Heterogeneous machines can be significantly better than homogeneous machines but for that an effective workload distribution policy is required. Maximum realization of the performance can be achieved when system designer will overcome load imbalance condition within the system. Load
distribution and load balancing policy together can reduce total execution time and increase system throughput.
In this paper; we provide algorithm analysis of a threshold based job allocation and load balancing policy for heterogeneous system where all incoming jobs are judiciously and transparently distributed among sharing nodes on the basis of jobs’ requirement and processor capability for the maximization of performance and decline in execution time. A brief discussion of job allocation, transfer and location policy is given with explanation of how load imbalance condition is solved within the system. A flow of scheme is given with essential code and analysis of present algorithm is given to show how this algorithm is better.
Modified Active Monitoring Load Balancing with Cloud Computingijsrd.com
Cloud computing is internet-based computing in which large groups of remote servers are networked to allow the centralized data storage, and online access to computer services or resources. Load Balancing is essential for efficient operations in distributed environments. As Cloud Computing is growing rapidly and clients are demanding more services and better results, load balancing for the Cloud has become a very interesting and important research area. In the absence of proper load balancing strategy/technique the growth of CC will never go as per predictions. The main focus of this paper is to verify the approach that has been proposed in the model paper [3]. An efficient load balancing algorithm has the ability to reduce the data center processing time, overall response time and to cope with the dynamic changes of cloud computing environments. The traditional load balancing Active Monitoring algorithm has been modified to achieve better data center processing time and overall response time. The algorithm presented in this paper efficiently distributes the requests to all the VMs for their execution, considering the CPU utilization of all VMs.
Scalable Distributed Job Processing with Dynamic Load Balancingijdpsjournal
We present here a cost effective framework for a robust scalable and distributed job processing system that
adapts to the dynamic computing needs easily with efficient load balancing for heterogeneous systems. The
design is such that each of the components are self contained and do not depend on each other. Yet, they
are still interconnected through an enterprise message bus so as to ensure safe, secure and reliable
communication based on transactional features to avoid duplication as well as data loss. The load
balancing, fault-tolerance and failover recovery are built into the system through a mechanism of health
check facility and a queue based load balancing. The system has a centralized repository with central
monitors to keep track of the progress of various job executions as well as status of processors in real-time.
The basic requirement of assigning a priority and processing as per priority is built into the framework.
The most important aspect of the framework is that it avoids the need for job migration by computing the
target processors based on the current load and the various cost factors. The framework will have the
capability to scale horizontally as well as vertically to achieve the required performance, thus effectively
minimizing the total cost of ownership
A study of load distribution algorithms in distributed schedulingeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Effective Load Balance Scheduling Schemes for Heterogeneous Distributed System IJECEIAES
Importance of distributed systems for distributing the workload on the processors is globally accepted. It is an agreed fact that divides and conquers is the effective strategy for load balancing problems. In today’s time, load balancing is the major issue for scheduling algorithm such as in Parallel and Distributed Systems including Grid and Cloud Computing and many more. Load Balancing is the phenomena of spreading or distributing the workload among the processors so that all processors keep busy for all the time, in order to prevent ideal time of processors. In this work, presents a load balancing algorithm for heterogeneous distributed system (HeDS) with aim of minimizing the load imbalance factor (LIF). The proposed algorithm is using optimization techniques such as Max-Max and Min-Max strategy and applied on Folded Crossed Cube (FCC) network. Makespan, speedup and average resource utilization are also evaluated for performance matrices. The experimental results of the proposed algorithms have showed better in comparison to with previous work under various test conditions.
A study on dynamic load balancing in grid environmentIJSRD
Grid computing is a collection of computer resources from multiple locations to reach a common goal. Grid computing distinguishes from conventional high performance computing systems that are heterogeneous and geographically dispersed than cluster computer. One of the major issues in grid computing is load balancing. Classification of load balancing is: Static – Dynamic, Centralized – Decentralized, Homogeneous – Heterogeneous. Techniques like: Ant Colony Optimization, Threshold based and Optimal Heterogeneous are used by some researcher to balance the load. This survey paper discusses set of parameters to be used for comparing performance of each of them. In addition to that it says which technique is more useful for grid environment.
Comparative Analysis of Job Scheduling for Grid Environment ............................................................1
Neeraj Pandey, Ashish Arya and Nitin Kumar Agrawal
Hackers Portfolio and its Impact on Society ........................................................................................1
Dr. Adnan Omar and Terrance Sanchez, M.S.
Ontology Based Multi-Viewed Approach for Requirements Engineering ..............................................1
R. Subha and S. Palaniswami
Modified Colonial Competitive Algorithm: An Approach for Graph Coloring Problem ..........................1
Hojjat Emami and Parvaneh Hasanzadeh
Security and Privacy in E-Passport Scheme using Authentication Protocols and Multiple Biometrics
Technology ........................................................................................................................................1
V. K. Narendira Kumar and B. Srinivasan
Comparative Study of WLAN, WPAN, WiMAX Technologies ................................................................1
Prof. Mangesh M. Ghonge and Prof. Suraj G. Gupta
A New Method for Web Development using Search Engine Optimization ............................................1
Chutisant Kerdvibulvech and Kittidech Impaiboon
A New Design to Improve the Security Aspects of RSA Cryptosystem ..................................................1
Sushma Pradhan and Birendra Kumar Sharma
A Hybrid Model of Multimodal Approach for Multiple Biometrics Recognition ...................................1
P. Prabhusundhar, V.K. Narendira Kumar and B. Srinivasan
A vm scheduling algorithm for reducing power consumption of a virtual machine...eSAT Journals
Abstract This paper concentrates on methods which provide efficient processing time of a virtual machine, CPU utilization time of a virtual machine. As the user increases, the performance may be significantly reduced if the tasks are not scheduled in a proper order. In this paper the performance of two already existing algorithms DSP (Dependency Structural Prioritization) algorithm and credit scheduling algorithm are analyzed and compared. A single virtual machine’s processing time and CPU utilization time are measured .Satisfactory results are achieved while comparing the two algorithms. This study concludes that the DSP algorithm can perform efficiently than the credit scheduling algorithm. Keywords: Virtual Machine, DSP algorithm, credit scheduling algorithm
A vm scheduling algorithm for reducing power consumption of a virtual machine...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Modified Active Monitoring Load Balancing with Cloud Computingijsrd.com
Cloud computing is internet-based computing in which large groups of remote servers are networked to allow the centralized data storage, and online access to computer services or resources. Load Balancing is essential for efficient operations in distributed environments. As Cloud Computing is growing rapidly and clients are demanding more services and better results, load balancing for the Cloud has become a very interesting and important research area. In the absence of proper load balancing strategy/technique the growth of CC will never go as per predictions. The main focus of this paper is to verify the approach that has been proposed in the model paper [3]. An efficient load balancing algorithm has the ability to reduce the data center processing time, overall response time and to cope with the dynamic changes of cloud computing environments. The traditional load balancing Active Monitoring algorithm has been modified to achieve better data center processing time and overall response time. The algorithm presented in this paper efficiently distributes the requests to all the VMs for their execution, considering the CPU utilization of all VMs.
Scalable Distributed Job Processing with Dynamic Load Balancingijdpsjournal
We present here a cost effective framework for a robust scalable and distributed job processing system that
adapts to the dynamic computing needs easily with efficient load balancing for heterogeneous systems. The
design is such that each of the components are self contained and do not depend on each other. Yet, they
are still interconnected through an enterprise message bus so as to ensure safe, secure and reliable
communication based on transactional features to avoid duplication as well as data loss. The load
balancing, fault-tolerance and failover recovery are built into the system through a mechanism of health
check facility and a queue based load balancing. The system has a centralized repository with central
monitors to keep track of the progress of various job executions as well as status of processors in real-time.
The basic requirement of assigning a priority and processing as per priority is built into the framework.
The most important aspect of the framework is that it avoids the need for job migration by computing the
target processors based on the current load and the various cost factors. The framework will have the
capability to scale horizontally as well as vertically to achieve the required performance, thus effectively
minimizing the total cost of ownership
A study of load distribution algorithms in distributed schedulingeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Effective Load Balance Scheduling Schemes for Heterogeneous Distributed System IJECEIAES
Importance of distributed systems for distributing the workload on the processors is globally accepted. It is an agreed fact that divides and conquers is the effective strategy for load balancing problems. In today’s time, load balancing is the major issue for scheduling algorithm such as in Parallel and Distributed Systems including Grid and Cloud Computing and many more. Load Balancing is the phenomena of spreading or distributing the workload among the processors so that all processors keep busy for all the time, in order to prevent ideal time of processors. In this work, presents a load balancing algorithm for heterogeneous distributed system (HeDS) with aim of minimizing the load imbalance factor (LIF). The proposed algorithm is using optimization techniques such as Max-Max and Min-Max strategy and applied on Folded Crossed Cube (FCC) network. Makespan, speedup and average resource utilization are also evaluated for performance matrices. The experimental results of the proposed algorithms have showed better in comparison to with previous work under various test conditions.
A study on dynamic load balancing in grid environmentIJSRD
Grid computing is a collection of computer resources from multiple locations to reach a common goal. Grid computing distinguishes from conventional high performance computing systems that are heterogeneous and geographically dispersed than cluster computer. One of the major issues in grid computing is load balancing. Classification of load balancing is: Static – Dynamic, Centralized – Decentralized, Homogeneous – Heterogeneous. Techniques like: Ant Colony Optimization, Threshold based and Optimal Heterogeneous are used by some researcher to balance the load. This survey paper discusses set of parameters to be used for comparing performance of each of them. In addition to that it says which technique is more useful for grid environment.
Comparative Analysis of Job Scheduling for Grid Environment ............................................................1
Neeraj Pandey, Ashish Arya and Nitin Kumar Agrawal
Hackers Portfolio and its Impact on Society ........................................................................................1
Dr. Adnan Omar and Terrance Sanchez, M.S.
Ontology Based Multi-Viewed Approach for Requirements Engineering ..............................................1
R. Subha and S. Palaniswami
Modified Colonial Competitive Algorithm: An Approach for Graph Coloring Problem ..........................1
Hojjat Emami and Parvaneh Hasanzadeh
Security and Privacy in E-Passport Scheme using Authentication Protocols and Multiple Biometrics
Technology ........................................................................................................................................1
V. K. Narendira Kumar and B. Srinivasan
Comparative Study of WLAN, WPAN, WiMAX Technologies ................................................................1
Prof. Mangesh M. Ghonge and Prof. Suraj G. Gupta
A New Method for Web Development using Search Engine Optimization ............................................1
Chutisant Kerdvibulvech and Kittidech Impaiboon
A New Design to Improve the Security Aspects of RSA Cryptosystem ..................................................1
Sushma Pradhan and Birendra Kumar Sharma
A Hybrid Model of Multimodal Approach for Multiple Biometrics Recognition ...................................1
P. Prabhusundhar, V.K. Narendira Kumar and B. Srinivasan
A vm scheduling algorithm for reducing power consumption of a virtual machine...eSAT Journals
Abstract This paper concentrates on methods which provide efficient processing time of a virtual machine, CPU utilization time of a virtual machine. As the user increases, the performance may be significantly reduced if the tasks are not scheduled in a proper order. In this paper the performance of two already existing algorithms DSP (Dependency Structural Prioritization) algorithm and credit scheduling algorithm are analyzed and compared. A single virtual machine’s processing time and CPU utilization time are measured .Satisfactory results are achieved while comparing the two algorithms. This study concludes that the DSP algorithm can perform efficiently than the credit scheduling algorithm. Keywords: Virtual Machine, DSP algorithm, credit scheduling algorithm
A vm scheduling algorithm for reducing power consumption of a virtual machine...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Task scheduling methodologies for high speed computing systemsijesajournal
High Speed computing meets ever increasing real-time computational demands through the leveraging of
flexibility and parallelism. The flexibility is achieved when computing platform designed with
heterogeneous resources to support multifarious tasks of an application where as task scheduling brings
parallel processing. The efficient task scheduling is critical to obtain optimized performance in
heterogeneous computing Systems (HCS). In this paper, we brought a review of various application
scheduling models which provide parallelism for homogeneous and heterogeneous computing systems. In
this paper, we made a review of various scheduling methodologies targeted to high speed computing
systems and also prepared summary chart. The comparative study of scheduling methodologies for high
speed computing systems has been carried out based on the attributes of platform & application as well.
The attributes are execution time, nature of task, task handling capability, type of host & computing
platform. Finally a summary chart has been prepared and it demonstrates that the need of developing
scheduling methodologies for Heterogeneous Reconfigurable Computing Systems (HRCS) which is an
emerging high speed computing platform for real time applications.
EFFICIENT SCHEDULING STRATEGY USING COMMUNICATION AWARE SCHEDULING FOR PARALL...ijdpsjournal
In the area of Computer Science, Parallel job scheduling is an important field of research. Finding a best
suitable processor on the high performance or cluster computing for user submitted jobs plays an
important role in measuring system performance. A new scheduling technique called communication aware
scheduling is devised and is capable of handling serial jobs, parallel jobs, mixed jobs and dynamic jobs.
This work focuses the comparison of communication aware scheduling with the available parallel job
scheduling techniques and the experimental results show that communication aware scheduling performs
better when compared to the available parallel job scheduling techniques.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Application of Fuzzy Logic in Load Balancing of Homogenous Distributed Systems1CSCJournals
Various studies have shown that distributing the work load evenly among processors of a
distributed system highly improves system performance and increases resource utilization. This
process is known as load balancing. Fuzzy logic has been applied in many fields of science and
industry to deal with uncertainties. Existing research in using fuzzy logic for the purpose of load
balancing has only concentrated in utilizing fuzzy logic concepts in describing processors load
and tasks execution length. The responsibility of the fuzzy-based load balancing process itself,
however, has not been discussed and in most reported work is assumed to be performed in a
distributed fashion by all nodes in the network. This paper proposes a new fuzzy dynamic load
balancing algorithm for homogenous distributed systems. The proposed algorithm utilizes fuzzy
logic in dealing with inaccurate load information, making load distribution decisions, and
maintaining overall system stability. In terms of control, we propose a new approach that specifies
how, when, and by which node the load balancing is implemented. Our approach is called
Centralized-But-Distributed (CBD). An evaluation study of the proposed algorithm shows that our
algorithm is able to reduce the average response time and average queue length as compared to
known load balancing algorithms reported in the literature.
Load shedding in power system using the AHP algorithm and Artificial Neural N...IJAEMSJORNAL
This paper proposes the load shedding method based on considering the load importance factor, primary frequency adjustment, secondary frequency adjustment and neuron network. Consideration the process of primary frequency control, secondary frequency control helps to reduce the amount of load shedding power and restore the system’s frequency to the permissible range. The amount of shedding power of each load bus is distributed based on the load importance factor. Neuron network is applied to distribute load shedding strategies in the power system at different load levels. The experimental and simulated results on the IEEE 37- bus system present the frequency can restore to allowed range and reduce the damage compared to the traditional load shedding method using under frequency relay- UFLS.
An enhanced adaptive scoring job scheduling algorithm with replication strate...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Cell Charge Approximation for Accelerating Molecular Simulation on CUDA-Enabl...ijcax
Methods for Molecular Dynamics(MD) simulations are investigated. MD simulation is the widely used computer simulation approach to study the properties of molecular system. Force calculation in MD is computationally intensive. Paral-lel programming techniques can be applied to improve those calculations.
The major aim of this paper is to speed up the MD simulation calculations by/using General Purpose Graphics Processing Unit(GPU) computing paradigm, an efficient and economical way for parallel computing. For that we are proposing a method called cell charge approximation which treats the
electrostatic interactions in MD simulations.This method reduces the complexity of force calculations.
A GUI-driven prototype for synthesizing self-adaptation decisionjournalBEEI
The ability to ensure an optimal decision is significant for self-adaptive systems especially when dealing with uncertainty. For this reason, a synthesis-driven approach can be used to capture and synthesize a decision that aims to satisfy the multi-objective properties. Assessing the quality of the synthesis-driven approach is challenging, since it involves a set of activities from modeling, simulating, and analyzing the outcomes. This paper presents the design and implementation of a graphical user interface (GUI)-based prototype for assessing synthesis outcome and performance of an adaptation decision. The prototype is designed and developed based on the component-based development approach that is able to integrate the existing and related libraries from PRISM-games model checker for the synthesis engine, JFreeChart libraries for the chart presentation, and Java Universal Network/Graph Framework libraries for the graph visualization. This paper also presents the implementation of the proposed prototype based on the cloud application deployment scenario to illustrate its applicability. This work contributes to provide a fundamental work towards automated synthesis for self-adaptive systems.
STUDY THE EFFECT OF PARAMETERS TO LOAD BALANCING IN CLOUD COMPUTINGIJCNCJournal
The rapid growth of users on the cloud service and number of services to the user increases the load on the
servers at cloud datacenter. This issue is becoming a challenge for the researchers. And requires used
effectively a load balancing technique not only to balance the resources for servers but also reduce the
negative impact to the end-user service. The current, load balancing techniques have solved the various
problems such as: (i) load balancing after a server was overloaded; (ii) load balancing and load forecast
for the allocation of resources; iii) improving the parameters affecting to load balancing in cloud. The
study of improving these parameters have great significance to improving system performance through
load balancing. From there, we can propose more effective methods of load balancing, in order to increase
system performance. Therefore, in this paper we researched some parameters affecting the performance
load balancing on the cloud computing.
Presenting an Algorithm for Tasks Scheduling in Grid Environment along with I...Editor IJCATR
Nowadays, human faces with huge data. With regard to expansion of computer technology and detectors, some terabytes are
produced. In order to response to this demand, grid computing is considered as one of the most important research fields. Grid technology
and concepts were used to provide resource subscription between scientific units. The purpose was using resources of grid environment
to solve complex problems.
In this paper, a new algorithm based on Mamdani fuzzy system has been proposed for tasks scheduling in computing grid. Mamdani
fuzzy algorithm is a new technique measuring criteria by using membership functions. In this paper, our considered criterion is response
time. The results of proposed algorithm implemented on grid systems indicate priority of the proposed method in terms of validation
criteria of scheduling algorithms like ending time of the task and etc. Also, efficiency increases considerably.
A survey of various scheduling algorithm in cloud computing environmenteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Reinforcement learning based multi core scheduling (RLBMCS) for real time sys...IJECEIAES
Embedded systems with multi core processors are increasingly popular because of the diversity of applications that can be run on it. In this work, a reinforcement learning based scheduling method is proposed to handle the real time tasks in multi core systems with effective CPU usage and lower response time. The priority of the tasks is varied dynamically to ensure fairness with reinforcement learning based priority assignment and Multi Core MultiLevel Feedback queue (MCMLFQ) to manage the task execution in multi core system
Comparative Analysis of Various Grid Based Scheduling Algorithmsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Survey of Job Scheduling Algorithms Whit Hierarchical Structure to Load Ba...Editor IJCATR
Due to the advances in human civilization, problems in science and engineering are becoming more complicated than ever
before. To solve these complicated problems, grid computing becomes a popular tool. a grid environment collects, integrates, and uses
heterogeneous or homogeneous resources scattered around the globe by a high-speed network. Scheduling problems are at the heart of
any Grid-like computational system. a good scheduling algorithm can assign jobs to resources efficiently and can balance the system
load. in this paper we survey three algorithms for grid scheduling and compare benefit and disadvantages of their based on makespan.
Task scheduling methodologies for high speed computing systemsijesajournal
High Speed computing meets ever increasing real-time computational demands through the leveraging of
flexibility and parallelism. The flexibility is achieved when computing platform designed with
heterogeneous resources to support multifarious tasks of an application where as task scheduling brings
parallel processing. The efficient task scheduling is critical to obtain optimized performance in
heterogeneous computing Systems (HCS). In this paper, we brought a review of various application
scheduling models which provide parallelism for homogeneous and heterogeneous computing systems. In
this paper, we made a review of various scheduling methodologies targeted to high speed computing
systems and also prepared summary chart. The comparative study of scheduling methodologies for high
speed computing systems has been carried out based on the attributes of platform & application as well.
The attributes are execution time, nature of task, task handling capability, type of host & computing
platform. Finally a summary chart has been prepared and it demonstrates that the need of developing
scheduling methodologies for Heterogeneous Reconfigurable Computing Systems (HRCS) which is an
emerging high speed computing platform for real time applications.
EFFICIENT SCHEDULING STRATEGY USING COMMUNICATION AWARE SCHEDULING FOR PARALL...ijdpsjournal
In the area of Computer Science, Parallel job scheduling is an important field of research. Finding a best
suitable processor on the high performance or cluster computing for user submitted jobs plays an
important role in measuring system performance. A new scheduling technique called communication aware
scheduling is devised and is capable of handling serial jobs, parallel jobs, mixed jobs and dynamic jobs.
This work focuses the comparison of communication aware scheduling with the available parallel job
scheduling techniques and the experimental results show that communication aware scheduling performs
better when compared to the available parallel job scheduling techniques.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Application of Fuzzy Logic in Load Balancing of Homogenous Distributed Systems1CSCJournals
Various studies have shown that distributing the work load evenly among processors of a
distributed system highly improves system performance and increases resource utilization. This
process is known as load balancing. Fuzzy logic has been applied in many fields of science and
industry to deal with uncertainties. Existing research in using fuzzy logic for the purpose of load
balancing has only concentrated in utilizing fuzzy logic concepts in describing processors load
and tasks execution length. The responsibility of the fuzzy-based load balancing process itself,
however, has not been discussed and in most reported work is assumed to be performed in a
distributed fashion by all nodes in the network. This paper proposes a new fuzzy dynamic load
balancing algorithm for homogenous distributed systems. The proposed algorithm utilizes fuzzy
logic in dealing with inaccurate load information, making load distribution decisions, and
maintaining overall system stability. In terms of control, we propose a new approach that specifies
how, when, and by which node the load balancing is implemented. Our approach is called
Centralized-But-Distributed (CBD). An evaluation study of the proposed algorithm shows that our
algorithm is able to reduce the average response time and average queue length as compared to
known load balancing algorithms reported in the literature.
Load shedding in power system using the AHP algorithm and Artificial Neural N...IJAEMSJORNAL
This paper proposes the load shedding method based on considering the load importance factor, primary frequency adjustment, secondary frequency adjustment and neuron network. Consideration the process of primary frequency control, secondary frequency control helps to reduce the amount of load shedding power and restore the system’s frequency to the permissible range. The amount of shedding power of each load bus is distributed based on the load importance factor. Neuron network is applied to distribute load shedding strategies in the power system at different load levels. The experimental and simulated results on the IEEE 37- bus system present the frequency can restore to allowed range and reduce the damage compared to the traditional load shedding method using under frequency relay- UFLS.
An enhanced adaptive scoring job scheduling algorithm with replication strate...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Cell Charge Approximation for Accelerating Molecular Simulation on CUDA-Enabl...ijcax
Methods for Molecular Dynamics(MD) simulations are investigated. MD simulation is the widely used computer simulation approach to study the properties of molecular system. Force calculation in MD is computationally intensive. Paral-lel programming techniques can be applied to improve those calculations.
The major aim of this paper is to speed up the MD simulation calculations by/using General Purpose Graphics Processing Unit(GPU) computing paradigm, an efficient and economical way for parallel computing. For that we are proposing a method called cell charge approximation which treats the
electrostatic interactions in MD simulations.This method reduces the complexity of force calculations.
A GUI-driven prototype for synthesizing self-adaptation decisionjournalBEEI
The ability to ensure an optimal decision is significant for self-adaptive systems especially when dealing with uncertainty. For this reason, a synthesis-driven approach can be used to capture and synthesize a decision that aims to satisfy the multi-objective properties. Assessing the quality of the synthesis-driven approach is challenging, since it involves a set of activities from modeling, simulating, and analyzing the outcomes. This paper presents the design and implementation of a graphical user interface (GUI)-based prototype for assessing synthesis outcome and performance of an adaptation decision. The prototype is designed and developed based on the component-based development approach that is able to integrate the existing and related libraries from PRISM-games model checker for the synthesis engine, JFreeChart libraries for the chart presentation, and Java Universal Network/Graph Framework libraries for the graph visualization. This paper also presents the implementation of the proposed prototype based on the cloud application deployment scenario to illustrate its applicability. This work contributes to provide a fundamental work towards automated synthesis for self-adaptive systems.
STUDY THE EFFECT OF PARAMETERS TO LOAD BALANCING IN CLOUD COMPUTINGIJCNCJournal
The rapid growth of users on the cloud service and number of services to the user increases the load on the
servers at cloud datacenter. This issue is becoming a challenge for the researchers. And requires used
effectively a load balancing technique not only to balance the resources for servers but also reduce the
negative impact to the end-user service. The current, load balancing techniques have solved the various
problems such as: (i) load balancing after a server was overloaded; (ii) load balancing and load forecast
for the allocation of resources; iii) improving the parameters affecting to load balancing in cloud. The
study of improving these parameters have great significance to improving system performance through
load balancing. From there, we can propose more effective methods of load balancing, in order to increase
system performance. Therefore, in this paper we researched some parameters affecting the performance
load balancing on the cloud computing.
Presenting an Algorithm for Tasks Scheduling in Grid Environment along with I...Editor IJCATR
Nowadays, human faces with huge data. With regard to expansion of computer technology and detectors, some terabytes are
produced. In order to response to this demand, grid computing is considered as one of the most important research fields. Grid technology
and concepts were used to provide resource subscription between scientific units. The purpose was using resources of grid environment
to solve complex problems.
In this paper, a new algorithm based on Mamdani fuzzy system has been proposed for tasks scheduling in computing grid. Mamdani
fuzzy algorithm is a new technique measuring criteria by using membership functions. In this paper, our considered criterion is response
time. The results of proposed algorithm implemented on grid systems indicate priority of the proposed method in terms of validation
criteria of scheduling algorithms like ending time of the task and etc. Also, efficiency increases considerably.
A survey of various scheduling algorithm in cloud computing environmenteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Reinforcement learning based multi core scheduling (RLBMCS) for real time sys...IJECEIAES
Embedded systems with multi core processors are increasingly popular because of the diversity of applications that can be run on it. In this work, a reinforcement learning based scheduling method is proposed to handle the real time tasks in multi core systems with effective CPU usage and lower response time. The priority of the tasks is varied dynamically to ensure fairness with reinforcement learning based priority assignment and Multi Core MultiLevel Feedback queue (MCMLFQ) to manage the task execution in multi core system
Comparative Analysis of Various Grid Based Scheduling Algorithmsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Survey of Job Scheduling Algorithms Whit Hierarchical Structure to Load Ba...Editor IJCATR
Due to the advances in human civilization, problems in science and engineering are becoming more complicated than ever
before. To solve these complicated problems, grid computing becomes a popular tool. a grid environment collects, integrates, and uses
heterogeneous or homogeneous resources scattered around the globe by a high-speed network. Scheduling problems are at the heart of
any Grid-like computational system. a good scheduling algorithm can assign jobs to resources efficiently and can balance the system
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ANALYSIS OF THRESHOLD BASED CENTRALIZED LOAD BALANCING POLICY FOR HETEROGENEOUS MACHINES
1. International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011
DOI : 10.5121/ijait.2011.1505 39
ANALYSIS OF THRESHOLD BASED
CENTRALIZED LOAD BALANCING POLICY
FOR HETEROGENEOUS MACHINES
Archana B.Saxena1
and Deepti Sharma2
1
Department of Computer Science, Jagan Institute of Management Studies, Affiliated to
GGSIPU, Delhi
archanabsaxena@gmail.com
2
Department of Computer Science, Jagan Institute of Management Studies, Affiliated to
GGSIPU, Delhi
deepti.jims@gmail.com
Abstract
Heterogeneous machines can be significantly better than homogeneous machines but for that an
effective workload distribution policy is required. Maximum realization of the performance can be
achieved when system designer will overcome load imbalance condition within the system. Load
distribution and load balancing policy together can reduce total execution time and increase system
throughput.
In this paper; we provide algorithm analysis of a threshold based job allocation and load balancing
policy for heterogeneous system where all incoming jobs are judiciously and transparently distributed
among sharing nodes on the basis of jobs’ requirement and processor capability for the maximization
of performance and decline in execution time. A brief discussion of job allocation, transfer and
location policy is given with explanation of how load imbalance condition is solved within the system.
A flow of scheme is given with essential code and analysis of present algorithm is given to show how
this algorithm is better.
Keywords
Heterogeneous Systems, Central Server, Threshold, Load Balancing, Load Distribution, Centralized
Load Balancing, Sender Initiative, Receiver Initiative.
Abbreviations
S: System, CS: Central Server, LB: Load Balancing, CM: Capability Matrix, LM: Load Matrix, Ji:
Job, n: Node, T:Threshold, L:Load, No: Node Overloaded, Nu: Node Under Loaded, SN: Suitable
Node, Nxj : Non Executing Jobs, Job_P: Pending Job, Job_E: Executing Job, M: No of machines
2. International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011
40
1. INTRODUCTION
Load balancing is a mechanism that enables jobs to move from one computer to another within
the distributed system. This creates faster job service e.g., minimize job response time and
enhances resource utilization. Various studies, e.g., (Barak., Shiloh, 1985) (Khan, 2010), have
shown that load balancing among nodes of a distributed system highly improves system
performance and increases resource utilization. The successful load balancing algorithm depends
on the following factors: job info, job requirement info, machine details, and current status of
machines. Load balancing algorithms can be static, dynamic and adaptive. System state is used in
dynamic load balancing algorithm to make load distribution decision where no such things are
required in static algorithm. Here, load distribution technique is hardwired on the basis of system
state. Where adaptive load balancing algorithm adapts their changes dynamically on the basis of
system state (Niranja, Krueger and Singhal, 1992), load balancing can be distributed and non-
distributed. In general, non-distributed based dynamic load balancing approach can be
implemented by two ways: centralized and semi distributed. Centralized load balancing require
fewer message to achieve load balancing condition with in system (Ali M. 2010) as nodes have to
interact with only CS not with any other neighbor node.
We have adopted centralized dynamic approach for load balancing. In this approach we consider
a system where M heterogeneous machines are registered with CS. These entire machines are
connected through high speed network with each other. Centralized Policy by considering system
state is used for load distribution and load balancing in this approach. Load distribution will
depend on processor capability, job requirement and threshold of machine. Load balancing is
accomplished by transferring load from heavily loaded system to lightly loaded system. This
movement can be initiated by heavily (Sender initiative policy) loaded machines or by lightly
(Receiver initiated policy) loaded machines. Load balancing policy will include: information,
transfer and location strategy. This proposed strategy is best suited for small size network as it is
proved through a study that centralized load balancing are best suited for network where node
strength < 100.
The proposed load balancing algorithm has following objectives:
- Greater overall improvement in the system by reducing job response time and increasing
system throughput.
- To have performance endurance in system in case of partial failure with system.
- To maintain system stability so that system performance does not decline and system
spends more time in executing jobs rather than passing jobs to other nodes.
Parameters used in Algorithm:
Load Measurement: In the current scheme CPU queue length is used as load descriptor because
it is simple to obtain and can give good idea about time required to finish currently assigned work
as we have assumed (execution time =job processing requirements / processing capabilities).
Performance Measurement: Proposed algorithm is both system performance oriented and user
performance oriented. System performance oriented is measured through job wait time and job
response time is indicator of user performance.
3. International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011
41
System Stability: In current scheme system stability depends on the time CS server is spending
in load balancing among nodes, it should not be more than certain percentage (p) of its total
working time.
2. RELATED WORK
In cluster computing era, load balancing has gained interest among researchers and lots of work
had been done in this regard to provide better systems. Mohammed A. M. Ibrahim [2010] used
a mobile agent based approach to improve load balancing of parallel task in heterogeneous
computing environment in which master process send mobile agents to every workstation to
measure the load level of local machine and send it to master server. In second phase master
server selects low loaded machine on the basis of threshold and form a cluster of such machines.
Finally this adaptive threshold policy adjusts the threshold according to the changes in system.
Ali M. Alakeel [2010] presented and analyzed factors viz. load estimation, information strategy,
transfer strategy, job resource requirement, performance indices and other issues which need to
be considered in building load balancing policy for the system. Leping Wang and Ying Lu
[2010] proposed a power management system for heterogeneous soft real time clusters. A
threshold based approach makes three important design decisions ordered server list, server
activation threshold and work load distribution. Server activation threshold and work load
distribution are designed to achieve optimal power consumption. Sandeep Sharma, Sarabjit
Singh, and Meenakshi Sharma [2008] have studied various Static(Round Robin and
Randomized Algorithms, Central Manager Algorithm, Threshold Algorithm) and Dynamic
(Central Queue Algorithm, Local Queue Algorithm) Load Balancing Algorithms in distributed
system and their results shows static load balancing algorithms are more stable with such
parameters. Neeraj Nehra, R.B. Patel, V.K. Bhat [2007] have proposed a DDLB(Dynamic
Distributed Load Balancing) scheme for minimizing the average completion time of application
running in parallel and improve the utilization of nodes. In order to achieve their target they will
make use of MA (Mobile agent) to distribute load among nodes in a cluster. Satoshi Azuchi,
Sojeong Hong, Jinoh Kim [2006] provided a new distributed priority-based computing
architecture to utilize the computing resource more efficiently and be tolerant against single point
of failure. Each computing server’s load balancing is achieved by dynamically changing its
priority and control task acceptance rate based on a computing server’s priority. Helen D.
Karatza and Ralph C. Hilzer [2002] have worked together to find a load sharing policy in
heterogeneous distributed environment where half of the total processor have double of the speed
of others and excepts only two types of jobs: first class and Generic. Kalim Qureshi and
Masahiko Hatanaka [2000] presented a short survey on HDC systems and identified load
balancing problems in parallel raytracing in such systems. Mutual authentication and capability
based access control model is used for security. Niranjan G. Shivaratri, Phillip Krueger, and
Mukesh Singhal Ohio State Universit [1992]. Anna Hac aud Theodore J. Johnson [1986] has
addressed a Load balancing problem in LOCUS distributed file system and proposed a CSS
(centralized synchronization sites) policy for optimal process and read site placement.
Mohammed Javeed Zaki, Wei Li, and Srinivasan Parthasarathy [1997] have worked on the
concept of Dynamic Customized Load Balancing for heterogeneous network. Their experiment
result shows that different load balancing schemes(Local, global, centralized and distributed) are
best for different application under varying processor, program and system parameter and
therefore application-driven customized dynamic load balancing becomes essential for good
performance.
4. International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011
42
3. FUNCTIONAL DESCRIPTIONS
In centralized load balancing policy, one centralized node CS (Central Server) acknowledges the
incoming jobs. Each job ji has its arrival time and processing requirements associated with it. CS
searches the Capability Matrix (CM) to assign the job to suitable node. A suitable node N is
eligible to process current job if N.capability > Ji.capability.
Fig.1. Flow Chart showing Functional Description of the Proposed Model
Once the job is assigned to suitable node, the system can be in any of the three states:
Load balanced: where Load = Threshold
Over Loaded : where Load > Threshold
Under Loaded : where Load < Threshold
5. International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011
43
If the system is in load balanced condition no further step is required and all processors will
execute assigned jobs. Load imbalance condition can be treated by two ways:
Sender Initiative Load Balancing (Overloaded Node: Load > Threshold): when a Node ‘No’
would like to share its workload, it sends a request through communication link to ‘CS’ for load
sharing. In order, to respond the requested node ‘No’, ‘CS’ proceeds with following actions:
1. To confirm job status of ‘No’, ‘CS’ checks ‘LM’ and request rejected if ‘No’ is
not overloaded, otherwise move to step 2.
2. ‘CS’ checks ‘LM’ for ‘Nxj’ with ‘No’ (Only non-executing jobs are considered
for migration). If there is no such job then procedure ends otherwise move to step
3.
3. After selecting the job for migration, ‘CS’ looks for (Through ‘CM’) node that
can process selected job. Move to step 4 if one of them is found to have enough
processing capabilities to execute selected job.
4. The last step involved job migration from Overloaded (‘No’) to newly selected
node and updating ‘LM’.
Receiver Initiative Load Balancing (Under loaded Node: Load < Threshold): when an under
loaded Node ‘Nu’ would like to share its workload, it sends a request through communication
link to ‘CS’ for load sharing. In order to respond the requested node ‘Nu’, ‘CS’ proceeds with
following actions:
1. To confirm job status of ‘Nu’, ‘CS’ checks ‘LM’ and request rejected if
‘Nu’ is not under loaded, otherwise move to step 2.
2. ‘CS’ checks the processing capabilities of required node ‘Nu’, in order to
decide what sort/type of job can be assign to his node ‘Nu’.
3. ‘CS’ checks ‘LM’ for non executing processes/jobs pending with any
node where job requirements matches with processing capabilities of
node ‘Nu’. (In present system only non-executing jobs are considered for
migration). If there is no such job then procedure ends otherwise move to
step 4.
4. The last step involved job migration from under loaded (‘Nu’) to newly
selected node and updating ‘LM’.
4. COMPONENTS OF PROPOSED MODEL
A model for providing load balancing in heterogeneous systems consists of information, transfer
and location strategy. The components in each strategy are shown below:
4.1 Information strategy
It is the information hub of the algorithm which is responsible for collecting information about
the nodes and supplying required information to the transfer and location strategy. Proposed
algorithm maintains required information in the form of three tables:
4.1.1 Job Queue (Arrival time, Processing requirements):
A job queue is a collection of pending jobs within the system at different clock timing.
6. International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011
44
Table 1. Job Queue
Arrival Time Processing Requirements
0 1590
2 716
3 2076
10 1581
11 2412
18 1263
4.1.2 Capability Matrix (Mac No, Model No, Clock speed, Capability (15 * Speed),
Execution Threshold (50 * Capability)): It saves details of registered nodes.
Table 2. Capability Matrix
Mac No Model No. Clock
Speed
Capability Execution
threshold
[CaP. * 50]
Mac I Core 2 Duo 5250 1.5 GHz 45 2250
Mac II Core2 DuoT5200 1.60 GHz 48 2400
Mac III Pentium 4-M 2.2 2.2 GHz 33 1650
Mac IV Pentium 4 HT 2.4C 2.4 GHz 36 1800
Mac V Pentium 4 HT 2.6C 2.6 GHz 39 1950
Mac VI Pentium 4 HT 2.8C 2.8 GHz 42 2100
Mac VII Pentium 4 HT 3.2 3.2 GHz 48 2400
Mac VIII Pentium 4 HT 3.4 3.4 GHz 51 2550
Mac IX Pentium 4 HT 651 3.4 GHz 51 2550
Mac X Pentium 4 HT 661 3.6 GHz 54 2700
Mac XI Pentium 4 Extreme
Edition 3.73
3.73 GHz
56 2800
4.1.3 Load Matrix (Arrival Clock, Job Requirement, Start Time, Machine No.,
Execution Time (Job requirement/processor capability), End Time (Start time+
Time Required for Execution) and status):
It saves load of a machine at a particular point of time.
Table 3. Load Matrix
Clock Job
Requirement
Start
Time
Machine
No.
Execution
Time
End
Time
Execution
Status
0 1590 0 Mac-III 1590/33=48 48 E
2 716 2 Mac-IV 716/36=20 22 E
3 2076 3 Mac-VI 2076/42=49 52 E
10 1581 10 Mac-V 1581/39=41 51 E
7. International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011
45
4.2 Transfer Strategy
This component of the proposed model is responsible for accepting incoming jobs and making a
decision where it should be transferred for execution. If there is load imbalance in the system then
this strategy decides which jobs to be moved from one location to another and thus balancing load
within system.
4.2.1 Job Allocation Policy
Jobs are allocated among eligible processors according to job allocation policy and related
information is saved in Load Matrix. A processor can be selected for job execution if any one or
all of the following conditions are met.
- Job requirements are within processing capabilities of the processor.
- If two or more processor can execute the same job then fastest processor will execute the job.
- If load is more than execution threshold then next fastest processor or the one who is ready
to work will execute the job.
- If all the processor that are capable enough to process the job are busy then compare the
pending workload of these processor and then allot the job to the one who is least loaded.
4.2.2 Threshold Policy
It is an integer value that represents load status of machine. A job is transferred if queue length of
a local node exceeds a certain threshold (T) otherwise executed locally.
4.2.3 Load Balancing Request
Proposed algorithm offers load balancing as sender initiated (Overloaded Node) and receiver
initiated (Under Loaded Node).
- Sender-Initiated (T<L): When a new job is assigned to the machine, it checks its status
and if overloaded can launch balancing request to CS and ask to provide node to share
some load.
- Receiver-Initiated (T>L): When a finish job departs from machine it checks its status and
sends a request to CS and shows its willingness to accept more load.
4.2.4 Job Transfer Policy
Algorithm adopts consider-new-only approach where only newly arrived and non executing jobs
are considered for transfer.
4.3 Location Strategy
The main job of Location strategy is to decide destination node for Load balancing. In current
scheme this decision is taken by CS by measuring current load (Job queue length which includes
executing and pending jobs) and processing capabilities of the node. A node is selected under
location strategy if any of the following conditions are met:
- If processing capabilities of current job is more than processing requirements of transferring job.
- If transfer of current job to selected processor will not cause its load to go above threshold (T).
8. International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011
46
- Among all the selected processor fastest processor, shortest distance from old node and with the
smallest pending queue length will be chosen.
Once the job is transferred to the required location, information is updated in load matrix.
Required information for various decisions by transfer strategy and location strategy will be
provided by information strategy.
5. ALGORITHM
Based on the above components of proposed model, an algorithm is designed. This algorithm
consists of two procedures: assignment and load balancing.
5.1 Assignment
Assignment algorithm is used to assign incoming jobs to nodes for execution with the help of
Job_Queue and Capability_Matrix.
Assignment is executed through two main functions: Random_jobs ( ) and Assignment( ).
Random_jobs ( ):
1. Generates job arrival time, saves it in an array and sort array in ascending order.
2. Generates job requirement time in an array.
3. Creates a 2D array for job arrival clock and processing requirement and finally sort the
array.
Assignment ( )
1. FCFS (First Come First Serve) assignment strategy considers capability matrix for
assignment.
2. Writes information in Load_Matrix.
Random_Job(No_of_jobs)
{
// generates job arrival time and stores it in an array
Random ran_job = new Random();
for (int i =0; i<no_of_jobs;i++)
{
arr_time[i] = ran_job.nextInt(100);
}
// sorting job arrival time in ascending order
for (int i=0; i < no_of_jobs ; i++)
{
for(int j=i+1; j < no_of_jobs ; j++)
{
if(arr_time[i]>arr_time[j])
{
mid1=arr_time[i];
arr_time[i] = arr_time[j];
9. International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011
47
arr_time[j]=mid1;
}
}
}
// generates job processing requirements and store in an array
Random ran = new Random ();
for (int i=0; i < no_of_jobs ; i++)
{
job[i] = ran.nextInt(5000);
}
// creating a 2d array
int [ ][ ] job_arr_time_combo= new int[No_of_jobs][2];
for (int i=0 ; i < no_of_jobs ; i++)
{
for(int j=0 ; j < 2 ; j++)
{
if(j= =0)
job_arr_time_combo[i][j]=arr_time[i];
else
job_arr_time_combo[i][j]=job[i];
}
}
Calculate current Load(Ni)
// calculating present load of node Ni and checking its execution status
{
int c_load = “select count(job_requirement) from Load_matrix where mac_no = “Ni” and
Execution status = “E” or Execution status = “P” ”
Return c_load // to assignment ( )
}
Assignment ( )
int processor[ ][ ]= select MacNo, Processing_capabilities from Capability_Matrix
// arrange processor array in descending order
for (int i=1; i< no_of_jobs;i++)
{
for(int j=i+1; j< no_of_jobs;j++)
{
10. International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011
48
if(job[i]<job[j])
{
mid1=job[i];
job[i] = job[j];
job[j]=mid1;
}
}
}
for (int i=0; i< no_of_jobs;i++)
{
for (int j=0; j< processor.length();j++)
{
if (jobi.req < nj.cap)
{
// calculating present load of machine
int c_load= Calculate current Load(Nj)
//if current load < threshold, assign current job to that node and updates load_matrix
if (c_load < T)
{
nj.assigned();
nj.update_Load_Matrix();
}
} } }
5.2 Load Balancing
In the current scheme load balancing operation can be initiated by over loaded or under loaded
nodes. In both cases, request has to be routed through central server. Server will listen to request
checks its validity, check for availability of non executing nodes and finally calls load balancer
accordingly. Load balancing is done through following functions:
Overloaded ( ) / Underloaded ( )
1. Validates request made by node.
2. Checks load and capability matrix for executing jobs.
3. Calls load balancer ( ).
Load_Balancing_Request(Ni)
1. Execution status (E/P) of a node is compared with threshold of machine.
2. Returns an integer value (0 for load balanced and 1 for under loaded or over loaded
nodes).
3. Updates the status of requested node in system.
Load_Matrix_Nxj(No)
1. Accepts Mac_No of Overloaded Node
2. Returns status of non executing jobs that can be transferred for balancing load within
system.
11. International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011
49
Check_CM(Job_req)
1. Accepts job_requirement and searches Capability Matrix.
2. Return Mac_no or 0, depending on the availability of node.
Load_Balancer (No,New_n,Job_id)
It accepts Mac_No of overloaded node, Mac_No of new node and Job Id of transferred job for
executing transfer procedure.
OverLoaded()
{
// validating request made by node
int status = Load_Balancing_Request(No);
if (status = = 1)
{
// checking load matrix for non executing jobs
int job_status = Load_Matrix_Nxj (No);
if (job_status = =1)
// checking capability matrix for best suitable node for executing selected jobs
int node_status=Check_CM();
if (node_status = = 1)
{
// calling load balancer to balance load
Load_Balancer()
}
} } }
UnderLoaded()
{
// validating request made by node
int status = Load_Balancing_Request(Ni);
if (status = = 0 )
{
// checking for non executable jobs from load matrix
int job_status = Load_Matrix_Nxj (No);
if (job_status = =1)
{
// checking suitable node from capability matrix
int node_status=Check_CM();
if (node_status = = 1)
{
// calling load balancer for balancing load
Load_Balancer()
}
} } }
12. International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011
50
Load_Balancing_Request(Ni)
{
int Job_P = “Select count(execution status) from Load_Matrix where Mac_No= Ni and
execution status = “P” “
if (Job_P >= 1 )
{
return 1 ;
} }
Load_Matrix_Nxj(No)
{
int job_count[ ][ ] ;
Statement stmt;
Resultset rs = stmt.executeQuery(select job_requirement and Job_Id from Load_Matrix where
Mac_no = “No” and Execution_status=”P”)
//move till end of result set
while(rs.next())
{
// entering data in 2d array
for (int i=0;i<rs.getRows();i++)
{
for (int j=0;j<2;j++)
{
// In first column add job_id and in second column add job_requirement
if (j= =0)
job_count[i][j]=rs.getString(“job_ID”);
else
job_count[i][j]=rs.getString(“job_requirement”);
} }
// Requirement details and Job_id of all non executing jobs are saved in job_count array
if ( job_count.length( ) = =0)
return 0; // No job is available for transfer
else
return 1; // Nxj are available for transfer
} }
Check_CM(job_req)
{
for (int i=0;i<Node.count();i++)
{
if(job_req < ni.capability)
13. International Journal of Advanced Information Technology (IJAIT) Vol. 1, No.5, October 2011
51
{
int count = select count(Execution status) from LoadMatrix where Mac_no=ni and
Execution status= “E”
//Query to check availability of current node through Load Matrix
If (count = 0)
{
return ni; // Node is free to take transfer
}
}
return 0;
} }
Load_Balancer(O_node, N_node,job_id )
{
Delete from Load_matrix where jobId = job_id and MachineNo = No;
Insert into Load_Matrix values() ;
}
6. ANALYSIS OF THE ALGORITHM
To analyze the algorithms, let there be ‘k’ jobs and ‘m’ number of servers.
Thus, T(n) = Sorting + Assignment
Sorting Algorithm: Here for every i = 0 to m-1 there are m-1 to 1 comparisons i.e. (m-1) + (m-2)
+--------------------2+1 = m (m-1)/2=m2
/2 comparisons.
So, time complexity = T (m2
/2).
6.1 Analysis for Assignment Algorithm
Best Case: When there is a single server, there can be maximum 1 comparison.
Worst Case: When there are m servers, there can be maximum 1.m1+1.m2 ----------------+1.mn .
If there are ‘m’ servers then the number of comparisons will be ‘m’.
Following are given, some cases with different values of k (number of jobs) and m (number of
servers).
Let T (n) = sorting + assignment
Case 1: k=1, m=1
T(n) = T(0)+T(0);
T(n) = 2T(0)
T(n)=1
Case 2: k=1, m>1
T(n)= T(m2
/2)+T(m)
T(n)= O(m2
/2)
Case 3: k>1, m=1
T(n) = T(1) + T(k)
T(n) = O(k)
Case 4: k>1, m>1
T(n) = T(m2
/2) + T(km)
T(n) = O(km2
/2)
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52
6.2 Analysis for Load Balancing Algorithm
Load Balancing
For each server there will be ‘n’ comparisons and the complexity remains T(m). But if there are
unbalanced nodes in the server then for every unbalanced nodes ‘j’ there will be (m-j)
comparisons.Let the number of unbalanced node = j. Following are the cases given with different
values of m (number of servers) and j (number of unbalanced jobs). Let the constant time T(1) for
inserting and deleting data in the database in loadbalancer( ). Query processing time is considered
as constant.
Case 1: When j=0, m>1
T(n) = T(m2
/2)+T(m)
T(n) = 2T(m2
/2)+T(0)
T(n)= O(m2
/2) , c=2
Case 2: When j=1, m>1
T(n) = T(m2
/2)+T(m)+T(m-p)+T(1),
Where p is the position of
Unbalanced node in the cluster
T(n) = 3T(m2
/2)+T(1)
T(n)= O(m2
/2) , c=3
Case 3: When j>1 and m>1
T(n)= T(m2
/2)+T(m)+T(j(m-p))+T(j.1)
,j<=m
T(n) = 4T(m2
)
T(n) = O(m2
) , c=4
7.Conclusion
In this paper, a threshold based job allocation policy for heterogeneous systems is presented. In
this, all incoming jobs are compared with the registered nodes and jobs being allocated according
to their capability and threshold value. Load imbalance conditions are treated by Sender Initiative
(Overloaded Node: Load > Threshold) or Receiver Initiative (Under loaded Node: Load <
Threshold) Load Balancing. Algorithm and its analysis are also given. The main objective is to
provide a Load Balancing mechanism so that overall performance can be maximized.
In the future, through simulation we will try to prove that our load balancing scheme improves
work throughput.
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Deepti Sharma is an Asst. Professor in Department of Computer Science at Jagan
Institute of Management Studies (Affiliated to Guru Gobind Singh Indraprastha
University), Rohini, Delhi. She is MPhil, MCA and pursuing her PhD (Computer
Science). She has more than seven years of teaching experience in the areas of C, C++,
Data Structures, Operating System and DBMS. Her research areas include “Load
Balancing in Hetrogenous Web Servers” and “Mobile Banking” on which papers have
been published in National and International conferences and journals. Various seminars,
workshops and FDP (AICTE) have been attended.
Archana B Saxena is working as Assistant Professor and Web Administrator in Jagan
Institute of Management Studies (JIMS) since Oct 2004. Presently she is perusing PhD
in Computer Science from GNDU on topic “Load Balancing in Heterogeneous
Distributed Systems”. She has attained MPhil Degree in CS from MKU, in 2006, MCA
from IGNOU in 2003 and Bachelors in Commerce from Delhi University. She has
served as guest Lecturer in ARSD, Delhi University and Faculty as NIIT. She has written
various papers on Load Balancing, Mobile Computing and Data Mining published in
various journals and conferences.