Yahyapour, Ramin

[["dc.contributor.author","González-García, José Luis"],["dc.contributor.author","Tchernykh, Andrei"],["dc.contributor.author","Yahyapour, Ramin"],["dc.date.accessioned","2020-04-03T17:23:20Z"],["dc.date.available","2020-04-03T17:23:20Z"],["dc.date.issued","2006"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63735"],["dc.title","Evaluación Experimental de Estrategias de Calendarización en Grid Computacional Utilizando un Esquema de Admisibilidad"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","77"],["dc.bibliographiccitation.lastpage","91"],["dc.contributor.author","Tchernykh, Andrei"],["dc.contributor.author","Schwiegelshohn, Uwe"],["dc.contributor.author","Yahyapour, Ramin"],["dc.contributor.author","Kuzjurin, Nikolai"],["dc.date.accessioned","2020-04-06T14:26:07Z"],["dc.date.available","2020-04-06T14:26:07Z"],["dc.date.issued","2008"],["dc.identifier.doi","10.1007/978-0-387-09455-7_6"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63888"],["dc.relation.isbn","978-0-387-09454-0"],["dc.relation.isbn","978-0-387-09455-7"],["dc.relation.ispartof","From Grids to Service and Pervasive Computing"],["dc.title","Online Hierarchical Job Scheduling on Grids"],["dc.type","book_chapter"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","965"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Future Generation Computer Systems"],["dc.bibliographiccitation.lastpage","976"],["dc.bibliographiccitation.volume","28"],["dc.contributor.author","Quezada-Pina, Ariel"],["dc.contributor.author","Tchernykh, Andrei"],["dc.contributor.author","González-García, José Luis"],["dc.contributor.author","Hirales-Carbajal, Adán"],["dc.contributor.author","Ramírez-Alcaraz, Juan Manuel"],["dc.contributor.author","Schwiegelshohn, Uwe"],["dc.contributor.author","Yahyapour, Ramin"],["dc.contributor.author","Miranda-López, Vanessa"],["dc.date.accessioned","2018-08-16T14:15:27Z"],["dc.date.available","2018-08-16T14:15:27Z"],["dc.date.issued","2012"],["dc.description.abstract","We evaluate job scheduling algorithms that integrate both tasks of Grid scheduling: job allocation to Grid sites and local scheduling at the sites. We propose and analyze an adaptive job allocation scheme named admissible allocation. The main idea of this scheme is to set job allocation constraints, and dynamically adapt them to cope with different workloads and Grid properties. We present 3-approximation and 5-competitive algorithms named MLB a + PS and MCT a + PS for the case that all jobs fit to the smallest machine, while we derive an approximation factor of 9 and a competitive factor of 11 for the general case. To show practical applicability of our methods, we perform a comprehensive study of the practical performance of the proposed strategies and their derivatives using simulation. To this end, we use real workload traces and corresponding Grid configurations. We analyze nine scheduling strategies that require a different amount of information on three Grid scenarios. We demonstrate that our strategies perform well across ten metrics that reflect both user-and system-specific goals."],["dc.identifier.doi","10.1016/j.future.2012.02.004"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15375"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.eissn","0167-739X"],["dc.relation.orgunit","Gesellschaft für wissenschaftliche Datenverarbeitung"],["dc.title","Adaptive parallel job scheduling with resource admissible allocation on two-level hierarchical grids"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","299"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Computación y Sistemas"],["dc.bibliographiccitation.lastpage","316"],["dc.bibliographiccitation.volume","17"],["dc.contributor.author","Lezama Barquet, Anuar"],["dc.contributor.author","Tchernykh, Andrei"],["dc.contributor.author","Yahyapour, Ramin"],["dc.date.accessioned","2019-02-18T09:05:33Z"],["dc.date.available","2019-02-18T09:05:33Z"],["dc.date.issued","2013"],["dc.description.abstract","In this paper, we present an experimental study of job scheduling algorithms in infrastructure as a service type in clouds. We analyze different system service levels which are distinguished by the amount of computing power a customer is guaranteed to receive within a time frame and a price for a processing time unit. We analyze different scenarios for this model. These scenarios combine a single service level with single and parallel machines. We apply our algorithmsin the context of executing real workload traces available to HPC community. In order to provide performance comparison, we make a joint analysis of several metrics. A case study is given."],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/57567"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.issn","1405-5546"],["dc.title","Performance Evaluation of Infrastructure as Service Clouds with SLA Constraints"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","95"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal"," Journal of Grid Computing"],["dc.bibliographiccitation.lastpage","116"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Ramírez-Alcaraz, Juan Manuel"],["dc.contributor.author","Tchernykh, Andrei"],["dc.contributor.author","Yahyapour, Ramin"],["dc.contributor.author","Schwiegelshohn, Uwe"],["dc.contributor.author","Quezada-Pina, Ariel"],["dc.contributor.author","González-García, José Luis"],["dc.contributor.author","Hirales-Carbajal, Adán"],["dc.date.accessioned","2018-08-16T14:02:01Z"],["dc.date.available","2018-08-16T14:02:01Z"],["dc.date.issued","2011"],["dc.description.abstract","We address non-preemptive non-clairvoyant online scheduling of parallel jobs on a Grid. We consider a Grid scheduling model with two stages. At the first stage, jobs are allocated to a suitable Grid site, while at the second stage, local scheduling is independently applied to each site. We analyze allocation strategies depending on the type and amount of information they require. We conduct a comprehensive performance evaluation study using simulation and demonstrate that our strategies perform well with respect to several metrics that reflect both user- and system-centric goals. Unfortunately, user run time estimates and information on local schedules does not help to significantly improve the outcome of the allocation strategies. When examining the overall Grid performance based on real data, we determined that an appropriate distribution of job processor requirements over the Grid has a higher performance than an allocation of jobs based on user run time estimates and information on local schedules. In general, our experiments showed that rather simple schedulers with minimal information requirements can provide a good performance."],["dc.identifier.doi","10.1007/s10723-011-9179-y"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15372"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.eissn","1570-7873"],["dc.relation.eissn","1572-9184"],["dc.title","Job Allocation Strategies with User Run Time Estimates for Online Scheduling in Hierarchical Grids"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","299"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Computación y Sistemas"],["dc.bibliographiccitation.lastpage","316"],["dc.bibliographiccitation.volume","17"],["dc.contributor.author","González-García, José Luis"],["dc.contributor.author","Yahyapour, Ramin"],["dc.contributor.author","Tchernykh, Andrei"],["dc.date.accessioned","2021-10-25T07:52:21Z"],["dc.date.available","2021-10-25T07:52:21Z"],["dc.date.issued","2013"],["dc.description.abstract","In this paper, we give an overview of efforts to improve current techniques of load-balancing and efficiency of finite element method (FEM) computations on large-scale parallel machines and introduce a multilevel load balancer to improve the local load imbalance. FEM is used to numerically approximate solutions of partial differential equations (PDEs) as well as integral equations. The PDEs domain is discretized into a mesh of information and usually solved using iterative methods. Distributing the mesh among the processors in a parallel computer, also known as the mesh-partitioning problem, was shown to be NPcomplete. Many efforts are focused on graphpartitioning to parallelize and distribute the mesh of information. Data partitioning is important to efficiently execute applications in distributed systems. To address this problem, a variety of general-purpose libraries and techniques have been developed providing great effectiveness. But the load-balancing problem is not yet well solved. Today’s large simulations require new techniques to scale on clusters of thousands of processors and to be resource aware due the increasing use of heterogeneous computing architectures as found in many-core computer systems. Existing libraries and algorithms need to be enhanced to support more complex applications and hardware architectures. We present trends in this field and discuss new ideas and approaches that take into account the new emerging requirements."],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91242"],["dc.language.iso","en"],["dc.relation.issn","1405-5546"],["dc.relation.orgunit","Gesellschaft für wissenschaftliche Datenverarbeitung"],["dc.title","Load Balacing for parallel Computations with the finite Element Method"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Armenta-Cano, Fermín"],["dc.contributor.author","Tchernykh, Andrei"],["dc.contributor.author","Cortés-Mendoza, Jorge"],["dc.contributor.author","Yahyapour, Ramin"],["dc.contributor.author","Drozdov, Alexander Yu."],["dc.contributor.author","Bouvry, Pascal"],["dc.contributor.author","Kliazovich, Dzmitry"],["dc.contributor.author","Avetisyan, Arutyun"],["dc.date.accessioned","2019-02-08T14:31:43Z"],["dc.date.available","2019-02-08T14:31:43Z"],["dc.date.issued","2015"],["dc.description.abstract","In this paper, we present an energy optimization model of Cloud computing, and formulate novel energy-aware resource allocation problem that provides energy-efficiency by heterogeneous job consolidation taking into account types of applications. Data centers process heterogeneous workloads that include CPU intensive, disk I/O intensive, memory intensive, network I/O intensive and other types of applications. When one type of applications creates a bottleneck and resource contention either in CPU, disk or network, it may result in degradation of the system performance and increasing energy consumption. We discuss energy characteristics of applications, and how an awareness of their types can help in intelligent allocation strategy to improve energy consumption."],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/57540"],["dc.language.iso","en"],["dc.notes.preprint","yes"],["dc.notes.status","final"],["dc.relation.conference","RuSCDays'15 - The Russian Supercomputing Days"],["dc.relation.eventlocation","Moscow, Russia"],["dc.relation.eventstart","2015-09-25"],["dc.relation.iserratumof","yes"],["dc.title","Heterogeneous Job Consolidation for Power Aware Scheduling with Quality of Service"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","65"],["dc.bibliographiccitation.lastpage","65"],["dc.bibliographiccitation.volume","8097"],["dc.contributor.author","Li, Zhihui"],["dc.contributor.author","Yahyapour, Ramin"],["dc.contributor.author","He, Yuxiong"],["dc.contributor.author","Koziris, Nectarios"],["dc.contributor.author","Mendelson, Bilha"],["dc.contributor.author","Sonigo, Veronika"],["dc.contributor.author","Streit, Achim"],["dc.contributor.author","Tchernykh, Andrei"],["dc.date.accessioned","2019-02-13T08:54:15Z"],["dc.date.available","2019-02-13T08:54:15Z"],["dc.date.issued","2013"],["dc.description.abstract","Despite significant effort parallel and distributed systems available today are still not fully utilized and exploited. Scheduling and load balancing techniques remain crucial for implementing efficient parallel and distributed applications and for making best use of existing parallel and distributed systems. The need for such techniques intensifies with the foreseen advent of exa-scale computer systems with many core and accelerator architectures. Similarly, cloud computing became a viable paradigm for some applications. Scheduling includes planning and optimization of the resource allocation as well as coping with the dynamics of the systems. These topics have been subject for research for many decades but remain one of the core topics in parallel and distributed computing."],["dc.identifier.doi","10.1007/978-3-642-40047-6_9"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/57557"],["dc.language.iso","en"],["dc.notes.preprint","yes"],["dc.notes.status","final"],["dc.relation.conference","Euro-Par 2013 Parallel Processing"],["dc.relation.crisseries","Lecture Notes in Computer Science"],["dc.relation.eventlocation","Aachen, Germany"],["dc.relation.eventstart","2013-08"],["dc.relation.isbn","978-3-642-40047-6"],["dc.relation.iserratumof","yes"],["dc.relation.ispartofseries","Lecture Notes in Computer Science"],["dc.title","Scheduling and Load Balancing"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","325"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Journal of Grid Computing"],["dc.bibliographiccitation.lastpage","346"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Hirales-Carbajal, Adán"],["dc.contributor.author","Tchernykh, Andrei"],["dc.contributor.author","Yahyapour, Ramin"],["dc.contributor.author","González-García, José Luis"],["dc.contributor.author","Röblitz, Thomas"],["dc.contributor.author","Ramírez-Alcaraz, Juan Manuel"],["dc.date.accessioned","2018-08-16T14:09:31Z"],["dc.date.available","2018-08-16T14:09:31Z"],["dc.date.issued","2012"],["dc.description.abstract","In this paper, we present an experimental study of deterministic non-preemptive multiple workflow scheduling strategies on a Grid. We distinguish twenty five strategies depending on the type and amount of information they require. We analyze scheduling strategies that consist of two and four stages: labeling, adaptive allocation, prioritization, and parallel machine scheduling. We apply these strategies in the context of executing the Cybershake, Epigenomics, Genome, Inspiral, LIGO, Montage, and SIPHT workflows applications. In order to provide performance comparison, we performed a joint analysis considering three metrics. A case study is given and corresponding results indicate that well known DAG scheduling algorithms designed for single DAG and single machine settings are not well suited for Grid scheduling scenarios, where user run time estimates are available. We show that the proposed new strategies outperform other strategies in terms of approximation factor, mean critical path waiting time, and critical path slowdown. The robustness of these strategies is also discussed."],["dc.identifier.doi","10.1007/s10723-012-9215-6"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15374"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.eissn","1572-9184"],["dc.relation.issn","1570-7873"],["dc.relation.orgunit","Gesellschaft für wissenschaftliche Datenverarbeitung"],["dc.title","Multiple Workflow Scheduling Strategies with User Run Time Estimates on a Grid"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","384"],["dc.bibliographiccitation.lastpage","398"],["dc.bibliographiccitation.volume","796"],["dc.contributor.author","Galaviz-Alejos, Luis-Angel"],["dc.contributor.author","Armenta-Cano, Fermín"],["dc.contributor.author","Tchernykh, Andrei"],["dc.contributor.author","Radchenko, Gleb"],["dc.contributor.author","Drozdov, Alexander Yu."],["dc.contributor.author","Sergiyenko, Oleg"],["dc.contributor.author","Yahyapour, Ramin"],["dc.date.accessioned","2019-01-25T08:23:44Z"],["dc.date.available","2019-01-25T08:23:44Z"],["dc.date.issued","2018"],["dc.description.abstract","In this paper, we address the problem of power-aware Virtual Machines (VMs) consolidation considering resource contention. Deployment of VMs can greatly influence host performance, especially, if they compete for resources on insufficient hardware. Performance can be drastically reduced and energy consumption increased. We focus on a bi-objective experimental evaluation of scheduling strategies for CPU and memory intensive jobs regarding the quality of service (QoS) and energy consumption objectives. We analyze energy consumption of the IBM System x3650 M4 server, with optimized performance for business-critical applications and cloud deployments built on IBM X-Architecture. We create power profiles for different types of applications and their combinations using SysBench benchmark. We evaluate algorithms with workload traces from Parallel Workloads and Grid Workload Archives and compare their non-dominated Pareto optimal solutions using set coverage and hyper volume metrics. Based on the presented case study, we show that our algorithms can provide the best energy and QoS trade-offs."],["dc.identifier.doi","10.1007/978-3-319-73353-1_27"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/57378"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.publisher","Springer"],["dc.relation.crisseries","Communications in Computer and Information Science"],["dc.relation.isbn","978-3-319-73352-4"],["dc.relation.ispartof","High Performance Computing. CARLA 2017"],["dc.relation.orgunit","Gesellschaft für wissenschaftliche Datenverarbeitung"],["dc.title","Bi-objective Heterogeneous Consolidation in Cloud Computing"],["dc.type","book_chapter"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]