Adaptive load sharing in homogeneous distributed systems
IEEE Transactions on Software Engineering
A comparison of receiver-initiated and sender-initiated adaptive load sharing
Performance Evaluation
Real-time system design
Distributed operating systems
A N algorithm for mutual exclusion in decentralized systems
ACM Transactions on Computer Systems (TOCS)
Issues in the Design of Adaptive Middleware Load Balancing
OM '01 Proceedings of the 2001 ACM SIGPLAN workshop on Optimization of middleware and distributed systems
Efficient Task Migration Algorithm for Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
Methodical Analysis of Adaptive Load Sharing Algorithms
IEEE Transactions on Parallel and Distributed Systems
A taxonomy of scheduling in general-purpose distributed computing systems
IEEE Transactions on Software Engineering
Experimental Studies in Load Balancing
Euro-Par '98 Proceedings of the 4th International Euro-Par Conference on Parallel Processing
ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
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One of the fundamental issues to ensure maximal performance improvement in a cluster computing environment is load distribution, which is commonly achieved by using polling-based load distribution algorithms. Such algorithms suffer from two weaknesses: (1) Load information exchanged during a polling session is confined to the two negotiating nodes only. (2) Such algorithms are not scalable in that growth of the distributed system is accompanied with increasing amount of polling sessions.In this paper, we proposed a LD algorithm which is based on anti-tasks and load state vectors. Anti-tasks travel around the distributed system for pairing up task senders and receivers. As an anti-task travels, timed load information is collected and disseminated over the entire system via the load state vector bundled with the anti-task. Guided by load state vectors, anti-tasks are spontaneously directed towards processing nodes having high transient workload, thus allowing their surplus workload to be relocated soonest possible. No peer-to-peer negotiations between senders and receivers are needed.To reduce the network bandwidth consumption caused by the anti-task algorithm, the number of hosts that an anti-task needs to travel to must be carefully limited. The algorithm achieves this by employing the mathematical notion of Finite Projective Plane (FPP). By employing FPP, the number of nodes that each anti-task has to travel is at most $\sqrt{N}$, where N is the number of nodes in the system, without sacrifying the spread of load information.