorithm based on the bloom filter data structure. The new algorithm uses a decentralized approach to resolve the problems that prevent the existing solutions from being scalable. The problems consist of an overloaded manager, a communication overhead among clients, and a memory overhead on the global cache. The new solution reduces the manager load and the communication overhead by distributing the global cache information among cooperating clients. Thus, the manager no longer maintains the global cache. Furthermore, the memory overhead is decreased due to a bloom filter data structure. The bloom filter saves memory space in the global cache and makes the new algorithm scalable. The correctness of the research hypothesis is verified by running experiments on the caching algorithms. The experiment results demonstrate that the new caching algorithm maintains a low block access time as existing algorithms. In addition, the new algorithm decreases the manager load by the factor of nine. Moreover, the communication overhead is reduced by nearly a factor of six as a result of distributing the global cache to clients. Finally, the results show a significant reduction in the memory overhead which also contributes to the scalability of the new algorithm

Algorithms and Architectures for Network Search Processors

The continuous growth in the Internet’s size, the amount of data traffic, and the complexity of processing this traffic gives rise to new challenges in building high-performance network devices. One of the most fundamental tasks performed by these devices is searching the network data for predefined keys. Address lookup, packet classification, and deep packet inspection are some of the operations which involve table lookups and searching. These operations are typically part of the packet forwarding mechanism, and can create a performance bottleneck. Therefore, fast and resource efficient algorithms are required. One of the most commonly used techniques for such searching operations is the Ternary Content Addressable Memory (TCAM). While TCAM can offer very fast search speeds, it is costly and consumes a large amount of power. Hence, designing cost-effective, power-efficient, and high-speed search techniques has received a great deal of attention in the research and industrial community. In this thesis, we propose a generic search technique based on Bloom filters. A Bloom filter is a randomized data structure used to represent a set of bit-strings compactly and support set membership queries. We demonstrate techniques to convert the search process into table lookups. The resulting table data structures are kept in the off-chip memory and their Bloom filter representations are kept in the on-chip memory. An item needs to be looked up in the off-chip table only when it is found in the on-chip Bloom filters. By filtering the off-chip memory accesses in this fashion, the search operations can be significantly accelerated. Our approach involves a unique combination of algorithmic and architectural techniques that outperform some of the current techniques in terms of cost-effectiveness, speed, and power-efficiency