Heuristic Algorithms for Task Assignment in Distributed Systems
IEEE Transactions on Computers
An efficient algorithm for a task allocation problem
Journal of the ACM (JACM)
On the task assignment problem: two new efficient heuristic algorithms
Journal of Parallel and Distributed Computing
Safety and Reliability Driven Task Allocation in Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
A hybrid heuristic to solve a task allocation problem
Computers and Operations Research
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Fast Algorithms for Distributed Resource Allocation
IEEE Transactions on Parallel and Distributed Systems
Declustering: A New Multiprocessor Scheduling Technique
IEEE Transactions on Parallel and Distributed Systems
Partial task assignment of task graphs under heterogeneous resource constraints
Proceedings of the 40th annual Design Automation Conference
A Dynamic Matching and Scheduling Algorithm for Heterogeneous Computing Systems
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
The task allocation problem with constant communication
Discrete Applied Mathematics - Special issue: The second international colloquium, "journées de l'informatique messine"
Multi-heuristic list scheduling genetic algorithm for task scheduling
Proceedings of the 2003 ACM symposium on Applied computing
AWSM: Allocation of workflows utilizing social network metrics
Decision Support Systems
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
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In any distributed processing environment, decisions need to be made concerning the assignment of computational task modules to various processors. Many versions of the task allocation problem have appeared in the literature. Intertask communication makes the assignment decision difficult; capacity limitations at the processors increase the difficulty. This problem is naturally formulated as a nonlinear integer program, but can be linearized to take advantage of commercial integer programming solvers. While traditional approaches to linearizing the problem perform well when only a few tasks communicate, they have considerable difficulty solving problems involving a large number of intercommunicating tasks. This paper introduces new mixed integer formulations for three variations of the task allocation problem. Results from extensive computational tests conducted over real and generated data indicate that the reformulations are particularly efficient when a large number of tasks communicate, solving reasonably large problems faster than other exact approaches available.