Virtual Savant for the Heterogeneous Computing Scheduling Problem

Массобрио, Рензо; Dorronsoro, Bernabè; Nesmachnow, Sergio

doi:10.1109/hpcs.2018.00133

Cited by 4 publications

(1 citation statement)

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“…Improvements of 5% on average over the baseline results were obtained for the problem instances studied. Our previous works were able to obtain promising results when applying Virtual Savant to a task scheduling problem [17,18,7,11]. The application of Virtual Savant to the 0/1 Knapsack Problem has been previously studied in [8,9].…”

Section: Related Workmentioning

confidence: 99%

Virtual Savant for the Knapsack Problem: learning for automatic resource allocation

Массобрио¹,

Díaz²,

Cánovas³

2019

Proceedings of ISP RAS

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This article presents the application of Virtual Savant to solve resource allocation problems, a widelystudied area with several real-world applications. Virtual Savant is a novel soft computing method that uses machine learning techniques to compute solutions to a given optimization problem. Virtual Savant aims at learning how to solve a given problem from the solutions computed by a reference algorithm, and its design allows taking advantage of modern parallel computing infrastructures. The proposed approach is evaluated to solve the Knapsack Problem, which models different variant of resource allocation problems, considering a set of instances with varying size and difficulty. The experimental analysis is performed on an Intel Xeon Phi many-core server. Results indicate that Virtual Savant is able to compute accurate solutions while showing good scalability properties when increasing the number of computing resources used.

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Section: Related Workmentioning

confidence: 99%