Multi-agent Optimized Load Balancing Using Spanning Tree for Mobile Services

communication and computing tasks in the fields can be integrated and applied in a distributed system. However, those resources are heterogeneous and dynamic in nature, connecting a broad range of resources. This study proposed a hybrid load balancing policy to maintain performance and stability of distributed system in Mobile services. Proposed work suggests to opt the use of some of advanced and efficient technologies like Multiagent. In this proposed implementation two models are developed. The worker model is used to describe the workload and its current distribution within the system. The master model defines for a given algorithm at any instant of time and shows the formal context for obtaining and evaluating the load distribution decisions. Multiagent computing on a cluster of workstations is widely envisioned to be a powerful paradigm for building useful distributed applications. The Mobile agents of the system span across all the machines of a cluster. Just like the case of traditional distributed systems. With different characteristics between ordinary processes and agents, it is interesting and useful to investigate whether conventional load-balancing strategies are also applicable and sufficient to cope with the newly emerging needs, such as coping with temporally continuous agents, devising a performance metric for multi agent systems, and taking into account the vast amount of communication and interaction among agent. This work discusses the above issues with reference to agent properties and load balancing techniques and outlines the space of load-balancing design choices in the arena of multi agent computing. The proposed algorithm works by associating a credit value with each agent. The credit of an agent depends on its affinity to a machine, its current workload, its communication behavior, and mobility. When a load imbalance occurs, the credits of all agents are examined and an agent with a lower credit value is migrated to relatively lightly loaded machine in the system. Proposed work considers the problem of load balancing to minimize the cost of dynamic computations, including the cost of migrations. We propose the Ripple load balancing paradigm, the load balancing service presented is a generic tool for enhancing performance of accessing distributed objects from the WAP interface.

[1]  Mukesh Singhal,et al.  Advanced Concepts In Operating Systems , 1994 .

[2]  Philip S. Yu,et al.  Dynamic Load Balancing on Web-Server Systems , 1999, IEEE Internet Comput..

[3]  Marco Furini,et al.  International Journal of Computer and Applications , 2010 .

[4]  George Coulouris,et al.  Distributed systems - concepts and design , 1988 .

[5]  Ali R. Hurson,et al.  Scheduling and Load Balancing in Parallel and Distributed Systems , 1995 .

[6]  Khin Mar Lar Tun,et al.  A Framework of Using Mobile Agent to Achieve Efficient Load Balancing in Cluster , 2005, 6th Asia-Pacific Symposium on Information and Telecommunication Technologies.

[7]  Shishir Kumar,et al.  Dispatcher Based Dynamic Load Balancing on Web Server System , 2012 .

[8]  Luís E. T. Rodrigues,et al.  Using a fairness monitoring service to improve load-balancing in DSR , 2005, 25th IEEE International Conference on Distributed Computing Systems Workshops.

[9]  Wei Gao,et al.  Research on Load Balance of Multi Clusters Architecture Based on Business Components Partition , 2005, Third International Conference on Information Technology and Applications (ICITA'05).

[10]  Reinhard Riedl,et al.  Classification of load distribution algorithms , 1996, Proceedings of 4th Euromicro Workshop on Parallel and Distributed Processing.

[11]  R. Moller.  Distributed Operating Systems: Concepts And Design , 1998, IEEE Concurrency.

[12]  Wang Fang-xiong,et al.  RESEARCH ON A DISTRIBUTED ARCHITECTURE OF MOBILE GIS BASED ON WAP , 2004 .

[13]  T. Koch,et al.  Adaptive load balancing in a distributed environment , 1994, Proceedings of IEEE Workshop on Services for Distributed and Networked Environments.

[14]  Philip S. Yu,et al.  Redirection algorithms for load sharing in distributed Web-server systems , 1999, Proceedings. 19th IEEE International Conference on Distributed Computing Systems (Cat. No.99CB37003).

[15]  William H. Sanders,et al.  Application-Driven Coordination-Free Distributed Checkpointing , 2005, 25th IEEE International Conference on Distributed Computing Systems (ICDCS'05).

[16]  Yu-Kwong Kwok,et al.  On Load Balancing for Distributed Multiagent Computing , 2002, IEEE Trans. Parallel Distributed Syst..

[17]  L. Y. Tseng,et al.  The Anatomy Study of Load Balancing in Distributed System , 2006, 2006 Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT'06).

[18]  Xin Guo,et al.  Towards efficient resource on-demand in Grid Computing , 2003, OPSR.

[19]  Sundaram Suresh,et al.  Divisible load scheduling in distributed system with buffer constraints: genetic algorithm and linear programming approach , 2006, Int. J. Parallel Emergent Distributed Syst..

[20]  Biswanath Mukherjee,et al.  A Better Approach to Reliable Multi-Path Provisioning , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[21]  Li Xiao,et al.  Improving distributed workload performance by sharing both CPU and memory resources , 2000, Proceedings 20th IEEE International Conference on Distributed Computing Systems.

[22]  Dariusz R. Kowalski,et al.  Explicit Combinatorial Structures for Cooperative Distributed Algorithms , 2005, 25th IEEE International Conference on Distributed Computing Systems (ICDCS'05).

[23]  Guy Bernard,et al.  Client side reconfiguration on software components for load balancing , 2001, Proceedings 21st International Conference on Distributed Computing Systems Workshops.

[24]  Jacques Mossière,et al.  Works in progress: the 2nd International Middleware Doctoral Symposium , 2006, IEEE Distributed Systems Online.

[25]  Randy H. Katz,et al.  Load balancing and stability issues in algorithms for service composition , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[26]  Sami Tabbane,et al.  A Hybrid Algorithm to Reconfigure Platforms of Radio Mobile Services , 2005, The IEEE Conference on Local Computer Networks 30th Anniversary (LCN'05)l.

[27]  Qi Zhang,et al.  Load Unbalancing to Improve Performance under Autocorrelated Traffic , 2006, 26th IEEE International Conference on Distributed Computing Systems (ICDCS'06).

[28]  Eric Jul,et al.  A framework for evolutionary, dynamically updatable, component-based systems , 2004, 24th International Conference on Distributed Computing Systems Workshops, 2004. Proceedings..

[29]  Taieb Znati,et al.  Using Spanning-Trees for Balancing Dynamic Load on Multiprocessors , 1991, The Sixth Distributed Memory Computing Conference, 1991. Proceedings.

[30]  Jeff Kramer,et al.  Methodical Analysis of Adaptive Load Sharing Algorithms , 1992, IEEE Trans. Parallel Distributed Syst..

[31]  Chi-Chung Cheung,et al.  Dynamic DNS for load balancing , 2003, 23rd International Conference on Distributed Computing Systems Workshops, 2003. Proceedings..

[32]  Thomas L. Casavant,et al.  A Taxonomy of Scheduling in General-Purpose Distributed Computing Systems , 1988, IEEE Trans. Software Eng..

[33]  Pradeep Kumar Sinha.  Distributed operating systems - concepts and design , 1996 .

[34]  Debasish Ghose,et al.  Scheduling Divisible Loads in Parallel and Distributed Systems , 1996 .

[35]  Yi Zhou,et al.  Agent-Based Rating Oriented Information Provision and Reallocation for High-Assurance in Open and Dynamic Environments , 2004, ICDCS 2004.