Abstract
This study proposes a novel load-balancing algorith m for managing virtual servers in a Peer-To-Peer (P 2P) system through mobile agents. The proposed algorith m is implemented in a fully decentralized manner fo r a structured P2P system. It uses mobile agents and is independent of the geometry of the P2P auxiliary networks. The load-balancing algorithm effectively reduces the load imbalance of the system using the load per unit capacity derived by the mobile agents. A u nique feature of the proposed algorithm is the mutu al swapping of virtual servers between overloaded and underloaded peers to efficiently use the available resources. The proposed solution has been verified in a P2P environment consisting of peers and embedded glassfish server instances, created dynamically to act as virtual servers.
Highlights
Load balancing is an important factor to consider when optimizing productivity in Peer-to-Peer (P2P) computing
The load per unit capacity (Lpc) of the system is defined as the sum of loads of all virtual servers divided by the sum of the capacities of all the peers
This Lpc is shared between Mobile Agents (MAs).Sum of loads of all virtual servers and capacities of peers which are shared among mobile agents are organized in the following manner: struct load_cap { double sum_load_vs,sum_cap; int ma_id; } mobile_agents[N]; Every MA maintains this and calculates the load per unit capacity after the information is globally shared
Summary
The system, the load imbalance factor is not effectively minimized. Sometimes an overloaded peer cannot. We propose a novel load balancing algorithm that exchanges virtual servers between overloaded and underloaded peers. This primarily helps to reduce the load imbalance factor of both overloaded and underloaded peers. Mobile Agents (MAs) are used to find the load per unit capacity and help overloaded peers to find underloaded peers to which to migrate their virtual servers Using this method, the load imbalance of the whole system is significantly reduced. It discusses the pros and cons of the proposed algorithm.
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