دانلود مقاله ISI انگلیسی شماره 105659
ترجمه فارسی عنوان مقاله

برنامه زمانبندی تصادفی انرژی برای مجموعه ای از مشاغل محدود محدودیت در سیستم محاسبات ناهمگن

عنوان انگلیسی
Energy-aware stochastic scheduler for batch of precedence-constrained jobs on heterogeneous computing system
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
105659 2017 27 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Energy, Volume 125, 15 April 2017, Pages 258-274

ترجمه کلمات کلیدی
سیستم محاسباتی ناهمگن، برنامه ریزی تصادفی، ولتاژ پویا و مقیاس فرکانس، اولویت کار محدود، بهینه سازی چند هدفه، انرژی، زمان تحویل،
کلمات کلیدی انگلیسی
Heterogeneous computing system; Stochastic scheduling; Dynamic Voltage and Frequency Scaling; Precedence-constrained jobs; Multi-objective optimization; Energy; Turnaround time;
پیش نمایش مقاله
پیش نمایش مقاله  برنامه زمانبندی تصادفی انرژی برای مجموعه ای از مشاغل محدود محدودیت در سیستم محاسبات ناهمگن

چکیده انگلیسی

The problem of optimal scheduling of precedence-constrained jobs as well as finding the Pareto-optimal sets for multi objective scheduling problem have been proven to be nondeterministic polynomial time (NP)-complete. The growing consumption of energy has compelled the researchers to consider energy consumption as an important parameter along with other parameters in multi-objective scheduling problem. Accordingly, many energy-aware precedence-constraints scheduling algorithms have been reported in the literature. Most of the algorithms have a limitation of treating this problem as a single objective optimization problem modelling with deterministic execution times rather than stochastic execution times. This work proposes energy-aware stochastic scheduler to schedule the batch of precedence-constrained jobs on dynamic voltage frequency scaling-enabled processors in order to optimize the energy consumption and the turnaround time. The execution and inter-communication times are stochastic which are drawn from independent probability distributions. A novel encoding for batch of precedence-constrained jobs, stochastic turnaround time and energy models are also proposed. Experimental results show that, compared with other algorithms, the proposed scheduler offers reduced turnaround time and reduced energy consumption.