Evolving toward an Optimal Scheduling Solution through Adaptivity

Oudshoorn, Michael J.; Huang, Lin

doi:10.1006/jpdc.2002.1844

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Only recently the scheduling problem has been addressed as a classification problem. Work in this field includes scheduling for open shop problems [9], scheduling in multiprocessors [15,18], as well as single-machine and parallel systems [6,7,11,12]. To our knowledge, no work has been done in the past for the design of scheduling policies in VC using GA or GP.…”

Section: Related Workmentioning

confidence: 99%

“…Jakobovic et al apply their method to parallel systems in [12]. In [18] and [15], the goal is to minimize the total execution time of the parallel application by using GA or GP. In [18], Seredybski and Zomaya base their method on convolution of cellular automata rather than rules and the combination of all the automata results is gathered in a global scheduling policy.…”

Section: Related Workmentioning

confidence: 99%

“…In [18], Seredybski and Zomaya base their method on convolution of cellular automata rather than rules and the combination of all the automata results is gathered in a global scheduling policy. In [15], Oudshoorn and Huang, outline communication and synchronization among task as part of their adapting scheduling process for multiprocessors. Because of the fact that VC systems are nondedicated and volatile, task execution time is not as relevant as the overall throughput; therefore in this paper we target throughput.…”

Section: Related Workmentioning

confidence: 99%

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A distributed evolutionary method to design scheduling policies for volunteer computing

Estrada

Fuentes

Taufer

2008

Proceedings of the 5th Conference on Computing Frontiers

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Volunteer Computing (VC) is a paradigm that uses idle cycles from computing resources donated by volunteers and connected through the Internet to compute large-scale, loosely-coupled simulations. A big challenge in VC projects is the scheduling of work-units across heterogeneous, volatile, and error-prone computers. The design of effective scheduling policies for VC projects involves subjective and time-demanding tuning that is driven by the knowledge of the project designer. VC projects are in need of a faster and projectindependent method to automate the scheduling design.To automatically generate a scheduling policy, we must explore the extremely large space of syntactically valid policies. Given the size of this search space, exhaustive search is not feasible. Thus in this paper we propose to solve the problem using an evolutionary method to automatically generate a set of scheduling policies that are project-independent, minimize errors, and maximize throughput in VC projects. Our method includes a genetic algorithm where the representation of individuals, the fitness function, and the genetic operators are specifically tailored to get effective policies in a short time. The effectiveness of our method is evaluated with SimBA, a Simulator of BOINC Applications. Contrary to manuallydesigned scheduling policies that often perform well only for the specific project they were designed for and require months of tuning, our resulting scheduling policies provide better overall throughput across the different VC projects considered in this work and were generated by our method in a time window of one week.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A distributed evolutionary method to design scheduling policies for volunteer computing

Estrada

Fuentes

Taufer

2008

Proceedings of the 5th Conference on Computing Frontiers

View full text Add to dashboard Cite

show abstract

“…Efficient algorithms that give the optimal schedule can only be obtained when some restrictions are imposed on the models representing the application and the multiprocessor or distributed system. There are only few known deterministic polynomial-time scheduling algorithms [10,17,18]; therefore, solving the general scheduling problem in polynomial-time requires the use of heuristic algorithms that provide near-optimal solutions.…”

Section: Introductionmentioning

confidence: 99%