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Solving the Task Starvation and Resources Problem Using Optimized SMPIA in Cloud

Computer Systems Science & Engineering 2022年第8期42卷 659-675页

作者： Mehran Mokhtari homayun motameni Peyman Bayat Department of Computer Sari BranchIslamic Azad UniversitySariIran Department of Computer Rasht BranchIslamic Azad UniversityRashtIran

In this study,a new feature is added to the smart message passing interface(SMPI)approach(SMPIA)based on the prioritization method,which cancompletely eliminate the task starvation and lack of sufficient resources problemsthrough prioritizing the *** proposed approach is based on prioritizing thetasks and the urgency of *** are prioritized based on executiontime,workload,the task with a more sensitive priority is executed earlier by thefree *** idea of demand-bound functions(DBFs)was extended to theSMPIA setting based on partitions and *** each task,two DBFs are constructed,DBFLO and DBFHI,for the LO and HI criticality modes,*** simulation results returned by MATLAB showed that with the optimizedSMPIA(O-SMPIA),the parameters of maximum service execution time,response time,delay time,and throughput improved in this *** addition,the results confirmed that the reduction of execution time,completion time,andresource consumption time did not affect the response time and throughput ofworkflow tasks and did not cause inefficient use of resources in virtual machines(VMs)and data centers(DCs).The evaluation of performance metrics showedthat the delay,response time of the Greedy algorithm was less than that ofMax-Min and *** the same time,the execution time of Max-Min wasless than the others and the throughput of the Greedy was *** effectand throughput of O-SMPIA became more obvious as change to the job countand the number of cloud workloads *** is also worth mentioning thatone of the main advantages of the O-SMPIA to other methods is the efficientuse of time to execute all the defined tasks by CPU.

关键词： Cloud computing O-SMPIA task starvation EDF-VD

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A supervised multimanifold method with locality preserving for face recognition using single sample per person

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Journal of Central South University 2017年第12期24卷 2853-2861页

作者： Nabipour Mehrasa Aghagolzadeh Ali motameni homayun Department of Computer Engineering Faculty of Engineering Sari Islamic Azad University Faculty of Electrical and Computer Engineering Babol Noshirvani University of Technology

Although real-world experiences show that preparing one image per person is more convenient, most of the appearance-based face recognition methods degrade or fail to work if there is only a single sample per person(SSPP). In this work, we introduce a novel supervised learning method called supervised locality preserving multimanifold(SLPMM) for face recognition with SSPP. In SLPMM, two graphs: within-manifold graph and between-manifold graph are made to represent the information inside every manifold and the information among different manifolds, respectively. SLPMM simultaneously maximizes the between-manifold scatter and minimizes the within-manifold scatter which leads to discriminant space by adopting locality preserving projection(LPP) concept. Experimental results on two widely used face databases FERET and AR face database are presented to prove the efficacy of the proposed approach.

关键词： face recognition locality preserving manifold learning single sample per person

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