This summarizes a research paper that proposes an adaptive load sharing algorithm for heterogeneous distributed systems. The algorithm aims to balance load across nodes by migrating tasks from overloaded nodes to underloaded nodes, taking into account factors like node processing capacities, link capacities, and communication delays. It formulates mathematical models to represent changes in waiting times as tasks are added, completed or migrated between nodes. The goal is to minimize overall response times through decentralized load balancing decisions made locally at each node.
This is a presentation for Chapter 7 Distributed system management
Book: DISTRIBUTED COMPUTING , Sunita Mahajan & Seema Shah
Prepared by Students of Computer Science, Ain Shams University - Cairo - Egypt
Load balancing is one of the main challenges of every structured peer-to-peer (P2P) system that uses
distributed hash tables to map and distribute data items (objects) onto the nodes of the system. In a typical
P2P system with N nodes, the use of random hash functions for distributing keys among peer nodes can
lead to O(log N) imbalance. Most existing load balancing algorithms for structured P2P systems are not
adaptable to objectsβ variant loads in different system conditions, assume uniform distribution of objects in
the system, and often ignore node heterogeneity. In this paper we propose a load balancing algorithm that
considers the above issues by applying node movement and replication mechanisms while load balancing.
Given the high overhead of replication, we postpone this mechanism as much as possible, but we use it
when necessary. Simulation results show that our algorithm is able to balance the load within 85% of the
optimal value.
Efficient load rebalancing for distributed file system in CloudsIJERA Editor
Β
Cloud computing is an upcoming era in software industry. Itβs a very vast and developing technology.
Distributed file systems play an important role in cloud computing applications based on map reduce
techniques. While making use of distributed file systems for cloud computing, nodes serves computing and
storage functions at the same time. Given file is divided into small parts to use map reduce algorithms in
parallel. But the problem lies here since in cloud computing nodes may be added, deleted or modified any time
and also operations on files may be done dynamically. This causes the unequal load distribution of load among
the nodes which leads to load imbalance problem in distributed file system. Newly developed distributed file
system mostly depends upon central node for load distribution but this method is not helpful in large-scale and
where chances of failure are more. Use of central node for load distribution creates a problem of single point
dependency and chances of performance of bottleneck are more. As well as issues like movement cost and
network traffic caused due to migration of nodes and file chunks need to be resolved. So we are proposing
algorithm which will overcome all these problems and helps to achieve uniform load distribution efficiently. To
verify the feasibility and efficiency of our algorithm we will be using simulation setup and compare our
algorithm with existing techniques for the factors like load imbalance factor, movement cost and network traffic.
A study of load distribution algorithms in distributed schedulingeSAT Publishing House
Β
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
Survey on Load Rebalancing for Distributed File System in CloudAM Publications
Β
Distributed file system is used as a key building block of cloud computing. In distributed file system, a
large file is divided into number of chunks and allocates each chunk to separate node to perform MapReduce function
parallel over each node. In cloud, if number of storage nodes, number of files and assesses to that file increases then
the central node (master in MapReduce) becomes bottleneck. The load rebalancing task is used to eliminate the load
on central node. Using load rebalancing algorithm the load of nodes is balanced as well as the movement cost is
reduced. In this survey paper the problem of load imbalancing is overcome.
This is a presentation for Chapter 7 Distributed system management
Book: DISTRIBUTED COMPUTING , Sunita Mahajan & Seema Shah
Prepared by Students of Computer Science, Ain Shams University - Cairo - Egypt
Load balancing is one of the main challenges of every structured peer-to-peer (P2P) system that uses
distributed hash tables to map and distribute data items (objects) onto the nodes of the system. In a typical
P2P system with N nodes, the use of random hash functions for distributing keys among peer nodes can
lead to O(log N) imbalance. Most existing load balancing algorithms for structured P2P systems are not
adaptable to objectsβ variant loads in different system conditions, assume uniform distribution of objects in
the system, and often ignore node heterogeneity. In this paper we propose a load balancing algorithm that
considers the above issues by applying node movement and replication mechanisms while load balancing.
Given the high overhead of replication, we postpone this mechanism as much as possible, but we use it
when necessary. Simulation results show that our algorithm is able to balance the load within 85% of the
optimal value.
Efficient load rebalancing for distributed file system in CloudsIJERA Editor
Β
Cloud computing is an upcoming era in software industry. Itβs a very vast and developing technology.
Distributed file systems play an important role in cloud computing applications based on map reduce
techniques. While making use of distributed file systems for cloud computing, nodes serves computing and
storage functions at the same time. Given file is divided into small parts to use map reduce algorithms in
parallel. But the problem lies here since in cloud computing nodes may be added, deleted or modified any time
and also operations on files may be done dynamically. This causes the unequal load distribution of load among
the nodes which leads to load imbalance problem in distributed file system. Newly developed distributed file
system mostly depends upon central node for load distribution but this method is not helpful in large-scale and
where chances of failure are more. Use of central node for load distribution creates a problem of single point
dependency and chances of performance of bottleneck are more. As well as issues like movement cost and
network traffic caused due to migration of nodes and file chunks need to be resolved. So we are proposing
algorithm which will overcome all these problems and helps to achieve uniform load distribution efficiently. To
verify the feasibility and efficiency of our algorithm we will be using simulation setup and compare our
algorithm with existing techniques for the factors like load imbalance factor, movement cost and network traffic.
A study of load distribution algorithms in distributed schedulingeSAT Publishing House
Β
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
Survey on Load Rebalancing for Distributed File System in CloudAM Publications
Β
Distributed file system is used as a key building block of cloud computing. In distributed file system, a
large file is divided into number of chunks and allocates each chunk to separate node to perform MapReduce function
parallel over each node. In cloud, if number of storage nodes, number of files and assesses to that file increases then
the central node (master in MapReduce) becomes bottleneck. The load rebalancing task is used to eliminate the load
on central node. Using load rebalancing algorithm the load of nodes is balanced as well as the movement cost is
reduced. In this survey paper the problem of load imbalancing is overcome.
Dynamic Load Calculation in A Distributed System using centralized approachIJARIIT
Β
The building of networks and the establishment of communication protocols have led to distributed systems, in which computers that are linked in a network cooperate on a task. The task is divided by the master node into small parts (sub problems) and is given to the nodes of the distributed system to solve, which gives better performance in time complexity to solve the problem compared to the time required to solve the problem in a single machine. Load balancing is the process of redistributing the work load among nodes of the distributed system to improve both resource utilization and job response time while also avoiding a situation where some nodes are heavily loaded while others are idle or doing little work. So before sending these parts of problem by the master to the nodes, master node should know the actual work load of all the nodes. We try a dynamic approach to find out the work load of each participating nodes in the distributed system by the master before sending the parts of the problem to the nodes.
This paper describes an algorithm which runs in the master machine and collects information from the nodes of the distributed system (client server application) and calculates the current work load of the nodes of the distributed system. The algorithm is developed in such a way that it can calculate the loads of the nodes dynamically. This means the loads can be evaluated if new nodes are added or deleted or during current performance of the nodes. The whole system is implemented on linux machine and local area network.
The Concept of Load Balancing Server in Secured and Intelligent NetworkIJAEMSJORNAL
Β
Hundreds and thousands of data packets are routed every second by computer networks which are complex systems. The data should be routed efficiently to handle large amounts of data in network. A core networking solution which is responsible for distribution of incoming traffic among servers hosting the same content is load balancing. For example, if there are ten servers within a network and two of them are doing 95% of the work, the network is not running very efficiently. If each server was handling about 10% of the traffic, the network would run much faster.Networks get more efficient with the help of Load balancing. The traffic is evenly distributed amongst the network making sure no single device is overwhelmed.When a request is balanced across multiple servers, it prevents any server from becoming a single point of failure. It improves overall availability and responsiveness. To evenly split the traffic load among several different servers web servers; often use load balancing.Load balancing requires hardware or software that divides incoming traffic amongst the available serverseither it is done on a local network or a large web server. High amount of traffic is received by a network that have one server dedicated to balance the load among other servers and devices in the network. This server is often known as load balancer. Load balancing is used by clusters or multiple computers that work together, to spread out processing jobs among the available systems.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
ADVANCED DIFFUSION APPROACH TO DYNAMIC LOAD-BALANCING FOR CLOUD STORAGEijdpsjournal
Β
Load-balancing techniques have become a critical function in cloud storage systems that consist of complex heterogeneous networks of nodes with different capacities. However, the convergence rate of any load-balancing algorithm as well as its performance deteriorated as the number of nodes in the system, the diameter of the network and the communication overhead increased. Therefore, this paper presents an approach aims at scaling the system out not up - in other words, allowing the system to be expanded by adding more nodes without the need to increase the power of each node while at the same time increasing the overall performance of the system. Also, our proposal aims at improving the performance by not only
considering the parameters that will affect the algorithm performance but also simplifying the structure of the network that will execute the algorithm. Our proposal was evaluated through mathematical analysis as well as computer simulations, and it was compared with the centralized approach and the original diffusion technique. Results show that our solution outperforms them in terms of throughput and response time.
Finally, we proved that our proposal converges to the state of equilibrium where the loads in all in-domain nodes are the same since each node receives an amount of load proportional to its capacity. Therefore, we conclude that this approach would have an advantage of being fair, simple and no node is privileged.
Transmission Time and Throughput analysis of EEE LEACH, LEACH and Direct Tran...acijjournal
Β
This paper gives a brief description about some routing protocols like EEE LEACH, LEACH and Direct
Transmission protocol (DTx) in Wireless Sensor Network (WSN) and a comparison study of these
protocols based on some performance matrices. Addition to this an attempt is done to calculate their
transmission time and throughput. To calculate these, MATLAB environment is used. Finally, on the basis
of the obtained results from the simulation, the above mentioned three protocols are compared. The
comparison results show that, the EEE LEACH routing protocol has a greater transmission time than
LEACH and DTx protocol and with smaller throughput.
Dynamic Cloud Partitioning and Load Balancing in Cloud Shyam Hajare
Β
Cloud computing is the emerging and transformational paradigm in the field of information technology. It mostly focuses in providing various services on demand and resource allocation and secure data storage are some of them. To store huge amount of data and accessing data from such metadata is new challenge. Distributing and balancing of the load over a cloud using cloud partitioning can ease the situation. Implementing load balancing by considering static as well as dynamic parameters can improve the performance cloud service provider and can improve the user satisfaction. Implementation the model can provide dynamic way of resource selection de-pending upon different situation of cloud environment at the time of accessing cloud provisions based on cloud partitioning. This model can provide effective load balancing algorithm over the cloud environment, better refresh time methods and better load status evaluation methods.
Base paper ppt-. A load balancing model based on cloud partitioning for the ...Lavanya Vigrahala
Β
A load balancing model based on cloud partitioning for the public cloud. -Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
(Paper) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Β
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Elastic neural network method for load prediction in cloud computing gridIJECEIAES
Β
Cloud computing still has no standard definition, yet it is concerned with Internet or network on-demand delivery of resources and services. It has gained much popularity in last few years due to rapid growth in technology and the Internet. Many issues yet to be tackled within cloud computing technical challenges, such as Virtual Machine migration, server association, fault tolerance, scalability, and availability. The most we are concerned with in this research is balancing servers load; the way of spreading the load between various nodes exists in any distributed systems that help to utilize resource and job response time, enhance scalability, and user satisfaction. Load rebalancing algorithm with dynamic resource allocation is presented to adapt with changing needs of a cloud environment. This research presents a modified elastic adaptive neural network (EANN) with modified adaptive smoothing errors, to build an evolving system to predict Virtual Machine load. To evaluate the proposed balancing method, we conducted a series of simulation studies using cloud simulator and made comparisons with previously suggested approaches in the previous work. The experimental results show that suggested method betters present approaches significantly and all these approaches.
IJORCS now welcomes research manuscripts for its next issue, Volume 2, Issue 6. Authors are encouraged to contribute to IJORCS by submitting articles that clarify new research results, projects, surveying works and industrial experiences that describe significant advances in field of computer science.
Commentz-Walter: Any Better than Aho-Corasick for Peptide Identification? IJORCS
Β
An algorithm for locating all occurrences of a finite number of keywords in an arbitrary string, also known as multiple strings matching, is commonly required in information retrieval (such as sequence analysis, evolutionary biological studies, gene/protein identification and network intrusion detection) and text editing applications. Although Aho-Corasick was one of the commonly used exact multiple strings matching algorithm, Commentz-Walter has been introduced as a better alternative in the recent past. Comments-Walter algorithm combines ideas from both Aho-Corasick and Boyer Moore. Large scale rapid and accurate peptide identification is critical in computational proteomics. In this paper, we have critically analyzed the time complexity of Aho-Corasick and Commentz-Walter for their suitability in large scale peptide identification. According to the results we obtained for our dataset, we conclude that Aho-Corasick is performing better than Commentz-Walter as opposed to the common beliefs.
Dynamic Load Calculation in A Distributed System using centralized approachIJARIIT
Β
The building of networks and the establishment of communication protocols have led to distributed systems, in which computers that are linked in a network cooperate on a task. The task is divided by the master node into small parts (sub problems) and is given to the nodes of the distributed system to solve, which gives better performance in time complexity to solve the problem compared to the time required to solve the problem in a single machine. Load balancing is the process of redistributing the work load among nodes of the distributed system to improve both resource utilization and job response time while also avoiding a situation where some nodes are heavily loaded while others are idle or doing little work. So before sending these parts of problem by the master to the nodes, master node should know the actual work load of all the nodes. We try a dynamic approach to find out the work load of each participating nodes in the distributed system by the master before sending the parts of the problem to the nodes.
This paper describes an algorithm which runs in the master machine and collects information from the nodes of the distributed system (client server application) and calculates the current work load of the nodes of the distributed system. The algorithm is developed in such a way that it can calculate the loads of the nodes dynamically. This means the loads can be evaluated if new nodes are added or deleted or during current performance of the nodes. The whole system is implemented on linux machine and local area network.
The Concept of Load Balancing Server in Secured and Intelligent NetworkIJAEMSJORNAL
Β
Hundreds and thousands of data packets are routed every second by computer networks which are complex systems. The data should be routed efficiently to handle large amounts of data in network. A core networking solution which is responsible for distribution of incoming traffic among servers hosting the same content is load balancing. For example, if there are ten servers within a network and two of them are doing 95% of the work, the network is not running very efficiently. If each server was handling about 10% of the traffic, the network would run much faster.Networks get more efficient with the help of Load balancing. The traffic is evenly distributed amongst the network making sure no single device is overwhelmed.When a request is balanced across multiple servers, it prevents any server from becoming a single point of failure. It improves overall availability and responsiveness. To evenly split the traffic load among several different servers web servers; often use load balancing.Load balancing requires hardware or software that divides incoming traffic amongst the available serverseither it is done on a local network or a large web server. High amount of traffic is received by a network that have one server dedicated to balance the load among other servers and devices in the network. This server is often known as load balancer. Load balancing is used by clusters or multiple computers that work together, to spread out processing jobs among the available systems.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
ADVANCED DIFFUSION APPROACH TO DYNAMIC LOAD-BALANCING FOR CLOUD STORAGEijdpsjournal
Β
Load-balancing techniques have become a critical function in cloud storage systems that consist of complex heterogeneous networks of nodes with different capacities. However, the convergence rate of any load-balancing algorithm as well as its performance deteriorated as the number of nodes in the system, the diameter of the network and the communication overhead increased. Therefore, this paper presents an approach aims at scaling the system out not up - in other words, allowing the system to be expanded by adding more nodes without the need to increase the power of each node while at the same time increasing the overall performance of the system. Also, our proposal aims at improving the performance by not only
considering the parameters that will affect the algorithm performance but also simplifying the structure of the network that will execute the algorithm. Our proposal was evaluated through mathematical analysis as well as computer simulations, and it was compared with the centralized approach and the original diffusion technique. Results show that our solution outperforms them in terms of throughput and response time.
Finally, we proved that our proposal converges to the state of equilibrium where the loads in all in-domain nodes are the same since each node receives an amount of load proportional to its capacity. Therefore, we conclude that this approach would have an advantage of being fair, simple and no node is privileged.
Transmission Time and Throughput analysis of EEE LEACH, LEACH and Direct Tran...acijjournal
Β
This paper gives a brief description about some routing protocols like EEE LEACH, LEACH and Direct
Transmission protocol (DTx) in Wireless Sensor Network (WSN) and a comparison study of these
protocols based on some performance matrices. Addition to this an attempt is done to calculate their
transmission time and throughput. To calculate these, MATLAB environment is used. Finally, on the basis
of the obtained results from the simulation, the above mentioned three protocols are compared. The
comparison results show that, the EEE LEACH routing protocol has a greater transmission time than
LEACH and DTx protocol and with smaller throughput.
Dynamic Cloud Partitioning and Load Balancing in Cloud Shyam Hajare
Β
Cloud computing is the emerging and transformational paradigm in the field of information technology. It mostly focuses in providing various services on demand and resource allocation and secure data storage are some of them. To store huge amount of data and accessing data from such metadata is new challenge. Distributing and balancing of the load over a cloud using cloud partitioning can ease the situation. Implementing load balancing by considering static as well as dynamic parameters can improve the performance cloud service provider and can improve the user satisfaction. Implementation the model can provide dynamic way of resource selection de-pending upon different situation of cloud environment at the time of accessing cloud provisions based on cloud partitioning. This model can provide effective load balancing algorithm over the cloud environment, better refresh time methods and better load status evaluation methods.
Base paper ppt-. A load balancing model based on cloud partitioning for the ...Lavanya Vigrahala
Β
A load balancing model based on cloud partitioning for the public cloud. -Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
(Paper) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Β
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Elastic neural network method for load prediction in cloud computing gridIJECEIAES
Β
Cloud computing still has no standard definition, yet it is concerned with Internet or network on-demand delivery of resources and services. It has gained much popularity in last few years due to rapid growth in technology and the Internet. Many issues yet to be tackled within cloud computing technical challenges, such as Virtual Machine migration, server association, fault tolerance, scalability, and availability. The most we are concerned with in this research is balancing servers load; the way of spreading the load between various nodes exists in any distributed systems that help to utilize resource and job response time, enhance scalability, and user satisfaction. Load rebalancing algorithm with dynamic resource allocation is presented to adapt with changing needs of a cloud environment. This research presents a modified elastic adaptive neural network (EANN) with modified adaptive smoothing errors, to build an evolving system to predict Virtual Machine load. To evaluate the proposed balancing method, we conducted a series of simulation studies using cloud simulator and made comparisons with previously suggested approaches in the previous work. The experimental results show that suggested method betters present approaches significantly and all these approaches.
IJORCS now welcomes research manuscripts for its next issue, Volume 2, Issue 6. Authors are encouraged to contribute to IJORCS by submitting articles that clarify new research results, projects, surveying works and industrial experiences that describe significant advances in field of computer science.
Commentz-Walter: Any Better than Aho-Corasick for Peptide Identification? IJORCS
Β
An algorithm for locating all occurrences of a finite number of keywords in an arbitrary string, also known as multiple strings matching, is commonly required in information retrieval (such as sequence analysis, evolutionary biological studies, gene/protein identification and network intrusion detection) and text editing applications. Although Aho-Corasick was one of the commonly used exact multiple strings matching algorithm, Commentz-Walter has been introduced as a better alternative in the recent past. Comments-Walter algorithm combines ideas from both Aho-Corasick and Boyer Moore. Large scale rapid and accurate peptide identification is critical in computational proteomics. In this paper, we have critically analyzed the time complexity of Aho-Corasick and Commentz-Walter for their suitability in large scale peptide identification. According to the results we obtained for our dataset, we conclude that Aho-Corasick is performing better than Commentz-Walter as opposed to the common beliefs.
IJORCS,Call For Papers- Volume 3, Issue 1,December 2012IJORCS
Β
Welcoming the research scholars, scientists around the globe in the Open Access Dimension, IJORCS is now
accepting manuscripts for its next issue (Volume 3, Issue 1). Authors are encouraged to contribute to the research
community by submitting to IJORCS, articles that clarify new research results, projects, surveying works and
industrial experiences that describe significant advances in field of computer science.
An Enhanced Framework for Improving Spatio-Temporal Queries for Global Positi...IJORCS
Β
To efficiently process continuous spatio-temporal queries, we need to efficiently and effectively handle large number of moving objects and continuous updates on these queries. In this paper, we propose a framework that employs a new indexing algorithm that is built on top of SQL Server 2008 and avoid the overhead related to R-Tree indexing. To answer range queries, we utilize dynamic materialized view concept to efficiently handle update queries. We propose an adaptive safe region to reduce communication costs between the client and the server and to minimize position update load. Caching of results was utilized to enhance the overall performance of the framework. To handle concurrent spatio-temporal queries, we utilize publish/subscribe paradigm to group similar queries and efficiently process these requests. Experiments show that the overall proposed framework performance was able to outperform R-Tree index and produce promising and satisfactory results.
Introductory Approach on Ad-hoc Networks and its Paradigms IJORCS
Β
An ad-hoc wireless network is a collection of wireless mobile nodes that self-configure to construct a network without the need for any established infrastructure or backbone. Ad hoc networks use mobile nodes to enable communication outside wireless transmission range. With the advancement in wireless communications, more and more wireless networks appear, e.g., Mobile Ad Hoc Network (MANET), Wireless Sensor Network (WSN), etc. So, in this paper we have discussed Ad Hoc Networks along with its energy issues, applications, QoS and challenges.
Welcoming the research scholars, scientists around the globeΒ in theΒ Open Access Dimension, IJORCS isΒ now accepting manuscripts for its next issue (Volume 4, Issue 4). Authors are encouraged to contribute to the research community by submitting to IJORCS, articles that clarify new research results, projects, surveying works and industrial experiences that describe significant advances in field of computer science.
All paper submissions (http://www.ijorcs.org/submit-paper) are received and managed electronically by IJORCS Team. Detailed instructions about the submission procedure are available on IJORCS website (http://www.ijorcs.org/author-guidelines)
Enhancement of DES Algorithm with Multi State LogicIJORCS
Β
The principal goal to design any encryption algorithm must be the security against unauthorized access or attacks. Data Encryption Standard algorithm is a symmetric key algorithm and it is used to secure the data. Enhanced DES algorithm works on increasing the key length or complex S-BOX design or increased the number of states in which the information is to be represented or combination of above criteria. By increasing the key length, the number of combinations for key will increase which is hard for the intruder to do the brute force attack. As the S-BOX design will become the complex there will be a good avalanche effect. As the number of states increases in which the information is represented, it is hard for the intruder to crack the actual information. Proposed algorithm replace the predefined XOR operation applied during the 16 round of the standard algorithm by a new operation called βHash functionβ depends on using two keys. One key used in βFβ function and another key consists of a combination of 16 states (0,1,2β¦13,14,15) instead of the ordinary 2 state key (0, 1). This replacement adds a new level of protection strength and more robustness against breaking methods.
ADVANCED DIFFUSION APPROACH TO DYNAMIC LOAD-BALANCING FOR CLOUD STORAGEijdpsjournal
Β
Load-balancing techniques have become a critical function in cloud storage systems that consist of
complex heterogeneous networks of nodes with different capacities. However, the convergence rate of any
load-balancing algorithm as well as its performance deteriorated as the number of nodes in the system, the
diameter of the network and the communication overhead increased. Therefore, this paper presents an
approach aims at scaling the system out not up - in other words, allowing the system to be expanded by
adding more nodes without the need to increase the power of each node while at the same time increasing
the overall performance of the system. Also, our proposal aims at improving the performance by not only
considering the parameters that will affect the algorithm performance but also simplifying the structure of
the network that will execute the algorithm. Our proposal was evaluated through mathematical analysis as
well as computer simulations, and it was compared with the centralized approach and the original diffusion
technique. Results show that our solution outperforms them in terms of throughput and response time.
Finally, we proved that our proposal converges to the state of equilibrium where the loads in all in-domain
nodes are the same since each node receives an amount of load proportional to its capacity. Therefore, we
conclude that this approach would have an advantage of being fair, simple and no node is privileged.
Load Rebalancing for Distributed Hash Tables in Cloud Computingiosrjce
Β
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.
Cloud Partitioning of Load Balancing Using Round Robin ModelIJCERT
Β
Abstract: The purpose of load balancing is to look up the performance of a cloud environment through an appropriate
circulation strategy. Good load balancing will construct cloud computing for more stability and efficiency. This paper
introduces a better round robin model for the public cloud based on the cloud partitioning concept with a switch mechanism
to choose different strategies for different situations. Load balancing is the process of giving out of workload among
different nodes or processor. It will introduces a enhanced approach for public cloud load distribution using screening and
game theory concept to increase the presentation of the system.
Comparative Study of Effects of Delay in Load Balancing Scheme for Highly Loa...idescitation
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Distributed computing architectures utilize a set
of computational elements (CEs) to achieve performance that
is not attainable on a single CE. Conventional load balancers
have proven effective in increasing the utilization of CPU,
memory, and disk I/O resources in a Distributed environment.
However, most of the existing load-balancing schemes ignore
network resources, leaving an opportunity to improve the
network resources like delay or effective bandwidth of
networks running parallel applications.Load balancing
becomes more challenging in interactive applications as load
variation is very large and the load on each server may change
dramatically over time, by the time when a server is to make
the load migration decision, the collected load status from
other servers may no longer be valid. This will affect the
accuracy, and hence the performance, of the load balancing
algorithms. All the existing methods neglect the effect of
network delay among the servers on the accuracy of the load
balancing solutions. In this paper, due to the change in the
load of the server, network delay would affect the performance
of the load balancing algorithm will be discussed.
ieee standard base paper.-Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
SCALING DISTRIBUTED DATABASE JOINS BY DECOUPLING COMPUTATION AND COMMUNICATIONijdms
Β
To process a large volume of data, modern data management systems use a collection of machines
connected through a network. This paper proposes frameworks and algorithms for processing
distributed joinsβa compute- and communication-intensive workload in modern data-intensive systems.
By exploiting multiple processing cores within the individual machines, we implement a system to
process database joins that parallelizes computation within each node, pipelines the computation with
communication, parallelizes the communication by allowing multiple simultaneous data transfers
(send/receive). Our experimental results show that using only four threads per node the framework
achieves a 3.5x gains in intra-node performance while compared with a single-threaded counterpart.
Moreover, with the join processing workload the cluster-wide performance (and speedup) is observed to
be dictated by the intra-node computational loads; this property brings a near-linear speedup with
increasing nodes in the system, a feature much desired in modern large-scale data processing system
SCALING DISTRIBUTED DATABASE JOINS BY DECOUPLING COMPUTATION AND COMMUNICATIONijdms
Β
To process a large volume of data, modern data management systems use a collection of machines
connected through a network. This paper proposes frameworks and algorithms for processing
distributed joinsβa compute- and communication-intensive workload in modern data-intensive systems.
By exploiting multiple processing cores within the individual machines, we implement a system to
process database joins that parallelizes computation within each node, pipelines the computation with
communication, parallelizes the communication by allowing multiple simultaneous data transfers
(send/receive). Our experimental results show that using only four threads per node the framework
achieves a 3.5x gains in intra-node performance while compared with a single-threaded counterpart.
Moreover, with the join processing workload the cluster-wide performance (and speedup) is observed to
be dictated by the intra-node computational loads; this property brings a near-linear speedup with
increasing nodes in the system, a feature much desired in modern large-scale data processing system.
SCALING DISTRIBUTED DATABASE JOINS BY DECOUPLING COMPUTATION AND COMMUNICATIONijdms
Β
To process a large volume of data, modern data management systems use a collection of machines
connected through a network. This paper proposes frameworks and algorithms for processing
distributed joinsβa compute- and communication-intensive workload in modern data-intensive systems.
By exploiting multiple processing cores within the individual machines, we implement a system to
process database joins that parallelizes computation within each node, pipelines the computation with
communication, parallelizes the communication by allowing multiple simultaneous data transfers
(send/receive). Our experimental results show that using only four threads per node the framework
achieves a 3.5x gains in intra-node performance while compared with a single-threaded counterpart.
Moreover, with the join processing workload the cluster-wide performance (and speedup) is observed to
be dictated by the intra-node computational loads; this property brings a near-linear speedup with
increasing nodes in the system, a feature much desired in modern large-scale data processing system.
A study on dynamic load balancing in grid environmentIJSRD
Β
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 ΓΒ’Γ’β¬Òβ¬Ε Dynamic, Centralized ΓΒ’Γ’β¬Òβ¬Ε Decentralized, Homogeneous ΓΒ’Γ’β¬Òβ¬Ε 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.
Load Balancing In Distributed ComputingRicha Singh
Β
Load Balancing In Distributed Computing
The goal of the load balancing algorithms is to maintain the load to each processing element such that all the processing elements become neither overloaded nor idle that means each processing element ideally has equal load at any moment of time during execution to obtain the maximum performance (minimum execution time) of the system
Cloud computing is an on demand service in which shared resources, information, software and other devices are provided to the end user as per their requirement at a specific time. A cloud consists of several elements such as clients, datacenters and distributed servers. There are n number of clients and end users involved in cloud environment. These clients may make requests to the cloud system simultaneously, making it difficult for the cloud to manage the entire load at a time. The load can be CPU load, memory load, delay or network load. This might cause inconvenience to the clients as there may be delay in the response time or it might affect the performance and efficiency of the cloud environment. So, the concept of load balancing is very important in cloud computing to improve the efficiency of the cloud. Good load balancing makes cloud computing more efficient and improves user satisfaction. This paper gives an approach to balance the incoming load in cloud environment by making partitions of the public cloud.
A LOAD BALANCING ALGORITHM BASED ON REPLICATION AND MOVEMENT OF DATA ITEMS FO...ijp2p
Β
Load balancing is one of the main challenges of every structured peer-to-peer (P2P) system that uses
distributed hash tables to map and distribute data items (objects) onto the nodes of the system. In a typical
P2P system with N nodes, the use of random hash functions for distributing keys among peer nodes can
lead to O(log N) imbalance. Most existing load balancing algorithms for structured P2P systems are not
adaptable to objectsβ variant loads in different system conditions, assume uniform distribution of objects in
the system, and often ignore node heterogeneity. In this paper we propose a load balancing algorithm that
considers the above issues by applying node movement and replication mechanisms while load balancing.
Given the high overhead of replication, we postpone this mechanism as much as possible, but we use it
when necessary. Simulation results show that our algorithm is able to balance the load within 85% of the
optimal value.
A LOAD BALANCING ALGORITHM BASED ON REPLICATION AND MOVEMENT OF DATA ITEMS FO...ijp2p
Β
Load balancing is one of the main challenges of every structured peer-to-peer (P2P) system that uses
distributed hash tables to map and distribute data items (objects) onto the nodes of the system. In a typical
P2P system with N nodes, the use of random hash functions for distributing keys among peer nodes can
lead to O(log N) imbalance. Most existing load balancing algorithms for structured P2P systems are not
adaptable to objectsβ variant loads in different system conditions, assume uniform distribution of objects in
the system, and often ignore node heterogeneity. In this paper we propose a load balancing algorithm that
considers the above issues by applying node movement and replication mechanisms while load balancing.
Given the high overhead of replication, we postpone this mechanism as much as possible, but we use it
when necessary. Simulation results show that our algorithm is able to balance the load within 85% of the
optimal value
A LOAD BALANCING ALGORITHM BASED ON REPLICATION AND MOVEMENT OF DATA ITEMS FO...ijp2p
Β
Load balancing is one of the main challenges of every structured peer-to-peer (P2P) system that uses
distributed hash tables to map and distribute data items (objects) onto the nodes of the system. In a typical
P2P system with N nodes, the use of random hash functions for distributing keys among peer nodes can
lead to O(log N) imbalance. Most existing load balancing algorithms for structured P2P systems are not
adaptable to objectsβ variant loads in different system conditions, assume uniform distribution of objects in
the system, and often ignore node heterogeneity. In this paper we propose a load balancing algorithm that
considers the above issues by applying node movement and replication mechanisms while load balancing.
Given the high overhead of replication, we postpone this mechanism as much as possible, but we use it
when necessary. Simulation results show that our algorithm is able to balance the load within 85% of the
optimal value.
A LOAD BALANCING ALGORITHM BASED ON REPLICATION AND MOVEMENT OF DATA ITEMS FO...ijp2p
Β
Load balancing is one of the main challenges of every structured peer-to-peer (P2P) system that uses
distributed hash tables to map and distribute data items (objects) onto the nodes of the system. In a typical
P2P system with N nodes, the use of random hash functions for distributing keys among peer nodes can
lead to O(log N) imbalance. Most existing load balancing algorithms for structured P2P systems are not
adaptable to objectsβ variant loads in different system conditions, assume uniform distribution of objects in
the system, and often ignore node heterogeneity. In this paper we propose a load balancing algorithm that
considers the above issues by applying node movement and replication mechanisms while load balancing.
Given the high overhead of replication, we postpone this mechanism as much as possible, but we use it
when necessary. Simulation results show that our algorithm is able to balance the load within 85% of the
optimal value.
Similar to An Adaptive Load Sharing Algorithm for Heterogeneous Distributed System (20)
Help the Genetic Algorithm to Minimize the Urban Traffic on IntersectionsIJORCS
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Control of traffic lights at the intersections of the main issues is the optimal traffic. Intersections to regulate traffic flow of vehicles and eliminate conflicting traffic flows are used. Modeling and simulation of traffic are widely used in industry. In fact, the modeling and simulation of an industrial system is studied before creating economically and when it is affordable. The aim of this article is a smart way to control traffic. The first stage of the project with the objective of collecting statistical data (cycle time of each of the intersection of the lights of vehicles is waiting for a red light) steps where the data collection found optimal amounts next it is. Introduced by genetic algorithm optimization of parameters is performed. GA begin with coding step as a binary variable (the range specified by the initial data set is obtained) will start with an initial population and then a new generation of genetic operators mutation and crossover and will Finally, the members of the optimal fitness values are selected as the solution set. The optimal output of Petri nets CPN TOOLS modeling and software have been implemented. The results indicate that the performance improvement project in intersections traffic control systems. It is known that other data collected and enforced intersections of evolutionary methods such as genetic algorithms to reduce the waiting time for traffic lights behind the red lights and to determine the appropriate cycle.
License plate recognition system is one of the core technologies in intelligent traffic control. In this paper, a new and tunable algorithm which can detect multiple license plates in high resolution applications is proposed. The algorithm aims at investigation into and identification of the novel Iranian and some European countries plate, characterized by both inclusion of blue area on it and its geometric shape. Obviously, the suggested algorithm contains suitable velocity due to not making use of heavy pre-processing operation such as image-improving filters, edge-detection operation and omission of noise at the beginning stages. So, the recommended method of ours is compatible with model-adaptation, i.e., the very blue section of the plate so that the present method indicated the fact that if several plates are included in the image, the method can successfully manage to detect it. We evaluated our method on the two Persian single vehicle license plate data set that we obtained 99.33, 99% correct recognition rate respectively. Further we tested our algorithm on the Persian multiple vehicle license plate data set and we achieved 98% accuracy rate. Also we obtained approximately 99% accuracy in character recognition stage.
FPGA Implementation of FIR Filter using Various Algorithms: A RetrospectiveIJORCS
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This Paper is a review study of FPGA implementation of Finite Impulse response (FIR) with low cost and high performance. The key observation of this paper is an elaborate analysis about hardware implementations of FIR filters using different algorithm i.e., Distributed Arithmetic (DA), DA-Offset Binary Coding (DA-OBC), Common Sub-expression Elimination (CSE) and sum-of-power-of-two (SOPOT) with less resources and without affecting the performance of the original FIR Filter.
Using Virtualization Technique to Increase Security and Reduce Energy Consump...IJORCS
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An approach has been presented in this paper in order to generate a secure environment on internet Based Virtual Computing platform and also to reduce energy consumption in green cloud computing. The proposed approach constantly checks the accuracy of stored data by means of a central control service inside the network environment and also checks system security through isolating single virtual machines using a common virtual environment. This approach has been simulated on two types of Virtual Machine Manager (VMM) Quick EMUlator (Qemu), HVM (Hardware Virtual Machine) Xen and outputs of the simulation in VMInsight show that when service is getting singly used, the overhead of its performance will be increased. As a secure system, the proposed approach is able to recognize malicious behaviors and assure service security by means of operational integrity measurement. Moreover, the rate of system efficiency has been evaluated according to the amount of energy consumption on five applications (Defragmentation, Compression, Linux Boot Decompression and Kernel Boot). Therefore, this has been resulted that to secure multi-tenant environment, managers and supervisors should independently install a security monitoring system for each Virtual Machines (VMs) which will come up to have the management heavy workload of. While the proposed approach, can respond to all VMβs with just one virtual machine as a supervisor.
Algebraic Fault Attack on the SHA-256 Compression FunctionIJORCS
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The cryptographic hash function SHA-256 is one member of the SHA-2 hash family, which was proposed in 2000 and was standardized by NIST in 2002 as a successor of SHA-1. Although the differential fault attack on SHA-1compression function has been proposed, it seems hard to be directly adapted to SHA-256. In this paper, an efficient algebraic fault attack on SHA-256 compression function is proposed under the word-oriented random fault model. During the attack, an automatic tool STP is exploited, which constructs binary expressions for the word-based operations in SHA-256 compression function and then invokes a SAT solver to solve the equations. The simulation of the new attack needs about 65 fault injections to recover the chaining value and the input message block with about 200 seconds on average. Moreover, based on the attack on SHA-256 compression function, an almost universal forgery attack on HMAC-SHA-256 is presented. Our algebraic fault analysis is generic, automatic and can be applied to other ARX-based primitives.
Hybrid Simulated Annealing and Nelder-Mead Algorithm for Solving Large-Scale ...IJORCS
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This paper presents a new algorithm for solving large scale global optimization problems based on hybridization of simulated annealing and Nelder-Mead algorithm. The new algorithm is called simulated Nelder-Mead algorithm with random variables updating (SNMRVU). SNMRVU starts with an initial solution, which is generated randomly and then the solution is divided into partitions. The neighborhood zone is generated, random number of partitions are selected and variables updating process is starting in order to generate a trail neighbor solutions. This process helps the SNMRVU algorithm to explore the region around a current iterate solution. The Nelder- Mead algorithm is used in the final stage in order to improve the best solution found so far and accelerates the convergence in the final stage. The performance of the SNMRVU algorithm is evaluated using 27 scalable benchmark functions and compared with four algorithms. The results show that the SNMRVU algorithm is promising and produces high quality solutions with low computational costs.
Welcoming the research scholars, scientists around the globeΒ in theΒ Open Access Dimension, IJORCS isΒ now accepting manuscripts for its next issue (Volume 4, Issue 2). Authors are encouraged to contribute to the research community by submitting to IJORCS, articles that clarify new research results, projects, surveying works and industrial experiences that describe significant advances in field of computer science.
To view complete list ofΒ topics coverage of IJORCS, Aim & Scope, please visit, www.ijorcs.org/scope
Welcoming the research scholars, scientists around the globe in the Open Access Dimension, IJORCS is now accepting manuscripts for its next issue (Volume 4, Issue 1). Authors are encouraged to contribute to the research community by submitting to IJORCS, articles that clarify new research results, projects, surveying works and industrial experiences that describe significant advances in field of computer science.
Voice Recognition System using Template MatchingIJORCS
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It is easy for human to recognize familiar voice but using computer programs to identify a voice when compared with others is a herculean task. This is due to the problem that is encountered when developing the algorithm to recognize human voice. It is impossible to say a word the same way in two different occasions. Human speech analysis by computer gives different interpretation based on varying speed of speech delivery. This research paper gives detail description of the process behind implementation of an effective voice recognition algorithm. The algorithm utilize discrete Fourier transform to compare the frequency spectra of two voice samples because it remained unchanged as speech is slightly varied. Chebyshev inequality is then used to determine whether the two voices came from the same person. The algorithm is implemented and tested using MATLAB.
Channel Aware Mac Protocol for Maximizing Throughput and FairnessIJORCS
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The proper channel utilization and the queue length aware routing protocol is a challenging task in MANET. To overcome this drawback we are extending the previous work by improving the MAC protocol to maximize the Throughput and Fairness. In this work we are estimating the channel condition and Contention for a channel aware packet scheduling and the queue length is also calculated for the routing protocol which is aware of the queue length. The channel is scheduled based on the channel condition and the routing is carried out by considering the queue length. This queue length will provide a measurement of traffic load at the mobile node itself. Depending upon this load the node with the lesser load will be selected for the routing; this will effectively balance the load and improve the throughput of the ad hoc network.
A Review and Analysis on Mobile Application Development Processes using Agile...IJORCS
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Over a last decade, mobile telecommunication industry has observed a rapid growth, proved to be highly competitive, uncertain and dynamic environment. Besides its advancement, it has also raised number of questions and gained concern both in industry and research. The development process of mobile application differs from traditional softwares as the users expect same features similar to their desktop computer applications with additional mobile specific functionalities. Advanced mobile applications require assimilation with existing enterprise computing systems such as databases, legacy applications and Web services. In addition, the lifecycle of a mobile application moves much faster than that of a traditional Web application and therefore the lifecycle management associated therein must be adjusted accordingly. The Security and application testing are more stimulating and interesting in mobile application than in Web applications since the technology in mobile devices progresses rapidly and developers must stay in touch with the latest developments, news and trends in their area of work. With the rising competence of software market, researchers are seeking more flexible methods that can adjust to dynamic situations where software system requirements are changing over time, producing valuable software in short duration and within low budget. The intrinsic uncertainty and complexity in any software project therefore requires an iterative developmental plan to cope with uncertainty and a large number of unknown variables. Agile Methodologies were thus introduced to meet the new requirements of the software development companies. The agile methodologies aim at facilitating software development processes where changes are acceptable at any stage and provide a structure for highly collaborative software development. Therefore, the present paper aims in reviewing and analysing different prevalent methodologies utilizing agile techniques that are currently in use for the development of mobile applications. This paper provides a detailed review and analysis on the use of agile methodologies in the proposed processes associated with mobile application skills and highlights its benefit and constraints. In addition, based on this analysis, future research needs are identified and discussed.
Congestion Prediction and Adaptive Rate Adjustment Technique for Wireless Sen...IJORCS
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In general, nodes in Wireless Sensor Networks (WSNs) are equipped with limited battery and computation capabilities but the occurrence of congestion consumes more energy and computation power by retransmitting the data packets. Thus, congestion should be regulated to improve network performance. In this paper, we propose a congestion prediction and adaptive rate adjustment technique for Wireless Sensor Networks. This technique predicts congestion level using fuzzy logic system. Node degree, data arrival rate and queue length are taken as inputs to the fuzzy system and congestion level is obtained as an outcome. When the congestion level is amidst moderate and maximum ranges, adaptive rate adjustment technique is triggered. Our technique prevents congestion by controlling data sending rate and also avoids unsolicited packet losses. By simulation, we prove the proficiency our technique. It increases system throughput and network performance significantly.
A Study of Routing Techniques in Intermittently Connected MANETsIJORCS
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A Mobile Ad hoc Network (MANET) is a self-configuring infrastructure less network of mobile devices connected by wireless. These are a kind of wireless Ad hoc Networks that usually has a routable networking environment on top of a Link Layer Ad hoc Network. The routing approach in MANET includes mainly three categories viz., Reactive Protocols, Proactive Protocols and Hybrid Protocols. These traditional routing schemes are not pertinent to the so called Intermittently Connected Mobile Ad hoc Network (ICMANET). ICMANET is a form of Delay Tolerant Network, where there never exists a complete end β to β end path between two nodes wishing to communicate. The intermittent connectivity araise when network is sparse or highly mobile. Routing in such a spasmodic environment is arduous. In this paper, we put forward the indication of prevailing routing approaches for ICMANET with their benefits and detriments
Improving the Efficiency of Spectral Subtraction Method by Combining it with ...IJORCS
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In the field of speech signal processing, Spectral subtraction method (SSM) has been successfully implemented to suppress the noise that is added acoustically. SSM does reduce the noise at satisfactory level but musical noise is a major drawback of this method. To implement spectral subtraction method, transformation of speech signal from time domain to frequency domain is required. On the other hand, Wavelet transform displays another aspect of speech signal. In this paper we have applied a new approach in which SSM is cascaded with wavelet thresholding technique (WTT) for improving the quality of speech signal by removing the problem of musical noise to a great extent. Results of this proposed system have been simulated on MATLAB.
The Design of Cognitive Social Simulation Framework using Statistical Methodo...IJORCS
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Modeling the behavior of the cognitive architecture in the context of social simulation using statistical methodologies is currently a growing research area. Normally, a cognitive architecture for an intelligent agent involves artificial computational process which exemplifies theories of cognition in computer algorithms under the consideration of state space. More specifically, for such cognitive system with large state space the problem like large tables and data sparsity are faced. Hence in this paper, we have proposed a method using a value iterative approach based on Q-learning algorithm, with function approximation technique to handle the cognitive systems with large state space. From the experimental results in the application domain of academic science it has been verified that the proposed approach has better performance compared to its existing approaches.
A PSO-Based Subtractive Data Clustering AlgorithmIJORCS
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There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods. In this paper, we present a hybrid Particle Swarm Optimization, Subtractive + (PSO) clustering algorithm that performs fast clustering. For comparison purpose, we applied the Subtractive + (PSO) clustering algorithm, PSO, and the Subtractive clustering algorithms on three different datasets. The results illustrate that the Subtractive + (PSO) clustering algorithm can generate the most compact clustering results as compared to other algorithms.
Dynamic Map and Diffserv Based AR Selection for Handoff in HMIPv6 Networks IJORCS
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In HMIPv6 Networks, most of the existing handoff decision mechanisms deal mainly with the selection of Mobility Anchor Point (MAP), ignoring the selection of access router (AR) under each MAP. In this paper, we propose a new mechanism called βDynamic MAP and Diffserv based ARs selection for Handoff in HMIPv6 networksβ and it deals with selecting the MAP as well as ARs. MAP will be selected dynamically by checking load, session mobility ratio (SMR), Binding update cost and Location Rate. After selecting the best MAP, the Diffserv approach is used to select the AR under the MAP, based on its resource availability. The AR is implemented at the edge router of Diffserv. DiffServ can be used to provide low-latency to critical network traffic such as voice or streaming media while providing simple best-effort service to non-critical services such as web traffic or file transfers. By using this mechanism, we can assure that better resource utilization and throughput can be attained during Handoff in HMIPv6 networks.
From Physical to Virtual Wireless Sensor Networks using Cloud Computing IJORCS
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In the modern world, billions of physical sensors are used for various dedications: Environment Monitoring, Healthcare, Education, Defense, Manufacturing, Smart Home, Agriculture Precision and others. Nonetheless, they are frequently utilized by their own applications and thereby snubbing the significant possibilities of sharing the resources in order to ensure the availability and performance of physical sensors. This paper assumes that the immense power of the Cloud can only be fully exploited if it is impeccably integrated into our physical lives. The principal merit of this work is a novel architecture where users can share several types of physical sensors easily and consequently many new services can be provided via a virtualized structure that allows allocation of sensor resources to different users and applications under flexible usage scenarios within which users can easily collect, access, process, visualize, archive, share and search large amounts of sensor data from different applications. Moreover, an implementation has been achieved using Arduino-Atmega328 as hardware platform and Eucalyptus/Open Stack with Orchestra-Juju for Private Sensor Cloud. Then this private Cloud has been connected to some famous public clouds such as Amazon EC2, ThingSpeak, SensorCloud and Pachube. The testing was successful at 80%. The recommendation for future work would be to improve the effectiveness of virtual sensors by applying optimization techniques and other methods.
Prediction of Atmospheric Pressure at Ground Level using Artificial Neural Ne...IJORCS
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Prediction of Atmospheric Pressure is one important and challenging task that needs lot of attention and study for analyzing atmospheric conditions. Advent of digital computers and development of data driven artificial intelligence approaches like Artificial Neural Networks (ANN) have helped in numerical prediction of pressure. However, very few works have been done till now in this area. The present study developed an ANN model based on the past observations of several meteorological parameters like temperature, humidity, air pressure and vapour pressure as an input for training the model. The novel architecture of the proposed model contains several multilayer perceptron network (MLP) to realize better performance. The model is enriched by analysis of alternative hybrid model of k-means clustering and MLP. The improvement of the performance in the prediction accuracy has been demonstrated by the automatic selection of the appropriate cluster.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Β
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Operation βBlue Starβ is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
How to Make a Field invisible in Odoo 17Celine George
Β
It is possible to hide or invisible some fields in odoo. Commonly using βinvisibleβ attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
A Strategic Approach: GenAI in EducationPeter Windle
Β
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
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The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesarβs dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empireβs birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empireβs society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
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Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Hanβs Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insiderβs LMA Course, this piece examines the courseβs effects via a variety of Tim Han LMA course reviews and Success Insider comments.
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An Adaptive Load Sharing Algorithm for Heterogeneous Distributed System
1. International Journal of Research in Computer Science
eISSN 2249-8265 Volume 3 Issue 3 (2013) pp. 9-15
www.ijorcs.org, A Unit of White Globe Publications
doi: 10.7815/ijorcs. 33.2013.063
www.ijorcs.org
AN ADAPTIVE LOAD SHARING ALGORITHM FOR
HETEROGENEOUS DISTRIBUTED SYSTEM
P. Neelakantan
Department of CSE, SVUCE, Tirupati, A.P, INDIA
E-mail: pneelakantan@rediffmail.com
Abstract: Due to the restriction of designing faster and
faster computers, one has to find the ways to maximize
the performance of the available hardware. A
distributed system consists of several autonomous
nodes, where some nodes are busy with processing,
while some nodes are idle without any processing. To
make better utilization of the hardware, the tasks or
load of the overloaded node will be sent to the under
loaded node that has less processing weight to
minimize the response time of the tasks. Load
balancing is a tool used effectively for balancing the
load among the systems. Dynamic load balancing
takes into account of the current system state for
migration of the tasks from heavily loaded nodes to the
lightly loaded nodes. In this paper, we devised an
adaptive load-sharing algorithm to balance the load
by taking into consideration of connectivity among the
nodes, processing capacity of each node and link
capacity.
Keywords: Load balancing, Distributed System,
heterogeneous, response time.
I. INTRODUCTION
An important attribute in a dynamic load balancing
policy is to initiate the load balancing activity. The
balancing activity specifies which node is responsible
for detecting imbalance of the load among the
nodes[9]. A load-balancing algorithm is invoked when
load imbalance among the nodes is detected. The
initiation of load balancing activity will have a higher
impact on complexity, overhead and scalability. The
load balancing algorithm is designed in such a way to
make the overloaded node to transfer its excess load to
the underloaded node which is called sender β initiated
and when underloaded node requests the load from the
overloaded node then it is called receiver- initiated [6]
[8].
Domain balancing is used to decentralize the
balancing process by minimizing its scope and
decrease the complexity of the load balancing
algorithm. A domain is defined as subset of nodes in a
system, such that a load balancing algorithm can be
applied for this subset of nodes in a single step.
Domain balancing is used in load balancing algorithms
to decentralize the balancing. The balancing domains
are further divided into two types: The first type is
overlapped domains, which consists of node initiating
the balancing activity and balances its load by
migrating the tasks or load units with the set of
surrounding nodes [3].
Global balancing is achieved by balancing every
domain and by diffusing the excess load throughout
the overlapped domains in a distributed system.
Another important attribute in load balancing
algorithm is the degree of information. The degree of
information plays an important role in making the load
balancing decisions. To achieve global load balancing
in a few steps, the load balancing should get absolute
information instead of getting the obsolete information
from the nodes. In general, the collection of
information by a node is restricted to the domain or
nearest neighboring nodes (which are directly
connected to a node) [4].
Although collecting information from all the nodes
in a distributed system gives the exact knowledge of
the system, it introduces large communication delay,
so from this perspective, it will have a negative impact
on the load balancing algorithm. In such cases, it has
been observed, that the average response time is kept
minimum without load balancing instead of doing the
load balancing which induces overhead in migrating
the load from one node to another node in the system
[5].
In this section, an abstract view of the software
details is presented for load balancing. The distributed
system consists of several nodes and the same load
balancing software is installed to run on all the nodes
in the distributed system. By installing the same
software in all the nodes, the load balancing decision is
taken by a node locally (decentralized) by collecting
the information from the neighboring nodes as
opposed to the centralized load balancing policy[14].
The program must use a multi-threaded concept to
implement load balancing in a distributed system. Two
communication ports are available: TCP and UDP.
UDP is preferable as it incurs less communication
overhead. In general the architecture provides three
2. 10 P. Neelakantan
www.ijorcs.org
layers: Communication layer, Load balancing process
and application layer[14][10]. For storing information
two data structures were used.
The communication link is responsible for four
phases: node status information phase, node status
reception, tasks reception and task migration. The node
status information is responsible for disseminating the
load information to the node that has requested it. The
exchange of the information has a profound effect on
the load balancing decision; it has to be done
according to the predefined intervals of time specified
on each node [7] [14].
The status reception is responsible for receiving the
status information from the other nodes and it will be
updated in the local node list which is running the
status reception phase. Here it is possible to distinguish
the old information from the new information. The
technique that is used to find is to associate the
timestamp for the information that it has received from
some node (say πππ
π
(πΌππ), the time stamp attached to
the information received from π to π). The local node
say π maintaining the status about the node π is kept in
the memory. If any estimate regarding node π exists in
the node π memory, it will be compared to the received
time stamp message and drops the old time stamp and
the new timestamp message has been saved in the
memory as the old time stamp has the obsolete
information[11][1][2].
Once a node collects the above information, then it
knows whether it is overloaded or underloaded. In case
if it is overloaded node, then it transmits the excess
tasks (loads) to the underloaded nodes in a βtasks
transmissionβ phase. The next initiation of load
balancing activity will be done only when the current
migration of load units to the underloaded nodes is
completed.
The βtask receptionβ is responsible for listening to
the requests and accepts the tasks sent from the other
nodes. As we can observe from the above situations,
the minimum time to initiate the new load balancing
activity takes three time instants. One instant for
receiving the status of all the nodes and second time
instant for determining the underloaded nodes and
computing the excess load and third time instant for
transferring the excess load to the underloaded nodes
which has been determined in the second time instant.
So, the new load balancing activity takes place only at
the fourth time instant [12] [14].
In a few papers [3] [9] [10], it is assumed that the
nodes will not fail. The problem arises when the nodes
fail which is common in the distributed systems.
Sometimes a communication link will also fail, so the
node will be unreachable. These two aspects i.e.,
failure of a node and the communication link will
affect greatly the load balancing algorithms. Let us
assume the following scenario. The overloaded node
has collected the load information from the
neighboring nodes and found some of the nodes are
low loaded as discussed earlier. Now at the given time
instant when the node tries to send its excess load to
the overloaded node, it will not succeed because of the
failure of the node. The node may fail after sending the
status information. If this happens, an alternative must
be chosen to avoid a failure of the load-balancing
algorithm.
II. NOTATIONS & ASSUMPTIONS
N : Number of nodes
V= {1, 2,β¦, N} a set of nodes in a system
xi(t) : Expected waiting time experienced by a task
inserted into the queue at the ith
node in time t
Ai(t) : rate of generation of waiting time on ith
node
caused by the addition of tasks in time t.
Si(t) : rate of reduction in waiting time caused by the
service of the tasks at the ith
node in time t.
ri(t) : rate of removal(transfer) of the tasks from node j
to node i at time t by the load balancing algorithm at
node j.
tsi : Average completion time of the task at node i.
bi : Average size of the task in bytes at node i when it
is transferred
dij : Transfer rate in bytes/sec between node i and node
j
π₯Μ π(π‘): Average size of the queue calculated by node i
based on its domain information at time t.
π·π: Neighboring nodes to i which is defined as
π·π = {π|π β π πππ (π, π) β πΈ} where V= {1, 2β¦N}
πΈπ(π‘): Excess number of tasks at node i at time t.
fij : Portion of the excess tasks of node i to be
transferred to node j decided by the load balancing
algorithm.
The following assumptions were made in this paper:
1. It is assumed that a distributed system consists of N
heterogeneous nodes interconnected by an
underlying arbitrary communication network. Each
node i in a system has a processing weight Pi >0
and processing capacity Si>0. The load is defined to
be Li= Pi/Si. In homogenous case the value of
Li=Pi.
2. It has been assumed that tasks arrive at node i
according to Poisson process with rate ππ(π‘). A task
arrived at node i may be processed locally or
migrated through the network to another node j for
remote processing. Once the task is migrated, it
remains there until its completion.
3. An Adaptive Load Sharing Algorithm for Heterogeneous Distributed System 11
www.ijorcs.org
3. It is assumed that there is a communication delay
incurred when task is transferred from one node to
another before the task can be processed in the
system. The communication delays are different for
each link.
Each node contains an independent queue where
arrived tasks are added to the queue, which results in
accumulation of waiting time. Load balancing must be
done repeatedly to maintain load balance in the
system. The proposed algorithm is distributed in nature
meaning that each node runs load-balancing algorithm
autonomously.
The second level of the system is a load-balancing
layer, which consists of load balancing algorithms. The
load balancing process is initiated by suing predefined
time instants or randomly generating which is kept in a
file. The algorithm determines the portion of the
excess load to be sent to the underloaded node based
on the current state of the node and availability of the
nodes in the network. The load balancing algorithm
must consider the communication delay while
migrating the tasks to the other nodes. The algorithm
selects the tasks to migrate to other nodes by setting
their status as inactive to avoid execution of the tasks
by current node application during the transition
period. After completion of the task transmission
activity, the status of the tasks is set to active when
they are not transmitted to any node. when the tasks
are transmitted to other nodes during the task
transmission phase then those tasks are removed from
the task queue of the current node.
Application layer consists of two threads of control:
Task input and task execution threads. The task input
creates a number of tasks defined in the initialization
file and inserts them in the task queue. This task input
is also responsible for adding the new tasks to the task
queue either from the current node or from other nodes
in the system. The task execution thread is responsible
for execution of the tasks and updating the QSize
variable by removing the task from the task queue.
The load balancing policy must take into account of
processing capacity of the node while migrating the
tasks to it. The selected node may become a candidate
for one or more overloaded node in a given time
instant because of the decentralized policy. Another
issue to be considered is variable task completion
times. Taking these issues in priori is not possible so a
load balancing strategy must be adaptive to the
dynamic state changes in the system and act
accordingly to transfer the tasks. Even this can result
in task shuttle between the nodes, so a migration limit
for a task should be set to avoid task thrashing.
Another issue to be considered while migrating the
tasks from one node to another node in a system is
communication overhead. Large communication
delays will have a negative impact on the load
balancing policy, so, the transfer delays must be taken
into account while migrating the task. When the
completion of the task time in current node is greater
than the completion time on task in another node
inclusive of communication overhead, then only a task
is considered for migration.
III. MATHEMATICAL MODEL
The mathematical model for load balancing in a
given node i is given by [1][2]
ππ€ π(π‘)
ππ‘
=π΄π β ππ + ππ(π‘) β β πππ
π‘π π
π‘π π
β π π
π=1 ππ(π‘ β πππ) (1)
πΈπ(π‘)= ππ(π‘)- ποΏ½π(π‘)
ππ(π‘) = πΊπ(πΈπ(π‘))
πππ β₯ 0, πππ=0, β πππ = 1
β ππ
π=1
πΈπ(π‘) = οΏ½
πΈ ππ π¦ β₯ 0
0 ππ π¦ < 0
When a task is inserted into the task queue of node
i, then it experiences the expected waiting time which
is denoted by wi(t).
Let the number of tasks in ith
node is denoted by ππ(t).
Let the average time needed to service the task at node
i π‘π π .
The expected (average) waiting time is given by at
node i is given by π€π(π‘) = ππ(π‘)π‘π π.
Note that π€π(π‘)/π‘π π = ππ is the number of tasks in
the node i queue.
Similarly π€ π(π‘)/π‘π π = π π is the queue length of
some node k. If tasks on node i were transferred to
some node k, then the waiting time transferred is
ππ π‘π π=
π€ π(π‘)π‘π π
π‘π π
, so that the fraction π‘π π/π‘π π converts
waiting time on node i to waiting time on node k.
π΄π βΆ Waiting time generated by adding the task in the
ith
node.
ππ : Rate of reduction in waiting time caused by the
service of tasks at the ith
node is given by ππ = (1 β
π‘ππ)/π‘ππ=1 for all π€π(π‘) > 0.
ππ(π‘) βΆ The rate of removal (transfer) of the tasks
from node i at time t by the load balancing algorithm at
node i. πππ is the fraction of ith
node tasks to be sent out
to the jth
node. In more detail fijri(t) is the rate at which
node i sends waiting time (tasks) to node i at time t
where fii>=0 and πππ=0.That is, the transfer from node i
of expected waiting time (tasks) β« πΈπ(π‘)ππ‘
π‘2
π‘1
in the
interval of time [π‘1, π‘2] to the other nodes is carried out
with the π π‘β
node receiving the fraction πππ(π‘ π π
/
π‘ ππ
) β« π’π(π‘)ππ‘
π‘2
π‘1
where the ratio π‘ π π
/π‘ ππ
converts the
task from waiting time on node i to waiting time on
4. 12 P. Neelakantan
www.ijorcs.org
node j. As β (π
π=1 πππ β« πΈπ(π‘)ππ‘
π‘2
π‘1
) = β« πΈπ(π‘)ππ‘
π‘2
π‘1
, this
results in removing all of the waiting time β« πΈπ(π‘)ππ‘
π‘2
π‘1
from node i.The quantity πππ πΈπ(π‘ β πππ) is the rate of
increase (rate of transfer) of the expected waiting time
(tasks) at time t from node i by (to) node j where
πππ(πππ = 0) is the time delay for the task transfer from
node i to node j.
In this model, all rates are in units of the rate of
change of expected waiting time, or time/time which is
dimensionless. As πΈπ(π‘) β₯ 0, node i can only send
tasks to other nodes and cannot initiate transfers from
another node to itself. A delay is experienced by
transmitted tasks before they are received at the other
node. The control law πΈπ(π‘) = πΊπ β πΈπ(π‘) states that if
the π π‘β
node output π€π(π‘) is above the domain average
(β π π(π‘ β πππ))π
π=1 /n, then it sends data to the other
nodes, while if it is less than the domain average
nothing is sent. The π π‘β
node receives the fraction
β« πΉππ
π‘2
π‘1
(π‘ ππ
/π‘ π π
) π’π(π‘)ππ‘ of transferred waiting time
β« πΈπ(π‘)ππ‘
π‘2
π‘1
delayed by the time πππ.The model
described in (1) is the basic model for load balancing,
but an important feature is to determine fij for each
underloaded node j. One approach is to distribute the
excess load equally to all the underloaded neighbors.
πππ =
1
πβ1
for iβ j.
Another approach is to use the load information
collected from the neighbors to determine the deficit
load of the neighbors. The deficit load of the neighbors
shall be determined by node i by using the formula (2)
π π(π‘-πππ) β ποΏ½π (2)
The above formula is used by node i to compute the
deficiency waiting times in the queue of node j with
respect to the domain load average of node i.
If node j queue is above the domain average
waiting time, then node i do not send tasks to it.
Therefore (ποΏ½π β π π(π‘-πππ)) is a measure by node i as
how much node j is behind the domain average waiting
time. Node i performs this computation for all the
other nodes which are directly connected to it and then
portions out its tasks among the other nodes that fall
below the domain queue average of node i.
πππ =
(ποΏ½ πβπ π(π‘βπ ππ))
β (ποΏ½ πβπ π(π‘βπ ππ)
π π
π=1
(3)
If the denominator β (ποΏ½π β π π(π‘ β πππ)
π π
π=1 =0 then fij
are defined to be zero then no waiting times are
transferred. If the denominator β (ποΏ½π β π π(π‘ β
π π
π=1
πππ)=0, then(ποΏ½π β π π(π‘ β πππ) β€ 0βπ β ππ.
However by definition of the average β (ποΏ½π β
π π
π=1
π π(π‘ β πππ)+ποΏ½π β ππ(π‘) =β (ποΏ½π β π π(π‘ β πππ)
π π
π=1 )=0
which implies
ποΏ½π β π π(π‘)=β (ποΏ½π β π π(π‘ β πππ)
π π
π=1 ) > 0
That is, if the denominator is zero, the node j is not
greater than its domain queue average, so Ei(t)= Gi
.Ei(t))=0, where G is Gain Factor.fij : Portion of the
excess tasks of node i to be transferred to node j
decided by the load balancing algorithm.
Except the last three parameters remaining
information is known at the time of load balancing
process. Before the instance of load balancing activity,
every variable is updated.
IV. ALGORITHM
A. Algorithm ALS
The current node i, performs the followings:
a. Calculate the average queue size ( ποΏ½π)based on the
information received from the neighbouring nodes.
ποΏ½π=
1
β ππ+1
β (ππ + π π
π‘π π
π‘π π
β ππ
π=1 )
if (ππ > ποΏ½π)then Ei =(ππ-ποΏ½π) * G
else Exit.
b. Determine the participant nodes in load sharing
process.
Participants= {j| π π<ποΏ½π, βjβNi}
c. Calculate the fraction of the load ( πππ
β²
) to be sent to
the participants
πππ
β²
=
ποΏ½πβ(
π‘π π
π‘π π
)π π
β ( ποΏ½πβ(
π π
π π
)π π
π π
π=1
d. Calculate maximum portion of the excess load
(πππ
β²β²
)
πππ
β²β²
=
(ππβEi) π‘π π dij
Eibi
e. πππ = Min (πππ
β²
, πππ
β²β²
)
f. For jβ Participants
i. Announce to node j about its willingness to
send Tij= πππ *Ei tasks;
ii. nowReceived = call procedure acceptanceFrom
Nodej()
iii. if(nowReceived >0)
1. Transfer NowReceived to j
2. Tij=Tij- NowReceived
End if
g. Repeat steps from (a) to (f).
Procedure acceptanceFromNodej()
if ((π π+ Tij)< ποΏ½ πnowSend=ποΏ½ π β π π;
else nowSend=-1;
5. An Adaptive Load Sharing Algorithm for Heterogeneous Distributed System 13
www.ijorcs.org
return now Send;
end acceptanceFromNodej
In general it is assumed that keeping the Gain factor
G=1 will give the good performance. But in a
distributed system with largest delays and the nodes
that have domain queue average outdated gives poor
result. This phenomenon was first observed by the load
balancing group at the University of New Mexico [7].
So the G values are set in the way that yields an
optimal result. Another step that is added in the above
algorithm is to test the node availability. It checks both
node availability as well as the amount of waiting
times it can receive. The node executing the ALS is
permitted to send the tasks to the neighbors after
receiving the acknowledgement specifying the amount
of the load they can be able to process.. The time
complexity of the proposed algorithm is O(d) shown in
the table 1.
Table 1: ALS Operations
Sno Actions Operation
Quantity,
(d is the
number of
neighbors)
1
Compute
average
queue size
Addition
Division
Multiplication
d+1
d
d
2 Compute Ei
Subtraction
Multiplication
1
1
3
Determine
the
participant
nodes
Comparison d
4
Compute
πππ
β²
Subtraction
Division
Multiplication
d+1
d+1
d+1
5
Compute
πππ
β²β²
Subtraction
Division
Multiplication
1
1
3
6 Compute Tij Multiplication d
7
Message to
node
Transfer d
8
Compute
nowReceived
Addition
Comparison
Message
Transfer
d
d
d
V. SIMULATION
To test the performance of the newly proposed
load-balancing policy, a Java program is developed to
test the performance of the existing and proposed
algorithms. The existing algorithms, ELISA and
DOLB are used to compare with the proposed
algorithm ALS. The DOLB is very much related to the
above problem. The initial settings and parameters are
shown in Table 2. The average network transfer rates
between each node are represented by the cost
adjacency matrix.
The proposed algorithm ALS is tested with DOLB
& ELISA for the gain values G between 0.3 and 1 with
0.1 incremental steps. The πΌ parameter introduced in
the previous section was set to 0.05 by running several
experiments and observing the behavior of the tsi
parameter. Note that, the first time the load-balancing
process was triggered was after 40s from the start of
the system and then the strategy was executed
regularly at 20s interval.
Table 2: Simulation Parameters
Number of nodes 16,32,64
Initial task
distribution
[100..1000] tasks distributed
randomly at each node
Average task
processing time(π‘π
in ms)
Processing time is randomly
distributed in a
range[300β¦800]
Size of task( in Mb) 1
Load balancing
instance
First time the load balancing
was triggered at 5s then for
every 10s the load balancing
is initiated
Bandwidth
distribution (πππ)
A cost adjacency matrix
denotes the transfer rate
between the nodes.
Number of nodes 16,32,64
Initial task
distribution
[100..1000] tasks distributed
randomly at each node
Average task
processing time(π‘π
in ms)
Processing time is randomly
distributed in a
range[300β¦800]
This was done to ensure that the ts parameter had
enough time to adapt and reflect the current
computational power of each node before the
occurrence of any tasks migration between the nodes.
Note that the ratio
π‘π π
π‘π π
are fixed over time. The
proposed and rival methods were evaluated by
conducting 10 runs for each value of G between 0.3
and 1 with 0.1 incremental step.
6. 14 P. Neelakantan
www.ijorcs.org
Figure 1: Completion time averaged over 5 runs vs different
gain values K. The graphs shows the results of three policies
for system size=64.
Figure 2: Completion time averaged over 5 runs vs.
different gain values K. The graphs shows the results of
three policies for system size=32.
Figure 3: Total number of tasks exchanged averaged over 5
runs Vs different Gain values K. The graphs shows the
performance of the three policies for system size=16.
VI. CONCLUSION
The proposed algorithm is better when compared to
the existing algorithms in the literature. In simulation,
we assumed the tasks with no precedence and with no
deadlines. However, as a future work, the algorithm
must focus on considering the tasks with dead line and
tasks with precedence relations.
VII. REFERENCES
[1] M. M. Hayat, S . Dhakal, C. T. Abdallah I βDynamic
time delay models for load balancing. Part II: Stochastic
analysis of the effect of delay uncertainty, CNRS-NSF
Workshop: Advances in Control of tirne-delay Systems,
Paris France, January 2003.
[2] J. Ghanem, C. T. Abdallah, M. M. Hayat, S. Dhakal,
J.D Birdwell, J. Chiasson, and Z. Tang. Implementation
of load balancing algorithms over a local area network
and the internet. 43rd IEEE Conference on Decision
and Control, Submitted, Bahamas, 2004. doi:
10.1109/CDC.2004.1429411
[3] L. Anand, D. Ghose, and V. Mani, βELISA: An
Estimated Load Information Scheduling Algorithm for
Distributed Computing Systems,β Intβl J. Computers
and Math With Applications, vol. 37, no. 8, pp. 57-85,
Apr. 1999. doi: 10.1016/S0898-1221(99)00101-7
[4] WEI Wen-hong, XIANG Fei, WANG Wen-feng, et al.
Load Balancing Algorithm in Structure P2P
Systems[J],Computer Science, 2010, 37(4):82-85.
[5] Khalifa, A.S.; Fergany, T.A.; Ammar, R.A.; Tolba,
M.F,β Dynamic online Allocation of Independent tasks
onto heterogeneous computing systems to maximize
load balancing,β IEEE International Symposium on
Signal Processing and Information Technology, ISSPIT
2008,Page(s): 418 β 425. doi: 10.1109/ISSPIT.
2008.4775659
[6] Andras Veres and Miklos Boda. The chaotic nature of
TCP congestion control. In Proceedings of the IEEE
Infocom, pages 1715-1723, 2000. doi:
10.1109/INFCOM.2000.832571
[7] J. Chiasson, J. D. Birdwell, Z. Tang, and C.T. Abdallah.
The effect of time delays in the stability of load
balancing algorithms for parallel computations. IEEE
Conference on Decision and Control, Maui, Hawaii,
2003. doi: 10.1109/CDC.2003.1272626
[8] Ming wu and Xian-He sun, A General Self Adaptive
Task Scheduling System for Non Dedicated
Heterogeneous Computing, In Proceedings of IEEE
International Conference on Cluster Computing, PP
354-361, Dec 2003. doi: 10.1109/CLUSTR.
2003.1253334
[9] Z. Zeng and B. Veeravalli, "Design and Performance
Evaluation of Queue-and-Rate-Adjustment Dynamic
Load Balancing Policies for Distributed Networks",
presented at IEEE Trans. Computers, 2006, pp.1410-
1422. doi: 10.1109/TC.2006.180
[10] K. Lu, R. Subrata, and A. Y. Zomaya, Towards
decentralized load balancing in a computational grid
environment, in: Proceedings of the first International
Conference on Grid and Pervasive Computing, 2006,
Vol. 3947, pp. 466-477, Springer-Verlag Press. doi:
10.1007/11745693_46
[11] Acker, D., Kulkarni, S. 2007. A Dynamic Load
Dispersion Algorithm for Load Balancing in a
Heterogeneous Grid System. IEEE Sarnoff
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40
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80
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160
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
AverageTaskCompletiontime
Gain
ALS
DOLB
ELISA
0
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0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
NUmberofTasksexchanged
ALS
DOLB
ELISA
7. An Adaptive Load Sharing Algorithm for Heterogeneous Distributed System 15
www.ijorcs.org
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How to cite
P. Neelakantan, " An Adaptive Load Sharing Algorithm for Heterogeneous Distributed System ". International
Journal of Research in Computer Science, 3 (3): pp. 9-15, May 2013. doi: 10.7815/ijorcs. 33.2013.063