Relevant bibliographies by topics / Workload scheduling

Academic literature on the topic 'Workload scheduling'

Author: Grafiati
Published: 13 May 2022

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Journal articles on the topic "Workload scheduling":

1

Bani-Mohammad, Saad. "The Effect of Real Workloads and Synthetic Workloads on the Performance of Job Scheduling for Non-Contiguous Allocation in 2D Mesh Multicomputers." International Journal of Distributed Systems and Technologies 6, no. 1 (January 2015): 53–68. http://dx.doi.org/10.4018/ijdst.2015010104.

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The performance of non-contiguous allocation has been traditionally carried out by means of simulations based on synthetic workloads, and also it can be significantly affected by the job scheduling strategy used for determining the order in which jobs are selected for execution. To validate the performance of the non-contiguous allocation algorithms, there has been a need to evaluate the algorithm's performance based on a real workload trace. In this paper, the performance of the well-known Greedy Available Busy List (GABL) non-contiguous allocation strategy for 2D mesh-connected multicomputers is revisited considering several important job scheduling strategies based on a real workload trace, and the results are compared to those obtained from using a synthetic workload. The scheduling strategies used are the First-Come-First-Served (FCFS), Out-of-Order (OO), and Window-Based job scheduling strategies. These strategies have been selected because they are common and they have been used in related works (Ababneh & Bani-Mohammad, 2011). Extensive simulation results based on synthetic and real workload models indicate that the Window-Based job scheduling strategy can improve both overall system performance and fairness (i.e., maximum job waiting delays) by adopting a large job scheduling window. Moreover, the relative performance merits of the scheduling strategies when a real workload trace is used are in general compatible with those obtained when a synthetic workload is used.
2

Lüthi, Hans-Jakob, and Andrés Polyméris. "Scheduling to Minimize Maximum Workload." Management Science 31, no. 11 (November 1985): 1409–15. http://dx.doi.org/10.1287/mnsc.31.11.1409.

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Andre, Anthony D., Susan T. Heers, Robert S. McCann, and Patricia A. Cashion. "Preview and Practice: Effects on Scheduling Behavior in a Simulated Flight Task." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 1 (October 1993): 132–36. http://dx.doi.org/10.1177/154193129303700131.

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The present study examined pilot scheduling behavior in the context of simulated instrument flight. Over the course of the flight, pilots flew along specified routes while concurrently performing three different flight-related secondary tasks. Seven pilots flew the simulation with no preview of future workload conditions, while another seven received preview information in the form of both written instruction and practice. The results show evidence for both macro and micro scheduling strategies. Specifically, those pilots with preview of future workload demands adopted an efficient macro strategy of scheduling more of the difficult secondary tasks during the low workload phase of flight. Subjects in both groups engaged in micro scheduling strategies as a function of flight path workload and secondary task workload.
4

Zhang, Xiaomei, Kang Li, Xiang Hu Zhao, Tian Yan, and Yong Sun. "Multicore workload scheduling in JUNO[1]." EPJ Web of Conferences 214 (2019): 03048. http://dx.doi.org/10.1051/epjconf/201921403048.

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The Jiangmen Underground Neutrino Observatory (JUNO) is going to apply parallel computing in its software to accelerate JUNO data processing and fully use capability of multi-core and manycore CPUs. Therefore, it is necessary for the JUNO distributed computing system to explore the way to support single-core and multi-core jobs in a consistent way. To support multi-core jobs, a series of changes to the job descriptions, scheduling, monitoring needs to be considered, in which the pilot-based scheduling for a hybrid of single-core and multi-core jobs is the most complicated part. Two scheduling modes and their efficiency are presented and compared in this paper, and also a way to optimize efficiency is provided.
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Sodinapalli, Nagendra Prasad, Subhash Kulkarni, Nawaz Ahmed Sharief, and Prasanth Venkatareddy. "An efficient resource utilization technique for scheduling scientific workload in cloud computing environment." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (March 1, 2022): 367. http://dx.doi.org/10.11591/ijai.v11.i1.pp367-378.

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<span lang="EN-US">Recently, number of data intensive workflow have been generated with growth of internet of things (IoT’s) technologies. Heterogeneous cloud framework has been emphasized by existing methodologies for executing these data-intensive workflows. Efficient resource scheduling plays a very important role provisioning workload execution on Heterogeneous cloud framework. Building tradeoff model in meeting energy constraint and workload task deadline requirement is challenging. Recently, number of multi-objective-based workload scheduling aimed at minimizing power budget and meeting task deadline constraint. However, these models induce significant overhead when demand and number of processing core increases. For addressing research problem here, the workload is modelled by considering each sub-task require dynamic memory, cache, accessible slots, execution time, and I/O access requirement. Thus, for utilizing resource more efficiently better cache resource management is needed. Here efficient resource utilization (ERU) model is presented. The ERU model is designed to utilize cache resource more efficiently and reduce last level cache failure and meeting workload task deadline prerequisite. The ERU model is very efficient when compared with standard resource management methodology in terms of reducing execution time, power consumption, and energy consumption for execution scientific workloads on heterogeneous cloud platform.</span>
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Buttazzo, G. C., G. Lipari, M. Caccamo, and L. Abeni. "Elastic scheduling for flexible workload management." IEEE Transactions on Computers 51, no. 3 (March 2002): 289–302. http://dx.doi.org/10.1109/12.990127.

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Tan, Kian-Lee, and Hongjun Lu. "Workload scheduling for multiple query processing." Information Processing Letters 55, no. 5 (September 1995): 251–57. http://dx.doi.org/10.1016/0020-0190(95)00088-t.

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Mao, Li, Deyu Qi, Weiwei Lin, and Chaoyue Zhu. "A Self-Adaptive Prediction Algorithm for Cloud Workloads." International Journal of Grid and High Performance Computing 7, no. 2 (April 2015): 65–76. http://dx.doi.org/10.4018/ijghpc.2015040105.

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It is difficult to analyze the workload in complex cloud computing environments with a single prediction algorithm as each algorithm has its own shortcomings. A self-adaptive prediction algorithm combining the advantages of linear regression (LR) and a BP neural network to predict workloads in clouds is proposed in this paper. The main idea of the self-adaptive prediction algorithm is to choose the better prediction method of the future workload. Some experiments of prediction algorithms are conducted with workloads on the public cloud servers. The experimental results show that the proposed algorithm has a relatively high accuracy on the workload predictions compared with the BP neural network and LR. Furthermore, in order to use the proposed algorithm in a cloud data center, a dynamic scheduling architecture of cloud resources is designed to improve resource utilization and reduce energy consumption.
9

Prasad, V. Rajendra, Mike Graul, Perakath Benjamin, Richard Mayer, and Patrick D. Cahill. "Resource-Constrained Shop-Level Scheduling in a Shipyard." Journal of Ship Production 19, no. 02 (May 1, 2003): 65–75. http://dx.doi.org/10.5957/jsp.2003.19.2.65.

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Ship production planning and scheduling at higher levels do not explicitly consider scheduling details at the level of individual workshops. However, the schedule of major events in ship production is collectively influenced by the actual shop-level, short-interval production schedules, which depend on resource and material availability and also on the due dates and priorities of the workloads. This necessitates development of robust, resource-constrained, shop-level scheduling systems that can support higher-level schedules in ship production. WorkShip (Knowledge Based Systems, Inc., College Station, TX) is a software tool for scheduling workload over short, regular intervals in workshops of a shipyard. It is driven by a powerful scheduling engine that is based on a generic model of resource-constrained job-shop scheduling and an efficient scheduling technique. Similar scheduling systems are being developed in other shops so that all systems can be used in tandem to support higher-level scheduling and help achieve optimal productivity for the shipyard.
10

WU, MIN-YOU. "PARALLEL INCREMENTAL SCHEDULING." Parallel Processing Letters 05, no. 04 (December 1995): 659–70. http://dx.doi.org/10.1142/s0129626495000588.

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Parallel incremental scheduling is a new approach for load balancing. In parallel scheduling, all processors cooperate together to balance the workload. Parallel scheduling accurately balances the load by using global load information. In incremental scheduling, the system scheduling activity alternates with the underlying computation work. This paper provides an overview of parallel incremental scheduling. Different categories of parallel incremental scheduling are discussed.
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You might also be interested in the extended bibliographies on the topic 'Workload scheduling' for particular source types:
Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Workload scheduling":

1

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.

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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.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.
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.
3

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.

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Xu, Luna. "A Workload-aware Resource Management and Scheduling System for Big Data Analysis." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/87469.

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The big data era has driven the needs for data analysis in every aspect of our daily lives. With the rapid growth of data size and complexity of data analysis models, modern big data analytic applications face the challenge to provide timely results often with limited resources. Such demand drives the growth of new hardware resources including GPUs and FPGAs, as well as storage devices such as SSDs and NVMs. It is challenging to manage the resources available in a cost restricted environment to best serve the applications with different characteristics. Extant approaches are agnostic to such heterogeneity in both underlying resources and workloads and require user knowledge and manual configuration for best performance. In this dissertation, we design, and implement a series of novel techniques, algorithms, and frameworks, to realize workload-aware resource management and scheduling. We demonstrate our techniques for efficient resource management across memory resource for in-memory data analytic platforms, processing resources for compute-intensive machine learning applications, and finally we design and develop a workload and heterogeneity-aware scheduler for general big data platforms. The dissertation demonstrates that designing an effective resource manager requires efforts from both application and system side. The presented approach makes and joins the efforts on both sides to provide a holistic heterogeneity-aware resource manage and scheduling system. We are able to avoid task failure due to resource unavailability by workload-aware resource management, and improve the performance of data processing frameworks by carefully scheduling tasks according to the task characteristics and utilization and availability of the resources.
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.
5

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.

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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.

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A scheduler, as a central components of a computing site, aggregates computing resources and is responsible to distribute the incoming load (jobs) between the resources. Under such an environment, the optimum performance of the system against the service level agreement (SLA) based workloads, can be achieved by calculating the priority of SLA bound jobs using integrated heuristic. The SLA defines the service obligations and expectations to use the computational resources. The integrated heuristic is the combination of different SLA terms. It combines the SLA terms with a specific weight for each term. Theweights are computed by applying parameter sweep technique in order to obtain the best schedule for the optimum performance of the system under the workload. The sweepingof parameters on the integrated heuristic observed to be computationally expensive. The integrated heuristic becomes more expensive if no value of the computed weights result in improvement in performance with the resulting schedule. Hence, instead of obtaining optimum performance it incurs computation cost in such situations. Therefore, there is a need of detection of situations where the integrated heuristic can be exploited beneficially. For that reason, in this thesis we propose a metric based on the concept of utilization, to evaluate the SLA based parallel workloads of independent jobs to detect any impact of integrated heuristic on the workload.
7

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.

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Scheduling algorithm is the algorithm to allocate system resources among processes and data flows. Joint channel-assignment and workload-based (CAWS) is a recently developed algorithm for scheduling in the downlink of multi-channel wireless systems, such as OFDM. Compared to well known algorithms, CAWS algorithm has been proved to throughput optimal with flow-level dynamics. In this master thesis project, we design a system that accounts for power control and for the characteristics of common radio channels. We evaluate the efficiency of the algorithm under a diverse set of conditions. We also do analysis of CAWS algorithm under different traffic density.
8

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.

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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.

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The Grid can be seen as a collection of services each of which performs some functionality. Users of the Grid seek to use combinations of these services to perform the overall task they need to achieve. In general this can be seen as a set of services with a workflow document describing how these services should be combined. The user may also have certain constraints on the workflow operations, such as execution time or cost to the user, specified in the form of a Quality of Service (QoS) document. The users submit their workflow to a brokering service along with the QoS document. The brokering service's task is to map any given workflow to a subset of the Grid services taking the QoS and state of the Grid into account -- service availability and performance. We propose an approach for generating constraint equations describing the workflow, the QoS requirements and the state of the Grid. This set of equations may be solved using Mixed-Integer Linear Programming (MILP), which is the traditional method. We further develop a novel 2-stage stochastic MILP which is capable of dealing with the volatile nature of the Grid and adapting the selection of the services during the lifetime of the workflow. We present experimental results comparing our approaches, showing that the 2-stage stochastic programming approach performs consistently better than other traditional approaches. Next we addresses workload allocation techniques for Grid workflows in a multi-cluster Grid. We model individual clusters as MIMIk queues and obtain a numerical solution for missed deadlines (failures) of tasks of Grid workflows. We also present an efficient algorithm for obtaining workload allocations of clusters. Next we model individual cluster resources as G/G/l queues and solve an optimisation problem that minimises QoS requirement violation, provides QoS guarantee and outperforms reservation based scheduling algorithms. Both approaches are evaluated through an experimental simulation and the results confirm that the proposed workload allocation strategies combined with traditional scheduling algorithms performs considerably better in terms of satisfying QoS requirements of Grid workflows than scheduling algorithms that don't employ such workload allocation techniques. Next we develop a novel method for Grid brokers that aims at maximising profit whilst satisfying end-user needs with a sufficient guarantee in a volatile utility Grid. We develop a develop a 2-stage stochastic MILP which is capable of dealing with the volatile nature of the Grid and obtaining cost bounds that ensure that end-user cost is minimised or satisfied and broker's profit is maximised with sufficient guarantee. These bounds help brokers know beforehand whether the budget limits of end-users can be satisfied and, if not, then obtain appropriate future leases from service providers. Experimental results confirm the efficacy of our approach.
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Martin, Megan Wydick. "Computational Studies in Multi-Criteria Scheduling and Optimization." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78699.

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Multi-criteria scheduling provides the opportunity to create mathematical optimization models that are applicable to a diverse set of problem domains in the business world. This research addresses two different employee scheduling applications using multi-criteria objectives that present decision makers with trade-offs between global optimality and the level of disruption to current operating resources. Additionally, it investigates a scheduling problem from the product testing domain and proposes a heuristic solution technique for the problem that is shown to produce very high-quality solutions in short amounts of time. Chapter 2 addresses a grant administration workload-to-staff assignment problem that occurs in the Office of Research and Sponsored Programs at land-grant universities. We identify the optimal workload assignment plan which differs considerably due to multiple reassignments from the current state. To achieve the optimal workload reassignment plan we demonstrate a technique to identify the n best reassignments from the current state that provides the greatest progress toward the utopian solution. Solving this problem over several values of n and plotting the results allows the decision maker to visualize the reassignments and the progress achieved toward the utopian balanced workload solution. Chapter 3 identifies a weekly schedule that seeks the most cost-effective set of coach-to-program assignments in a gymnastics facility. We identify the optimal assignment plan using an integer linear programming model. The optimal assignment plan differs greatly from the status quo; therefore, we utilize a similar approach from Chapter 2 and use a multiple objective optimization technique to identify the n best staff reassignments. Again, the decision maker can visualize the trade-off between the number of reassignments and the resulting progress toward the utopian staffing cost solution and make an informed decision about the best number of reassignments. Chapter 4 focuses on product test scheduling in the presence of in-process and at-completion inspection constraints. Such testing arises in the context of the manufacture of products that must perform reliably in extreme environmental conditions. Each product receives a certification at the successful completion of a predetermined series of tests. Operational efficiency is enhanced by determining the optimal order and start times of tests so as to minimize the make span while ensuring that technicians are available when needed to complete in-process and at-completion inspections We first formulate a mixed-integer programming model (MILP) to identify the optimal solution to this problem using IBM ILOG CPLEX Interactive Optimizer 12.7. We also present a genetic algorithm (GA) solution that is implemented and solved in Microsoft Excel. Computational results are presented demonstrating the relative merits of the MILP and GA solution approaches across a number of scenarios.
Ph. D.
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Books on the topic "Workload scheduling":

1

Bailey, Michael P. Empirical methods for estimating workload capacity. Monterey, Calif: Naval Postgraduate School, 1992.

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Wiskow, Christiane. Workload measurement in determining staffing levels: A literature review. Geneva, Switzerland: ICN, 2004.

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Gucer, Vasfi. Getting started with IBM Tivoli workload scheduler V8.3: Best practices and performance improvements. [Poughkeepsie, NY]: IBM International Technical Support Organization, 2006.

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Jones, James Patton. NAS requirements checklist for job queuing/scheduling software. [Washington, D.C: National Aeronautics and Space Administration, 1996.

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Pettinger, Richard. Managing the flexible workforce. Oxford: Capstone, 2002.

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Pettinger, Richard. Managing the flexible workforce. London: FT Pitman, 1997.

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Pettinger, Richard. Managing the flexible workforce. Hitchin: Technical Communications, 1996.

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Pettinger, Richard. Managing the flexible workforce. London: Cassell, 1998.

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Zhu, Xingwen. ZnO bao mo zhi bei ji qi guang, dian xing neng yan jiu. 8th ed. Shanghai Shi: Shanghai da xue chu ban she, 2010.

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Redbooks, IBM. End-To-End Scheduling With Tivoli Workload Scheduler 8.1. Ibm, 2002.

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Book chapters on the topic "Workload scheduling":

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Kousalya, G., P. Balakrishnan, and C. Pethuru Raj. "Workload Consolidation Through Automated Workload Scheduling." In Computer Communications and Networks, 157–76. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56982-6_9.

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Plattner, Hasso. "Workload Management and Scheduling." In A Course in In-Memory Data Management, 149–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36524-9_22.

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Plattner, Hasso. "Workload Management and Scheduling." In A Course in In-Memory Data Management, 153–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55270-0_22.

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Plattner, Hasso. "Workload-Management und Scheduling." In Lehrbuch In-Memory Data Management, 153–55. Wiesbaden: Springer Fachmedien Wiesbaden, 2013. http://dx.doi.org/10.1007/978-3-658-03213-5_22.

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Ernemann, Carsten, Baiyi Song, and Ramin Yahyapour. "Scaling of Workload Traces." In Job Scheduling Strategies for Parallel Processing, 166–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/10968987_9.

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Jann, Joefon, Pratap Pattnaik, Hubertus Franke, Fang Wang, Joseph Skovira, and Joseph Riordan. "Modeling of workload in MPPs." In Job Scheduling Strategies for Parallel Processing, 95–116. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63574-2_18.

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Lo, Virginia, Jens Mache, and Kurt Windisch. "A comparative study of real workload traces and synthetic workload models for parallel job scheduling." In Job Scheduling Strategies for Parallel Processing, 25–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0053979.

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Li, Hui, David Groep, and Lex Wolters. "Workload Characteristics of a Multi-cluster Supercomputer." In Job Scheduling Strategies for Parallel Processing, 176–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11407522_10.

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Song, Baiyi, Carsten Ernemann, and Ramin Yahyapour. "Parallel Computer Workload Modeling with Markov Chains." In Job Scheduling Strategies for Parallel Processing, 47–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11407522_3.

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Söhngen, Ludwig. "Planning Shift Work and Duty Roster for Personnel with Variable Workload." In Computer-Aided Transit Scheduling, 119–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-85966-3_11.

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Conference papers on the topic "Workload scheduling":

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Faisal, Abdullah Bin, Hafiz Mohsin Bashir, Ihsan Ayyub Qazi, Zartash Uzmi, and Fahad R. Dogar. "Workload adaptive flow scheduling." In CoNEXT '18: The 14th International Conference on emerging Networking EXperiments and Technologies. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3281411.3281429.

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Kuchumov, R., and V. Korkhov. "HPC WORKLOAD BALANCING ALGORITHM FOR CO- SCHEDULING ENVIRONMENTS." In 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". Crossref, 2021. http://dx.doi.org/10.54546/mlit.2021.21.34.001.

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The goal of this research work is to reduce wait time of HPC (high performance computing) applications in schedulers queue by applying a co-scheduling strategy. This strategy allows the execution of more than one task with different non-overlapping requirements for computational resources simultaneously. Co-scheduling strategy reduces task queue wait time and improves utilization of cluster resources when compared to the scheduling strategies that do not allow for parallel task execution on the same machine. We have proposed a method for measuring application processing speed in its run-time, which can be used as a feedback for scheduling strategies. In this work, we have formalized the co-scheduling problem and proposed strategies for solving it. For some strategies we have shown analytically the upper bounds values of their competitive ratios. Besides that for the proposed scheduling strategies we ran numerical experiments using imitation models to show how they compare to the optimal strategy.
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Naik, Ketaki, G. Meera Gandhi, and S. H. Patil. "Multiobjective Workload Scheduling in Distributed Datacenters." In 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2019. http://dx.doi.org/10.1109/icecct.2019.8869271.

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Roitzsch, M., S. Wachtler, and H. Hartig. "Atlas: Look-ahead scheduling using workload metrics." In 2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS). IEEE, 2013. http://dx.doi.org/10.1109/rtas.2013.6531074.

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Goponenko, Alexander V., Ramin Izadpanah, Jim M. Brandt, and Damian Dechev. "Towards workload-adaptive scheduling for HPC clusters." In 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2020. http://dx.doi.org/10.1109/cluster49012.2020.00064.

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Jones, J. P., B. Nitzberg, and B. Henderson. "Workload management: more than just job scheduling." In Proceedings 2001 IEEE International Conference on Cluster Computing. IEEE, 2001. http://dx.doi.org/10.1109/clustr.2001.959959.

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Ferdouse, Lilatul, Mushu Li, Ling Guan, and Alagan Anpalagan. "Bayesian workload scheduling in multimedia cloud networks." In 2016 IEEE 21st International Workshop on Computer-Aided Modelling and Design of Communication Links and Networks (CAMAD). IEEE, 2016. http://dx.doi.org/10.1109/camad.2016.7790335.

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Jin, Yibo, Yuan Gao, Zhuzhong Qian, Mingyu Zhai, Hui Peng, and Sanglu Lu. "Workload-Aware Scheduling Across Geo-distributed Data Centers." In 2016 IEEE Trustcom/BigDataSE/I​SPA. IEEE, 2016. http://dx.doi.org/10.1109/trustcom.2016.0228.

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Stavrinides, Georgios L., and Helen D. Karatza. "Periodic scheduling of mixed workload in distributed systems." In 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC). IEEE, 2017. http://dx.doi.org/10.1109/ice.2017.8279875.

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Gulati, Divya P., Changkyu Kim, Simha Sethumadhavan, Stephen W. Keckler, and Doug Burger. "Multitasking workload scheduling on flexible-core chip multiprocessors." In the 17th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1454115.1454142.

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