Adaptive Job Scheduling Via Predictive Job Resource Allocation

2007 ◽  
pp. 115-140 ◽  
Author(s):  
Lawrence Barsanti ◽  
Angela C. Sodan
Keyword(s):  
Resource Allocation ◽  
Job Scheduling

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.

QoS-based Job Scheduling and Resource Management Strategies for Grid Computing

2011 ◽  
pp. 110-127
Author(s):  
Kuo-Chan Huang ◽  
Po-Chi Shih ◽  
Yeh-Ching Chung
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
Job Scheduling ◽  
Performance Metrics ◽  
Management Strategies ◽  
Load Sharing ◽  
Computational Grid ◽  
Qos Performance ◽  
Grid Level

This chapter elaborates the quality of service (QoS) aspect of load sharing activities in a computational grid environment. Load sharing is achieved through appropriate job scheduling and resource allocation mechanisms. A computational grid usually consists of several geographically distant sites each with different amount of computing resources. Different types of grids might have different QoS requirements. In most academic or experimental grids the computing sites volunteer to join the grids and can freely decide to quit the grids at any time when they feel joining the grids bring them no benefits. Therefore, maintaining an appropriate QoS level becomes an important incentive to attract computing sites to join a grid and stay in it. This chapter explores the QoS issues in such type of academic and experimental grids. This chapter first defines QoS based performance metrics for evaluating job scheduling and resource allocation strategies. According to the QoS performance metrics appropriate grid-level load sharing strategies are developed. The developed strategies address both user-level and site-level QoS concerns. A series of simulation experiments were performed to evaluate the proposed strategies based on real and synthetic workloads.

scholarly journals Optimized Load Balancing Strategy In Cloud Computing : A Review

2016 ◽  
Vol 15 (4) ◽  
pp. 6681-6685
Author(s):  
Parveen Kaur ◽  
Monika Sachdeva
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Resource Utilization ◽  
Distributed System ◽  
Job Scheduling ◽  
Work Load ◽  
Memory Capacity ◽  
Network Load

Now a days every organization is migrating towards  cloud computing as cloud computing is considered being more flexible and scalable as compared to other technologies. The technology simply means to provide the computing resources and services through a network. This paper discusses the existing approaches for scheduling algorithms that can maintain the load balancing and provides better improved strategies through efficient job scheduling and modified resource allocation techniques. The load can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of a distributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that all the processor in the system or every node in the network does approximately the equal amount of work at any instant of time. 

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