Modern human life is heavily dependent on computing systems and one of the core components affecting the performance of these systems is underlying operating system.Operating systems need to be upgraded to match the n...Modern human life is heavily dependent on computing systems and one of the core components affecting the performance of these systems is underlying operating system.Operating systems need to be upgraded to match the needs of modern-day systems relying on Internet of Things,Fog computing and Mobile based applications.The scheduling algorithm of the operating system dictates that how the resources will be allocated to the processes and the Round Robin algorithm(RR)has been widely used for it.The intent of this study is to ameliorate RR scheduling algorithm to optimize task scheduling.We have carried out an experimental study where we have developed four variations of RR,each algorithm considers three-time quanta and the performance of these variations was compared with the RR algorithm,and results highlighted that these variations performed better than conventional RR algorithm.In the future,we intend to develop an automated scheduler that can determine optimal algorithm based on the current set of processes and will allocate time quantum to the processes intelligently at the run time.This way the task performance of modern-day systems can be improved to make them more efficient.展开更多
The proliferation of the global datasphere has forced cloud storage systems to evolve more complex architectures for different applications.The emergence of these application session requests and system daemon service...The proliferation of the global datasphere has forced cloud storage systems to evolve more complex architectures for different applications.The emergence of these application session requests and system daemon services has created large persistent flows with diverse performance requirements that need to coexist with other types of traffic.Current routing methods such as equal-cost multipath(ECMP)and Hedera do not take into consideration specific traffic characteristics nor performance requirements,which make these methods difficult to meet the quality of service(QoS)for high-priority flows.In this paper,we tailored the best routing for different kinds of cloud storage flows as an integer programming problem and utilized grey relational analysis(GRA)to solve this optimization problem.The resulting method is a GRAbased service-aware flow scheduling(GRSA)framework that considers requested flow types and network status to select appropriate routing paths for flows in cloud storage datacenter networks.The results from experiments carried out on a real traffic trace show that the proposed GRSA method can better balance traffic loads,conserve table space and reduce the average transmission delay for high-priority flows compared to ECMP and Hedera.展开更多
文摘Modern human life is heavily dependent on computing systems and one of the core components affecting the performance of these systems is underlying operating system.Operating systems need to be upgraded to match the needs of modern-day systems relying on Internet of Things,Fog computing and Mobile based applications.The scheduling algorithm of the operating system dictates that how the resources will be allocated to the processes and the Round Robin algorithm(RR)has been widely used for it.The intent of this study is to ameliorate RR scheduling algorithm to optimize task scheduling.We have carried out an experimental study where we have developed four variations of RR,each algorithm considers three-time quanta and the performance of these variations was compared with the RR algorithm,and results highlighted that these variations performed better than conventional RR algorithm.In the future,we intend to develop an automated scheduler that can determine optimal algorithm based on the current set of processes and will allocate time quantum to the processes intelligently at the run time.This way the task performance of modern-day systems can be improved to make them more efficient.
基金supported by National Natural Science Foundation of China(Nos.61861013,61662018)Science and Technology Major Project of Guangxi(No.AA18118031)+2 种基金Guangxi Natural Science Foundation of China(No.2018 GXNSFAA050028)the Doctoral Research Foundation of Guilin University of Electronic Science and Technology(No.UF19033Y)Director Fund project of Key Laboratory of Cognitive Radio and Information Processing of Ministry of Education(No.CRKL190102)。
文摘The proliferation of the global datasphere has forced cloud storage systems to evolve more complex architectures for different applications.The emergence of these application session requests and system daemon services has created large persistent flows with diverse performance requirements that need to coexist with other types of traffic.Current routing methods such as equal-cost multipath(ECMP)and Hedera do not take into consideration specific traffic characteristics nor performance requirements,which make these methods difficult to meet the quality of service(QoS)for high-priority flows.In this paper,we tailored the best routing for different kinds of cloud storage flows as an integer programming problem and utilized grey relational analysis(GRA)to solve this optimization problem.The resulting method is a GRAbased service-aware flow scheduling(GRSA)framework that considers requested flow types and network status to select appropriate routing paths for flows in cloud storage datacenter networks.The results from experiments carried out on a real traffic trace show that the proposed GRSA method can better balance traffic loads,conserve table space and reduce the average transmission delay for high-priority flows compared to ECMP and Hedera.
