A Distributed Drafting Algorithm for Load Balancing
IEEE Transactions on Software Engineering
Adaptive load sharing in homogeneous distributed systems
IEEE Transactions on Software Engineering
Distributed Scheduling of Tasks with Deadlines and Resource Requirements
IEEE Transactions on Computers
Analysis of the Effects of Delays on Load Sharing
IEEE Transactions on Computers
A Performance Study of Robust Distributed Load Sharing Strategies
IEEE Transactions on Parallel and Distributed Systems
A uniform framework for dynamic load balancing strategies in distributed processing systems
Journal of Parallel and Distributed Computing
Dynamic load balancing of data parallel applications on a distributed network
ICS '95 Proceedings of the 9th international conference on Supercomputing
Prediction-Based Dynamic Load-Sharing Heuristics
IEEE Transactions on Parallel and Distributed Systems
Analytic Models of Adaptive Load Sharing Schemes in Distributed Real-Time Systems
IEEE Transactions on Parallel and Distributed Systems
Design and Evaluation of Effective Load Sharing in Distributed Real-Time Systems
IEEE Transactions on Parallel and Distributed Systems
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The location policy in distributed load balancing schemes locates the destination nodes to or from which tasks will be transferred. It should evenly distribute workload to the entire nodes with minimal delay for transferring task. The traditional policies can be classified into dynamic selection, random selection, and state polling. However, the policies representatively cause unpredictable state, excessive task transfers, and useless polling problems, respectively. An efficient adaptive location policy is required in the sense that it can react to changes in system state and achieve high performance. We propose on advanced state polling policy based on predictable system state information. The system state information is composed of the state information collected at run time and the predefined static information that is a global priority order of each node for transferring tasks. The global priority order is generated by the global priority network. When load balancing is triggered at a heavily loaded node, the proposed location policy dynamically predicts lightly loaded nodes and other heavily loaded ones by exploiting predictable state information. Then it adaptively finds a good lightly loaded node that minimizes useless polling and maximizes even load distribution. An analytic model is developed to compare the presented policy with other well known policies. The validity of the model is checked with an event driven simulation, and it is shown that the proposed policy exhibits a significant performance improvement over other policies.