Grid computing can involve lot of computational tasks which requires trustworthy computational nodes. Load balancing in grid computing is a technique which overall optimizes the whole process of assigning computational tasks to processing nodes. Grid computing is a form of distributed computing but different from conventional distributed computing in a manner that it tends to be heterogeneous, more loosely coupled and dispersed geographically. Optimization of this process must contains the overall maximization of resources utilization with balance load on each processing unit and also by decreasing the overall time or output. Evolutionary algorithms like genetic algorithms have studied so far for the implementation of load balancing across the grid networks. But problem with these genetic algorithm is that they are quite slow in cases where large number of tasks needs to be processed. In this paper we give a novel approach of parallel genetic algorithms for enhancing the overall performance and optimization of managing the whole process of load balancing across the grid nodes.
Effective and Efficient Job Scheduling in Grid ComputingAditya Kokadwar
The integration of remote and diverse resources and the increasing computational needs of Grand Challenges problems combined with the faster growth of the internet and communication technologies leads to the development of global computational grids. Grid computing is a prevailing technology, which unites underutilized resources in order to support sharing of resources and services distributed across numerous administrative region. An efficient and effective scheduling system is essentially required in order to achieve the promising capacity of grids. The main goal of scheduling is to maximize the resource utilization and minimize processing time and cost of the jobs. In this research, the objective is to prioritize the jobs based on execution cost and then allocate the resources with minimum cost by merging it with conventional job grouping strategy to provide the solution for better and more efficient job scheduling which is beneficial to both user and resource broker. The proposed scheduling approach in grid computing employs a dynamic cost-based job scheduling algorithm for making an efficient mapping of a job to available resources in the grid. It also improves communication to computation ratio (CCR) and utilization of available resources by grouping the user jobs before resource allocation.
DGBSA : A BATCH JOB SCHEDULINGALGORITHM WITH GA WITH REGARD TO THE THRESHOLD ...IJCSEA Journal
In this paper , we will provide a scheduler on batch jobs with GA regard to the threshold detector. In The algorithm proposed in this paper, we will provide the batch independent jobs with a new technique ,so we can optimize the schedule them. To do this, we use a threshold detector then among the selected jobs, processing resources can process batch jobs with priority. Also hierarchy of tasks in each batch, will be determined with using DGBSA algorithm. Now , with the regard to the works done by previous ,we can provide an algorithm that by adding specific parameters to fitness function of the previous algorithms ,develop a optimum fitness function that in the proposed algorithm has been used. According to assessment done on DGBSA algorithm, in compare with the similar algorithms, it has more performance. The effective parameters that used in the proposed algorithm can reduce the total wasting time in compare with previous algorithms. Also this algorithm can improve the previous problems in batch processing with a new technique.
OPTIMIZED RESOURCE PROVISIONING METHOD FOR COMPUTATIONAL GRID ijgca
Grid computing is an accumulation of heterogeneous, dynamic resources from multiple administrative areas which are geographically distributed that can be utilized to reach a mutual end. Development of resource provisioning-based scheduling in large-scale distributed environments like grid computing brings in new requirement challenges that are not being believed in traditional distributed computing environments. Computational grid is applying the resources of many systems in a network to a single problem at the same time. Grid scheduling is the method by which work specified by some means is assigned to the resources that complete the work in the environment which cannot fulfill the user requirements considerably. The satisfaction of users while providing the resources might increase the beneficiary level of resource suppliers. Resource scheduling has to satisfy the multiple constraints specified by the user. The option of resource with the satisfaction of multiple constraints is the most tedious process. This trouble is solved by bringing out the particle swarm optimization based heuristic scheduling algorithm which attempts to select the most suitable resource from the set of available resources. The primary parameters that are taken in this work for selecting the most suitable resource are the makespan and cost. The experimental result shows that the proposed method yields optimal scheduling with the atonement of all user requirements.
Qo s aware scientific application scheduling algorithm in cloud environmentAlexander Decker
This document summarizes a research paper that proposes a scheduling algorithm for scientific applications in cloud environments. The algorithm aims to schedule tasks in workflows based on user preferences for quality of service (QoS), like time and cost. It ranks tasks and uses an UPFF function to select resources that meet the user's desired QoS. The algorithm is compared to other similar algorithms through scenarios, and results show it has better efficiency. The full paper provides more details on scientific workflows, cloud computing, related work on workflow scheduling algorithms, and defines the problem of scheduling tasks to resources while considering costs and times.
T AXONOMY OF O PTIMIZATION A PPROACHES OF R ESOURCE B ROKERS IN D ATA G RIDSijcsit
A novel taxonomy of replica selection techniques is proposed. We studied some data grid approaches
where the selection strategies of data management is different. The aim of the study is to determine the
common concepts and observe their performance and to compare their performance with our strategy
A novel scheduling algorithm for cloud computing environmentSouvik Pal
The document describes a proposed genetic algorithm-based scheduling approach for cloud computing environments. It aims to minimize waiting time and queue length. The algorithm first permutes task burst times and finds minimum waiting times using FCFS and genetic algorithms. It then applies a queuing model to the sequences with minimum waiting time from each approach. Experimental results on 4 sample tasks show the genetic algorithm reduces waiting time compared to FCFS. The genetic operators of selection, crossover and mutation are applied to evolve optimal task scheduling sequences.
Job Scheduling on the Grid Environment using Max-Min Firefly AlgorithmEditor IJCATR
Grid computing indeed is the next generation of distributed systems and its goals is creating a powerful virtual, great, and
autonomous computer that is created using countless Heterogeneous resource with the purpose of sharing resources. Scheduling is one
of the main steps to exploit the capabilities of emerging computing systems such as the grid. Scheduling of the jobs in computational
grids due to Heterogeneous resources is known as an NP-Complete problem. Grid resources belong to different management domains
and each applies different management policies. Since the nature of the grid is Heterogeneous and dynamic, techniques used in
traditional systems cannot be applied to grid scheduling, therefore new methods must be found. This paper proposes a new algorithm
which combines the firefly algorithm with the Max-Min algorithm for scheduling of jobs on the grid. The firefly algorithm is a new
technique based on the swarm behavior that is inspired by social behavior of fireflies in nature. Fireflies move in the search space of
problem to find the optimal or near-optimal solutions. Minimization of the makespan and flowtime of completing jobs simultaneously
are the goals of this paper. Experiments and simulation results show that the proposed method has a better efficiency than other
compared algorithms.
FDMC: Framework for Decision Making in Cloud for EfficientResource Management IJECEIAES
An effective resource management is one of the critical success factors for precise virtualization process in cloud computing in presence of dynamic demands of the user. After reviewing the existing research work towards resource management in cloud, it was found that there is still a large scope of enhancement. The existing techniques are found not to completely utilize the potential features of virtual machine in order to perform resource allocation. This paper presents a framework called FDMC or Framework for Decision Making in Cloud that gives better capability for the VMs to perform resource allocation. The contribution of FDMC is a joint operation of VM to ensure faster processing of task and thereby withstand more number of increasing traffic. The study outcome was compared with some of the existing systems to find FDMC excels better performance in the scale of task allocation time, amount of core wasted, amount of storage wasted, and communication cost.
Effective and Efficient Job Scheduling in Grid ComputingAditya Kokadwar
The integration of remote and diverse resources and the increasing computational needs of Grand Challenges problems combined with the faster growth of the internet and communication technologies leads to the development of global computational grids. Grid computing is a prevailing technology, which unites underutilized resources in order to support sharing of resources and services distributed across numerous administrative region. An efficient and effective scheduling system is essentially required in order to achieve the promising capacity of grids. The main goal of scheduling is to maximize the resource utilization and minimize processing time and cost of the jobs. In this research, the objective is to prioritize the jobs based on execution cost and then allocate the resources with minimum cost by merging it with conventional job grouping strategy to provide the solution for better and more efficient job scheduling which is beneficial to both user and resource broker. The proposed scheduling approach in grid computing employs a dynamic cost-based job scheduling algorithm for making an efficient mapping of a job to available resources in the grid. It also improves communication to computation ratio (CCR) and utilization of available resources by grouping the user jobs before resource allocation.
DGBSA : A BATCH JOB SCHEDULINGALGORITHM WITH GA WITH REGARD TO THE THRESHOLD ...IJCSEA Journal
In this paper , we will provide a scheduler on batch jobs with GA regard to the threshold detector. In The algorithm proposed in this paper, we will provide the batch independent jobs with a new technique ,so we can optimize the schedule them. To do this, we use a threshold detector then among the selected jobs, processing resources can process batch jobs with priority. Also hierarchy of tasks in each batch, will be determined with using DGBSA algorithm. Now , with the regard to the works done by previous ,we can provide an algorithm that by adding specific parameters to fitness function of the previous algorithms ,develop a optimum fitness function that in the proposed algorithm has been used. According to assessment done on DGBSA algorithm, in compare with the similar algorithms, it has more performance. The effective parameters that used in the proposed algorithm can reduce the total wasting time in compare with previous algorithms. Also this algorithm can improve the previous problems in batch processing with a new technique.
OPTIMIZED RESOURCE PROVISIONING METHOD FOR COMPUTATIONAL GRID ijgca
Grid computing is an accumulation of heterogeneous, dynamic resources from multiple administrative areas which are geographically distributed that can be utilized to reach a mutual end. Development of resource provisioning-based scheduling in large-scale distributed environments like grid computing brings in new requirement challenges that are not being believed in traditional distributed computing environments. Computational grid is applying the resources of many systems in a network to a single problem at the same time. Grid scheduling is the method by which work specified by some means is assigned to the resources that complete the work in the environment which cannot fulfill the user requirements considerably. The satisfaction of users while providing the resources might increase the beneficiary level of resource suppliers. Resource scheduling has to satisfy the multiple constraints specified by the user. The option of resource with the satisfaction of multiple constraints is the most tedious process. This trouble is solved by bringing out the particle swarm optimization based heuristic scheduling algorithm which attempts to select the most suitable resource from the set of available resources. The primary parameters that are taken in this work for selecting the most suitable resource are the makespan and cost. The experimental result shows that the proposed method yields optimal scheduling with the atonement of all user requirements.
Qo s aware scientific application scheduling algorithm in cloud environmentAlexander Decker
This document summarizes a research paper that proposes a scheduling algorithm for scientific applications in cloud environments. The algorithm aims to schedule tasks in workflows based on user preferences for quality of service (QoS), like time and cost. It ranks tasks and uses an UPFF function to select resources that meet the user's desired QoS. The algorithm is compared to other similar algorithms through scenarios, and results show it has better efficiency. The full paper provides more details on scientific workflows, cloud computing, related work on workflow scheduling algorithms, and defines the problem of scheduling tasks to resources while considering costs and times.
T AXONOMY OF O PTIMIZATION A PPROACHES OF R ESOURCE B ROKERS IN D ATA G RIDSijcsit
A novel taxonomy of replica selection techniques is proposed. We studied some data grid approaches
where the selection strategies of data management is different. The aim of the study is to determine the
common concepts and observe their performance and to compare their performance with our strategy
A novel scheduling algorithm for cloud computing environmentSouvik Pal
The document describes a proposed genetic algorithm-based scheduling approach for cloud computing environments. It aims to minimize waiting time and queue length. The algorithm first permutes task burst times and finds minimum waiting times using FCFS and genetic algorithms. It then applies a queuing model to the sequences with minimum waiting time from each approach. Experimental results on 4 sample tasks show the genetic algorithm reduces waiting time compared to FCFS. The genetic operators of selection, crossover and mutation are applied to evolve optimal task scheduling sequences.
Job Scheduling on the Grid Environment using Max-Min Firefly AlgorithmEditor IJCATR
Grid computing indeed is the next generation of distributed systems and its goals is creating a powerful virtual, great, and
autonomous computer that is created using countless Heterogeneous resource with the purpose of sharing resources. Scheduling is one
of the main steps to exploit the capabilities of emerging computing systems such as the grid. Scheduling of the jobs in computational
grids due to Heterogeneous resources is known as an NP-Complete problem. Grid resources belong to different management domains
and each applies different management policies. Since the nature of the grid is Heterogeneous and dynamic, techniques used in
traditional systems cannot be applied to grid scheduling, therefore new methods must be found. This paper proposes a new algorithm
which combines the firefly algorithm with the Max-Min algorithm for scheduling of jobs on the grid. The firefly algorithm is a new
technique based on the swarm behavior that is inspired by social behavior of fireflies in nature. Fireflies move in the search space of
problem to find the optimal or near-optimal solutions. Minimization of the makespan and flowtime of completing jobs simultaneously
are the goals of this paper. Experiments and simulation results show that the proposed method has a better efficiency than other
compared algorithms.
FDMC: Framework for Decision Making in Cloud for EfficientResource Management IJECEIAES
An effective resource management is one of the critical success factors for precise virtualization process in cloud computing in presence of dynamic demands of the user. After reviewing the existing research work towards resource management in cloud, it was found that there is still a large scope of enhancement. The existing techniques are found not to completely utilize the potential features of virtual machine in order to perform resource allocation. This paper presents a framework called FDMC or Framework for Decision Making in Cloud that gives better capability for the VMs to perform resource allocation. The contribution of FDMC is a joint operation of VM to ensure faster processing of task and thereby withstand more number of increasing traffic. The study outcome was compared with some of the existing systems to find FDMC excels better performance in the scale of task allocation time, amount of core wasted, amount of storage wasted, and communication cost.
Peer-to-Peer Data Sharing and Deduplication using Genetic AlgorithmIRJET Journal
This document proposes a peer-to-peer data sharing and deduplication system using genetic algorithms. The system would allow organizations in a corporate network to share data by registering with a P2P service provider and launching peer instances. It addresses challenges of scalability, performance, and security for inter-organizational data sharing. The system integrates cloud computing, databases, and P2P technologies. It uses genetic algorithms for deduplication to reduce redundant data storage. The system is intended to provide flexible, scalable, and cost-effective data sharing services for corporate networks based on a pay-as-you-go model.
AN ENTROPIC OPTIMIZATION TECHNIQUE IN HETEROGENEOUS GRID COMPUTING USING BION...ijcsit
This document summarizes a research paper that proposes a new method for improving both fault tolerance and load balancing in grid computing networks. The method converts the tree structure of grid computing nodes into a distributed R-tree index structure and then applies an entropy estimation technique. This entropy estimation helps discard nodes with high entropy from the tree, reducing complexity. The method then uses thresholding and control algorithms to select optimal route paths based on load balance and fault tolerance. Various optimization techniques like genetic algorithms, ant colony optimization, and particle swarm optimization are also applied to reach better solutions. Experimental results showed the proposed method improved performance over other existing methods.
The Cloud computing becomes an important topic
in the area of high performance distributed computing. On the
other hand, task scheduling is considered one the most significant
issues in the Cloud computing where the user has to pay for the
using resource based on the time. Therefore, distributing the
cloud resource among the users' applications should maximize
resource utilization and minimize task execution Time. The goal
of task scheduling is to assign tasks to appropriate resources that
optimize one or more performance parameters (i.e., completion
time, cost, resource utilization, etc.). In addition, the scheduling
belongs to a category of a problem known as an NP-complete
problem. Therefore, the heuristic algorithm could be applied to
solve this problem. In this paper, an enhanced dependent task
scheduling algorithm based on Genetic Algorithm (DTGA) has
been introduced for mapping and executing an application’s
tasks. The aim of this proposed algorithm is to minimize the
completion time. The performance of this proposed algorithm has
been evaluated using WorkflowSim toolkit and Standard Task
Graph Set (STG) benchmark.
This document summarizes an article from the International Journal of Computer Engineering and Technology (IJCET) that proposes an algorithm called Replica Placement in Graph Topology Grid (RPGTG) to optimally place data replicas in a graph-based data grid while ensuring quality of service (QoS). The algorithm aims to minimize data access time, balance load among replica servers, and avoid unnecessary replications, while restricting QoS in terms of number of hops and deadline to complete requests. The article describes how the algorithm converts the graph structure of the data grid to a hierarchical structure to better manage replica servers and proposes services to facilitate dynamic replication, including a replica catalog to track replica locations and a replica manager to perform replication
A Novel Approach for Clustering Big Data based on MapReduce IJECEIAES
Clustering is one of the most important applications of data mining. It has attracted attention of researchers in statistics and machine learning. It is used in many applications like information retrieval, image processing and social network analytics etc. It helps the user to understand the similarity and dissimilarity between objects. Cluster analysis makes the users understand complex and large data sets more clearly. There are different types of clustering algorithms analyzed by various researchers. Kmeans is the most popular partitioning based algorithm as it provides good results because of accurate calculation on numerical data. But Kmeans give good results for numerical data only. Big data is combination of numerical and categorical data. Kprototype algorithm is used to deal with numerical as well as categorical data. Kprototype combines the distance calculated from numeric and categorical data. With the growth of data due to social networking websites, business transactions, scientific calculation etc., there is vast collection of structured, semi-structured and unstructured data. So, there is need of optimization of Kprototype so that these varieties of data can be analyzed efficiently.In this work, Kprototype algorithm is implemented on MapReduce in this paper. Experiments have proved that Kprototype implemented on Mapreduce gives better performance gain on multiple nodes as compared to single node. CPU execution time and speedup are used as evaluation metrics for comparison.Intellegent splitter is proposed in this paper which splits mixed big data into numerical and categorical data. Comparison with traditional algorithms proves that proposed algorithm works better for large scale of data.
This document evaluates the performance of the First Come First Serve (FCFS) and Easy Backfilling (EBF) resource allocation algorithms in grid computing systems. It compares the resource utilization and throughput of the two algorithms when gridlet size increases linearly and non-linearly. The results show that EBF achieves better resource utilization and throughput than FCFS in both linear and non-linear cases. EBF is more efficient at scheduling jobs to maximize resource usage and the amount of work completed per time period.
Task Scheduling methodology in cloud computing Qutub-ud- Din
This document outlines a proposed methodology for developing efficient task scheduling strategies in cloud computing. It begins with introductions to cloud computing and task scheduling. It then reviews several relevant existing task scheduling algorithms from literature that focus on objectives like reducing costs, minimizing completion time, and maximizing resource utilization. The problem statement indicates the goals are to reduce costs, minimize completion time, and maximize resource allocation. An overview of the proposed methodology's flow is then provided, followed by references.
Literature Survey on Buliding Confidential and Efficient Query Processing Usi...paperpublications3
Abstract: Hosting data query services with the deployed cloud computing infrastructure increase the scalability and high performance evaluations with low cost. However, some data owners might not be interested to the save their in the cloud environment because of data confidentiality and query processing privacy should be guaranteed by the cloud service providers. Secured Query should able to provide very high efficient of query processing and also should reduce in – house workload. In this paper we proposed RASP data perturbation techniques combines various objectives like random noise injection, dimensionality expansion, efficient encryption and random projection, henceforth RASP methodology are also used to preserves multidimensional ranges. KNN – R algorithm used to work with RASP range for processing KNN queries. The experimental result of our project carried out to define realistic security and threat model approaches for improved efficient and security.
The document discusses optimization of resource allocation in cloud environments using a modified particle swarm optimization (PSO) approach. It proposes a Modified Resource Allocation Mutation PSO (MRAMPSO) strategy that uses an Extended Multi Queue Scheduling algorithm to schedule tasks based on resource availability and reschedules failed tasks. The MRAMPSO strategy is compared to standard PSO and other algorithms to show it can reduce execution time, makespan, transmission cost, and round trip time.
This document summarizes an article from the International Journal of Computer Engineering and Technology (IJCET) that proposes a new dynamic data replication and job scheduling strategy for data grids. The strategy aims to improve data access time and reduce bandwidth consumption by replicating data based on file popularity, storage limitations at nodes, and data category. It replicates more popular files that are in the same category as frequently accessed data to nodes close to where jobs are run. This is intended to optimize performance by locating data and jobs close together. The document provides context on related work and outlines the proposed system architecture and replication/scheduling approach.
Survey on Synchronizing File Operations Along with Storage Scalable MechanismIRJET Journal
The document summarizes research on efficient file operations and storage scalability mechanisms. It discusses how data is divided into chunks and distributed to nodes for transmission in peer-to-peer networks. The proposed system aims to provide efficient load balancing, eliminate single points of failure, and ensure synchronization and security during data transmission. It uses synchronization algorithms and a hybrid distribution model combining features of peer-to-peer and client-server networks. The system is designed to securely handle insertions, deletions, splits, and concatenations of file chunks in a distributed storage system.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document provides an overview of stream data mining techniques. It discusses how traditional data mining cannot be directly applied to data streams due to their continuous, rapid nature. The document outlines some essential methodologies for analyzing data streams, including sampling, load shedding, sketching, and data summarization techniques like reservoirs, histograms, and wavelets. It also discusses challenges in applying these techniques to data streams and open problems in the emerging field of stream data mining.
A survey of various scheduling algorithm in cloud computing environmenteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A latency-aware max-min algorithm for resource allocation in cloud IJECEIAES
Cloud computing is an emerging distributed computing paradigm. However, it requires certain initiatives that need to be tailored for the cloud environment such as the provision of an on-the-fly mechanism for providing resource availability based on the rapidly changing demands of the customers. Although, resource allocation is an important problem and has been widely studied, there are certain criteria that need to be considered. These criteria include meeting user’s quality of service (QoS) requirements. High QoS can be guaranteed only if resources are allocated in an optimal manner. This paper proposes a latency-aware max-min algorithm (LAM) for allocation of resources in cloud infrastructures. The proposed algorithm was designed to address challenges associated with resource allocation such as variations in user demands and on-demand access to unlimited resources. It is capable of allocating resources in a cloud-based environment with the target of enhancing infrastructure-level performance and maximization of profits with the optimum allocation of resources. A priority value is also associated with each user, which is calculated by analytic hierarchy process (AHP). The results validate the superiority for LAM due to better performance in comparison to other state-of-the-art algorithms with flexibility in resource allocation for fluctuating resource demand patterns.
Cloud computing Review over various scheduling algorithmsIJEEE
Cloud computing has taken an importantposition in the field of research as well as in thegovernment organisations. Cloud computing uses virtualnetwork technology to provide computer resources tothe end users as well as to the customer’s. Due tocomplex computing environment the use of high logicsand task scheduler algorithms are increase which resultsin costly operation of cloud network. Researchers areattempting to build such kind of job scheduling algorithms that are compatible and applicable in cloud computing environment.In this paper, we review research work which is recently proposed by researchers on the base of energy saving scheduling techniques. We also studying various scheduling algorithms and issues related to them in cloud computing.
This document summarizes a research paper on developing an improved LEACH (Low-Energy Adaptive Clustering Hierarchy) communication protocol for energy efficient data mining in multi-feature sensor networks. It begins with background on wireless sensor networks and issues like energy efficiency. It then discusses the existing LEACH protocol and its drawbacks. The proposed improved LEACH protocol includes cluster heads, sub-cluster heads, and cluster nodes to address LEACH's limitations. This new version aims to minimize energy consumption during cluster formation and data aggregation in multi-feature sensor networks.
This document summarizes a research paper that proposes a new density-based clustering technique called Triangle-Density Based Clustering Technique (TDCT) to efficiently cluster large spatial datasets. TDCT uses a polygon approach where the number of data points inside each triangle of a polygon is calculated to determine triangle densities. Triangle densities are used to identify clusters based on a density confidence threshold. The technique aims to identify clusters of arbitrary shapes and densities while minimizing computational costs. Experimental results demonstrate the technique's superiority in terms of cluster quality and complexity compared to other density-based clustering algorithms.
Demand-driven Gaussian window optimization for executing preferred population...IJECEIAES
Scheduling is one of the essential enabling technique for Cloud computing which facilitates efficient resource utilization among the jobs scheduled for processing. However, it experiences performance overheads due to the inappropriate provisioning of resources to requesting jobs. It is very much essential that the performance of Cloud is accomplished through intelligent scheduling and allocation of resources. In this paper, we propose the application of Gaussian window where jobs of heterogeneous in nature are scheduled in the round-robin fashion on different Cloud clusters. The clusters are heterogeneous in nature having datacenters with varying sever capacity. Performance evaluation results show that the proposed algorithm has enhanced the QoS of the computing model. Allocation of Jobs to specific Clusters has improved the system throughput and has reduced the latency.
This document discusses load balancing strategies for grid computing. It proposes a dynamic tree-based model to represent grid architecture in a hierarchical way that supports heterogeneity and scalability. It then develops a hierarchical load balancing strategy and algorithms based on neighborhood properties to decrease communication overhead. Conventional scheduling algorithms like Min-Min, Max-Min, and Sufferage are discussed but determined to ignore dynamic network status, which is important for load balancing. Genetic algorithms are also mentioned as a potential solution.
A Survey of Job Scheduling Algorithms Whit Hierarchical Structure to Load Ba...Editor IJCATR
Due to the advances in human civilization, problems in science and engineering are becoming more complicated than ever
before. To solve these complicated problems, grid computing becomes a popular tool. a grid environment collects, integrates, and uses
heterogeneous or homogeneous resources scattered around the globe by a high-speed network. Scheduling problems are at the heart of
any Grid-like computational system. a good scheduling algorithm can assign jobs to resources efficiently and can balance the system
load. in this paper we survey three algorithms for grid scheduling and compare benefit and disadvantages of their based on makespan.
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
Grid computing enlarge with computing platform which is collection of heterogeneous computing resources connected by a network across dynamic and geographically dispersed organization to form a distributed high performance computing infrastructure. Grid computing solves the complex computing
problems amongst multiple machines. Grid computing solves the large scale computational demands in a high performance computing environment. The main emphasis in the grid computing is given to the resource management and the job scheduler .The goal of the job scheduler is to maximize the resource utilization and minimize the processing time of the jobs. Existing approaches of Grid scheduling doesn’t give much emphasis on the performance of a Grid scheduler in processing time parameter. Schedulers allocate resources to the jobs to be executed using the First come First serve algorithm. In this paper, we have provided an optimize algorithm to queue of the scheduler using various scheduling methods like Shortest Job First, First in First out, Round robin. The job scheduling system is responsible to select best suitable machines in a grid for user jobs. The management and scheduling system generates job schedules for each machine in the grid by taking static restrictions and dynamic parameters of jobs and machines
into consideration. The main purpose of this paper is to develop an efficient job scheduling algorithm to maximize the resource utilization and minimize processing time of the jobs. Queues can be optimized by using various scheduling algorithms depending upon the performance criteria to be improved e.g. response
time, throughput. The work has been done in MATLAB using the parallel computing toolbox.
Peer-to-Peer Data Sharing and Deduplication using Genetic AlgorithmIRJET Journal
This document proposes a peer-to-peer data sharing and deduplication system using genetic algorithms. The system would allow organizations in a corporate network to share data by registering with a P2P service provider and launching peer instances. It addresses challenges of scalability, performance, and security for inter-organizational data sharing. The system integrates cloud computing, databases, and P2P technologies. It uses genetic algorithms for deduplication to reduce redundant data storage. The system is intended to provide flexible, scalable, and cost-effective data sharing services for corporate networks based on a pay-as-you-go model.
AN ENTROPIC OPTIMIZATION TECHNIQUE IN HETEROGENEOUS GRID COMPUTING USING BION...ijcsit
This document summarizes a research paper that proposes a new method for improving both fault tolerance and load balancing in grid computing networks. The method converts the tree structure of grid computing nodes into a distributed R-tree index structure and then applies an entropy estimation technique. This entropy estimation helps discard nodes with high entropy from the tree, reducing complexity. The method then uses thresholding and control algorithms to select optimal route paths based on load balance and fault tolerance. Various optimization techniques like genetic algorithms, ant colony optimization, and particle swarm optimization are also applied to reach better solutions. Experimental results showed the proposed method improved performance over other existing methods.
The Cloud computing becomes an important topic
in the area of high performance distributed computing. On the
other hand, task scheduling is considered one the most significant
issues in the Cloud computing where the user has to pay for the
using resource based on the time. Therefore, distributing the
cloud resource among the users' applications should maximize
resource utilization and minimize task execution Time. The goal
of task scheduling is to assign tasks to appropriate resources that
optimize one or more performance parameters (i.e., completion
time, cost, resource utilization, etc.). In addition, the scheduling
belongs to a category of a problem known as an NP-complete
problem. Therefore, the heuristic algorithm could be applied to
solve this problem. In this paper, an enhanced dependent task
scheduling algorithm based on Genetic Algorithm (DTGA) has
been introduced for mapping and executing an application’s
tasks. The aim of this proposed algorithm is to minimize the
completion time. The performance of this proposed algorithm has
been evaluated using WorkflowSim toolkit and Standard Task
Graph Set (STG) benchmark.
This document summarizes an article from the International Journal of Computer Engineering and Technology (IJCET) that proposes an algorithm called Replica Placement in Graph Topology Grid (RPGTG) to optimally place data replicas in a graph-based data grid while ensuring quality of service (QoS). The algorithm aims to minimize data access time, balance load among replica servers, and avoid unnecessary replications, while restricting QoS in terms of number of hops and deadline to complete requests. The article describes how the algorithm converts the graph structure of the data grid to a hierarchical structure to better manage replica servers and proposes services to facilitate dynamic replication, including a replica catalog to track replica locations and a replica manager to perform replication
A Novel Approach for Clustering Big Data based on MapReduce IJECEIAES
Clustering is one of the most important applications of data mining. It has attracted attention of researchers in statistics and machine learning. It is used in many applications like information retrieval, image processing and social network analytics etc. It helps the user to understand the similarity and dissimilarity between objects. Cluster analysis makes the users understand complex and large data sets more clearly. There are different types of clustering algorithms analyzed by various researchers. Kmeans is the most popular partitioning based algorithm as it provides good results because of accurate calculation on numerical data. But Kmeans give good results for numerical data only. Big data is combination of numerical and categorical data. Kprototype algorithm is used to deal with numerical as well as categorical data. Kprototype combines the distance calculated from numeric and categorical data. With the growth of data due to social networking websites, business transactions, scientific calculation etc., there is vast collection of structured, semi-structured and unstructured data. So, there is need of optimization of Kprototype so that these varieties of data can be analyzed efficiently.In this work, Kprototype algorithm is implemented on MapReduce in this paper. Experiments have proved that Kprototype implemented on Mapreduce gives better performance gain on multiple nodes as compared to single node. CPU execution time and speedup are used as evaluation metrics for comparison.Intellegent splitter is proposed in this paper which splits mixed big data into numerical and categorical data. Comparison with traditional algorithms proves that proposed algorithm works better for large scale of data.
This document evaluates the performance of the First Come First Serve (FCFS) and Easy Backfilling (EBF) resource allocation algorithms in grid computing systems. It compares the resource utilization and throughput of the two algorithms when gridlet size increases linearly and non-linearly. The results show that EBF achieves better resource utilization and throughput than FCFS in both linear and non-linear cases. EBF is more efficient at scheduling jobs to maximize resource usage and the amount of work completed per time period.
Task Scheduling methodology in cloud computing Qutub-ud- Din
This document outlines a proposed methodology for developing efficient task scheduling strategies in cloud computing. It begins with introductions to cloud computing and task scheduling. It then reviews several relevant existing task scheduling algorithms from literature that focus on objectives like reducing costs, minimizing completion time, and maximizing resource utilization. The problem statement indicates the goals are to reduce costs, minimize completion time, and maximize resource allocation. An overview of the proposed methodology's flow is then provided, followed by references.
Literature Survey on Buliding Confidential and Efficient Query Processing Usi...paperpublications3
Abstract: Hosting data query services with the deployed cloud computing infrastructure increase the scalability and high performance evaluations with low cost. However, some data owners might not be interested to the save their in the cloud environment because of data confidentiality and query processing privacy should be guaranteed by the cloud service providers. Secured Query should able to provide very high efficient of query processing and also should reduce in – house workload. In this paper we proposed RASP data perturbation techniques combines various objectives like random noise injection, dimensionality expansion, efficient encryption and random projection, henceforth RASP methodology are also used to preserves multidimensional ranges. KNN – R algorithm used to work with RASP range for processing KNN queries. The experimental result of our project carried out to define realistic security and threat model approaches for improved efficient and security.
The document discusses optimization of resource allocation in cloud environments using a modified particle swarm optimization (PSO) approach. It proposes a Modified Resource Allocation Mutation PSO (MRAMPSO) strategy that uses an Extended Multi Queue Scheduling algorithm to schedule tasks based on resource availability and reschedules failed tasks. The MRAMPSO strategy is compared to standard PSO and other algorithms to show it can reduce execution time, makespan, transmission cost, and round trip time.
This document summarizes an article from the International Journal of Computer Engineering and Technology (IJCET) that proposes a new dynamic data replication and job scheduling strategy for data grids. The strategy aims to improve data access time and reduce bandwidth consumption by replicating data based on file popularity, storage limitations at nodes, and data category. It replicates more popular files that are in the same category as frequently accessed data to nodes close to where jobs are run. This is intended to optimize performance by locating data and jobs close together. The document provides context on related work and outlines the proposed system architecture and replication/scheduling approach.
Survey on Synchronizing File Operations Along with Storage Scalable MechanismIRJET Journal
The document summarizes research on efficient file operations and storage scalability mechanisms. It discusses how data is divided into chunks and distributed to nodes for transmission in peer-to-peer networks. The proposed system aims to provide efficient load balancing, eliminate single points of failure, and ensure synchronization and security during data transmission. It uses synchronization algorithms and a hybrid distribution model combining features of peer-to-peer and client-server networks. The system is designed to securely handle insertions, deletions, splits, and concatenations of file chunks in a distributed storage system.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document provides an overview of stream data mining techniques. It discusses how traditional data mining cannot be directly applied to data streams due to their continuous, rapid nature. The document outlines some essential methodologies for analyzing data streams, including sampling, load shedding, sketching, and data summarization techniques like reservoirs, histograms, and wavelets. It also discusses challenges in applying these techniques to data streams and open problems in the emerging field of stream data mining.
A survey of various scheduling algorithm in cloud computing environmenteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A latency-aware max-min algorithm for resource allocation in cloud IJECEIAES
Cloud computing is an emerging distributed computing paradigm. However, it requires certain initiatives that need to be tailored for the cloud environment such as the provision of an on-the-fly mechanism for providing resource availability based on the rapidly changing demands of the customers. Although, resource allocation is an important problem and has been widely studied, there are certain criteria that need to be considered. These criteria include meeting user’s quality of service (QoS) requirements. High QoS can be guaranteed only if resources are allocated in an optimal manner. This paper proposes a latency-aware max-min algorithm (LAM) for allocation of resources in cloud infrastructures. The proposed algorithm was designed to address challenges associated with resource allocation such as variations in user demands and on-demand access to unlimited resources. It is capable of allocating resources in a cloud-based environment with the target of enhancing infrastructure-level performance and maximization of profits with the optimum allocation of resources. A priority value is also associated with each user, which is calculated by analytic hierarchy process (AHP). The results validate the superiority for LAM due to better performance in comparison to other state-of-the-art algorithms with flexibility in resource allocation for fluctuating resource demand patterns.
Cloud computing Review over various scheduling algorithmsIJEEE
Cloud computing has taken an importantposition in the field of research as well as in thegovernment organisations. Cloud computing uses virtualnetwork technology to provide computer resources tothe end users as well as to the customer’s. Due tocomplex computing environment the use of high logicsand task scheduler algorithms are increase which resultsin costly operation of cloud network. Researchers areattempting to build such kind of job scheduling algorithms that are compatible and applicable in cloud computing environment.In this paper, we review research work which is recently proposed by researchers on the base of energy saving scheduling techniques. We also studying various scheduling algorithms and issues related to them in cloud computing.
This document summarizes a research paper on developing an improved LEACH (Low-Energy Adaptive Clustering Hierarchy) communication protocol for energy efficient data mining in multi-feature sensor networks. It begins with background on wireless sensor networks and issues like energy efficiency. It then discusses the existing LEACH protocol and its drawbacks. The proposed improved LEACH protocol includes cluster heads, sub-cluster heads, and cluster nodes to address LEACH's limitations. This new version aims to minimize energy consumption during cluster formation and data aggregation in multi-feature sensor networks.
This document summarizes a research paper that proposes a new density-based clustering technique called Triangle-Density Based Clustering Technique (TDCT) to efficiently cluster large spatial datasets. TDCT uses a polygon approach where the number of data points inside each triangle of a polygon is calculated to determine triangle densities. Triangle densities are used to identify clusters based on a density confidence threshold. The technique aims to identify clusters of arbitrary shapes and densities while minimizing computational costs. Experimental results demonstrate the technique's superiority in terms of cluster quality and complexity compared to other density-based clustering algorithms.
Demand-driven Gaussian window optimization for executing preferred population...IJECEIAES
Scheduling is one of the essential enabling technique for Cloud computing which facilitates efficient resource utilization among the jobs scheduled for processing. However, it experiences performance overheads due to the inappropriate provisioning of resources to requesting jobs. It is very much essential that the performance of Cloud is accomplished through intelligent scheduling and allocation of resources. In this paper, we propose the application of Gaussian window where jobs of heterogeneous in nature are scheduled in the round-robin fashion on different Cloud clusters. The clusters are heterogeneous in nature having datacenters with varying sever capacity. Performance evaluation results show that the proposed algorithm has enhanced the QoS of the computing model. Allocation of Jobs to specific Clusters has improved the system throughput and has reduced the latency.
This document discusses load balancing strategies for grid computing. It proposes a dynamic tree-based model to represent grid architecture in a hierarchical way that supports heterogeneity and scalability. It then develops a hierarchical load balancing strategy and algorithms based on neighborhood properties to decrease communication overhead. Conventional scheduling algorithms like Min-Min, Max-Min, and Sufferage are discussed but determined to ignore dynamic network status, which is important for load balancing. Genetic algorithms are also mentioned as a potential solution.
A Survey of Job Scheduling Algorithms Whit Hierarchical Structure to Load Ba...Editor IJCATR
Due to the advances in human civilization, problems in science and engineering are becoming more complicated than ever
before. To solve these complicated problems, grid computing becomes a popular tool. a grid environment collects, integrates, and uses
heterogeneous or homogeneous resources scattered around the globe by a high-speed network. Scheduling problems are at the heart of
any Grid-like computational system. a good scheduling algorithm can assign jobs to resources efficiently and can balance the system
load. in this paper we survey three algorithms for grid scheduling and compare benefit and disadvantages of their based on makespan.
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
Grid computing enlarge with computing platform which is collection of heterogeneous computing resources connected by a network across dynamic and geographically dispersed organization to form a distributed high performance computing infrastructure. Grid computing solves the complex computing
problems amongst multiple machines. Grid computing solves the large scale computational demands in a high performance computing environment. The main emphasis in the grid computing is given to the resource management and the job scheduler .The goal of the job scheduler is to maximize the resource utilization and minimize the processing time of the jobs. Existing approaches of Grid scheduling doesn’t give much emphasis on the performance of a Grid scheduler in processing time parameter. Schedulers allocate resources to the jobs to be executed using the First come First serve algorithm. In this paper, we have provided an optimize algorithm to queue of the scheduler using various scheduling methods like Shortest Job First, First in First out, Round robin. The job scheduling system is responsible to select best suitable machines in a grid for user jobs. The management and scheduling system generates job schedules for each machine in the grid by taking static restrictions and dynamic parameters of jobs and machines
into consideration. The main purpose of this paper is to develop an efficient job scheduling algorithm to maximize the resource utilization and minimize processing time of the jobs. Queues can be optimized by using various scheduling algorithms depending upon the performance criteria to be improved e.g. response
time, throughput. The work has been done in MATLAB using the parallel computing toolbox.
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
This document describes a proposed grouping based job scheduling algorithm for grid computing that aims to maximize resource utilization and minimize job processing times. It discusses related work on job scheduling algorithms and then presents the steps of the proposed algorithm. The algorithm uses shortest job first, first-in first-out, and round robin scheduling to process jobs in groups. The algorithm is evaluated experimentally in MATLAB and shown to reduce total job processing time compared to using only first-in first-out scheduling. Graphs demonstrate the processing time improvements achieved by the combined scheduling approach.
RSDC (Reliable Scheduling Distributed in Cloud Computing)IJCSEA Journal
This document summarizes the PPDD algorithm for scheduling divisible loads originating from multiple sites in distributed computing environments. The PPDD algorithm is a two-phase approach that first derives a near-optimal load distribution and then considers actual communication delays when transferring load fractions. It guarantees a near-optimal solution and improved performance over previous algorithms like RSA by avoiding unnecessary load transfers between processors.
Propose a Method to Improve Performance in Grid Environment, Using Multi-Crit...Editor IJCATR
The most important purpose of grid networks is resource subscription in a dynamic and heterogeneous environment.
They are accessible through using various methods. Subscription has mainly computational, scientific and other implications. In
order to reach grid purposes and to use available resources in grid environment, subtasks are distributed among resources and are
scheduled by considering the quality of service. It has been tried to distribute subtasks between resources in a way that maximum
QOS can be obtained. In this study, a method has been presented. In this method, three parameters; namely, sent and transferred
time between RMS and resource, process time of subtask by the resource, and the load of available tasks in resources row, have
been taken into account. In this way, multi-criteria decision is made by using TOPSIS method and this priority of the resources
are determined to assign them to subtasks. Finally, time response, as an efficient parameter, has been improved and optimized by
optimal assignment of the resources to subtasks.
The document discusses using a genetic algorithm to schedule tasks in a cloud computing environment. It aims to minimize task execution time and reduce computational costs compared to the traditional Round Robin scheduling algorithm. The proposed genetic algorithm mimics natural selection and genetics to evolve optimal task schedules. It was tested using the CloudSim simulation toolkit and results showed the genetic algorithm provided better performance than Round Robin scheduling.
An enhanced adaptive scoring job scheduling algorithm with replication strate...eSAT Publishing House
This document describes an enhanced adaptive scoring job scheduling algorithm with replication strategy for grid environments. The algorithm aims to improve upon an existing adaptive scoring job scheduling algorithm by identifying whether jobs are data-intensive or computation-intensive. It then divides large jobs into subtasks, replicates the subtasks, and allocates the replicas to clusters based on a computed cluster score in order to improve resource utilization and job completion times. The algorithm is evaluated through simulation using the GridSim toolkit.
Optimized Resource Provisioning Method for Computational Gridijgca
Grid computing is an accumulation of heterogeneous, dynamic resources from multiple administrative areas which are geographically distributed that can be utilized to reach a mutual end. Development of resource provisioning-based scheduling in large-scale distributed environments like grid computing brings in new requirement challenges that are not being believed in traditional distributed computing environments. Computational grid is applying the resources of many systems in a network to a single problem at the same time. Grid scheduling is the method by which work specified by some means is assigned to the resources that complete the work in the environment which cannot fulfill the user requirements considerably. The satisfaction of users while providing the resources might increase the beneficiary level of resource suppliers. Resource scheduling has to satisfy the multiple constraints specified by the user. The option of resource with the satisfaction of multiple constraints is the most tedious process. This trouble is solved by bringing out the particle swarm optimization based heuristic scheduling algorithm which attempts to select the most suitable resource from the set of available resources. The primary parameters that are taken in this work for selecting the most suitable resource are the makespan and cost. The experimental result shows that the proposed method yields optimal scheduling with the atonement of all user requirements
This document evaluates the performance of a hybrid differential evolution-genetic algorithm (DE-GA) approach for load balancing in cloud computing. It first provides background on cloud computing and load balancing. It then describes the DE-GA approach, which uses differential evolution initially and switches to genetic algorithm if needed. The results show that the hybrid DE-GA approach improves performance over differential evolution and genetic algorithm alone, reducing makespan, average response time, and improving resource utilization. The study demonstrates the benefits of the hybrid evolutionary algorithm for an important problem in cloud computing.
This document proposes a genetic algorithm called Workflow Scheduling for Public Cloud Using Genetic Algorithm (WSGA) to optimize the cost of executing workflows in the public cloud. It discusses how genetic algorithms can be applied to the workflow scheduling problem to generate optimal schedules. The WSGA represents potential scheduling solutions as chromosomes, uses a fitness function to evaluate scheduling costs, and applies genetic operators like selection, crossover and mutation to evolve new schedules over multiple iterations. The goal is to minimize total execution cost while meeting workflow dependencies and deadline constraints. An experimental setup is described and the WSGA approach is claimed to reduce costs more than other heuristic scheduling algorithms for communication-intensive workflows.
This document summarizes a paper that presents a novel method for passive resource discovery in cluster grid environments. The method monitors network packet frequency from nodes' network interface cards to identify nodes with available CPU cycles (<70% utilization) by detecting latency signatures from frequent context switching. Experiments on a 50-node testbed showed the method can consistently and accurately discover available resources by analyzing existing network traffic, including traffic passed through a switch. The paper also proposes algorithms for distributed two-level resource discovery, replication and utilization to optimize resource allocation and access costs in distributed computing environments.
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
This document discusses a hierarchical scheduling method for efficiently scheduling varying length tasks in grid computing. It proposes using a two-level hierarchical approach. The first level uses a permutation-based method like Chemical Reaction Optimization (CRO) to schedule jobs to resources. The second level uses Shortest Job First to select and prioritize shorter jobs on each resource. This prevents shorter jobs from waiting for longer jobs to finish. Results show the hierarchical method reduces flowtime compared to CRO alone and improves performance for varying length job scheduling.
A survey of various scheduling algorithm in cloud computing environmenteSAT Journals
Abstract Cloud computing is known as a provider of dynamic services using very large scalable and virtualized resources over the Internet. Due to novelty of cloud computing field, there is no many standard task scheduling algorithm used in cloud environment. Especially that in cloud, there is a high communication cost that prevents well known task schedulers to be applied in large scale distributed environment. Today, researchers attempt to build job scheduling algorithms that are compatible and applicable in Cloud Computing environment Job scheduling is most important task in cloud computing environment because user have to pay for resources used based upon time. Hence efficient utilization of resources must be important and for that scheduling plays a vital role to get maximum benefit from the resources. In this paper we are studying various scheduling algorithm and issues related to them in cloud computing. Index Terms: cloud computing, scheduling, algorithm
Qo s aware scientific application scheduling algorithm in cloud environmentAlexander Decker
The document describes a QoS-aware scientific application scheduling algorithm for cloud environments. It proposes an algorithm that ranks tasks in a workflow and uses a user preference fitness function to select resources based on the user's desired quality of service, such as time and cost. The algorithm is compared to other similar works through several scenarios, and results show the proposed algorithm has better efficiency. Key aspects considered include task dependencies, data sizes, compute times, data transfer times, workflow makespan, resource costs and attributes.
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.
MAP/REDUCE DESIGN AND IMPLEMENTATION OF APRIORIALGORITHM FOR HANDLING VOLUMIN...acijjournal
Apriori is one of the key algorithms to generate frequent itemsets. Analysing frequent itemset is a crucial
step in analysing structured data and in finding association relationship between items. This stands as an
elementary foundation to supervised learning, which encompasses classifier and feature extraction
methods. Applying this algorithm is crucial to understand the behaviour of structured data. Most of the
structured data in scientific domain are voluminous. Processing such kind of data requires state of the art
computing machines. Setting up such an infrastructure is expensive. Hence a distributed environment
such as a clustered setup is employed for tackling such scenarios. Apache Hadoop distribution is one of
the cluster frameworks in distributed environment that helps by distributing voluminous data across a
number of nodes in the framework. This paper focuses on map/reduce design and implementation of
Apriori algorithm for structured data analysis.
Cost-Efficient Task Scheduling with Ant Colony Algorithm for Executing Large ...Editor IJCATR
This document summarizes a research paper that proposes an optimized ant colony optimization (ACO) algorithm for task scheduling in cloud computing. The goal is to minimize makespan and cost while improving fairness and load balancing. The ACO algorithm is adapted to prioritize and fairly allocate tasks to machines based on their performance. Simulations show the proposed ACO algorithm reduces makespan by 80% compared to Berger and greedy algorithms. It also increases processor utilization and balances loads across machines better than the other algorithms. The researchers conclude the optimized ACO approach improves resource usage and user satisfaction for task scheduling in cloud computing.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
International Conference on NLP, Artificial Intelligence, Machine Learning an...
Use of genetic algorithm for
1. International Journal on Information Theory (IJIT), Vol.4, No.3, July 2015
DOI: 10.5121/ijit.2015.4302 1
USE OF GENETIC ALGORITHM FOR
BALANCING THE GRID LOAD
Saad Masood Butt1
, Moaz Masood Butt2
and Azura Onn3
12
Computer and Software Engineering Department, Bahria University Islamabad,
Pakistan
saadmasoodbutt668@yahoo.com
moazbutt786@hotmail.com
3
Department of Management and Human Resource, Universiti Tenaga National,
Malaysia
azura@uniten.edu.my
ABSTRACT
Grid computing can involve lot of computational tasks which requires trustworthy computational nodes.
Load balancing in grid computing is a technique which overall optimizes the whole process of assigning
computational tasks to processing nodes. Grid computing is a form of distributed computing but different
from conventional distributed computing in a manner that it tends to be heterogeneous, more loosely
coupled and dispersed geographically. Optimization of this process must contains the overall
maximization of resources utilization with balance load on each processing unit and also by decreasing
the overall time or output. Evolutionary algorithms like genetic algorithms have studied so far for the
implementation of load balancing across the grid networks. But problem with these genetic algorithm is
that they are quite slow in cases where large number of tasks needs to be processed. In this paper we give
a novel approach of parallel genetic algorithms for enhancing the overall performance and optimization
of managing the whole process of load balancing across the grid nodes.
KEYWORDS
Network Protocols, Grid Computing, Load Balancing, Distributed Computing, Bubble Sort, 2-element
Insertion Sort
1. INTRODUCTION
With the advent of distributed computing we encounter large number issues regarding
processing powers and load balancing across the distributed networks specially like grid
computing. In order to get an optimized results without any time delay and with maximum
throughput we need enhanced algorithms across the networks. Hence this faster processing
results can be obtained by introducing a single processing unit or it can be inflated to diversity
of processing types like multiple processing nodes, geographically distributed processing units
and parallel processing. Grid computing is a modern and diverse form of distributed computing.
It focused on the ability to support computation across multiple administrative domains that sets
it apart from traditional distributed computing. Grids presents a method of using the
computational resources optimally within an association involving different computing
resources. Its supports multiple administrative domains and security authentication and other
organizational mechanisms that capable it to be distributed locally or dispersed on the multiple
geographic location in form of wide area network. Within the network connected nodes can
share their resources over a network domain. These resources can be utilized by any other node
that is connects to the grid network. I we ponder over this fact then we consider resource sharing
across the grid computing network is an important factor over the internet networks (Neil Y,
2012).
Multiple sharing resources are accumulated across the grid network. These resources involves
number of dimensions including computational powers, processing capabilities. In grid
2. International Journal on Information Theory (IJIT), Vol.4, No.3, July 2015
2
computing environment all computational nodes are connected in such a manner that one node
across the network is used for the admittance point for the whole set of resources which are
physically dispersed across the network [1]. So on the whole it seems for every user that their
computer has powers analogous to super computers.
This assorted nature of gird has numerous challenges like resource management of multiple
resources like storage capacity and processing powers, hardware and software based
heterogeneity, multiple security issues while connecting to different administrative domain.
When connecting to the grid. So on the whole the multi folded nature of grid has generated
some stern concerns needed to be considered. Load balancing across the network manages the
concept of load across the network in a manner that no node is over loaded or under loaded
while the resources are used. These load balancing algorithms deal with the overall optimization
of grid network in terms of resource sharing.
Evolutionary algorithms like genetic algorithms are from the family of heuristic techniques
which are in used from long time for finding optimized solutions. These algorithms incorporates
the mechanism inspired from biological evolution [2]. There are many variants of genetic
algorithm including different mutation and cross over procedure. Parallel genetic algorithms are
quite new in this perspective but different from conventional genetic algorithm methodologies
as they are faster and efficient in processing and gave more optimized results. There are many
variants of parallel genetic algorithm including master slave model, coarse grain genetic
algorithm, and dual species genetic algorithm
2. RELATED WORK
There is lot of examples in literature which deals with parallel processing in distributed
environment. In order to design well managed two-way applications writer has proposed the
infrastructure named as middleware. This paper gives idea on a middleware concept and figure
out the problems of service discovery, organization of communication between devices,
harmonization, data-security and minimization of communication between the devices within
distributed and collaborative environments [3].
Distributed systems along with the concept of P2P systems is also a form of computer networks.
By considering the strategies of mutual understanding and trust between the peers in P2P
systems writer has proposed that by using different protocols set of reliable peers can be
generated [4]. Schedulers based on genetic algorithms are quite efficient for jobs allocation to
different computing resources in a grid environment. Author proposes a widespread study on the
utilization of genetic algorithm for designing resourceful grid based schedulers for overall
minimization of total completion time. Two schemes based on encoding has been focused and
the majority of genetic algorithm operators for each of these schemes are implemented and
logically studied. Dealing with grid infrastructure at its service level generates two important
issues which needs to be managed, these issues includes management of workloads and resource
management in an optimized manner. Using neighbourhood property and focusing on the tree
based techniques generated optimized results in for managing load balancing across the grid
networks [5]. Genetic algorithms to resolve the predicament of load balancing resourcefully.
Proposed algorithm considers multi purposes in its resolution of assessment and resolves the
scheduling problem in a way that concomitantly minimizes maxspan and overall decreases
communication cost, and capitalize the processors efficiency and utilization [6]. Proposed
genetic algorithm make use of dynamic load-balancing methodology for solving issue of
resource management across the networks Algorithm generated high-quality results when size
of tasks to be scheduled become bigger. Implementation of central scheduler in grid
environment overall optimized the scheduling process by considering the concept of least
communication for making load balancing decisions along the grid networks [7].
3. International Journal on Information Theory (IJIT), Vol.4, No.3, July 2015
3
[8] proposed approach to solves the task scheduling for grid local search algorithm based on the
concept of gravitational attraction search is incorporated with genetic algorithm in order to
enhance its capacity to search more sharply in problem based search space and attain more
precise response in optimized time. Comparison between the results o HYGAGA and genetic
algorithm emphasizes major improvement in the recital of search algorithm[9][10].
2.1. Proposed Methodology
Grid Model:
We have taken in account the grid information system model for solving the issue o load
balancing across the grid nodes. GIA is a grid model provides the information regarding the
number of resources, number of tasks and state information of resources along with the
information that which task is being served by which resource across the grid network. This
model is good enough to use in the place where some kind of heuristics is being implemented.
We enhance this model by adding the concept of parallel genetic algorithm. We consider that
there is a scheduler present in the grid. This scheduler according to GIS model has all the
information related to the number o task. Let us consider that set of task come to the scheduler
in GIS model of grid. These tasks are selected on the basis o criteria we define based on the total
completion time of tasks. We select those tasks first whose completion time is approaching to
zero
Tct 0
Suppose a new array of tasks based on their completion time reaches to scheduler for
scheduling. The central scheduler has all the information about the current state of the system.
At this point of time we mainly focuses on the selection of optimized set of tasks for assigning
to multiple resources for processing. In order to find the optimized set of tasks we use the
concept of parallel genetic algorithm. Parallel genetic algorithm is quite efficient as compare to
conventional genetic algorithm techniques for finding optimized solutions. As the number of
tasks are growing continuously so the concept of processing by only single processor is not
optimized solution. The main scheduler selects three more schedulers for finding the set of
optimized tasks. The main scheduler is called master scheduler while the rest of the three
schedulers are known as slave schedulers. These slave schedulers are selected on the basis of
state information present at the master scheduler. Let us consider the figure 1 for the illustration
of this kind o grid model.
4. International Journal on Information Theory (IJIT), Vol.4, No.3, July 2015
4
Figure 1. Master and slave nodes in scheduler
Figure 2: Group of nodes connected to grid
Suppose we have currently five nodes connecting to the grid. Then the grid node N1 is
consider to be the master node for assigning the tasks to the slave node and let’s say we
have N2, N3 and N5 are slave nodes depending upon the state of nodes and the current
load on the node. These three nodes are selected for the processing of genetic algorithm
and they return the optimized set of tasks that can be assigned to respective nodes for
processing. The master node selects the node N2, N3 and N5 for the processing and
implementation of genetic algorithm as these are the nodes with less loads. Now we see
that how genetic algorithm is being practically implemented on these grid nodes.
2.2. SELECTION OF OPTIMIZED SET O JOBS FOR ASSIGNING TO
RESOURCES
Genetic Algorithm
The master node N1 implements the first two steps of genetic algorithm. Let’s assume that node
N1 gets the set of tasks or in other words an array of tasks which are selected on the basis of
heuristic names as first come first serve. On the basis of FCFS basis the node N1perofrms the
next step named as fitness function over the first set of population which are actually the tasks
and we present these tasks in the form of numbers. We consider the tasks completion time as the
population which passes through the fitness function. We consider that at first on the basis of
first come first serve we have the following set of tasks in the form of an array.
Initial Population
A Initial population based on the completion time of each task:
This is actually the completion time of each task in milliseconds. Genetic algorithm has initial
population Most of the genetic algorithm selects the initial population without any define
criteria. But we introduce the heuristic that is first come first serve for the selection of initial
population. Now at next step the population passes through the fitness function.
5. International Journal on Information Theory (IJIT), Vol.4, No.3, July 2015
5
Fitness Function
Fitness unction actually approves that which set of population is capable of reaching to the next
step of cross over and mutations in genetic algorithm. Hence fitness unction is responsible for
doing most of the task in genetic algorithm, hence we tried that we consider all those factors
which can overall increases the efficiency of load balancing procedure and can minimize the
makes pan. We have taken in account the following factors for fitness function. Fitness function
based on our algorithm comprise of important factors. The following equation gives our fitness
function.
• WT = Waiting time for each task before it is selected for processing
• TPT =Total processing time needed by a task to complete
• CLP=Current load on processor
• LS=Length of population string (which is selected on the basis of FCFS)
• DF=Delay factor (if some kind of delay is occurred from any processing node)
Current load on the processing node can be measure by taking in consideration the following
factors. is the tasks that are currently being executed by processor as well as size of all tasks that
are waiting in processors queue. This load on overall processor can find by using following
equation.
• CL= Current load present on particular processor
• RT= Time left for task that is currently processing by the processing node
• TS=Total size of task in terms of time (time taken by each task to complete)
• j=Number of processing nodes which are capable of processing
Our input string that passes through the fitness function are based on the total completion
needed by each task. The result of fitness function is categorized in two ways.
F(pop) > 0
F(pop)<=0
The matrix defined below shows the fitness function of each population and its probability of
selection. The highest probability strings are selected for mutation and cross over and for
generating new child population to be used in next generation. Each string has to pass through
this fitness function in order to select for the future population. The following table shows the
selection of strings.
6. International Journal on Information Theory (IJIT), Vol.4, No.3, July 2015
6
Table 1: Fitness values of selected strings
2.4. Selection
For selection the population we have used the proportionate selection method/This kind of
selection methodology is similar to the roulette wheel selection methodology in which selection
is done on the basis of slice of wheel. The proportionate selection methodology uses the fitness
value for the selection of individuals.
Considering the equation based on the table 1
pi = Fi / SFi
Where pi is the probability of each string to be selected
F is the fitness value of each chromosome within the string
Where SFi is the sum of all the fitness values of each individual within the string. So all out of
population strings, the ones with lesser fitness value that is approaching to zero are selected as
the parents.
Crossover and Mutation
Crossover and mutation are the two important steps to be followed after the completion of
fitness function and selection procedures based on genetic algorithm. Parallel genetic algorithm
breaks down the procedure of GA into two parts and hence the crossover and mutation
procedure is performed by slave nodes selected by master node. These slave nodes are selected
on the basis of following conditions.
Total work load on each node → 0
Hence all those nodes whose total work load approaches to zero can be selected as a slave node.
Suppose in our case there are three nodes N2, N3 and N5 that are selected for performing the
crossover and mutation procedures. We have used the single arithmetic crossover for
implementing the rest of PGA. It works like this
Suppose we have two parents: and <y1,…,yn> and <z1,…,zn>
We will select gene (k) at random after that some selected point and mix values. Then the child
become
(y1,…. , yk,α.zk +(1-α).yk,…., yn )
7. International Journal on Information Theory (IJIT), Vol.4, No.3, July 2015
7
Suppose we have these set of parents generated from master node and sent to slave node for
cross over.
Suppose we have α=4
After crossover the above mentioned results come out. Now the mutation that is also performed
by the slave nodes is done after crossover operation
3. CONCLUSIONS
Hence our algorithm that is based on the PGA based strategy is resourceful in manner that it
optimizes the overall procedure by decreasing the total execution time, total completion time
and total waiting time for all tasks. Hence as the time decreases the overall efficiency of the
parallel genetic algorithm increases. So in case of grid computing the implementation of parallel
genetic algorithm gives optimal results in selection of tasks to be assigned to decreases the
overall execution time for tasks in case where the numbers of tasks are continuously increasing.
The selection of minimum loaded node in grid computing overall increases the efficiency of
proposed algorithm. So in case of grid computing where the resources have to exploit and
nodes are quite heterogeneous this strategy gives the optimal results and of great use. Both
priorities and sizes of tasks are of uneven length. This algorithm is implemented on the local
grid created during this research work.java based software gridsim is use to implement on the
grid. Infrastructure for checking the load balancing issues and results in terms of time. As a
future work some new techniques for load balancing, like Ant colony optimization and particle
swarm optimization will be taken into consideration.
REFERENCES
[1] Yen, N. Y., & Kuo, S. Y. (2012). An intergrated approach for internet resources mining and
searching. J. Converg, 3(2), 37-44.
[2] Pyshkin, E., & Kuznetsov, A. (2010). Approaches for web search user interfaces. FTRA Journal of
Convergence, 1(1).
[3] Ling, A. P. A., & Masao, M. (2011, May). Selection of model in developing information security
criteria on smart grid security system. In Parallel and Distributed Processing with Applications
Workshops (ISPAW), 2011 Ninth IEEE International Symposium on (pp. 91-98). IEEE.
[4] Schmid, O., & Hirsbrunner, B. (2012, March). Middleware for distributed collaborative ad-hoc
environments. In Pervasive Computing and Communications Workshops (PERCOM Workshops),
2012 IEEE International Conference on (pp. 435-438). IEEE.
[5] Aikebaier, A., Enokido, T., & Takizawa, M. (2011). Trustworthy group making algorithm in
distributed systems. Human-centric computing and information sciences, 1(1), 1-15.
[6] Maheshwari, P. (1996, January). A dynamic load balancing algorithm for a heterogeneous
computing environment. In System Sciences, 1996, Proceedings of the Twenty-Ninth Hawaii
International Conference on, (Vol. 1, pp. 338-346). IEEE.
8. International Journal on Information Theory (IJIT), Vol.4, No.3, July 2015
8
[7] Li, Y., Yang, Y., Ma, M., & Zhou, L. (2009). A hybrid load balancing strategy of sequential tasks
For grid computing environments. Future Generation Computer Systems, 25(8), 819-828.
[8] Zomaya, A. Y., & Teh, Y. H. (2001). Observations on using genetic algorithms for dynamic load-
balancing. Parallel and Distributed Systems, IEEE Transactions on, 12(9), 899-911.
[9] Nikravan, M., & Kashani, M. H. (2007, June). A genetic algorithm for process scheduling in
distributed operating systems considering load balancing. InProceedings 21st European Conference
on Modelling and Simulation Ivan Zelinka, Zuzana Oplatkova, Alessandra Orsoni, ECMS.
[10] Naseri, N. K., & Jula, A. (2012, August). A hybrid genetic algorithm-gravitational attraction
search algorithm (HYGAGA) to solve grid task scheduling problem. In International Conference
on Soft Computing and its Applications (ICSCA'2012).
Authors
Engineer. Saad Masood Butt received his BS (Software Engineering) degree
from Bahria University Islamabad, Pakistan in 2008. He completed his MS
(Software Engineering) degree in 2010 from Bahria University Islamabad,
Pakistan. He is the recognized Engineer of Pakistan approved by Higher
Education Commission and Pakistan Engineering Council (PEC). He has got
more than 7 years international work experience and was associated with
various organizations and universities in Pakistan and Malaysia.