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Many enterprises or institutes are building private clouds within their own data centers. Data centers may have different batches of physical machines due to annual upgrades, but the number of machines is fixed most of the time. Consequently it is crucial to schedule jobs with different resource requirements and characteristics to meet different job timing constraints, in such heterogeneous yet most...
Virtualization has recently become popular in the embedded-systems area. In order to apply virtualization techniques on embedded-systems, we need a hypervisor that provides a software virtualization environment. A hypervisor scheduler assigns virtual cores in a virtual machine to physical cores. Since traditional load-balanced scheduling strategy is not aware of the asymmetry of cores, its deployment...
This paper describes the important issue of energy conservation for data centers. We consider the problem of provisioning physical servers to a sequence of jobs, and reducing the total energy consumption. The performance metric is the wasted energy — the over-provisioned computing power provided by the physical servers, but exceeding the requirement of the jobs. We propose three new strategies for...
This paper proposes scheduling algorithms for assigning jobs with different release time and execution time, to machines with heterogeneous processor cardinality. We show that this scheduling problem is NP-complete, and propose dynamic programming to find the optimal schedules. Since the dynamic programming is time-consuming we propose techniques that improve the efficiency of the dynamic programming...
This paper introduces techniques in scheduling jobs on a master/workers platform where the bandwidth is shared by all workers. The goal is to minimize the total makespan. The jobs are independent and each job requires a fixed amount of bandwidth to download input data before execution. The master can communicate with multiple workers simultaneously, provided that the bandwidth used by the master and...
Almost every computation job requires input data in order to find the solution, and the computation cannot proceed without the required data becoming available. As a result a proper interleaving of data transfer and job execution has a significant impact on the overall efficiency. In this paper we analyze the computational complexity of the shared data job scheduling problem, with and without consideration...
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