As the Internet has become a more central aspect for information technology, so have concerns with supplying enough bandwidth and serving web requests to end users in an appropriate time frame. Web caching was introduced in the 1990s to help decrease network traffic, lessen user perceived lag, and reduce loads on origin servers by storing copies of web objects on servers closer to end users as opposed to forwarding all requests to the origin servers. Since web caches have limited space, web caches must effectively decide which objects are worth caching or replacing for other objects. This problem is known as cache replacement. We used neural networks to solve this problem and proposed the Neural Network Proxy Cache Replacement (NNPCR) method. The goal of this research is to implement NNPCR in a real environment like Squid proxy server. In order to do so, we propose an improved strategy of NNPCR referred to as NNPCR-2. We show how the improved model can be trained with up to twelve times more data and gain a 5–10% increase in Correct Classification Ratio (CCR) than NNPCR. We implemented NNPCR-2 in Squid proxy server and compared it with four other cache replacement strategies. In this paper, we use 84 times more data than NNPCR was tested against and present exhaustive test results for NNPCR-2 with different trace files and neural network structures. Our results demonstrate that NNPCR-2 made important, balanced decisions in relation to the hit rate and byte hit rate; the two performance metrics most commonly used to measure the performance of web proxy caches.

A neural network proxy cache replacement strategy and its implementation in the Squid proxy server

Web caching has been used extensively to enhance content delivery to the clients by minimizing client-observed latency, reducing network bandwidth usage and improving scalability of the network. Caching performance can be improved by designing good replacement policies, prefetching techniques, clustering of web users and proper placement of proxy caches in the network. In this paper, we discuss the various approaches that were designed based on neural networks, genetic algorithms and fuzzy logic to optimize the performance of web caching. The approaches discussed here proved to be more effective in solving the problems as compared to the conventional techniques that were used earlier in this problem domain. Neural networks and evolutionary algorithms can be considered for further exploration in the various issues related to web caching and content delivery.

A Survey on Applications of Neural Networks and Evolutionary Techniques in Web Caching

We presented swarm grammars as an extension of Lindenmayer systems. Instead of applying a single ('turtle') agent to convert linear strings into 3D structures, we use a swarm of agents "which navigate in 3D space and-as a side effect-place structural building blocks into their environment. The swarm grammars are used to specify how the setup of agent types changes over time. Additional agent parameters determine the agents' behaviors and their interaction dynamics. Both the grammar rules and the agent parameters are evolvable and can change over time-either automatically at replication and collision events among the agents, or triggered by external 'tinkering' from a supervising breeder. When swarm grammars are applied to concrete problems, constraints on the developmental processes as "well as on the emerging structures may provide the basis for an automatic evolutionary algorithm.

The evolution of swarm grammars- growing trees, crafting art, and bottom-up design

We recently introduced swarm grammars as an extension of Lindenmayer systems to model dynamic growth processes in 3D space through a large number of interacting (swarm) agents. Grammatical rewrite rules define different types of agents and their evolution over time. Sets of parameters determine specific interaction behaviors among the generated swarms. As we will show, swarm grammars lend themselves to creating an ecology of interacting entities and dynamic structures that are built by a multitude of agents. In addition to a rather traditional approach of evolving swarm grammars through interactive genetic programming, we explore new ways of designing ecologies of swarm agents by immersing the breeder into the growth and evolution processes. The system designer takes on the role of a 'tinkerer' or 'gardener', who is equipped with tools to influence and shape the on-going growth, evolutionary, and other dynamic processes within the swarm grammar ecology. Spatial genetic operators can be directed to specific locations within the evolving swarms. This enables the breeder to overview large numbers of phenotypic developmental processes and implicitly direct their evolution.

Genetic swarm grammar programming: Ecological breeding like a gardener

We consider the human body as a well-orchestrated system of interacting swarms. Utilizing swarm intelligence techniques, we present our latest virtual simulation and experimentation environment, IMMS:VIGO::3D, to explore key aspects of the human immune system. Immune system cells and related entities (viruses, bacteria, cytokines) are represented as virtual agents inside 3-dimensional, decentralized and compartmentalized environments that represent primary and secondary lymphoid organs as well as vascular and lymphatic vessels. Specific immune system responses emerge as by-products from collective interactions among the involved simulated ‘agents' and their environment. We demonstrate simulation results for clonal selection and primary and secondary collective responses after viral infection, as well as the key response patterns encountered during bacterial infection. We see this simulation environment as an essential step towards a hierarchical whole-body simulation of the immune system, both for educational and research purposes.

The swarming body: simulating the decentralized defenses of immunity

We demonstrate a 3-dimensional, agent-based model of the lambda-switch gene regulatory system, which simulates the interactions of proteins, promoters and operators during the phases of lysogenic and lytic growth in the phage lambda infected bacterium Escherichia coli. Following a decentralized approach, all regulatory mechanisms result from local interactions of the biomolecular agents within a simulated 3D cytoplasm. We show that our model displays the regulatory lambda switch behavior observed in E. coli, where proteins regulate their own production. Our lambda switch simulations provide a versatile testbed for studying gene regulation in silico, complementing in vitro experiments.

Swarms and Genes: Exploring &#955; - Switch Gene Regulation through Swarm Intelligence

This paper studies the Web proxy cache placement problem, in which m caching proxies are to be placed in a network so as to minimize the average response time for users accessing Web content. We compare an evolutionary approach to this network optimization problem with two classical approaches, namely dynamic programming and packet-level simulation. The results show that the evolutionary approach produces results as good as or better than the other approaches. Furthermore, the evolutionary approach is computationally faster, enabling the study of larger network scenarios than possible with the other approaches.

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C. Jacob

Papers

Swarms and Genes: Exploring λ - Switch Gene Regulation through Swarm Intelligence

An Evolutionary Approach to Optimal Web Proxy Cache Placement

C. Jacob

Papers

Swarms and Genes: Exploring &#955; - Switch Gene Regulation through Swarm Intelligence

An Evolutionary Approach to Optimal Web Proxy Cache Placement

Swarms and Genes: Exploring λ - Switch Gene Regulation through Swarm Intelligence