Дисертації з теми "Workload scheduling"
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Wu, Zuobao. "Multi-agent workload control and flexible job shop scheduling." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001193.
Tan, Chin Jiat. "Workload analysis and scheduling policies for a document processing centre." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38286.
Includes bibliographical references.
This thesis is the result of a six-month internship at the Steel Stock Department of Keppel FELS Singapore, a company which is involved in the design and construction of oil-rigs. The primary objective of this project is to reduce the tardiness of the delivery of steel materials and identify the reasons behind the delay. The initial stage of this attachment is devoted to understanding the process flow of the department. Analysis has been done to pinpoint to the exact causes of the delay, which is at the stages of document processing and dispatching to the storage areas. The workload at each stage of document processing has been analyzed using a queuing model and it has been found that the stage that the issue vouchers have to be generated and printed out is the bottleneck in the entire process flow. Some recommendations have been proposed to alleviate the problem. The second part of this thesis focuses on the reasons why scheduling rules should be utilized when dispatching the issue vouchers to the storage areas. Three scheduling rules have been tested and their performances with regards to tardiness have been studied.
by Chin Jiat Tan.
M.Eng.
Kettimuthu, Rajkumar. "Type- and Workload-Aware Scheduling of Large-Scale Wide-Area Data Transfers." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437747493.
Xu, Luna. "A Workload-aware Resource Management and Scheduling System for Big Data Analysis." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/87469.
Ph. D.
Clusters of multiple computers connected through internet are often deployed in industry for larger scale data processing or computation that cannot be handled by standalone computers. In such a cluster, resources such as CPU, memory, disks are integrated to work together. It is important to manage a pool of such resources in a cluster to efficiently work together to provide better performance for workloads running on top. This role is taken by a software component in the middle layer called resource manager. Resource manager coordinates the resources in the computers and schedule tasks to them for computation. This dissertation reveals that current resource managers often partition resources statically hence cannot capture the dynamic resource needs of workloads as well as the heterogeneous configurations of the underlying resources. For example, some computers in a clsuter might be older than the others with slower CPU, less memory, etc. Workloads can show different resource needs. Watching YouTube require a lot of network resource while playing games demands powerful GPUs. To this end, the disseration proposes novel approaches to manage resources that are able to capture the heterogeneity of resources and dynamic workload needs, based on which, it can achieve efficient resource management, and schedule the right task to the right resource.
BELL, RUBEN LIONEL. "A STUDY OF WORKLOAD SCHEDULING AND RESOURCE PLANNING AT AN OVERHAUL FACILITY." University of Cincinnati / OhioLINK, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=ucin975507800.
Ali, Syed Zeeshan. "An investigation into parallel job scheduling using service level agreements." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/an-investigation-into-parallel-job-scheduling-using-service-level-agreements(f4685321-374e-41c4-86da-d07f09ea4bac).html.
Nguyen, Minh Duc. "Application-aware Scheduling in Multichannel Wireless Networks with Power Control." Thesis, KTH, Kommunikationsnät, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-99194.
Rehman, Attiqa [Verfasser]. "Workload Modeling and Prediction for Workflow Scheduling in Dynamic Grid Environments / Attiqa Rehman." Hagen : Fernuniversität Hagen, 2014. http://d-nb.info/104711464X/34.
Patel, Yash. "Stochastic scheduling and workload allocation : QoS support and profitable brokering in computing grids." Thesis, Imperial College London, 2007. http://hdl.handle.net/10044/1/8081.
Martin, Megan Wydick. "Computational Studies in Multi-Criteria Scheduling and Optimization." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78699.
Ph. D.
Stigge, Martin. "Real-Time Workload Models : Expressiveness vs. Analysis Efficiency." Doctoral thesis, Uppsala universitet, Avdelningen för datorteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-219307.
Qian, Fei. "Scheduling problems for fractional airlines." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/39641.
Pan, Xinwei. "FORECASTING THE WORKLOAD WITH A HYBRID MODEL TO REDUCE THE INEFFICIENCY COST." UKnowledge, 2017. http://uknowledge.uky.edu/me_etds/91.
Varisteas, Georgios. "Cooperative user- and system-level scheduling of task-centric parallel programs." Licentiate thesis, KTH, Programvaruteknik och Datorsystem, SCS, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-127708.
QC 20130910
Varisteas, Georgios. "Effective cooperative scheduling of task-parallel applications on multiprogrammed parallel architectures." Doctoral thesis, KTH, Programvaruteknik och Datorsystem, SCS, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175461.
QC 20151016
Staats, Raymond William. "An Airspace Planning and Collaborative Decision Making Model Under Safety, Workload, and Equity Considerations." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/26844.
Ph. D.
Serrano, Gómez Mónica. "Scheduling Local and Remote Memory in Cluster Computers." Doctoral thesis, Editorial Universitat Politècnica de València, 2013. http://hdl.handle.net/10251/31639.
Cluster computers represent a cost-effective alternative solution to supercomputers. In these systems, it is common to constrain the memory address space of a given processor to the local motherboard. Constraining the system in this way is much cheaper than using a full-fledged shared memory implementation among motherboards. However, memory usage among motherboards may be unfairly balanced depending on the memory requirements of the applications running on each motherboard. This situation can lead to disk-swapping, which severely degrades system performance, although there may be unused memory on other motherboards. A straightforward solution is to increase the amount of available memory in each motherboard, but the cost of this solution may become prohibitive. On the other hand, remote memory access (RMA) hardware provides fast interconnects among the motherboards of a cluster computer. In recent works, this characteristic has been used to extend the addressable memory space of selected motherboards. In this work, the baseline machine uses this capability as a fast mechanism to allow the local OS to access to DRAM memory installed in a remote motherboard. In this context, efficient memory scheduling becomes a major concern since main memory latencies have a strong impact on the overall execution time of the applications, provided that remote memory accesses may be several orders of magnitude higher than local accesses. Additionally, changing the memory distribution is a slow process which may involve several motherboards, hence the memory scheduler needs to make sure that the target distribution provides better performance than the current one. This dissertation aims to address the aforementioned issues by proposing several memory scheduling policies. First, an ideal algorithm and a heuristic strategy to assign main memory from the different memory regions are presented. Additionally, a Quality of Service control mechanism has been devised in order to prevent unacceptable performance degradation for the running applications. The ideal algorithm finds the optimal memory distribution but its computational cost is prohibitive for a high number of applications. This drawback is handled by the heuristic strategy, which approximates the best local and remote memory distribution among applications at an acceptable computational cost. The previous algorithms are based on profiling. To deal with this potential shortcoming we focus on analytical solutions. This dissertation proposes an analytical model that estimates the execution time of a given application for a given memory distribution. This technique is used as a performance predictor that provides the input to a memory scheduler. The estimates are used by the memory scheduler to dynamically choose the optimal target memory distribution for each application running in the system in order to achieve the best overall performance. Scheduling at a higher granularity allows simpler scheduler policies. This work studies the feasibility of scheduling at OS page granularity. A conventional hardware-based block interleaving and an OS-based page interleaving have been assumed as the baseline schemes. From the comparison of the two baseline schemes, we have concluded that only the performance of some applications is significantly affected by page-based interleaving. The reasons that cause this impact on performance have been studied and have provided the basis for the design of two OS-based memory allocation policies. The first one, namely on-demand (OD), is a simple strategy that works by placing new pages in local memory until this region is full, thus benefiting from the premise that most of the accessed pages are requested and allocated before than the least accessed ones to improve the performance. Nevertheless, in the absence of this premise for some benchmarks, OD performs worse. The second policy, namely Most-accessed in-local (Mail), is proposed to avoid this problem
Serrano Gómez, M. (2013). Scheduling Local and Remote Memory in Cluster Computers [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/31639
Alfresco
Le, Trung. "Towards Sustainable Cloud Computing: Reducing Electricity Cost and Carbon Footprint for Cloud Data Centers through Geographical and Temporal Shifting of Workloads." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23082.
Chapman, Dona Elizabeth. "A decision support system for the faculty/course assignment problem." Thesis, This resource online, 1985. http://scholar.lib.vt.edu/theses/available/etd-10022008-063148/.
March, Cabrelles José Luis. "Dynamic Power-Aware Techniques for Real-Time Multicore Embedded Systems." Doctoral thesis, Editorial Universitat Politècnica de València, 2015. http://hdl.handle.net/10251/48464.
March Cabrelles, JL. (2014). Dynamic Power-Aware Techniques for Real-Time Multicore Embedded Systems [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48464
TESIS
Tierney, Shirley J. "Nursing Unit Staffing: An Innovative Model Incorporating Patient Acuity and Patient Turnover: A Dissertation." eScholarship@UMMS, 2010. https://escholarship.umassmed.edu/gsn_diss/18.
Colin, Emerson Carlos. "Distribuição de carga e variação de capacidade na programação da produção: resultados na inserção de espera e na utilização de capacidade adicional." Universidade de São Paulo, 2000. http://www.teses.usp.br/teses/disponiveis/3/3136/tde-19112003-145354/.
This thesis analyses two cases of one-machine problem regarding to production scheduling with fixed sequence. In both problems, modeling with mathematical programming, and (pseudo)polynomial-time algorithms are suggested. The first problem deals with idle time insertion in the problem where the objective function (represented by a sum of costs) considers that each job has costs described as any convex function of its completion time. The second problem considers earliness and tardiness with distinct costs for each job considering the possible use of additional capacity. For the additional capacity we assume that there are distinct costs for each time period where jobs can be processed. A procedure dealing with options of either to change the number of shifts or to utilize overtime considering total costs is suggested. Analysis and generalizations based on the utilization of several contiguous time periods with distinct costs and a heuristic extension for the multiple-machine case are also presented
Polo, Jordà. "Multi-constraint scheduling of MapReduce workloads." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/276174.
Polo, Bardès Jordà. "Multi-constraint scheduling of MapReduce workloads." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/276174.
Netti, Alessio. "Development of Data-Driven Dispatching Heuristics for Heterogeneous HPC Systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14541/.
Delgado, Javier. "Scheduling Medical Application Workloads on Virtualized Computing Systems." FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/633.
Hung, Hui-Chih. "Allocation of jobs and resources to work centers." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1141849609.
Amaral, Marcelo. "Improving resource efficiency in virtualized datacenters." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/666753.
En els últims anys hi ha hagut un gran creixement del Internet of Things (IoT) i els seus protocols. La creixent difusió de dispositius electrònics amb capacitats d'identificació, computació i comunicació esta establint les bases de l’aparició de serveis altament distribuïts i del seu entorn de xarxa. L’esmentada situació implica que hi ha una creixent demanda de plataformes de processament i gestió avançada de dades per IoT. Aquestes plataformes requereixen suport per a múltiples protocols al Edge per connectivitat amb el objectes, però també necessiten d’una organització de dades interna i capacitats avançades de processament de dades per satisfer les demandes de les aplicacions i els serveis que consumeixen dades IoT. Una de les aproximacions inicials per abordar aquesta demanda és la integració entre IoT i el paradigma del Cloud computing. Hi ha molts avantatges d'integrar IoT amb el Cloud. IoT genera quantitats massives de dades i el Cloud proporciona una via perquè aquestes dades viatgin a la seva destinació. Però els models actuals del Cloud no s'ajusten del tot al volum, varietat i velocitat de les dades que genera l'IoT. Entre les noves tecnologies que sorgeixen al voltant del IoT per proporcionar un escenari nou, el paradigma del Fog Computing s'ha convertit en la més rellevant. Fog Computing es va introduir fa uns anys com a resposta als desafiaments que plantegen moltes aplicacions IoT, incloent requisits com baixa latència, operacions en temps real, distribució geogràfica extensa i mobilitat. També aquest entorn està cobert per l'arquitectura de xarxa MEC (Mobile Edge Computing) que proporciona serveis de TI i capacitats Cloud al edge per la xarxa mòbil dins la Radio Access Network (RAN) i a prop dels subscriptors mòbils. El Fog aborda casos d’us amb requisits que van més enllà de les capacitats de solucions només Cloud. La interacció entre Cloud i Fog és crucial per a l'evolució de l'anomenat IoT, però l'abast i especificació d'aquesta interacció és un problema obert. Aquesta tesi té com objectiu trobar les decisions de disseny i les tècniques adequades per construir un sistema distribuït escalable per IoT sota el paradigma del Fog Computing per a ingerir i processar dades. L'objectiu final és explorar els avantatges/desavantatges i els desafiaments en el disseny d'una solució des del Edge al Cloud per abordar les oportunitats que les tecnologies actuals i futures portaran d'una manera integrada. Aquesta tesi descriu un enfocament arquitectònic que aborda alguns dels reptes tècnics que hi ha darrere de la convergència entre IoT, Cloud i Fog amb especial atenció a reduir la bretxa entre el Cloud i el Fog. Amb aquesta finalitat, s'introdueixen nous models i tècniques per explorar solucions per entorns IoT. Aquesta tesi contribueix a les propostes arquitectòniques per a la ingesta i el processament de dades IoT mitjançant 1) proposant la caracterització d'una plataforma per a l'allotjament de workloads IoT en el Cloud que proporcioni capacitats de processament de flux de dades multi-tenant, les interfícies a través d'una tecnologia centrada en dades incloent la construcció d'una infraestructura avançada per avaluar el rendiment i validar la solució proposada. 2) estudiar un enfocament arquitectònic seguint el paradigma Fog que aborda alguns dels reptes tècnics que es troben en la primera contribució. La idea és estudiar una extensió del model que abordi alguns dels reptes centrals que hi ha darrere de la convergència de Fog i IoT. 3) Dissenyar una plataforma distribuïda i escalable per a realitzar operacions IoT en un entorn de dades en moviment. La idea després d'estudiar el processament de dades en el Cloud, i després d'estudiar la conveniència del paradigma Fog per resoldre els desafiaments de IoT a prop del Edge, és definir els protocols, les interfícies i la gestió de dades per resoldre la ingestió i processament de dades d’una manera més eficient
Georgiou, Yiannis. "Contributions for resource and job management in high performance computing." Grenoble, 2010. http://www.theses.fr/2010GRENM079.
High Performance Computing is characterized by the latest technological evolutions in computing architectures and by the increasing needs of applications for computing power. A particular middleware called Resource and Job Management System (RJMS), is responsible for delivering computing power to applications. The RJMS plays an important role in HPC since it has a strategic place in the whole software stack because it stands between the above two layers. However, the latest evolutions in hardware and applications layers have provided new levels of complexities to this middleware. Issues like scalability, management of topological constraints, energy efficiency and fault tolerance have to be particularly considered, among others, in order to provide a better system exploitation from both the system and user point of view. This dissertation provides a state of the art upon the fundamental concepts and research issues of Resources and Jobs Management Systems. It provides a multi-level comparison (concepts, functionalities, performance) of some Resource and Jobs Management Systems in High Performance Computing. An important metric to evaluate the work of a RJMS on a platform is the observed system utilization. However, studies and logs of production platforms show that HPC systems in general suffer of significant un-utilization rates. Our study deals with these clusters' un-utilization periods by proposing methods to aggregate otherwise un-utilized resources for the benefit of the system or the application. More particularly this thesis explores RJMS level mechanisms: 1) for increasing the jobs valuable computation rates in the high volatile environments of a lightweight grid context, 2) for improving system utilization with malleability techniques and 3) providing energy efficient system management through the exploitation of idle computing machines. The experimentation and evaluation in this type of contexts provide important complexities due to the inter-dependency of multiple parameters that have to be taken into control. In this thesis we have developed a methodology based upon real-scale controlled experimentation with submission of synthetic or real workload traces
Gonzalo, P. Rodrigo. "HPC scheduling in a brave new world." Doctoral thesis, Umeå universitet, Institutionen för datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-132983.
Work also supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR) and we used resources at the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility, supported by the Officece of Science of the U.S. Department of Energy, both under Contract No. DE-AC02-05CH11231.
Stanley, Leisa J. "Association among neonatal mortality, weekend or nighttime admissions and staffing in a Neonatal Intensive Care Unit." [Tampa, Fla.] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002421.
Broberg, James Andrew, and james@broberg com au. "Effective task assignment strategies for distributed systems under highly variable workloads." RMIT University. Computer Science and Information Technology, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080130.150130.
Vanga, Manohar [Verfasser], and Björn [Akademischer Betreuer] Brandenburg. "High-Throughput and Predictable VM Scheduling for High-Density Workloads / Manohar Vanga ; Betreuer: Björn Brandenburg." Kaiserslautern : Technische Universität Kaiserslautern, 2021. http://d-nb.info/1240674538/34.
LI, JIAN-FU, and 李建賦. "Workload Aware CPU Scheduling Algorithm for Xen Platforms." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/wv72nq.
國立屏東大學
資訊工程學系碩士班
105
Virtualization is the most popular technology in recent years. It shares the physical resources of the physical machine to multiple virtual machines. The cost and space of server management can be reduced significantly if the physical resources are allocated wisely. The Xen Project is a well-know virtualization platform created by Keir Fraser and Ian Pratt. In this thesis, we shall target the physical resources allocation problem for xen virtualization systems. In particular, we are interested in dynamic CPU scheduling for virtual machines such that the performance of the entire system could be improved. At the current stage, existing CPU scheduler for xen are all in static manner. In other words, it allocation physical CPUs to virtual CPUs offline. However, we can not predict workload (i.e, number of tasks of each virtual machine at the runtime.In this thesis, an RTDS-based CPU scheduler, called enhanced real-time deferrable server(ERTDS), is proposed an additional capacity to virtual CPUs when their run-time requirements are higher than expected. The proposed ERTDS has been implemented in Xen 4.7 and a series of experiments has been conducted for which we have some encouraging results.
CHEN, KUAN-FU, and 陳冠甫. "Workload- and resource-aware list-based workflow scheduling." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/7y888u.
國立臺中教育大學
資訊工程學系
107
Workflow scheduling is an NP-complete problem, and has always been an important research topic on parallel job scheduling. Different types of scheduling heuristics have been developed for tackling the challenging workflow scheduling problem. List-based scheduling is one of the most widely used categories for different workflow structures and optimization goals. Among various list-based workflow scheduling algorithms, HEFT and PEFT are two typical and well-known representatives with important innovations. However, none of them can consistently retain advantage over the other across various workflow properties and system scales. That motivated our research work in this thesis, focusing on the problem of task-parallel workflow scheduling on homogeneous parallel systems which have become feasible and common on current cloud computing platforms with virtualization technology. Based on thorough experimental analysis of HEFT and PEFT, we found drawbacks of them, and then developed a new workload- and resource-aware list-based workflow scheduling approach featuring three new mechanisms, including structural task ranking, task ranking based on allocated critical path, and adaptable task allocation. The proposed approach was evaluated with a series of simulation experiments based on both synthetic workflow structures and real-world workflow models, and compared to HEFT and PEFT. The experimental results demonstrate that our approach can achieve significantly superior performance in most circumstances in terms of both average makespan and best ratio.
Ting, Chen Yen, and 陳彥廷. "Workload Partitioning and Scheduling on Heterogeneous Multi-Core Systems." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/76455374582739431719.
國立中正大學
資訊工程研究所
101
Due to the diversity in computing capabilities of processors in heterogeneous multicore systems, it is difficult to come up with a perfect task scheduling algorithm that can avoid all processors from becoming idle at some point in time during the whole schedule. The situation becomes worse when the capabilities of processors differ by a large margin or the ratio of communication time between tasks to the computation time of tasks is very large. Nevertheless, it is this imperfection that motivates this Thesis to propose a re-scheduling scheme that leverages the characteristics of divisible tasks by partitioning the workload across two different processors so as to fill the holes (idle time slots) in the schedule. Based on the type of hole, constant or varying, different strategies are proposed, including a profiling-based partitioning and an on-the-fly partitioning. Re-scheduling based on a combination of these two strategies results in a decrease in the makespan and total amount of idle time of processors. Experiment results show that the makespan can be decreased by 14% and the total amount of idle time by 50%.
Chang, Ting-Chi, and 張廷吉. "Vehicle Routing and Scheduling Problems with Time Constraints and Balanced Workload." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/97128069794615487288.
逢甲大學
工業工程學系
88
In this study, issues of vehicle routing and scheduling problems arising in local distribution centers are addressed. Due to the evolution of local economy from production-oriented markets to customer-oriented markets, a large number of distribution centers and the associated convenience stores are established in major metropolitan areas. Since the distribution services of commodities provided by distribution centers to convenience stores have tremendous impacts on the cost-effective performance of a firm and the level of customer services, issues of planning and management of commodity distribution have been received great concerns in practice. Theoretically, the planning of commodity distribution services is one type of vehicle routing and scheduling problems and can be improved through the application of optimization techniques. The vehicle routing and scheduling problem can be treated as a combinatorial optimization problem in nature and is NP-hard problem. First, we analyze the vehicle routing and scheduling problem arising in local distribution centers. The vehicle routing and scheduling problem under consideration is analyzed in terms of the following criteria: (1) to minimize the number of vehicles needed to provide the required distribution service, (2) to minimize the total travel distance needed to complete the total distribution service, (3) to ensure that every route is balanced in terms of workload, and (4) to ensure that the delivery time of every route is within the required duration. Next, an optimization model for describing this problem is explored and formulated. A heuristic-type solution method is developed by exploiting the property of the developed model. The developed heuristic consists of three stages. First, we generate an initial solution using savings method. Second we devise five procedures using one-point movement, two-point exchange, 2-opt improvement, reinitialization, and infeasibility improvement. Third, we ensure that every route is balanced in terms of workload. A real-world data is collected from a local distribution center and its associated convenience stores. The collected data is used for testing and implementation of the proposed approach. Results suggest that the proposed approach can achieve the foregoing objectives. The developed heuristic is fast and has improved upon previous best-known solution. It can obtain solution of higher accuracy and provide more scheduling information that has substantial contribution to operate the planning and management of commodity distribution.
CHAO-WEI, HUANG, and 黃昭為. "Improvement of Workload Balancing Using Parallel Loop Self-Scheduling on Xeon Phi." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/89262948380098450456.
東海大學
資訊工程學系
103
In this paper, we will examine how to improve workload balancing on a computing cluster by a parallel loop self-scheduling scheme. We use hybrid MPI and OpenMP parallel programming in C language. The block partition loop is according to the performance weighting of compute nodes. This study implements parallel loop self-scheduling use Xeon Phi, with its characteristics to improve workload balancing between heterogeneous nodes. The parallel loop self-scheduling is composed of the static and dynamic allocation. A weighting algorithm is adopted in the static part while the well-known loop self-scheduling scheme is adopted in the dynamic part. In recent years, Intel promotes its new product Xeon Phi coprocessor, which is similar to the x86 architecture coprocessor. It has about 60 cores and can be regarded as a single computing node, with the computing power that cannot be ignored. In our experiment, we will use a plurality of computing nodes. We compute four applications, i.e., matrix multiplication, sparse matrix multiplication, Mandelbrot set computation, and the circuit satisfiability problem. Our results will show how to do the weight allocation and how to choose a scheduling scheme to achieve the best performance in the parallel loop self-scheduling.
Lin, Ming Ham, and 林明翰. "Energy Efficient Workload-Aware DVS Scheduling for Multi-core Real-time Embedded Systems." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/79906963934612974983.
國立交通大學
網路工程研究所
96
Memory is an important shared resource in a multi-core real-time embedded system. The memory contentions between cores will lengthen the total execution time due to waiting for memory requests being served. In this thesis, we focus on the tasks partition scheduling problem while considering memory contentions in multi-core real-time embedded systems. We propose an energy efficient scheduling mechanism with consideration to the memory workload of tasks, called WAS-DVS (workload-aware scheduling-dynamic voltage scaling), which is an improvement of an existing method, LTF-MES (Largest-Task-First-Minimize-Energy-Scheduling). The main difference between ours and LTF-MES is that we consider the execution order of tasks that may reduce the frequency of memory contentions. Simulation results show that by reducing memory contentions between tasks, the slack time will increase and the proposed WAS-DVS can use it to lower total execution time and total energy consumption on a variety of workloads in multi-core systems. The proposed WAS-DVS can lower the total execution time from 2% to 10.3% before applying DVS and improve the total energy consumption from 3.85% to 19% compared to LTF-MES, under various numbers of tasks and 2 to 16 cores after applying DVS.
Ishakian, Vatche. "Strategic and operational services for workload management in the cloud." Thesis, 2013. https://hdl.handle.net/2144/13128.
LIU, RONG-CHAO, and 劉榮超. "The effects of system parameters and workload characteristics on the performance of load balancing and scheduling policies in distributed parallel computing systems." Thesis, 1991. http://ndltd.ncl.edu.tw/handle/32020725085064600753.
"Affinity scheduling of unbalanced workloads." Thesis, 1993. http://hdl.handle.net/10388/etd-11012011-110443.
Maia, John Camilo Ferreira. "Scheduling scientific workloads on an heterogeneous server." Master's thesis, 2016. http://hdl.handle.net/1822/47830.
The goal of this dissertation is to explore techniques to improve the efficiency and performance level of scientific applications on computing platforms that are equipped with multiple multi-core devices and at least one many-core device, such as Intel MIC and/or NVidia GPU devices. These platforms are known as heterogeneous servers, which are becoming increasingly popular both in research environments as in our daily gadgets. To fully exploit the performance capabilities of the heterogeneous servers, it is crucial to have an efficient workload distribution among the available devices; however the heterogeneity of the server and the workload irregularity dramatically increases the challenge. Most state of the art schedulers efficiently balance regular workloads among heterogeneous devices, although some lack adequate mechanisms for irregular workloads. Scheduling these type of workloads is particularly complex due to their unpredictability, namely on their execution time. To overcome this issue, this dissertation presents an efficient dynamic adaptive scheduler that efficiently balances irregular workloads among multiple devices in a heterogeneous environment. To validate the scheduling mechanism, the case study used in this thesis is an irregular scientific application that has a set of independent embarrassingly parallel tasks applied to a very large number of input datasets, whose tasks durations have an unpredictable range larger than 1:100. By dynamically adapting the size of the workloads that were distributed among the multiple devices in run-time, the scheduler featured in this dissertation had an occupancy rate of every computing resources over 97% of the application’s run-time while generating an overhead well below 0.001%.
O objetivo desta dissertação é o de explorar técnicas que possam melhorar a eficiência e o nível de performance de aplicações cientificas em plataformas de computação que estão equipadas com vários dispositivos multi-core e pelo menos um dispositivo many-core, como por exemplo um Intel MIC e/ou um GPU da NVidia. Estas plataformas são conhecidas como servidores heterogéneos e estão a se tornar cada vez mais populares, tanto em ambientes de investigação como em nossos gadgets diários. Para explorar completamente as capacidades de desempenho dos servidores heterogéneos, é crucial ter uma distribuição eficiente da carga de trabalho entre os vários dispositivos disponíveis; no entanto a heterogeneidade do servidor e a irregularidade das cargas de trabalho aumentam drasticamente o desafio. A maioria dos escalonadores mais avançados são capazes de equilibrar eficientemente cargas de trabalho regulares entre dispositivos heterogéneos, embora alguns deles não disponham de mecanismos adequados para cargas de trabalho irregulares. O escalonamento desse tipo de cargas de trabalho é particularmente complexo devido à sua imprevisibilidade, nomeadamente ao seu tempo de execução. Para superar este problema, esta dissertação apresenta um escalonador dinâmico e adaptativo que equilibra de forma eficiente cargas de trabalho irregulares entre vários dispositivos de uma plataforma heterogénea. Para validar o escalonador, o caso de estudo utilizado nesta tese é uma aplicação científica irregular que possui um conjunto de tarefas independentes, que são embaraçosamente paralelas, aplicadas a um grande número de conjuntos de dados, cujas tarefas têm durações com um n´nível de imprevisibilidade maior do que 1:100. Ao adaptar dinamicamente o tamanho das cargas de trabalho, que são distribuídas entre os vários dispositivos, em tempo de execução, o escalonador apresentado nesta dissertação apresenta uma taxa de ocupação de cada dispositivo acima de 97 % do tempo total de execução da aplicação e tem um peso que é bem abaixo dos 0,001 %.
Nair, Jayakrishnan U. "Scheduling for Heavy-Tailed and Light-Tailed Workloads in Queueing Systems." Thesis, 2012. https://thesis.library.caltech.edu/7121/1/thesis.pdf.
In much of classical queueing theory, workloads are assumed to be light-tailed, with job sizes being described using exponential or phase type distributions. However, over the past two decades, studies have shown that several real-world workloads exhibit heavy-tailed characteristics. As a result, there has been a strong interest in studying queues with heavy-tailed workloads. So at this stage, there is a large body of literature on queues with light-tailed workloads, and a large body of literature on queues with heavy-tailed workloads. However, heavy-tailed workloads and light-tailed workloads differ considerably in their behavior, and these two types of workloads are rarely studied jointly.
In this thesis, we design scheduling policies for queueing systems, considering both heavy-tailed as well as light-tailed workloads. The motivation for this line of work is twofold. First, since real world workloads can be heavy-tailed or light-tailed, it is desirable to design schedulers that are robust in their performance to distributional assumptions on the workload. Second, there might be scenarios where a heavy-tailed and a light-tailed workload interact in a queueing system. In such cases, it is desirable to design schedulers that guarantee fairness in resource allocation for both workload types.
In this thesis, we study three models involving the design of scheduling disciplines for both heavy-tailed as well as light-tailed workloads. In Chapters 3 and 4, we design schedulers that guarantee robust performance across heavy-tailed and light-tailed workloads. In Chapter 5, we consider a setting in which a heavy-tailed and a light-tailed workload complete for service. In this setting, we design scheduling policies that guarantee good response time tail performance for both workloads, while also maintaining throughput optimality.