A New Design Framework for Heterogeneous Uncoded Storage Elastic Computing
A New Design Framework for Heterogeneous Uncoded Storage Elastic Computing
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DOI：
10.23919/wiopt56218.2022.9930566
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Mingyue Ji;Xiang Zhang;Kai Wan
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文献类型：
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作者：
Mingyue Ji;Xiang Zhang;Kai Wan
弹性是现代云计算系统的一个重要特征，会导致计算失败或显著增加计算时间。这种灵活性意味着，如果出现高优先级作业，云上的虚拟机可以在短时间内(例如，几小时或几分钟)被抢占；另一方面，随着时间的推移，新的虚拟机可能会变得可用来补偿计算资源。编码存储弹性计算(CSEC)由Yang等人提出。2018年是克服弹性的一种有效和高效的方法，它的存储和计算负载相对较小。然而，CSEC的局限性之一是它可能仅应用于某些类型的计算(例如，线性)，并且可能难以应用于更复杂的计算，因为通常需要编码数据存储和近似。因此，可能优选通过直接将数据复制到虚拟机中来使用未编码的存储。此外，根据我们自己的测量，Amazon EC2群集上的虚拟机通常具有不同的计算速度，即使它们具有完全相同的配置(例如，CPU、RAM、I/O成本)。针对非编码存储弹性计算(USEC)系统的计算速度异构性，提出了一种新的优化框架，以最小化整体计算时间。在此框架下，我们提出了具有和不具有散布容限的USEC系统在不同存储布局下的最优解。我们提出的算法在Amazon EC2上使用POWER迭代应用程序进行了评估。
Elasticity is one important feature in modern cloud computing systems and can result in computation failure or significantly increase computing time. Such elasticity means that virtual machines over the cloud can be preempted under a short notice (e.g., hours or minutes) if a high-priority job appears; on the other hand, new virtual machines may become available over time to compensate the computing resources. Coded Storage Elastic Computing (CSEC) introduced by Yang et al. in 2018 is an effective and efficient approach to overcome the elasticity and it costs relatively less storage and computation load. However, one of the limitations of the CSEC is that it may only be applied to certain types of computations (e.g., linear) and may be challenging to be applied to more involved computations because the coded data storage and approximation are often needed. Hence, it may be preferred to use uncoded storage by directly copying data into the virtual machines. In addition, based on our own measurement, virtual machines on Amazon EC2 clusters often have heterogeneous computation speed even if they have exactly the same configurations (e.g., CPU, RAM, I/O cost). In this paper, we introduce a new optimization framework on Uncoded Storage Elastic Computing (USEC) systems with heterogeneous computing speed to minimize the overall computation time. Under this framework, we propose optimal solutions of USEC systems with or without straggler tolerance using different storage placements. Our proposed algorithms are evaluated using power iteration applications on Amazon EC2.