Grid load balancing using intelligent agents

Abstract

Abstract. Workload and resource management are essential functionalities in the software infrastructure for grid computing. The management and scheduling of dynamic grid resources in a scalable way requires new technologies to implement a next generation intelligent grid environment. This work demonstrates that AI technologies can be utilised to achieve effective workload and resource management. A combination of intelligent agents and multi-agent approaches is applied for both local grid resource scheduling and global grid load balancing. Each agent is a representative of a local grid resource and utilises predictive application performance data and iterative heuristic algorithms to engineer local load balancing across multiple hosts. At a higher level of the global grid, agents cooperate with each other to balance workload using a peer-to-peer service advertisement and discovery mechanism. A case study is included with corresponding experimental results to demonstrate that intelligent agents are effective to achieve resource scheduling and load balancing, improve application execution performance and maximise resource utilisation.

Self-Healing Distributed Scheduling Platform

“…This compromises the robustness of the system by introducing a bottleneck as well as a single point of failure for the entire scheduling mechanism. Cao et al proposed a load balancing method for MAS aimed at Grids [18]. Although their system implements scheduling algorithms based on artificial intelligence (AI) techniques, it lacks support both for overcoming security issues when multiple providers are used and for a customizable negotiation module.…”

Resource allocation in decentralised computational systems: an evolutionary market-based approach

“…In the field of grid computing, examples include the hierarchical agent-based approach found in Cao et al [9], and TURBO [1]. In the latter, allocations are achieved through the reliance on altruistic behaviour between cooperating peers, which collaborate in order to reach a global objective.…”

Increasing efficiency in Grid by using glow worm Algorithm

“…Whole process that has been repeated several times is called propagation or reproduction [2]. The advantages of computing Grid are as the following: sources, parallel computing ability, creating source and virtual organization, using source, reliability, management and more source availability [3]. Since artificial intelligence algorithms are efficient in optimization, they have been considered as good options for solving the problems such as load balance in distributed system.…”