Novel fairness-aware co-scheduling for shared cache contention game on chip multiprocessors

Xiao, Zheng; Chen, Liwen; Wang, Bangyong; Du, Jiayi; Li, Keqin

doi:10.1016/j.ins.2020.03.078

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Zhong et al [22] addressed the fair scheduling problem of multicloud workflow tasks and proposed a reinforcement learning-based algorithm. In response to cache contention issues in on-chip multiprocessors, a thread cooperative scheduling technique considering fairness was proposed by Xiao et al [23]. It was based on non-cooperative game theory.…”

Section: Related Workmentioning

confidence: 99%

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Multiprocessor Fair Scheduling Based on an Improved Slime Mold Algorithm

Dai,

Jiang

2023

Algorithms

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An improved slime mold algorithm (IMSMA) is presented in this paper for a multiprocessor multitask fair scheduling problem, which aims to reduce the average processing time. An initial population strategy based on Bernoulli mapping reverse learning is proposed for the slime mold algorithm. A Cauchy mutation strategy is employed to escape local optima, and the boundary-check mechanism of the slime mold swarm is optimized. The boundary conditions of the slime mold population are transformed into nonlinear, dynamically changing boundaries. This adjustment strengthens the slime mold algorithm’s global search capabilities in early iterations and strengthens its local search capability in later iterations, which accelerates the algorithm’s convergence speed. Two unimodal and two multimodal test functions from the CEC2019 benchmark are chosen for comparative experiments. The experiment results show the algorithm’s robust convergence and its capacity to escape local optima. The improved slime mold algorithm is applied to the multiprocessor fair scheduling problem to reduce the average execution time on each processor. Numerical experiments showed that the IMSMA performs better than other algorithms in terms of precision and convergence effectiveness.

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Section: Related Workmentioning

confidence: 99%

“…Xiao et al [23] They proposed a fairness-aware thread collaborative scheduling algorithm based on uncooperative game theory, and the on-chip multiprocessor cache congestion problem was addressed.…”

Section: Related Workmentioning

confidence: 99%

Multiprocessor Fair Scheduling Based on an Improved Slime Mold Algorithm

Dai,

Jiang

2023

Algorithms

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“…Authors of [17] propose a novel fairness-aware thread co-scheduling algorithm based on non-cooperative game to reduce L2 cache misses. The execution time of a thread varies depending on which threads are running on other cores of the same chip, because different thread combinations result in different levels of cache contention.…”

Section: Introductionmentioning

confidence: 99%

Multi-Core Processor Scheduling with Respect to Data Bus Bandwidth

Eremeev¹,

Malakhov²,

Sakhno³

et al. 2020

Preprint

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The paper considers the problem of scheduling software modules on a multi-core processor, taking into account the limited bandwidth of the data bus and the precedence constraints. Two problem formulations with different levels of problem-specific detail are suggested and both shown to be NP-hard. A mixed integer linear programming (MILP) model is proposed for the first problem formulation, and a greedy algorithm is developed for the second one. An experimental comparison of the results of the greedy algorithm and the MILP solutions found by CPLEX solver is carried out.

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