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
Task Allocation and Precedence Relations for Distributed Real-Time Systems
IEEE Transactions on Computers
A Trace-Driven Simulation Study of Dynamic Load Balancing
IEEE Transactions on Software Engineering
Heuristic Algorithms for Task Assignment in Distributed Systems
IEEE Transactions on Computers
Adaptive load sharing in heterogeneous distributed systems
Journal of Parallel and Distributed Computing
IEEE Transactions on Computers
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
Multiprocessor Scheduling with the Aid of Network Flow Algorithms
IEEE Transactions on Software Engineering
Optimal Load Balancing in a Multiple Processor System with Many Job Classes
IEEE Transactions on Software Engineering
Autonomic microcell assignment in massively distributed online virtual environments
Journal of Network and Computer Applications
Autonomic service hosting for large-scale distributed MOVE-services
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Hybrid parallel task placement in X10
Proceedings of the third ACM SIGPLAN X10 Workshop
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A load distribution (LD) algorithm achieves better system performance by smoothing out any workload imbalance that may exist in a distributed system. This is done by relocating application tasks from busy nodes to lightly loaded (or idle) nodes. Previous studies on LD algorithms allow only one single task to be transferred for each sender-receiver negotiation session. While this approach is effective for a homogeneous system, it is too conservative to be applied to a heterogeneous system where nodes may have drastically different processing speeds. In this paper, we propose a class of LD algorithms that allow a batch of tasks to be transferred during each negotiation session. The core of the algorithms is a protocol that ensures a sender-receiver pair to negotiate and arrive at a suitable batch size. The protocol takes into consideration the processing speeds of the sender and receiver, as well as their relative workload, thus ensuring the maximal benefit for each negotiation session. An additional advantage of the algorithms is that a task batch can be composed according to different performance objectives.