Competitive and Cooperative-Based Strength Pareto Evolutionary Algorithm for Green Distributed Heterogeneous Flow Shop Scheduling

This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem(DHPFSP)with minimizing makespan and total energy consumption(TEC).To solve this NP-hard problem,this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm(CCSPEA)which contains the following features:1)An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence.2)A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution.3)A competitive selection is designed which divides the population into a winner and a loser swarms based on the comprehensive metric.4)A cooperative evolutionary schema is proposed for winner and loser swarms to accelerate the convergence of global search.5)Five local search strategies based on problem knowledge are designed to improve convergence.6)Aproblem-based energy-saving strategy is presented to reduce TEC.Finally,to evaluate the performance of CCSPEA,it is compared to four state-of-art and run on 22 instances based on the Taillard benchmark.The numerical experiment results demonstrate that 1)the proposed comprehensive metric can efficiently represent the heuristic information of each solution to help the later step divide the population.2)The global search based on the competitive and cooperative schema can accelerate loser solutions convergence and further improve the winner’s exploration.3)The problembased initialization,local search,and energy-saving strategies can efficiently reduce the makespan and TEC.4)The proposed CCSPEA is superior to the state-of-art for solving DHPFSP.

A Resource Scheduling Scheme for Spectrum Aggregation in Cognitive Radio Based Heterogeneous Networks

In spectrum aggregation(SA), two or more component carriers(CCs) of different bandwidths in different bands can be aggregated to support wider transmission bandwidth. The current resource scheduling schemes for spectrum aggregation are not optimal or suitable for CR based heterogeneous networks(Het Nets). Consequently, the authors propose a novel resource scheduling scheme for spectrum aggregation in CR based Het Nets, termed as cognitive radio based resource scheduling(CR-RS) scheme. CR-RS has a three-level structure. Under a dynamic traffic model, an equivalent throughput of the CCs based on the knowledge of primary users(PUs) is given. On this basis, the CR users data transmission time of each CC is equal in CR-RS. The simulation results show that CR-RS has the better performance than the current resource scheduling schemes in the CR based Het Nets. Meanwhile, CR-RS is also effective in other spectrum aggregation systems which are not CR based HetNets.

A Nonprecedence－based Query Schedule Algorithm on Heterogeneous Multiprocessor System

There has been a number of algorithms designed to handle intra-query or inter-query scheduling in multiprocessor-based parallel database system. They all have the assumption that the processorsare identical. But in some cases, such as shared nothing environment, this assumption would not be fullyvalid. In this paper we devise and evaluate a scheduling algorithm ELLIST to handle nonprecedence-basedheterogeneous malleable scheduling problem. It uses LLIST-NM as subroutine that handles nonmalleablescheduling without precedence and assuming the processors are identical. Even though the problem we considered is NP-hard in the strong sense, the schedule generated by our algorithm is seen experimentally toachieve results that are close to optimum when there are enough tasks to be scheduled.

HOPE:a heterogeneity-oriented parallel execution engine for inference on mobiles

It is significant to efficiently support artificial intelligence(AI)applications on heterogeneous mobile platforms,especially coordinately execute a deep neural network(DNN)model on multiple computing devices of one mobile platform.This paper proposes HOPE,an end-to-end heterogeneous inference framework running on mobile platforms to distribute the operators in a DNN model to different computing devices.The problem is formalized into an integer linear programming(ILP)problem and a heuristic algorithm is proposed to determine the near-optimal heterogeneous execution plan.The experimental results demonstrate that HOPE can reduce up to 36.2%inference latency(with an average of 22.0%)than MOSAIC,22.0%(with an average of 10.2%)than StarPU and 41.8%(with an average of 18.4%)thanμLayer respectively.

Erratum to: Dynamic task scheduling modeling in unstructured heterogeneous multiprocessor systems

The original version of this article unfortunately contained a mistake. The affiliation of the first author was incorrect. The correct affiliation is: Department of Computer Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran.