Thesis (M.Sc.)-Cairo University, 2023.

Bibliography: pages 98-99.

Cloud computing is one of the most famous examples of high performance computing (HPC). It has gained a lot of attention all over the world. Workflow systems have become an important method for developing scientific applications. Therefore, workflow scheduling is one of the most important issues in cloud computing. It is about planning tasks on cloud resources (for example, virtual machines (VMs)), to improve scheduling performance. IN this thesis, we propose a new scheduling algorithm based on Heterogeneous Earliest Finish Time (HEFT) algorithm; called Modified Heterogeneous Earliest Finish Time (M-HEFT); to reduce the tradeoff among make span, resource utilization, and load balance. The proposed M-HEFT consists of two phases; task prioritization and task-VM mapping. In Task prioritization phase, a priority will be provided to each task in Directed Acyclic Graph (DAG) as in the original HEFT algorithm. According to task-VM phase, tasks allocate to resources according to length of tasks and the load of available VMs with considering load balance. In addition an enhancement has been done to improve our M-HEFT with respect to make span, resource utilization, and load balance; called Enhanced Modified Heterogeneous Earliest Finish Time (EM-HEFT). The enhanced EM-HEFT algorithm consists of two phases; task prioritization and task-VM mapping. In Task prioritization phase, a priority will be provided to each task in Directed Acyclic Graph (DAG) by introducing new factors in priority value to be more aware about task requirements. According to task-VM phase, tasks are allocated to resources as in our previous M-HEFT algorithm. Experimental results show that our proposed M-HEFT and E-MEHEFT algorithms can reduce the tradeoff among make span, resource utilization, and load balance. The proposed M-HEFT algorithm outperforms the other algorithms by minimizing make span by 29%, improve resource utilization by 53% and load balance by 18% in average. And the Enhanced EM-HEFT algorithm outperforms the other algorithms by minimizing make span by 25%, improving resource utilization by 43% and load balance by 14% in average.

تعد جدولة المهام إحدى المشكلات الرئيسية لتحقيق الأداء الجيد عبر بيئة السحابة. . في هذه الأطروحة ، تم إدخال جدولين للمهام M-HEFT ، وخوارزميات EM-HEFT المحسّنة لتحسين أداء HEFT الحالي فيما يتعلق بالامتداد ، واستخدام الموارد ، وتوازن التحميل. لتقييم أداء خوارزميات M-HEFT و EM-HEFT المحسّنة المقترحة ، تم الاتصال بدراسة مقارنة باستخدام معيارين ، LIGO و EPIGENOMICS ، مع تنفيذ 100 و 1000 مهمة على جهاز محاكاةWorkflowSimمع مراعاة الأجهزة الافتراضية غير المتجانسة

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Text in English and abstract in Arabic & English.