Relevant bibliographies by topics / Heterogenous scheduling

Academic literature on the topic 'Heterogenous scheduling'

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Published: 11 March 2023

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Journal articles on the topic "Heterogenous scheduling"

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Patil, Shailesh. "Measurement-Based Opportunistic Scheduling for Heterogenous Wireless Systems." IEEE Transactions on Communications 57, no. 9 (September 2009): 2745–53. http://dx.doi.org/10.1109/tcomm.2009.09.0800902.

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Al-Saqabi, Khaled, Syed Sarwar, and Kassem Saleh. "Distributed gang scheduling in networks of heterogenous workstations." Computer Communications 20, no. 5 (July 1997): 338–48. http://dx.doi.org/10.1016/s0140-3664(97)00020-0.

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Sun, Donglai, Yang Liu, Jianhua Li, and Yue Wu. "Collaborative Opportunistic Scheduling in Heterogeneous Networks: A Distributed Approach." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/414689.

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We consider a collaborative opportunistic scheduling problem in a decentralized network with heterogeneous users. While most related researches focus on solutions for optimizing decentralized systems’ total performance, we proceed in another direction. Two problems are specifically investigated. (1) With heterogenous users having personal demands, is it possible to have it met by designing distributed opportunistic policies? (2) With a decentralized mechanism, how can we prevent selfish behaviors and enforce collaboration? In our research, we first introduce a multiuser network model along with a scheduling problem constrained by individual throughput requirement at each user’s side. An iterative algorithm is then proposed to characterize a solution for the scheduling problem, based on which collaborative opportunistic scheduling scheme is enabled. Properties of the algorithm, including convergence, will be discussed. Furthermore in order to keep the users staying with the collaboration state, an additional punishment strategy is designed. Therefore selfish deviation can be detected and disciplined so that collaboration is enforced. We demonstrate our main findings with both analysis and simulations.
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Ponsy. "Balanced Scheduling of Independent File-Sharing Tasks in Heterogenous Environment." Journal of Computer Science 7, no. 12 (December 1, 2011): 1793–97. http://dx.doi.org/10.3844/jcssp.2011.1793.1797.

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Lai, Shouwen, Binoy Ravindran, and Hyeonjoong Cho. "Heterogenous Quorum-Based Wake-Up Scheduling in Wireless Sensor Networks." IEEE Transactions on Computers 59, no. 11 (November 2010): 1562–75. http://dx.doi.org/10.1109/tc.2010.20.

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Chraibi, Amine, Said Ben Alla, and Abdellah Ezzati. "An efficient cloudlet scheduling via bin packing in cloud computing." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (June 1, 2022): 3226. http://dx.doi.org/10.11591/ijece.v12i3.pp3226-3237.

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<p>In this ever-developing technological world, one way to manage and deliver services is through cloud computing, a massive web of heterogenous autonomous systems that comprise adaptable computational design. Cloud computing can be improved through task scheduling, albeit it being the most challenging aspect to be improved. Better task scheduling can improve response time, reduce power consumption and processing time, enhance makespan and throughput, and increase profit by reducing operating costs and raising the system reliability. This study aims to improve job scheduling by transferring the job scheduling problem into a bin packing problem. Three modifies implementations of bin packing algorithms were proposed to be used for task scheduling (MBPTS) based on the minimisation of makespan. The results, which were based on the open-source simulator CloudSim, demonstrated that the proposed MBPTS was adequate to optimise balance results, reduce waiting time and makespan, and improve the utilisation of the resource in comparison to the current scheduling algorithms such as the particle swarm optimisation (PSO) and first come first serve (FCFS).</p>
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Incrocci, Luca, Paolo Marzialetti, Giorgio Incrocci, Andrea Di Vita, Jos Balendonck, Carlo Bibbiani, Serafino Spagnol, and Alberto Pardossi. "Substrate water status and evapotranspiration irrigation scheduling in heterogenous container nursery crops." Agricultural Water Management 131 (January 2014): 30–40. http://dx.doi.org/10.1016/j.agwat.2013.09.004.

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Hamdi, S., E. Bouazizi, and S. Faiz. "QOS MANAGEMENT IN REAL-TIME SPATIAL BIG DATA USING FEEDBACK CONTROL SCHEDULING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (August 19, 2015): 243–48. http://dx.doi.org/10.5194/isprsannals-ii-3-w5-243-2015.

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Geographic Information System (GIS) is a computer system designed to capture, store, manipulate, analyze, manage, and present all types of spatial data. Spatial data, whether captured through remote sensors or large scale simulations has always been big and heterogenous. The issue of real-time and heterogeneity have been extremely important for taking effective decision. Thus, heterogeneous real-time spatial data management has become a very active research domain. Existing research has principally focused on querying of real-time spatial data and their updates. But the unpredictability of access to data maintain the behavior of the real-time GIS unstable. In this paper, we propose the use of the real-time Spatial Big Data and we define a new architecture called FCSA-RTSBD (Feedback Control Scheduling Architecture for Real-Time Spatial Big Data). The main objectives of this architecture are the following: take in account the heterogeneity of data, guarantee the data freshness, enhance the deadline miss ratio even in the presence of conflicts and unpredictable workloads and finally satisfy the requirements of users by the improving of the quality of service (QoS).
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Kaur, Sumanpreet, and Mr Navtej Singh Ghumman. "ALLOCATION OF HETEROGENOUS CLOUDLETS ON PRIORITY BASIS IN CLOUD ENVIRONMENT." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 16, no. 3 (June 5, 2017): 6240–46. http://dx.doi.org/10.24297/ijct.v16i3.6177.

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Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. In the natural environment, the cloudlets will be processed in the FIFO (First in First Out approach). We propose an improved load balancing algorithm for job scheduling in the Grid environment. Hence, in this research work, various types of leases have been assigned to the cloudlets like cancellable, suspendable and non-preemtable. The leases have been assigned on the basis of cost assigned to them and the requirement specified by the user. The datacenter broker will receive the list of all the virtual machines and will categorize them into two classes i.e. Class A and Class B. Class A will have high end virtual machines and will process the non-preemptable cloudlets. Class B will contain the low end virtual machines and will process the suspendable and cancellable cloudlets. The machines in each class will be further sorted in descending order according to their MIPS. Multiple parameters have been evaluated like waiting time, turnaround time, execution time and processing cost. Further, this research also provides the anticipated results with the implementation of the proposed algorithm. In the cloud storage, load balancing is a key issue. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. The main contributions of the research work are to balance the entire system load while trying to minimize the make span of a given set of jobs. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.
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Lin, Na, Huimin Yang, Ya Li, and Xuping Wang. "Scheduling multi-pattern precooling service resources for post-harvest fruits and vegetables using the adaptive large neighborhood search." Journal of Physics: Conference Series 2425, no. 1 (February 1, 2023): 012006. http://dx.doi.org/10.1088/1742-6596/2425/1/012006.

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Abstract This study focuses on the multi-pattern precooling service resources scheduling (MPPSRS) issue recently derived from the practice of small-scale farming. In this issue, a heterogeneous fleet of vehicles belonging to different service pattens is considered to fulfil precooling requests of farmers in the service time windows. The studied issue is regarded as a variant of heterogeneous fleet vehicle routing problem with time windows (HFVRPTW) in which heterogeneous service efficiencies among vehicles are considered. To the best of our knowledge, this is the first time that such a characteristic of vehicles’ heterogenous service efficiencies is considered in vehicle routing problems. This new characteristic increases the potential combination ways of nodes in the temporal aspect, which expands the search space of the problem and leads to a more challenging optimization issue. This study formulates the MPPSRS issue using a mixed-integer programming model in which details from the real world are considered. Motivated by the challenge of computational time, an adaptive large neighborhood search (ALNS) metaheuristic is proposed to solve the model. Results obtained from a case study of the apple industry from Luochuan County, China show advantages of using multi-pattern precooling service resources in reducing total operating cost.
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Dissertations / Theses on the topic "Heterogenous scheduling"

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Durdak, Yavuz. "The Air Cargo Scheduling Problem With Heterogenous Fleet." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615358/index.pdf.

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In this study, we consider the Air Cargo Scheduling Problem based on a real life application. The aim is to move cargo and passengers that have different priorities and delivery time window, from a number of origin airports to destination airports by means of a transportation system. The system has predefined carrier routes and a heterogeneous fleet of aircraft. The problem is formulated as a heterogeneous vehicle, multi commodity, pick-up, and delivery network flow problem with a large set of system specific constraints. The proposed model determines set of movement requirements assigned on each route leg and number and type of aircraft assigned for each route in a reasonable amount of time. The model is tested with the real and generated data and the results are compared with the current methodology under different scenarios. The model produced better results in a short amount of time compared to the current methodology.
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Hernandez, Jesus Israel. "Reactive scheduling of DAG applications on heterogeneous and dynamic distributed computing systems." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/2336.

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Emerging technologies enable a set of distributed resources across a network to be linked together and used in a coordinated fashion to solve a particular parallel application at the same time. Such applications are often abstracted as directed acyclic graphs (DAGs), in which vertices represent application tasks and edges represent data dependencies between tasks. Effective scheduling mechanisms for DAG applications are essential to exploit the tremendous potential of computational resources. The core issues are that the availability and performance of resources, which are already by their nature heterogeneous, can be expected to vary dynamically, even during the course of an execution. In this thesis, we first consider the problem of scheduling DAG task graphs onto heterogeneous resources with changeable capabilities. We propose a list-scheduling heuristic approach, the Global Task Positioning (GTP) scheduling method, which addresses the problem by allowing rescheduling and migration of tasks in response to significant variations in resource characteristics. We observed from experiments with GTP that in an execution with relatively frequent migration, it may be that, over time, the results of some task have been copied to several other sites, and so a subsequent migrated task may have several possible sources for each of its inputs. Some of these copies may now be more quickly accessible than the original, due to dynamic variations in communication capabilities. To exploit this observation, we extended our model with a Copying Management(CM) function, resulting in a new version, the Global Task Positioning with copying facilities (GTP/c) system. The idea is to reuse such copies, in subsequent migration of placed tasks, in order to reduce the impact of migration cost on makespan. Finally, we believe that fault tolerance is an important issue in heterogeneous and dynamic computational environments as the availability of resources cannot be guaranteed. To address the problem of processor failure, we propose a rewinding mechanism which rewinds the progress of the application to a previous state, thereby preserving the execution in spite of the failed processor(s). We evaluate our mechanisms through simulation, since this allow us to generate repeatable patterns of resource performance variation. We use a standard benchmark set of DAGs, comparing performance against that of competing algorithms from the scheduling literature.
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Binotto, Alécio Pedro Delazari. "A dynamic scheduling runtime and tuning system for heterogeneous multi and many-core desktop platforms." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/34768.

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Atualmente, o computador pessoal (PC) moderno poder ser considerado como um cluster heterogênedo de um nodo, o qual processa simultâneamente inúmeras tarefas provenientes das aplicações. O PC pode ser composto por Unidades de Processamento (PUs) assimétricas, como a Unidade Central de Processamento (CPU), composta de múltiplos núcleos, a Unidade de Processamento Gráfico (GPU), composta por inúmeros núcleos e que tem sido um dos principais co-processadores que contribuiram para a computação de alto desempenho em PCs, entre outras. Neste sentido, uma plataforma de execução heterogênea é formada em um PC para efetuar cálculos intensivos em um grande número de dados. Na perspectiva desta tese, a distribuição da carga de trabalho de uma aplicação nas PUs é um fator importante para melhorar o desempenho das aplicações e explorar tal heterogeneidade. Esta questão apresenta desafios uma vez que o custo de execução de uma tarefa de alto nível em uma PU é não-determinístico e pode ser afetado por uma série de parâmetros não conhecidos a priori, como o tamanho do domínio do problema e a precisão da solução, entre outros. Nesse escopo, esta pesquisa de doutorado apresenta um sistema sensível ao contexto e de adaptação em tempo de execução com base em um compromisso entre a redução do tempo de execução das aplicações - devido a um escalonamento dinâmico adequado de tarefas de alto nível - e o custo de computação do próprio escalonamento aplicados em uma plataforma composta de CPU e GPU. Esta abordagem combina um modelo para um primeiro escalonamento baseado em perfis de desempenho adquiridos em préprocessamento com um modelo online, o qual mantém o controle do tempo de execução real de novas tarefas e escalona dinâmicamente e de modo eficaz novas instâncias das tarefas de alto nível em uma plataforma de execução composta de CPU e de GPU. Para isso, é proposto um conjunto de heurísticas para escalonar tarefas em uma CPU e uma GPU e uma estratégia genérica e eficiente de escalonamento que considera várias unidades de processamento. A abordagem proposta é aplicada em um estudo de caso utilizando uma plataforma de execução composta por CPU e GPU para computação de métodos iterativos focados na solução de Sistemas de Equações Lineares que se utilizam de um cálculo de stencil especialmente concebido para explorar as características das GPUs modernas. A solução utiliza o número de incógnitas como o principal parâmetro para a decisão de escalonamento. Ao escalonar tarefas para a CPU e para a GPU, um ganho de 21,77% em desempenho é obtido em comparação com o escalonamento estático de todas as tarefas para a GPU (o qual é utilizado por modelos de programação atuais, como OpenCL e CUDA para Nvidia) com um erro de escalonamento de apenas 0,25% em relação à combinação exaustiva.
A modern personal computer can be now considered as a one-node heterogeneous cluster that simultaneously processes several applications’ tasks. It can be composed by asymmetric Processing Units (PUs), like the multi-core Central Processing Unit (CPU), the many-core Graphics Processing Units (GPUs) - which have become one of the main co-processors that contributed towards high performance computing - and other PUs. This way, a powerful heterogeneous execution platform is built on a desktop for data intensive calculations. In the perspective of this thesis, to improve the performance of applications and explore such heterogeneity, a workload distribution over the PUs plays a key role in such systems. This issue presents challenges since the execution cost of a task at a PU is non-deterministic and can be affected by a number of parameters not known a priori, like the problem size domain and the precision of the solution, among others. Within this scope, this doctoral research introduces a context-aware runtime and performance tuning system based on a compromise between reducing the execution time of the applications - due to appropriate dynamic scheduling of high-level tasks - and the cost of computing such scheduling applied on a platform composed of CPU and GPUs. This approach combines a model for a first scheduling based on an off-line task performance profile benchmark with a runtime model that keeps track of the tasks’ real execution time and efficiently schedules new instances of the high-level tasks dynamically over the CPU/GPU execution platform. For that, it is proposed a set of heuristics to schedule tasks over one CPU and one GPU and a generic and efficient scheduling strategy that considers several processing units. The proposed approach is applied in a case study using a CPU-GPU execution platform for computing iterative solvers for Systems of Linear Equations using a stencil code specially designed to explore the characteristics of modern GPUs. The solution uses the number of unknowns as the main parameter for assignment decision. By scheduling tasks to the CPU and to the GPU, it is achieved a performance gain of 21.77% in comparison to the static assignment of all tasks to the GPU (which is done by current programming models, such as OpenCL and CUDA for Nvidia) with a scheduling error of only 0.25% compared to exhaustive search.
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Neeracher, Matthias. "Scheduling for heterogeneous opportunistic workstation clusters /." [S.l.] : [s.n.], 1998. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=12906.

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Wen, Yuan. "Multi-tasking scheduling for heterogeneous systems." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/23469.

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Heterogeneous platforms play an increasingly important role in modern computer systems. They combine high performance with low power consumption. From mobiles to supercomputers, we see an increasing number of computer systems that are heterogeneous. The most well-known heterogeneous system, CPU+GPU platforms have been widely used in recent years. As they become more mainstream, serving multiple tasks from multiple users is an emerging challenge. A good scheduler can greatly improve performance. However, indiscriminately allocating tasks based on availability leads to poor performance. As modern GPUs have a large number of hardware resources, most tasks cannot efficiently utilize all of them. Concurrent task execution on GPU is a promising solution, however, indiscriminately running tasks in parallel causes a slowdown. This thesis focuses on scheduling OpenCL kernels. A runtime framework is developed to determine where to schedule OpenCL kernels. It predicts the best-fit device by using a machine learning-based classifier, then schedules the kernels accordingly to either CPU or GPU. To improve GPU utilization, a kernel merging approach is proposed. Kernels are merged if their predicted co-execution can provide better performance than sequential execution. A machine learning based classifier is developed to find the best kernel pairs for co-execution on GPU. Finally, a runtime framework is developed to schedule kernels separately on either CPU or GPU, and run kernels in pairs if their co-execution can improve performance. The approaches developed in this thesis significantly improve system performance and outperform all existing techniques.
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Kreaseck, Barbara. "Dynamic autonomous scheduling on heterogeneous systmes /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3102539.

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Tzeng, Stanley. "Scheduling on Manycore and Heterogeneous Graphics Processors." Thesis, University of California, Davis, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3602240.

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Through custom software schedulers that distribute work differently than built-in hardware schedulers, data-parallel and heterogenous architectures can be retargeted towards irregular task-parallel graphics workloads. This dissertation examines the role of a GPU scheduler and how it may schedule complicated workloads onto the GPU for efficient parallel processing. This dissertation examines the scheduler through three different properties of workloads: granularity, irregularity, and dependency. Then it moves onto heterogenous architectures and examine how scheduling decisions differ when scheduling for discrete versus heterogeneous chips. The dissertation conclues with future work in scheduling for both discrete and heterogeneous architectures.

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Lee, Young Choon. "Problem-centric scheduling for heterogeneous computing systems." Thesis, The University of Sydney, 2007. http://hdl.handle.net/2123/9321.

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This project addresses key scheduling problems in heterogeneous computing environments. Heterogeneous computing systems (HCSs) have received increased attention since the 1990s, particularly over the past 10 years with the popularity of grid computing systems. These computing environments consist of a variety of resources interconnected by a high-speed network. Many parallel and distributed applications can take advantage of this computing platform; however, resource heterogeneity and dynamism impose scheduling restrictions. It is extremely difficult for a single scheduling scheme to efficiently and effectively handle the application scenarios that are required in grid computing environments. What further complicates the issue is that computing environments are controlled by different administrative authorities. Thus, application diversity, and resource heterogeneity and dynamism, point to the need to develop a set of scheduling algorithms to manage these scenarios. The thesis describes a number of key application and system models, and extensively discusses the characteristics of traditional multiprocessor scheduling and grid scheduling. The application models can be broadly classified as independent and precedence-constrained. The coupling of resources in our HCS model can be tight or loose; while static scheduling is applied to tightly coupled platforms, dynamic scheduling is adopted on loosely coupled platforms. The thesis presents the scheduling schemes that we have developed to address various challenging scheduling issues, and sets out and interprets the experimental results from our performance evaluation study. The data indicate that our novel scheduling algorithms—which appropriately incorporate application and system characteristics into their scheduling—demonstrate significantly superior performance than previous approaches.
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Okolo, Benjamin Uchenna. "Joint Routing and Scheduling in Heterogeneous Wireless Network." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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The idea of the Internet-of-Things (IoT), heralds a future where most things used in the daily life of people are connected in possibly one network and share information among themselves. As a result, IoT presents a hope of revolutionizing the daily life of its potential users, but also presents a challenge for engineers, who need to ensure good connectivity between heterogeneous devices in such network. The IoT vision will deal with devices that are based on different/heterogeneous technologies. This thesis presents a novel technique for routing and scheduling in an heterogeneous network, based on a realistic physical layer model. The routing and scheduling techniques presented in this thesis, follows some existing routing and scheduling algorithms, with the performance of the scheme proved to be very efficient, based on the simulation results obtained.
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Planas, Carbonell Judit. "Programming models and scheduling techniques for heterogeneous architectures." Doctoral thesis, Universitat Politècnica de Catalunya, 2015. http://hdl.handle.net/10803/327036.

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There is a clear trend nowadays to use heterogeneous high-performance computers, as they offer considerably greater computing power than homogeneous CPU systems. Extending traditional CPU systems with specialized units (accelerators such as GPGPUs) has become a revolution in the HPC world. Both the traditional performance-per-Watt and the performance-per-Euro ratios have been increased with the use of such systems. Heterogeneous machines can adapt better to different application requirements, as each architecture type offers different characteristics. Thus, in order to maximize application performance in these platforms, applications should be divided into several portions according to their execution requirements. These portions should then be scheduled to the device that better fits their requirements. Hence, heterogeneity introduces complexity in application development, up to the point of reaching the programming wall: on the one hand, source codes must be adapted to fit new architectures and, on the other, resource management becomes more complicated. For example, multiple memory spaces that require explicit data movements or additional synchronizations between different code portions that run on different units. For all these reasons, efficient programming and code maintenance in heterogeneous systems is extremely complex and expensive. Although several approaches have been proposed for accelerator programming, like CUDA or OpenCL, these models do not solve the aforementioned programming challenges, as they expose low level hardware characteristics to the programmer. Therefore, programming models should be able to hide all these complex accelerator programming by providing a homogeneous development environment. In this context, this thesis contributes in two key aspects: first, it proposes a general design to efficiently manage the execution of heterogeneous applications and second, it presents several scheduling mechanisms to spread application execution among all the units of the system to maximize performance and resource utilization. The first contribution proposes an asynchronous design to manage execution, data movements and synchronizations on accelerators. This approach has been developed in two steps: first, a semi-asynchronous proposal and then, a fully-asynchronous proposal in order to fit contemporary hardware restrictions. The experimental results tested on different multi-accelerator systems showed that these approaches could reach the maximum expected performance. Even if compared to native, hand-tuned codes, they could get the same results and outperform native versions in selected cases. The second contribution presents four different scheduling strategies. They focus and combine different aspects related to heterogeneous programming to minimize application's execution time. For example, minimizing the amount of data shared between memory spaces, or maximizing resource utilization by scheduling each portion of code on the unit that fits better. The experimental results were performed on different heterogeneous platforms, including CPUs, GPGPU and Intel Xeon Phi devices. As shown in these tests, it is particularly interesting to analyze how all these scheduling strategies can impact application performance. Three general conclusions can be extracted: first, application performance is not guaranteed across new hardware generations. Then, source codes must be periodically updated as hardware evolves. Second, the most efficient way to run an application on a heterogeneous platform is to divide it into smaller portions and pick the unit that better fits to run each portion. Hence, system resources can cooperate together to execute the application. Finally, and probably the most important, the requirements derived from the first and second conclusions can be implemented inside runtime frameworks, so the complexity of programming heterogeneous architectures is completely hidden to the programmer.
Actualment, hi ha una clara tendència per l'ús de sistemes heterogenis d'alt rendiment, ja que ofereixen una major potència de càlcul que els sistemes homogenis amb CPUs tradicionals. L'addició d'unitats especialitzades (acceleradors com ara GPGPUs) als sistemes amb CPUs s'ha convertit en una revolució en el món de la computació d'alt rendiment. Els sistemes heterogenis poden adaptar-se millor a les diferents necessitats de les aplicacions, ja que cada tipus d'arquitectura ofereix diferents característiques. Per tant, per maximitzar el rendiment, les aplicacions s'han de dividir en diverses parts d'acord amb els seus requeriments computacionals. Llavors, aquestes parts s'han d'executar al dispositiu que s'adapti millor a les seves necessitats. Per tant, l'heterogeneïtat introdueix una complexitat addicional en el desenvolupament d'aplicacions: d'una banda, els codis font s'han d'adaptar a les noves arquitectures i, de l'altra, la gestió de recursos es fa més complicada. Per exemple, múltiples espais de memòria que requereixen moviments explícits de dades o sincronitzacions addicionals entre diferents parts de codi que s'executen en diferents unitats. Per això, la programació i el manteniment del codi en sistemes heterogenis són extremadament complexos i cars. Tot i que hi ha diverses propostes per a la programació d'acceleradors, com CUDA o OpenCL, aquests models no resolen els reptes de programació descrits anteriorment, ja que exposen les característiques de baix nivell del hardware al programador. Per tant, els models de programació han de poder ocultar les complexitats dels acceleradors de cara al programador, proporcionant un entorn de desenvolupament homogeni. En aquest context, la tesi contribueix en dos aspectes fonamentals: primer, proposa un disseny per a gestionar de manera eficient l'execució d'aplicacions heterogènies i, segon, presenta diversos mecanismes de planificació per dividir l'execució d'aplicacions entre totes les unitats del sistema, per tal de maximitzar el rendiment i la utilització de recursos. La primera contribució proposa un disseny d'execució asíncron per gestionar els moviments de dades i sincronitzacions en acceleradors. Aquest enfocament s'ha desenvolupat en dos passos: primer, una proposta semi-asíncrona i després, una proposta totalment asíncrona per tal d'adaptar-se a les restriccions del hardware contemporani. Els resultats en sistemes multi-accelerador mostren que aquests enfocaments poden assolir el màxim rendiment esperat. Fins i tot, en determinats casos, poden superar el rendiment de codis nadius altament optimitzats. La segona contribució presenta quatre mecanismes de planificació diferents, enfocats a la programació heterogènia, per minimitzar el temps d'execució de les aplicacions. Per exemple, minimitzar la quantitat de dades compartides entre espais de memòria, o maximitzar la utilització de recursos mitjançant l'execució de cada porció de codi a la unitat que s'adapta millor. Els experiments s'han realitzat en diferents plataformes heterogènies, incloent CPUs, GPGPUs i dispositius Intel Xeon Phi. És particularment interessant analitzar com totes aquestes estratègies de planificació poden afectar el rendiment de l'aplicació. Com a resultat, es poden extreure tres conclusions generals: en primer lloc, el rendiment de l'aplicació no està garantit en les noves generacions de hardware. Per tant, els codis s'han d'actualitzar periòdicament a mesura que el hardware evoluciona. En segon lloc, la forma més eficient d'executar una aplicació en una plataforma heterogènia és dividir-la en porcions més petites i escollir la unitat que millor s'adapta per executar cada porció. Finalment, i probablement la conclusió més important, és que les exigències derivades de les dues primeres conclusions poden ser implementades dins de llibreries de sistema, de manera que la complexitat de programació d'arquitectures heterogènies quedi completament oculta per al programador.
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Books on the topic "Heterogenous scheduling"

1

Neeracher, Matthias. Scheduling for heterogeneous opportunistic workstation clusters. Konstanz: Hartung-Gorre, 1998.

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Xie, Guoqi, Gang Zeng, Renfa Li, and Keqin Li. Scheduling Parallel Applications on Heterogeneous Distributed Systems. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6557-7.

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Pop, Traian. Scheduling and optimisation of heterogeneous time/event-triggered distributed embedded systems. Linko ping: Univ., 2003.

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Pop, Traian. Analysis and optimisation of distributed embedded systems with heterogeneous scheduling policies. Linköping: Department of Computer and Information Science, Linköpings universitet, 2007.

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Systematic Methodology for Real-Time Cost-Effective Mapping of Dynamic Concurrent Task-Based Systems on Heterogenous Platforms. Springer, 2007.

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Li, Keqin, Gang Zeng, Guoqi Xie, and Renfa Li. Scheduling Parallel Applications on Heterogeneous Distributed Systems. Springer Singapore Pte. Limited, 2020.

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Li, Keqin, Gang Zeng, Guoqi Xie, and Renfa Li. Scheduling Parallel Applications on Heterogeneous Distributed Systems. Springer, 2019.

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Parallel job scheduling on heterogeneous networks of multiprocessor workstations. Ottawa: National Library of Canada, 1999.

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Beisel, Tobias. Management and Scheduling of Accelerators for Heterogeneous High-Performance Computing. Logos Verlag Berlin, 2015.

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Jacob, Joseph. Automatic scheduling and dynamic load sharing of parallel computations on heterogeneous workstation clusters. 1995.

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Book chapters on the topic "Heterogenous scheduling"

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He, Shiming, Kun Xie, and Dafang Zhang. "Completion Time-Aware Flow Scheduling in Heterogenous Networks." In Algorithms and Architectures for Parallel Processing, 492–507. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27119-4_34.

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Kale, Vivek, and William D. Gropp. "Composing Low-Overhead Scheduling Strategies for Improving Performance of Scientific Applications." In OpenMP: Heterogenous Execution and Data Movements, 18–29. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24595-9_2.

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Yang, Fudong, Xiaodong Ji, and Lei Li. "An Enhanced Cost Efficient Resource Scheduling Algorithm for Dense Heterogenous Networks." In Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems, 3–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49831-6_1.

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Liao, Qun, Shuangshuang Jiang, Qiaoxiang Hei, Tao Li, and Yulu Yang. "Scheduling Stochastic Tasks with Precedence Constrain on Cluster Systems with Heterogenous Communication Architecture." In Algorithms and Architectures for Parallel Processing, 85–99. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27161-3_8.

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Akkaya, Selen Burçak, and Mahmut Ali Gökçe. "Intelligent Scheduling and Routing of a Heterogenous Fleet of Automated Guided Vehicles (AGVs) in a Production Environment with Partial Recharge." In Lecture Notes in Networks and Systems, 568–76. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09176-6_65.

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Suter, Frédéric, Frédéric Desprez, and Henri Casanova. "From Heterogeneous Task Scheduling to Heterogeneous Mixed Parallel Scheduling." In Lecture Notes in Computer Science, 230–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27866-5_30.

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Sjöland, Thomas, Per Kreuger, and Martin Aronsson. "Heterogeneous Scheduling and Rotation." In Computational Logic: Logic Programming and Beyond, 655–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45628-7_24.

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Li, Kenli, Xiaoyong Tang, Jing Mei, Longxin Zhang, Wangdong Yang, and Keqin Li. "Scheduling Stochastic Tasks on Heterogeneous Cluster Systems." In Workflow Scheduling on Computing Systems, 53–72. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/b23006-4.

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Gupta, Anupam, Ravishankar Krishnaswamy, and Kirk Pruhs. "Scalably Scheduling Power-Heterogeneous Processors." In Automata, Languages and Programming, 312–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14165-2_27.

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Albers, Susanne, Evripidis Bampis, Dimitrios Letsios, Giorgio Lucarelli, and Richard Stotz. "Scheduling on Power-Heterogeneous Processors." In LATIN 2016: Theoretical Informatics, 41–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49529-2_4.

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Conference papers on the topic "Heterogenous scheduling"

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Schildt, Sebastian, Felix Busching, Enrico Jorns, and Lars Wolf. "CANDIS: Heterogenous Mobile Cloud Framework and Energy Cost-Aware Scheduling." In 2013 IEEE International Conference on Green Computing and Communications (GreenCom) and IEEE Internet of Things(iThings) and IEEE Cyber, Physical and Social Computing(CPSCom). IEEE, 2013. http://dx.doi.org/10.1109/greencom-ithings-cpscom.2013.372.

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Li, Na, Bo Gao, Zongfu Xie, Jinjin Liu, Yawei ji, and Xiaolong Shen. "Load balancing scheduling algorithm based on edge clustering in heterogenous systems." In 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）, edited by Xuexia Ye, Francisco Falcone, and Heming Cui. SPIE, 2022. http://dx.doi.org/10.1117/12.2636691.

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Mahmoud, Hadeer, Mostafa Thabet, Mohamed H. Khafagy, and Fatma A. Omara. "A Comparative Study of Heterogenous Task-based Scheduling Techniques in a Cloud Environment." In 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE). IEEE, 2020. http://dx.doi.org/10.1109/itce48509.2020.9047806.

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Deng, Xiangjun, Jing Huang, and Renfa Li. "A Locality-aware Task Scheduling Algorithm for Video Transcoding over Heterogenous MapReduce Cluster." In the 3rd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3220162.3220191.

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Hossain, M. J., M. S. Alouini, and V. K. Bhargava. "Two-User Opportunistic Scheduling Using Hierarchical Modulations in Wireless Networks with Heterogenous Average Link Gains." In 2008 IEEE International Conference on Communications. IEEE, 2008. http://dx.doi.org/10.1109/icc.2008.214.

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Fan, Wei, Jie Zhu, and Kexin Ding. "An Improved Task Duplication based Clustering Algorithm for DAG Task Scheduling in Heterogenous and Distributed Systems." In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2022. http://dx.doi.org/10.1109/smc53654.2022.9945255.

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Yu, Han. "A Hybrid GA-based Scheduling Algorithm for Heterogeneous Computing Environments." In 2007 IEEE Symposium on Computational Intelligence in Scheduling. IEEE, 2007. http://dx.doi.org/10.1109/scis.2007.367674.

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Li, Feng, Lin Zhang, and Yuanjun Laili. "Multi-Task Scheduling Based on QoS Evaluation in Cloud Manufacturing System." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2839.

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Abstract:
Cloud manufacturing (CMfg) mode provides an effective means to intensely utilize distributed resources and manufacturing capability for personalized production. Increasing personalized customization implies more and more heterogeneous tasks and hence more sorts of requirements for services. As the granularity of tasks vary with changing users and products, the solution (or scheme) of task scheduling should be different. In order to efficiently provide the most suitable solution for each kind of tasks, different scheduling ways should be adopted under different circumstances. In this paper, we study scheduling issues for heterogeneous tasks with variable granularity and present two kinds of optimal scheduling mode based on user-oriented comprehensive evaluation. Then different encoding schemes relied on the genetic algorithm are proposed according to different scheduling strategies.
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Kashansky, V., R. Prodan, and G. Radchenko. "SOME ASPECTS OF THE WORKFLOW SCHEDULING IN THE COMPUTING CONTINUUM SYSTEMS." In 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". Crossref, 2021. http://dx.doi.org/10.54546/mlit.2021.29.45.001.

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Contemporary computing systems are commonly characterized in terms of data-intensive workflows, that are managed by utilizing large number of heterogeneous computing and storage elements interconnected through complex communication topologies. As the scale of the system grows and workloads become more heterogeneous in both inner structure and the arrival patterns, scheduling problem becomes exponentially harder, requiring problem-specifc heuristics. Despite several decades of the active research on it, one issue that still requires effort is to enable efficient workflows scheduling in such complex environments, while preserving robustness of the results. Moreover, recent research trend coined under term "computing continuum" prescribes convergence of the multiscale computational systems with complex spatio-temporal dynamics and diverse sets of the management policies. This paper contributes with the set of recommendations and brief analysis for the existing scheduling algorithms.
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Kitajima, J. P. W., and S. C. S. Porto. "Estimated and Observed Performance of Heuristic Algorithrns for Task Scheduling on Heterogeneous Processors." In Simpósio Brasileiro de Arquitetura de Computadores e Processamento de Alto Desempenho. Sociedade Brasileira de Computação, 1997. http://dx.doi.org/10.5753/sbac-pad.1997.22629.

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Parallel applications with regular and well-known behavior, where task execution time estimates are fairly reliable, are suited to static task scheduling (in opposition to dynamic scheduling, performed during the execution of the application). This is the case of many parallel scientific applications. Task scheduling on a heterogeneous environment, where processors present different processing speeds, is even more complex than on a homogeneous one. Static task scheduling is, thus, performed based on estimated data about the parallel application and the system architecture, and has long taken a heuristic approach. Therefore, a realistic performance evaluation of a task scheduling algorithm can only be fully accomplished if practical results are also considered. In this sense, the present work analyzes the quality of greedy and tabu search task scheduling algorithrns comparing estimated deterministic results with the actual observed makespan of several parallel synthetic applications executing on real parallel machines following the static schedule previously determined.
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Reports on the topic "Heterogenous scheduling"

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Oliker, Leonid, Rupak Biswas, Hongzhang Shan, and Warren Smith. Job scheduling in a heterogenous grid environment. Office of Scientific and Technical Information (OSTI), February 2004. http://dx.doi.org/10.2172/860301.

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Andersson, Bjorn A., and Gurulingesh Raravi. Scheduling Constrained-Deadline Parallel Tasks on Two-type Heterogeneous Multiprocessors. Fort Belvoir, VA: Defense Technical Information Center, January 2015. http://dx.doi.org/10.21236/ada614630.

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