Analysis of computer job control under uncertainty

Golosov, P. E.; Kozlov, M. V.; Malashenko, Yu. E.; Назарова, И. А.; Ronzhin, A. F.

doi:10.1134/s1064230711060062

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…The use of sporadic control to regulate manufacturing processes can significantly improve the production efficiency. In this case, control mechanisms are greatly simplified due to no necessity to generate and, thereafter, optimize a target function [8]. Attribution of parameters to the tasks not only optimizes the output, but also regulates the number of finished details that were not used in the assembly of units.…”

Section: Discussionmentioning

confidence: 99%

Simulation Model of Process Sporadic Control

Gostev¹,

Golosov²

2022

IJMEM

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The control solutions for modern production with a large number of processes performed simultaneously require equal or more computing power than, for example, calculating hashes for bitcoin. Hereinafter the reference is made to the problem of maximizing the production of the quantity of some mechanical units, which include several types of details required to assemble the unit in a certain quantity. The details are assumed to be manufactured and delivered to the assembly shop in a certain set. It is required to ensure the production of details on a specified number of machines for the assembly of maximum units for a fixed time. Any detail is expected to be produced on any machine. The same details can be produced in parallel. The optimization parameters will be provided by number of details of the required nomenclature, as produced with limited machine support used for the time-constraint production of these details. The production of a unit results from the available details of required nomenclature and its assembly from such details. We consider the production simulation model focused on maximizing the output of assemblies and increasing the efficiency of the use of available equipment. To solve the problem, we use the sporadic control mechanism for the number of manufactured details.

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Section: Discussionmentioning

confidence: 99%

Simulation Model of Process Sporadic Control

Gostev¹,

Golosov²

2022

IJMEM

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“…The problem is considered to be solved once one manages to find the unique fragment with preliminary given characteristics, properties or param eters in the given set of data. Otherwise, one needs to make sure that there is no such fragment in the array, thus completing the task [10]. Requests on solving citu problems of different types are received by the sys tem at arbitrary instants without any particular attributes, preference relations or prescribed service order.…”

Section: Statement Of the Problemmentioning

confidence: 99%

“…The proposed concurrent quota allocation of jobs of particular type is similar to the SWAP scheduling rule proposed in [7,9]. When the whole group of requests arrives simultaneously, the SWAP rule allows solving "less labor intensive" ones first and complete the tasks that require exhaustive search last [10].…”

Section: Property Of Concurrent Allocation Of Tasksmentioning

confidence: 99%

Control of resource-intensive computations under uncertainty. III. Dynamic concurrent resource allocation

Malashenko

Назарова

2015

J. Comput. Syst. Sci. Int.

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Control problems for a heterogeneous system executing heterogeneous search tasks are considered. Data parallelization of tasks under objective uncertainty-for the unknown and rapidly changing flow of incoming requests-is studied. Critical modes of operation under big system work loads are analyzed. The modified sliding scheduling method allows correcting the processing proce dure in real time. A way of dynamic construction of a sequence of non discriminating concurrent allo cations of limited different type resources between users with equal rights is proposed. A concept of normative maximal performance of the system processing one type of tasks in the single user mode is introduced. Parametric guaranteed estimates are used to decompose tasks with respect to their type. Scheduling procedures according to which tasks that de facto require less computational costs receive the processing priority and are completed first of all are developed.

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“…The computing system receives tasks at arbitrary instants. Thus, when scheduling and managing computational jobs, one should take into account objective uncertainty associated with both the receiving time and composition of heterogeneous tasks and their processing duration [10,11]. From the point of view of users, all solutions are peer, equally important and must be obtained-each one separately and all together-as soon as possible.…”

Section: Introductionmentioning

confidence: 99%

Control of resource-intensive computations under uncertainty. II. Scheduling complex

Kupalov-Yaropolk

Malashenko

Назарова

et al. 2014

J. Comput. Syst. Sci. Int.

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The CORSAR model software complex designed to analyze scheduling rules for resource intensive heterogeneous problems of search type that can be parallelized with respect to data is described. In the course of searching for solutions, the original arrays are decomposed into separate informative fragments so that one can use sliding scheduling methods and optimize parallelizing pro cesses when processing requests. Tasks are executed on a high performance heterogeneous specialized computing system. The developed multiparametric model control scheme is invariant to types of com putational resources, ensures their operation efficiency and scalability when the system is updated and evolving.

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Analysis of computer job control under uncertainty

Cited by 10 publications

References 26 publications

Simulation Model of Process Sporadic Control

Simulation Model of Process Sporadic Control

Control of resource-intensive computations under uncertainty. III. Dynamic concurrent resource allocation

Control of resource-intensive computations under uncertainty. II. Scheduling complex

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