Parallel job scheduling for power constrained HPC systems

Etinski, Maja; Corbalan, Julita; Labarta, Jesús; Valero, Mateo

doi:10.1016/j.parco.2012.08.001

Cited by 44 publications

(29 citation statements)

References 14 publications

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“…Jobs running on HESs can be calculated by determining the number jobs at time T, on service queue of , which is divided by the service rate of at time T. Job of all HESs in a data center is calculated as follows: (8) Execution time of HES: (9) Execution time of all HESs: (10) Standard deviation of job: (11) JOB SCHEDULING DECISION After applying the jobs scheduling using the proposed technique, load balancing at run time will come in action. Load balancing will migrate some jobs of heavily loaded HESs to underloaded HESs.…”

Section: Job On Hessmentioning

confidence: 99%

See 1 more Smart Citation

Multiobjective Artificial Bee Colony based Job Scheduling for Cloud Computing Environment

Sethi¹,

Singh²,

Singh³

2018

IJMSC

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Cloud computing has become the hottest issue due to its wide range of services. Due to a large number of users, it becomes more significant to provide high availability of services to cloud users. The majority of existing scheduling techniques in the cloud environment is NP-Complete in nature. Many researchers have utilized meta-heuristic techniques to schedule the jobs in cloud data centers. The majority of existing techniques such as Genetic Algorithm, Ant colony optimization, Non-dominated Sorting Genetic Algorithm (NSGA-III), etc. suffer from poor convergence speed. Also, most of these techniques are either based upon scheduling or load balancing. Therefore, to overcome these issues, a new Variance Honey Bee Behavior with multi-objective optimization method (VHBBMO) is proposed in this paper. Extensive experiments have been conducted by considering the various set of jobs. The experimental results have shown that the proposed method provides more significant results than available methods.

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Section: Job On Hessmentioning

confidence: 99%

“…But this technique suffers from poor convergence speed. The agent-based prioritized dynamic round robin method is developed to efficiently schedule the jobs in cloud data centers [8]. But this technique is limited to a single objective problem only.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Artificial Bee Colony based Job Scheduling for Cloud Computing Environment

Sethi¹,

Singh²,

Singh³

2018

IJMSC

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“…The scheduling algorithm developed in [13] uses integer linear programming to assign a CPU frequency before executing a selected job in order to remain below the predetermined budget. Even though DVFS is common for CPU-based infrastructures [14], it is relatively new for heterogeneous systems based on GPUs.…”

Section: Related Workmentioning

confidence: 99%

Power-Aware Job Scheduling on Heterogeneous Multicore Architectures

Vanzolini

Mucci

Scarselli

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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This paper presents a power-aware scheduling algorithm based on efficient distribution of the computing workload to the resources on heterogeneous CPU-GPU architectures. The scheduler manages the resources of several computing nodes with a view to reducing the peak power. The algorithm can be used in concert with adjustable power state software services in order to further reduce the computing cost during high demand periods. Although our study relies on GPU workloads, the approach can be extended to other heterogeneous computer architectures. The algorithm has been implemented in a real CPU-GPU heterogeneous system. Experiments prove that the approach presented reduces peak power by 10% compared to a system without any power-aware policy and by up to 24% with respect to the worst case scenario with an execution time increase in the range of 2%. This leads to a reduction in the system and service costs.

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“…MaxJobPerf is a power-constrained scheduling algorithm for parallel jobs proposed in [15]. The algorithm relies on an integer linear programming model, which makes it unsuitable for online scheduling because the time to solve it grows exponentially with the number of jobs.…”

Section: Scheduling Algorithms For Hpc2mentioning

confidence: 99%