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A Virtual-Machine Task Scheduling Method Based on Localized Weight Evaluation
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我們創造了局部化權重排程法(Localized Weight Scheduling，LWS)。在模擬實驗結果的數據中和最快完成時間分配法(Heterogeneous Earliest Finish Time，HEFT)相較起來，在10000組工作的實驗當中我們縮短了約120秒左右的時間。也減少了整體虛擬機器的停滯時間，增加了雲端環境中的處理效能。|
In recent years, cloud computing technology develops rapidly. Through virtualization, many applications have been provided via internet, including scientific computing. As cloud services provided by enterprise increases, workload that datacenters have to process increases dramatically. So task scheduling becomes an important research issue on processing large workload in a short time period. We focus on how to allocate tasks on virtual machines for performance improvement. An application can be typically represented by a workflow graph. A workflow graph consists of many tasks. These tasks may have data dependency between each other. This means that some tasks need to wait for data which are produced by other tasks. We intend to design an efficient scheduling method to reduce the completion time of workflow. In level scheduling, priorities of tasks are calculated by accumulating weights level-by-level in a bottom-up fashion, so called upward ranking. Upward ranking allows us to figure out a scheduling order rapidly. However, it leads to indiscriminate allocation, and tasks are assigned on the basis of priority to resource whichever becomes available first. This may result in poor resource utilization. Other methods find critical paths in workflow graphs, and use critical paths for scheduling tasks with the guaranteed finish time. But these methods tend to ignore the communication effect on task finish time. Here we propose a scheduling method for allocating the resources of virtual machines, called localized weight scheduling and LWS for short. The proposed LWS method features communication minimization and utilization improvement. Our method is based upon an important observation that assigning dependent tasks to the same virtual machine helps reduce the communication cost. The LWS method evaluates weights of tasks with localizing communication in mind. It crosses the boundary of level to choose tasks for dynamical scheduling. In this manner, the proposed method can achieve higher resource utilization with minimized completion time. We evaluate the performance of the LWS method through simulation using CloudSim. Compared with HEFT, the proposed method reduces about 120 seconds in 10000 works. Virtual Machine’s idle time also reduced. We conclude that our LWS method significantly minimizes execution time and improves resource utilization of cloud computing.
|Appears in Collections:||資訊科學與工程學系所|
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