Abstract
Cloud computing is one of the most prominent parallel and distributed computing paradigm. It is used for providing solution to a huge number of scientific and business applications. Large scale scientific applications which are structured as scientific workflows are evaluated through cloud computing. Scientific workflows are data-intensive applications, as a single scientific workflow may consist of hundred thousands of tasks. Task failures, deadline constraints, budget constraints and improper management of tasks can also instigate inconvenience. Therefore, provision of fault-tolerant techniques with data-oriented scheduling is an important approach for execution of scientific workflows in Cloud computing. Accordingly, we have presented enhanced data-oriented scheduling with Dynamic-clustering fault-tolerant technique (EDS-DC) for execution of scientific workflows in Cloud computing. We have presented data-oriented scheduling as a proposed scheduling technique. We have also equipped EDS-DC with Dynamic-clustering fault-tolerant technique. To know the effectiveness of EDS-DC, we compared its results with three well-known enhanced heuristic scheduling policies referred to as: (a) MCT-DC, (b) Max-min-DC, and (c) Min-min-DC. We considered scientific workflow of CyberShake as a case study, because it contains most of the characteristics of scientific workflows such as integration, disintegration, parallelism, and pipelining. The results show that EDS-DC reduced make-span of 10.9% as compared to MCT-DC, 13.7% as compared to Max-min-DC, and 6.4% as compared to Min-min-DC scheduling policies. Similarly, EDS-DC reduced the cost of 4% as compared to MCT-DC, 5.6% as compared to Max-min-DC, and 1.5% as compared to Min-min-DC scheduling policies. These results in respect of make-span and cost are highly significant for EDS-DC as compared with above referred three scheduling policies. The SLA is not violated for EDS-DC in respect of time and cost constraints, while it is violated number of times for MCT-DC, Max-min-DC, and Min-min-DC scheduling techniques.
Highlights
Облачные вычисления – это универсальная и масштабная парадигма распределенных вычислений
In terms of characteristics of resources and specifications of scientific workflows submitted by the user, the detail description of the simulation environment is given below
Спецификация русурсов, используемых для симуляции Table 2: The specification of resources used for simulation
Summary
Облачные вычисления – это универсальная и масштабная парадигма распределенных вычислений. В потоках научных работ для решения каждой задачи требуется значительный объем. Ориентированное на данные планирование с применением отказоустойчивого метода динамической кластеризации для поддержки потоков научных работ в облаках. Для этого мы усовершенствовали ориентированное на данные планирование потоков научных работ с применением отказоустойчивого метода динамической. Мы предложили усовершенствованное ориентированное на данные планирование потоков научных работ в облаках с применением отказоустойчивого метода динамической кластеризации (Enhanced Data-oriented Scheduling with Dynamic-Clustering fault-tolerant technique, EDS-DC) [13]. Таких как (a) Minimum Completion Time (MCT) [24], (b) Maximum-minimum (Max-min) [25] и (c) Minimumminimum (Min-min) [26], которые также использовались для планирования независимых задачи при выполнении потоков научных работ [16]. Механизм кластеризации Fault-Tolerant Clustering (FTC) для потоков научных работ был представлен в [15]. Обеспечивает ресурсы для Пригоден только для групп потоков работ групп потоков работ [27]
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