مدل برنامه نویسی سطح bi چند هدفه جدید برای برنامه ریزی چند کار آگاه محل و انرژی در رایانش ابری
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|74234||2014||11 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Future Generation Computer Systems, Volume 36, July 2014, Pages 91–101
How to reduce power consumption of data centers has received worldwide attention. By combining the energy-aware data placement policy and locality-aware multi-job scheduling scheme, we propose a new multi-objective bi-level programming model based on MapReduce to improve the energy efficiency of servers. First, the variation of energy consumption with the performance of servers is taken into account; second, data locality can be adjusted dynamically according to current network state; last but not least, considering that task-scheduling strategies depend directly on data placement policies, we formulate the problem as an integer bi-level programming model. In order to solve the model efficiently, specific-design encoding and decoding methods are introduced. Based on these, a new effective multi-objective genetic algorithm based on MOEA/D is proposed. As there are usually tens of thousands of tasks to be scheduled in the cloud, this is a large-scale optimization problem and a local search operator is designed to accelerate convergent speed of the proposed algorithm. Finally, numerical experiments indicate the effectiveness of the proposed model and algorithm.