Job scheduling and resource allocation in parallel-machine system via a hybrid nested partition method

2018 ◽  
Vol 14 (4) ◽  
pp. 597-604 ◽  
Author(s):  
Yaping Fu ◽  
Guanjie Jiang ◽  
Guangdong Tian ◽  
Zhenling Wang
Keyword(s):  
Resource Allocation ◽  
Job Scheduling ◽  
Parallel Machine ◽  
Machine System ◽  
Nested Partition ◽  
Partition Method

Research of Parallel Machine Scheduling with Flexible Resources Based on Nested Partition Method

2013 ◽  
Vol 459 ◽  
pp. 488-493
Author(s):  
Hong Fei Sun ◽  
Xiao Dang Liu ◽  
Wei Hou
Keyword(s):  
Production Process ◽  
Production Efficiency ◽  
Market Competition ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Flexible Resources ◽  
Flexible Resource ◽  
Nested Partition ◽  
Partition Method

In the fierce market competition environment, to maximize the production efficiency, manufacturing enterprises mostly adopt the many varieties of small batch and discrete mode of production. The production process has the very strong flexibility. But the production process are lack of scheduling management or parallel machine production unit mostly adopts the scheduling method of static quota system, so the flexible resource in enterprise production system parallel machine scheduling began to reveal inadequate and it has reduce the production efficiency of the enterprise. This paper provide the Nested Partition Method for the problem, establishing the mathematic models for dynamic scheduling, and developing the corresponding algorithm, in order to improve the utilization rate of equipment and flexible resource.

scholarly journals A meta-heuristic algorithm for integrated optimization of dynamic resource allocation planning and production scheduling in parallel machine system

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989834
Author(s):  
Na Wang ◽  
Yaping Fu ◽  
Hongfeng Wang
Keyword(s):  
Resource Allocation ◽  
Heuristic Algorithm ◽  
Production Scheduling ◽  
Optimization Problem ◽  
Manufacturing System ◽  
Parallel Machine ◽  
Dynamic Resource Allocation ◽  
Integration Problem ◽  
Advanced Information Technology ◽  
Dynamic Resource

With the wide application of advanced information technology and intelligent equipment in the manufacturing system, the decisions of design and operation have become more interdependent and their integration optimization has gained great concerns from the community of operational research recently. This article investigates an optimization problem of integrating dynamic resource allocation and production schedule in a parallel machine environment. A meta-heuristic algorithm, in which heuristic-based partition, genetic-based sampling, promising index calculation, and backtracking strategies are employed, is proposed for solving the investigated integration problem in order to minimize the makespan of the manufacturing system. The experimental results on a set of random-generated test instances indicate that the presented model is effective and the proposed algorithm exhibits the satisfactory performance that outperforms two state-of-the-art algorithms from literature.

Mongrel of FA-CRA and CG-CRA: Self Healing Resource Allocation with Optimized Job-Scheduling in SDN-Accelerated HPC Infrastructure

2019 ◽  
Vol 36 (6) ◽  
pp. 6195-6206
Author(s):  
S. Vamshi Krishna ◽  
Azad Srivastava ◽  
Sunil J. Wagh ◽  
Santhi Sabbi
Keyword(s):  
Resource Allocation ◽  
Job Scheduling ◽  
Self Healing

A Comprehensive Survey on SLA Compliant Energy Aware Resource Allocation in Cloud Datacenters

2018 ◽  
Vol 6 (6) ◽  
pp. 335
Author(s):  
Anitha R ◽  
C Vidya Raj
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Load Balancing ◽  
Job Scheduling ◽  
Cost Effective ◽  
Service Users ◽  
Energy Aware ◽  
Art Works ◽  
Comprehensive Survey ◽  
Service Agreement

Cloud Computing has achieved immense popularity due to its unmatched benefits and characteristics. With its increasing popularity and round the clock demand, cloud based data centers often suffer with problems due to over-usage of resources or under-usage of capable servers that ultimately leads to wastage of energy and overall elevated cost of operation. Virtualization plays a key role in providing cost effective solution to service users. But on datacenters, load balancing and scheduling techniques remain inevitable to provide better Quality of Service to the service users and maintenance of energy efficient operations in datacenters. Energy-Aware resource allocation and job scheduling mechanisms in VMs has helped datacenter providers to reduce their cost incurrence through predictive job scheduling and load balancing. But it is quite difficult for any SLA oriented systems to maintain equilibrium between QoS and cost incurrence while considering their legal assurance of quality, as there should not be any violations in their service agreement. This paper presents some state-of-the-art works by various researchers and experts in the arena of cloud computing systems and particularly emphasizes on energy aware resource allocations, job scheduling techniques, load balancing and price prediction methods. Comparisons are made to demonstrate usefulness of the mechanisms in different scenarios.

QoS Oriented Enhancement based on the Analysis of Dynamic Job Scheduling in HPC

2014 ◽  
Vol 6 (4) ◽  
pp. 28-45 ◽  
Author(s):  
Reshmi Raveendran ◽  
D. Shanthi Saravanan
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
High Performance ◽  
Large Scale ◽  
Job Scheduling ◽  
Dynamic Scheduling ◽  
Future Research ◽  
Dynamic Allocation ◽  
The Impact

With the advent of High Performance Computing (HPC) in the large-scale parallel computational environment, better job scheduling and resource allocation techniques are required to deliver Quality of Service (QoS). Therefore, job scheduling on a large-scale parallel system has been studied to minimize the queue time, response time, and to maximize the overall system utilization. The objective of this paper is to touch upon the recent methods used for dynamic resource allocation across multiple computing nodes and the impact of scheduling algorithms. In addition, a quantitative approach which explains a trend line analysis on dynamic allocation for batch processors is depicted. Throughout the survey, the trends in research on dynamic allocation and parallel computing is identified, besides, highlights the potential areas for future research and development. This study proposes the design for an efficient dynamic scheduling algorithm based on the Quality-of-Service. The analysis provides a compelling research platform to optimize dynamic scheduling of jobs in HPC.

QoS Oriented Enhancement based on the Analysis of Dynamic Job Scheduling in HPC

2016 ◽  
pp. 1800-1817
Author(s):  
Reshmi Raveendran ◽  
D. Shanthi Saravanan
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
Large Scale ◽  
Job Scheduling ◽  
Dynamic Scheduling ◽  
Future Research ◽  
Dynamic Allocation ◽  
System Utilization ◽  
Batch Processors

With the advent of High Performance Computing (HPC) in the large-scale parallel computational environment, better job scheduling and resource allocation techniques are required to deliver Quality of Service (QoS). Therefore, job scheduling on a large-scale parallel system has been studied to minimize the queue time, response time, and to maximize the overall system utilization. The objective of this paper is to touch upon the recent methods used for dynamic resource allocation across multiple computing nodes and the impact of scheduling algorithms. In addition, a quantitative approach which explains a trend line analysis on dynamic allocation for batch processors is depicted. Throughout the survey, the trends in research on dynamic allocation and parallel computing is identified, besides, highlights the potential areas for future research and development. This study proposes the design for an efficient dynamic scheduling algorithm based on the Quality-of-Service. The analysis provides a compelling research platform to optimize dynamic scheduling of jobs in HPC.

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