Heterogeneous Coded Computation across Heterogeneous Workers

Sun, Yuxuan; Zhao, Junlin; Zhou, Sheng; Gündüz, Deniz

doi:10.1109/globecom38437.2019.9014006

Cited by 19 publications

(4 citation statements)

References 18 publications

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“…Future research directions include to consider coded computation techniques [15], [43]- [45] and task cancellation principles [26], [27], to further improve the efficiency of resource utilization, while guaranteeing the quality of service of computation tasks.…”

Section: Discussionmentioning

confidence: 99%

Distributed Task Replication for Vehicular Edge Computing: Performance Analysis and Learning-based Algorithm

Sun

Zhou

Niu

2020

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In a vehicular edge computing (VEC) system, vehicles can share their surplus computation resources to provide cloud computing services. The highly dynamic environment of the vehicular network makes it challenging to guarantee the task offloading delay. To this end, we introduce task replication to the VEC system, where the replicas of a task are offloaded to multiple vehicles at the same time, and the task is completed upon the first response among replicas. First, the impact of the number of task replicas on the offloading delay is characterized, and the optimal number of task replicas is approximated in closedform. Based on the analytical result, we design a learning-based task replication algorithm (LTRA) with combinatorial multi-armed bandit theory, which works in a distributed manner and can automatically adapt itself to the dynamics of the VEC system. A realistic traffic scenario is used to evaluate the delay performance of the proposed algorithm. Results show that, under our simulation settings, LTRA with an optimized number of task replicas can reduce the average offloading delay by over 30% compared to the benchmark without task replication, and at the same time can improve the task completion ratio from 97% to 99.6%.

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Section: Discussionmentioning

confidence: 99%

Distributed Task Replication for Vehicular Edge Computing: Performance Analysis and Learning-based Algorithm

Sun

Zhou

Niu

2020

Preprint

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“…The statistical knowledge of the computation and communication latency for each worker can be acquired over time, and used for a more efficient allocation of computation tasks (e.g. as in [28], [35], [36]) as well as the coding scheme employed, for the sake of simplicity we assume homogeneous workers in this work.…”

Section: B Coded Distributed Matrix-vector Multiplicationmentioning

confidence: 99%

Coded Distributed Computing with Partial Recovery

Ozfatura¹,

Ulukuş²,

Gündüz³

2020

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Coded computation techniques provide robustness against straggling workers in distributed computing. However, most of the existing schemes require exact provisioning of the straggling behaviour and ignore the computations carried out by straggling workers. Moreover, these schemes are typically designed to recover the desired computation results accurately, while in many machine learning and iterative optimization algorithms, faster approximate solutions are known to result in an improvement in the overall convergence time. In this paper, we first introduce a novel coded matrix-vector multiplication scheme, called coded computation with partial recovery (CCPR), which benefits from the advantages of both coded and uncoded computation schemes, and reduces both the computation time and the decoding complexity by allowing a trade-off between the accuracy and the speed of computation. We then extend this approach to distributed implementation of more general computation tasks by proposing a coded communication scheme with partial recovery, where the results of subtasks computed by the workers are coded before being communicated. Numerical simulations on a large linear regression task confirm the benefits of the proposed distributed computation scheme with partial recovery in terms of the trade-off between the computation accuracy and latency.

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“…For example, experiments on Amazon EC2 instances show that some workers can be five times slower than the typical performance [2]. There have been several attempts in the literature to mitigate the straggler effect by adding redundancy to the distributed computing system via coding [3], [4] or via scheduling computation tasks [5], [6]. However, these works overlooked the inherent heterogeneity in the computing capacity of different workers.…”

Section: Introductionmentioning

confidence: 99%

Motivating Workers in Federated Learning: A Stackelberg Game Perspective

2020

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Due to the large size of the training data, distributed learning approaches such as federated learning have gained attention recently. However, the convergence rate of distributed learning suffers from heterogeneous worker performance.In this paper, we consider an incentive mechanism for workers to mitigate the delays in completion of each batch. We analytically obtained equilibrium solution of a Stackelberg game. Our numerical results indicate that with a limited budget, the model owner should judiciously decide on the number of workers due to trade off between the diversity provided by the number of workers and the latency of completing the training.Y. Sarikaya (ysarikaya@sabanciuniv.edu) and O. Ercetin (oercetin@sabanciuniv.edu) are with the

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Heterogeneous Coded Computation across Heterogeneous Workers

Cited by 19 publications

References 18 publications

Distributed Task Replication for Vehicular Edge Computing: Performance Analysis and Learning-based Algorithm

Distributed Task Replication for Vehicular Edge Computing: Performance Analysis and Learning-based Algorithm

Coded Distributed Computing with Partial Recovery

Motivating Workers in Federated Learning: A Stackelberg Game Perspective

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