arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2008.05141cs.ITcs.DCmath.ITmath.OC
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存储和计算速度异构机器上的编码弹性计算

Coded Elastic Computing on Machines with Heterogeneous Storage and Computation Speed

Nicholas Woolsey, Rong-Rong Chen, Mingyue Ji

摘要:

我们研究了异构编码弹性计算(CEC)的优化设计,其中机器具有不同的计算速度和存储。Yang等人在2018年引入的CEC是一个减轻弹性事件影响的框架,机器可以在任意时间加入和离开。在CEC中,数据使用最大距离可分离(MDS)代码在机器之间分发,以便机器的子集可以执行所需的计算。然而,最先进的CEC设计仅在同构网络上运行,其中机器具有相同的速度和存储。这可能不切实际。在这项工作中,基于MDS存储分配,我们开发了一种新的计算分配方法,异构CEC网络,以尽量减少总的计算时间。我们首先考虑机器具有异构计算速度但具有相同存储的情况,然后考虑两种异构性都存在的情况。我们提出了一种新的组合优化公式,并解决它通过分解成一个凸优化问题,找到最佳的计算负载和“填充问题”,找到确切的计算分配。一个低复杂度的“填充算法”是适应,并可以在一定数量的迭代等于最多的可用机器的数量内完成。

英文摘要:

We study the optimal design of heterogeneous Coded Elastic Computing (CEC) where machines have varying computation speeds and storage. CEC introduced by Yang et al. in 2018 is a framework that mitigates the impact of elastic events, where machines can join and leave at arbitrary times. In CEC, data is distributed among machines using a Maximum Distance Separable (MDS) code such that subsets of machines can perform the desired computations. However, state-of-the-art CEC designs only operate on homogeneous networks where machines have the same speeds and storage. This may not be practical. In this work, based on an MDS storage assignment, we develop a novel computation assignment approach for heterogeneous CEC networks to minimize the overall computation time. We first consider the scenario where machines have heterogeneous computing speeds but same storage and then the scenario where both heterogeneities are present. We propose a novel combinatorial optimization formulation and solve it exactly by decomposing it into a convex optimization problem for finding the optimal computation load and a "filling problem" for finding the exact computation assignment. A low-complexity "filling algorithm" is adapted and can be completed within a number of iterations equals at most the number of available machines.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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