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

2012 ◽  
pp. 1315-1331
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.

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.

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.

Quantifying Operator Benefits of Wireless Load Distribution

2010 ◽  
pp. 86-102
Author(s):  
S. J. Lincke ◽  
J. Brandner
Keyword(s):  
Quality Of Service ◽  
Mathematical Models ◽  
Performance Metrics ◽  
Load Sharing ◽  
Integrated Networks ◽  
Data Rates ◽  
Potential Benefits ◽  
Configuration Simulation ◽  
Shared Network

Although simulation studies show performance increases when load sharing wireless integrated networks, these studies assume a limited, defined configuration. Simulation examples of load sharing consider only performance of specific scenarios, and do not estimate capacity or other benefits for a generic network. This study discusses other potential benefits of a load shared network, such as flexibility, survivability, modularity, service focus, quality of service, and auto-reconfigurability. We evaluate these other benefits by developing mathematical models and measurements to quantify a set of potential benefits of load sharing. In addition, we consider capacity considerations against a best-case model. Varied overflow algorithms are then simulated assuming standard HSPA+ and WLAN data rates. The results are compared to the estimated and best-case performance metrics.

scholarly journals Performance metrics in wireless sensor networks :a survey and outlook

2018 ◽  
Vol 7 (2.26) ◽  
pp. 25
Author(s):  
E Ramya ◽  
R Gobinath
Keyword(s):  
Wireless Sensor Networks ◽  
Quality Of Service ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Performance Metrics ◽  
Resource Constraints ◽  
Sensor Data ◽  
Wireless Sensor

Data mining plays an important role in analysis of data in modern sensor networks. A sensor network is greatly constrained by the various challenges facing a modern Wireless Sensor Network. This survey paper focuses on basic idea about the algorithms and measurements taken by the Researchers in the area of Wireless Sensor Network with Health Care. This survey also catego-ries various constraints in Wireless Body Area Sensor Networks data and finds the best suitable techniques for analysing the Sensor Data. Due to resource constraints and dynamic topology, the quality of service is facing a challenging issue in Wireless Sensor Networks. In this paper, we review the quality of service parameters with respect to protocols, algorithms and Simulations. 

scholarly journals NSIS-Based Quality of Service and Resource Allocation in Ethernet Networks

2006 ◽  
pp. 132-142
Author(s):  
M. Carmo ◽  
B. Carvalho ◽  
J. Sá Silva ◽  
E. Monteiro ◽  
P. Simões ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
Ethernet Networks

Voice and Video over Gigabit Ethernet and ATM Backbone Networks

2014 ◽  
pp. 174-184
Author(s):  
Nurul I. Sarkar ◽  
Yash Dole
Keyword(s):  
Quality Of Service ◽  
Network Performance ◽  
Performance Metrics ◽  
Asynchronous Transfer Mode ◽  
Atm Networks ◽  
Gigabit Ethernet ◽  
Corporate Networks ◽  
Backbone Networks ◽  
Network Technologies

This chapter aims to report on the performance of voice and video traffic over two popular backbone network technologies, namely Gigabit Ethernet (GbE) and Asynchronous Transfer Mode (ATM). ATM networks are being used by many universities and organizations for their unique characteristics such as scalability and guaranteed Quality of Service (QoS), especially for voice and video applications. Gigabit Ethernet matches ATM functionality by providing higher bandwidth at much lower cost, less complexity, and easier integration into the existing Ethernet technologies. It is useful to be able to compare these two technologies against various network performance metrics to find out which technology performs better for transporting voice and video conferencing. This chapter provides an in-depth performance analysis and comparison of GbE and ATM networks by extensive OPNET-based simulation. The authors measure the Quality of Service (QoS) parameters, such as voice and video throughput, end-to-end delay, and voice jitter. The analysis and simulation results reported in this chapter provide some insights into the performance of GbE and ATM backbone networks. This chapter may help network researchers and engineers in selecting the best technology for the deployment of backbone campus and corporate networks.

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