Optimal static load balancing in distributed computer systems
Journal of the ACM (JACM)
Adaptive load sharing in homogeneous distributed systems
IEEE Transactions on Software Engineering
Using stochastic learning automata for job scheduling in distributed processing systems
Journal of Parallel and Distributed Computing
A Trace-Driven Simulation Study of Dynamic Load Balancing
IEEE Transactions on Software Engineering
Learning automata: an introduction
Learning automata: an introduction
Load Sharing in Distributed Real-Time Systems with State-Change Broadcasts
IEEE Transactions on Computers
Analysis of the Effects of Delays on Load Sharing
IEEE Transactions on Computers
Optimal load balancing and scheduling in a distributed computer system
Journal of the ACM (JACM)
Load balancing with network partitioning using host groups
Parallel Computing
A taxonomy of scheduling in general-purpose distributed computing systems
IEEE Transactions on Software Engineering
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Each job scheduler in large decentralized load balancing systems generally must consider whether it is advantageous to offload jobs to remote computation servers when the local load is too high. Although processing power may appear to be available at a very distant server, two problems arise due to the transmission delay between the scheduler and server. Predictably, the response time of the job is adversely affected as the job spends valuable time in transit, but a more subtle problem involves the value, or reliability, of the state information regarding job queues. The longer the delay between scheduler and server, the less a scheduler should value the state information of the server (given that the state changes over time). We examine the performance of schedulers in topologies with different average proximity and show a probabilistic algorithm that allows schedulers to dynamically form efficient clusters in the network.