Messor: Load-Balancing through a Swarm of Autonomous Agents

Montresor, Alberto; Meling, Hein; Babaoğlu, Özalp

doi:10.1007/3-540-45074-2_12

Cited by 99 publications

(88 citation statements)

References 12 publications

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“…As autonomous agents [17] have the capabilities required by autonomic elements, it is believed that multi-agent systems are a suitable technique for the implementation of autonomic behaviour [21]. Examples of such techniques already successfully applied in decentralized large-scale susyems include ant colony optimization in load balancing [10], particle swarm optimization in wireless networks [7], evolutionary computing in routing [5] and reinforcement learning (RL) in load balancing [4].…”

Section: Autonomic Systemsmentioning

confidence: 99%

Using Reinforcement Learning for Multi-policy Optimization in Decentralized Autonomic Systems – An Experimental Evaluation

Dusparić

Cahill

2009

Lecture Notes in Computer Science

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Abstract. Large-scale autonomic systems are required to self-optimize with respect to high-level policies, that can differ in terms of their priority, as well as their spatial and temporal scope. Decentralized multiagent systems represent one approach to implementing the required selfoptimization capabilities. However, the presence of multiple heterogeneous policies leads to heterogeneity of the agents that implement them. In this paper we evaluate the use of Reinforcement Learning techniques to support the self-optimization of heterogeneous agents towards multiple policies in decentralized systems. We evaluate these techniques in an Urban Traffic Control simulation and compare two approaches to supporting multiple policies. Our results suggest that approaches based on W-learning, which learn separately for each policy and then select between nominated actions based on current action importance, perform better than combining policies into a single learning process over a single state space. The results also indicate that explicitly supporting multiple policies simultaneously can improve waiting times over policies dedicated to optimizing for a single vehicle type.

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Section: Autonomic Systemsmentioning

confidence: 99%

Using Reinforcement Learning for Multi-policy Optimization in Decentralized Autonomic Systems – An Experimental Evaluation

Dusparić

Cahill

2009

Lecture Notes in Computer Science

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“…Other P2P balancing methods include the virtual-server approach originally used in Chord [13], [16], [37], the Messor system [31], proximity-aware balancing [42], cluster-based balancing [30], and several other dynamic algorithms [1], [18], [35], which provide alternative mechanisms for balancing P2P graphs and are orthogonal to our analysis. This paper is organized as follows.…”

Section: A Main Results and Paper Structurementioning

confidence: 99%

Load-Balancing Performance of Consistent Hashing: Asymptotic Analysis of Random Node Join

Wang

Loguinov

2007

IEEE/ACM Trans. Networking

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Abstract-Balancing of structured peer-to-peer graphs, including their zone sizes, has recently become an important topic of distributed hash table (DHT) research. To bring analytical understanding into the various peer-join mechanisms based on consistent hashing, we study how zone-balancing decisions made during the initial sampling of the peer space affect the resulting zone sizes and derive several asymptotic bounds for the maximum and minimum zone sizes that hold with high probability. Several of our results contradict those of prior work and shed new light on the theoretical performance limitations of consistent hashing. We use simulations to verify our models and compare the performance of the various methods using the example of recently proposed de Bruijn DHTs.Index Terms-Asymptotic bounds, balls-into-bins, consistent hashing, load balancing, peer-to-peer (P2P).

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“…Messor [9] uses adaptive system approach. It uses an algorithm that emulates the ant behavior to distribute workload among distributed nodes.…”

Section: Related Workmentioning

confidence: 99%

An Analysis Architecture for Communications in Multi-agent Systems

Gutiérrez¹

2013

IJIMAI

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-Evaluation tools are significant from the Agent Oriented Software Engineering (AOSE) point of view. Defective designs of communications in Multi-agent Systems (MAS) may overload one or several agents, causing a bullying effect on them. Bullying communications have avoidable consequences, as high response times and low quality of service (QoS). Architectures that perform evaluation functionality must include features to measure the bullying activity and QoS, but it is also recommendable that they have reusability and scalability features. Evaluation tools with these features can be applied to a wide range of MAS, while minimizing designer's effort. This work describes the design of an architecture for communication analysis, and its evolution to a modular version, that can be applied to different types of MAS. Experimentation of both versions shows differences between its executions.

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Messor: Load-Balancing through a Swarm of Autonomous Agents

Abstract: Abstract

Cited by 99 publications

References 12 publications

Using Reinforcement Learning for Multi-policy Optimization in Decentralized Autonomic Systems – An Experimental Evaluation

Using Reinforcement Learning for Multi-policy Optimization in Decentralized Autonomic Systems – An Experimental Evaluation

Load-Balancing Performance of Consistent Hashing: Asymptotic Analysis of Random Node Join

An Analysis Architecture for Communications in Multi-agent Systems

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