Scheduling parallel machines on-line
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Approximating total flow time on parallel machines
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Minimizing the flow time without migration
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Flow and stretch metrics for scheduling continuous job streams
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Developments from a June 1996 seminar on Online algorithms: the state of the art
Minimizing total flow time and total completion time with immediate dispatching
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures
Communications of the ACM - Web science
On-line hierarchical job scheduling on grids with admissible allocation
Journal of Scheduling
Two level job-scheduling strategies for a computational grid
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
Are user runtime estimates inherently inaccurate?
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
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In this paper, we investigate the problem of online task scheduling of jobs such as MapReduce jobs, Monte Carlo simulations and generating search index from web documents, on cloud computing infrastructures. We consider the virtualized cloud computing setup comprising machines that host multiple identical virtual machines (VMs) under pay-as-you-go charging, and that booting a VM requires a constant setup time. The cost of job computation depends on the number of VMs activated, and the VMs can be activated and shutdown on demand. We propose a new bi-objective algorithm to minimize the maximum task delay, and the total cost of the computation. We study both the clairvoyant case, where the duration of each task is known upon its arrival, and the more realistic non-clairvoyant case.