کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4950275 1364283 2018 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Dynamic energy-aware scheduling for parallel task-based application in cloud computing
ترجمه فارسی عنوان
برنامه ریزی پویا انرژی آگاه برای کاربرد موازی کاری مبتنی بر محاسبات ابری
کلمات کلیدی
محاسبات توزیع شده، پردازش ابری، محاسبات سبز، برنامه های مبتنی بر کار، زمان بندی انرژی آگاه، تخصیص منابع چند هوریستی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی


- Energy-aware run-time scheduler for task-based applications.
- Model for estimating the application Energy consumption.
- Methodology to automatically generate the required power consumption profile.
- Multi-heuristic resource allocation algorithm to get solutions in polynomial time.
- Energy saving/performance trade-off evaluation for different scenarios.

Green Computing is a recent trend in computer science, which tries to reduce the energy consumption and carbon footprint produced by computers on distributed platforms such as clusters, grids, and clouds. Traditional scheduling solutions attempt to minimize processing times without taking into account the energetic cost. One of the methods for reducing energy consumption is providing scheduling policies in order to allocate tasks on specific resources that impact over the processing times and energy consumption. In this paper, we propose a real-time dynamic scheduling system to execute efficiently task-based applications on distributed computing platforms in order to minimize the energy consumption. Scheduling tasks on multiprocessors is a well known NP-hard problem and optimal solution of these problems is not feasible, we present a polynomial-time algorithm that combines a set of heuristic rules and a resource allocation technique in order to get good solutions on an affordable time scale. The proposed algorithm minimizes a multi-objective function which combines the energy-consumption and execution time according to the energy-performance importance factor provided by the resource provider or user, also taking into account sequence-dependent setup times between tasks, setup times and down times for virtual machines (VM) and energy profiles for different architectures. A prototype implementation of the scheduler has been tested with different kinds of DAG generated at random as well as on real task-based COMPSs applications. We have tested the system with different size instances and importance factors, and we have evaluated which combination provides a better solution and energy savings. Moreover, we have also evaluated the introduced overhead by measuring the time for getting the scheduling solutions for a different number of tasks, kinds of DAG, and resources, concluding that our method is suitable for run-time scheduling.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 78, Part 1, January 2018, Pages 257-271
نویسندگان
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