Cloud computing is an upcoming technology in dispersed computing facilitating paying for each model as
for each user demand and need. Cloud incorporates a set of virtual machine which comprises both storage
and computational facility. The fundamental goal of cloud computing is to offer effective access to isolated
and geographically circulated resources. Cloud is growing every day and experiences numerous problems
such as scheduling. Scheduling means a collection of policies to regulate the order of task to be executed
by a computer system. An excellent scheduler derives its scheduling plan in accordance with the type of
work and the varying environment. This research paper demonstrates a generalized precedence algorithm
for effective performance of work and contrast with Round Robin and FCFS Scheduling. Algorithm needs
to be tested within CloudSim toolkit and outcome illustrates that it provide good presentation compared
some customary scheduling algorithm.
Task scheduling methodologies for high speed computing systemsijesajournal
High Speed computing meets ever increasing real-time computational demands through the leveraging of
flexibility and parallelism. The flexibility is achieved when computing platform designed with
heterogeneous resources to support multifarious tasks of an application where as task scheduling brings
parallel processing. The efficient task scheduling is critical to obtain optimized performance in
heterogeneous computing Systems (HCS). In this paper, we brought a review of various application
scheduling models which provide parallelism for homogeneous and heterogeneous computing systems. In
this paper, we made a review of various scheduling methodologies targeted to high speed computing
systems and also prepared summary chart. The comparative study of scheduling methodologies for high
speed computing systems has been carried out based on the attributes of platform & application as well.
The attributes are execution time, nature of task, task handling capability, type of host & computing
platform. Finally a summary chart has been prepared and it demonstrates that the need of developing
scheduling methodologies for Heterogeneous Reconfigurable Computing Systems (HRCS) which is an
emerging high speed computing platform for real time applications.
Energy Efficient Heuristic Base Job Scheduling Algorithms in Cloud ComputingIOSRjournaljce
Cloud computing environment provides the cost efficient solution to customers by the resource provisioning and flexible customized configuration. The interest of cloud computing is growing around the globe at very fast pace because it provides scalable virtualized infrastructure by mean of which extensive computing capabilities can be used by the cloud clients to execute their submitted jobs. It becomes challenge for the cloud infrastructure to manage and schedule these jobs originated by different cloud users to available resources in such a manner to strengthen the overall performance of the system. As the number of user increases the job scheduling become an intensive task. Energy efficient job scheduling is one constructive solution to streamline the resource utilization as well as to reduce the energy consumption. Though there are several scheduling algorithms available, this paper intends to present job scheduling based on two Heuristic approaches i.e. Efficient MQS (Multi-queue job scheduling) and ACO (Ant colony optimization) and further evaluating the effectiveness of both approaches by considering the parameter of energy consumption and time in cloud computing.
Cost Optimization in Multi Cloud Platforms using Priority Assignmentijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Optimized Assignment of Independent Task for Improving Resources Performance ...ijgca
Grid computing has emerged from category of distributed and parallel computing where the
heterogeneous resources from different network are used simultaneously to solve a particular problem that
need huge amount of resources. Potential of Grid computing depends on my issues such as security of
resources, heterogeneity of resources, fault tolerance & resource discovery and job scheduling. Scheduling
is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing
resources and is an NP-complete problem. To achieve the promising potential of grid computing, an
effective and efficient job scheduling algorithm is proposed, which will optimized two important criteria to
improve the performance of resources i.e. makespan time & resource utilization. With this, we have
classified various tasks scheduling heuristic in grid on the basis of their characteristics.
Task scheduling methodologies for high speed computing systemsijesajournal
High Speed computing meets ever increasing real-time computational demands through the leveraging of
flexibility and parallelism. The flexibility is achieved when computing platform designed with
heterogeneous resources to support multifarious tasks of an application where as task scheduling brings
parallel processing. The efficient task scheduling is critical to obtain optimized performance in
heterogeneous computing Systems (HCS). In this paper, we brought a review of various application
scheduling models which provide parallelism for homogeneous and heterogeneous computing systems. In
this paper, we made a review of various scheduling methodologies targeted to high speed computing
systems and also prepared summary chart. The comparative study of scheduling methodologies for high
speed computing systems has been carried out based on the attributes of platform & application as well.
The attributes are execution time, nature of task, task handling capability, type of host & computing
platform. Finally a summary chart has been prepared and it demonstrates that the need of developing
scheduling methodologies for Heterogeneous Reconfigurable Computing Systems (HRCS) which is an
emerging high speed computing platform for real time applications.
Energy Efficient Heuristic Base Job Scheduling Algorithms in Cloud ComputingIOSRjournaljce
Cloud computing environment provides the cost efficient solution to customers by the resource provisioning and flexible customized configuration. The interest of cloud computing is growing around the globe at very fast pace because it provides scalable virtualized infrastructure by mean of which extensive computing capabilities can be used by the cloud clients to execute their submitted jobs. It becomes challenge for the cloud infrastructure to manage and schedule these jobs originated by different cloud users to available resources in such a manner to strengthen the overall performance of the system. As the number of user increases the job scheduling become an intensive task. Energy efficient job scheduling is one constructive solution to streamline the resource utilization as well as to reduce the energy consumption. Though there are several scheduling algorithms available, this paper intends to present job scheduling based on two Heuristic approaches i.e. Efficient MQS (Multi-queue job scheduling) and ACO (Ant colony optimization) and further evaluating the effectiveness of both approaches by considering the parameter of energy consumption and time in cloud computing.
Cost Optimization in Multi Cloud Platforms using Priority Assignmentijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Optimized Assignment of Independent Task for Improving Resources Performance ...ijgca
Grid computing has emerged from category of distributed and parallel computing where the
heterogeneous resources from different network are used simultaneously to solve a particular problem that
need huge amount of resources. Potential of Grid computing depends on my issues such as security of
resources, heterogeneity of resources, fault tolerance & resource discovery and job scheduling. Scheduling
is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing
resources and is an NP-complete problem. To achieve the promising potential of grid computing, an
effective and efficient job scheduling algorithm is proposed, which will optimized two important criteria to
improve the performance of resources i.e. makespan time & resource utilization. With this, we have
classified various tasks scheduling heuristic in grid on the basis of their characteristics.
STUDY THE EFFECT OF PARAMETERS TO LOAD BALANCING IN CLOUD COMPUTINGIJCNCJournal
The rapid growth of users on the cloud service and number of services to the user increases the load on the
servers at cloud datacenter. This issue is becoming a challenge for the researchers. And requires used
effectively a load balancing technique not only to balance the resources for servers but also reduce the
negative impact to the end-user service. The current, load balancing techniques have solved the various
problems such as: (i) load balancing after a server was overloaded; (ii) load balancing and load forecast
for the allocation of resources; iii) improving the parameters affecting to load balancing in cloud. The
study of improving these parameters have great significance to improving system performance through
load balancing. From there, we can propose more effective methods of load balancing, in order to increase
system performance. Therefore, in this paper we researched some parameters affecting the performance
load balancing on the cloud computing.
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal
Currently cloud computing has turned into a promising technology and has become a great key for satisfying a flexible service oriented , online provision and storage of computing resources and user’s information in lesser expense with dynamism framework on pay per use basis.In this technology Job Scheduling Problem is acritical issue. For well-organizedmanaging and handling resources, administrations, scheduling plays a vital role. This paper shares out the improved Hyper- Heuristic Scheduling Approach to schedule resources, by taking account of computation time and makespan with two detection operators. Operators are used to select the low-level heuristics automatically. Conditional
Revealing Algorithm (CRA)idea is applied for finding the job failures while allocating the resources. We believe that proposed hyper-heuristic achieve better results than other individual heuristics.
PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHMecij
The running generation of world, cloud computing has become the most powerful, chief and also lightning technology. IT based companies has already changed their way to buy and design hardware through this technology. It is a high utility which can also make software more attractive. Load balancing research in
cloud technology is one of the burning technologies in modern time. In this paper, pointing various proposed algorithms, the topic of load balancing in Cloud Computing are researched and compared to provide a gist of the latest way in this research area. By using Genetic Algorithm the balance is most
flexible which is represented here.
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.
Cloud Computing Load Balancing Algorithms Comparison Based SurveyINFOGAIN PUBLICATION
Cloud computing is an online primarily based computing. This computing paradigm has increased the employment of network wherever the potential of 1 node may be used by alternative node. Cloud provides services on demand to distributive resources like info, servers, software, infrastructure etc. in pay as you go basis. Load reconciliation is one amongst the vexing problems in distributed atmosphere. Resources of service supplier have to be compelled to balance the load of shopper request. Totally different load reconciliation algorithms are planned so as to manage the resources of service supplier with efficiency and effectively. This paper presents a comparison of assorted policies used for load reconciliation.
LOAD BALANCING ALGORITHM TO IMPROVE RESPONSE TIME ON CLOUD COMPUTINGijccsa
Load balancing techniques in cloud computing can be applied at different levels. There are two main
levels: load balancing on physical server and load balancing on virtual servers. Load balancing on a
physical server is policy of allocating physical servers to virtual machines. And load balancing on virtual
machines is a policy of allocating resources from physical server to virtual machines for tasks or
applications running on them. Depending on the requests of the user on cloud computing is SaaS (Software
as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service) that has a proper load
balancing policy. When receiving the task, the cloud data center will have to allocate these tasks efficiently
so that the response time is minimized to avoid congestion. Load balancing should also be performed
between different datacenters in the cloud to ensure minimum transfer time. In this paper, we propose a
virtual machine-level load balancing algorithm that aims to improve the average response time and
average processing time of the system in the cloud environment. The proposed algorithm is compared to the
algorithms of Avoid Deadlocks [5], Maxmin [6], Throttled [8] and the results show that our algorithms
have optimized response times.
Dynamic Three Stages Task Scheduling Algorithm on Cloud Computing
Naglaa Sayed Abdelrehem, Fathi Ahmed Amer, Imane Aly Saroit,
Department of Information Technology, Faculty of Computer and Artificial Intelligence, Cairo University, Cairo, Egypt.
Information Technology Disaster Recovery Plans (DRPs) are becoming an essential component for any organization with IT infrastructure. However, DRPs varies in performance and cost; therefore, based on requirements and resources, an organization can design their DRP. Typically, DRPs depends on data and/or system replication, data needs to be backed up frequently, and a plan to restore the system to running state within the allowed time. Hence, DRP designer must know the needed business requirements in terms of recovery time objective (RTO) and recovery point objective (RPO). Then, the appropriate technical requirements will be set. At the same time, the cost factor can play a role in choosing the appropriate DRP. The industry has a widely accepted seven-tier system of how DRP can be designed. In this work, we design and implement a software tool that can simulate the IT DPR systems and therefore help designers to design, optimize, and test their design before it is physically implemented. This tool will run a simulated system with DRP specific design and the designer can exercise with the system to show it’s RTO, RPO, and cost that can significantly improve DRP design.
Comparative Analysis of Various Grid Based Scheduling Algorithmsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Scheduling Divisible Jobs to Optimize the Computation and Energy Costsinventionjournals
ABSTRACT : The important challenge in cloud computing environment is to design a scheduling strategy to handle jobs, and to process them in a heterogeneous environment with shared data centers. In this paper, we attempt to investigate a new analytical framework model that enables an existing private cloud data-center for scheduling jobs and minimizing the overall computation and energy cost together. Our model is based on Divisible Load Theory (DLT) model to derive closed-form solution for the load fractions to be assigned to each machines considering computation and energy cost. Our analysis also attempts to schedule the jobs such a way that cloud provider can gain maximum benefit for his service and Quality of Service (QoS) requirement user’s job. Finally, we quantify the performance of the strategies via rigorous simulation studies.
Job Resource Ratio Based Priority Driven Scheduling in Cloud Computingijsrd.com
Cloud Computing is an emerging technology in the area of parallel and distributed computing. Clouds consist of a collection of virtualized resources, which include both computational and storage facilities that can be provisioned on demand, depending on the users' needs. Job scheduling is one of the major activities performed in all the computing environments. Cloud computing is one the upcoming latest technology which is developing drastically. To efficiently increase the working of cloud computing environments, job scheduling is one the tasks performed in order to gain maximum profit. In this paper we proposed a new scheduling algorithm based on priority and that priority is based on ratio of job and resource. To calculate priority of job we use analytical hierarchy process. In this paper we also compare result with other algorithm like First come first serve and round robin algorithms.
Stochastic Scheduling Algorithm for Distributed Cloud Networks using Heuristi...Eswar Publications
Rule based heuristic scheduling algorithms in real time and cloud computing Systems employ for resource or task scheduling since they are suitable to implement for NP-complete problems. However, they are simple but there is much room to improve these algorithms. This study presents a heuristic scheduling algorithm, called High performance hyper-heuristic scheduling algorithm (HHSA) using detection operator, to find better scheduling solutions for real and cloud computing systems. The two operators - diversity detection and
improvement detection operators - are employed in this algorithm to determine the timing to determine the heuristic algorithm.. These two are employed to dynamically determine a low level heuristic that can be used to find better solution. To evaluate the performance of this method, authors examined the above method with several scheduling algorithms and results prove that Hyper Heuristic Scheduling Algorithm can
significantly decrease the makespan of task scheduling when compared with all other scheduling algorithms.
A novel high-performance hyper-heuristic algorithm is proposed for scheduling on cloud computing systems to
reduce the makespan. This algorithm can be applied to both sequence dependent and sequence independent scheduling problems.
Dynamic Scheduling of Data Using Genetic Algorithm in Cloud Computingijcoa
Cloud Computing is the utilization of pool of resources for remote users through internet that can be easily accessible, scalable and utilization of resources. To attain maximum utilization of resources the tasks need to be scheduled. The problem in scheduling is allocating the correct resources to the arrived tasks. Dynamic scheduling is that the task arrival is uncertain at run time and allocating resources are tedious as several tasks arrive at the same time. To avoid this scheduling problem, Genetic Algorithm is used. Genetic algorithm is a heuristic method that deals with the natural selection of solution from all possible solutions. Using genetic algorithm the tasks are scheduled according to the computation and memory usage. The tasks are scheduled dynamically. The execution time is reduced by parallel processing. The scheduled data is stored in cloud. By using GA to obtain global optimization.
Cloud computing is an emerging technology. It process huge amount of data so scheduling mechanism
works as a vital role in the cloud computing. Thus my protocol is designed to minimize the switching time,
improve the resource utilization and also improve the server performance and throughput. This method or
protocol is based on scheduling the jobs in the cloud and to solve the drawbacks in the existing protocols.
Here we assign the priority to the job which gives better performance to the computer and try my best to
minimize the waiting time and switching time. Best effort has been made to manage the scheduling of jobs
for solving drawbacks of existing protocols and also improvise the efficiency and throughput of the server.
A Review on Scheduling in Cloud Computingijujournal
Cloud computing is the requirement based on clients that this computing which provides software,
infrastructure and platform as a service as per pay for use norm. The scheduling main goal is to achieve
the accuracy and correctness on task completion. The scheduling in cloud environment which enables the
various cloud services to help framework implementation. Thus the far reaching way of different type of
scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling
and grid scheduling. The survey gives an elaborate idea about grid, cloud, workflow scheduling to
minimize the energy cost, efficiency and throughput of the system.
STUDY THE EFFECT OF PARAMETERS TO LOAD BALANCING IN CLOUD COMPUTINGIJCNCJournal
The rapid growth of users on the cloud service and number of services to the user increases the load on the
servers at cloud datacenter. This issue is becoming a challenge for the researchers. And requires used
effectively a load balancing technique not only to balance the resources for servers but also reduce the
negative impact to the end-user service. The current, load balancing techniques have solved the various
problems such as: (i) load balancing after a server was overloaded; (ii) load balancing and load forecast
for the allocation of resources; iii) improving the parameters affecting to load balancing in cloud. The
study of improving these parameters have great significance to improving system performance through
load balancing. From there, we can propose more effective methods of load balancing, in order to increase
system performance. Therefore, in this paper we researched some parameters affecting the performance
load balancing on the cloud computing.
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal
Currently cloud computing has turned into a promising technology and has become a great key for satisfying a flexible service oriented , online provision and storage of computing resources and user’s information in lesser expense with dynamism framework on pay per use basis.In this technology Job Scheduling Problem is acritical issue. For well-organizedmanaging and handling resources, administrations, scheduling plays a vital role. This paper shares out the improved Hyper- Heuristic Scheduling Approach to schedule resources, by taking account of computation time and makespan with two detection operators. Operators are used to select the low-level heuristics automatically. Conditional
Revealing Algorithm (CRA)idea is applied for finding the job failures while allocating the resources. We believe that proposed hyper-heuristic achieve better results than other individual heuristics.
PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHMecij
The running generation of world, cloud computing has become the most powerful, chief and also lightning technology. IT based companies has already changed their way to buy and design hardware through this technology. It is a high utility which can also make software more attractive. Load balancing research in
cloud technology is one of the burning technologies in modern time. In this paper, pointing various proposed algorithms, the topic of load balancing in Cloud Computing are researched and compared to provide a gist of the latest way in this research area. By using Genetic Algorithm the balance is most
flexible which is represented here.
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.
Cloud Computing Load Balancing Algorithms Comparison Based SurveyINFOGAIN PUBLICATION
Cloud computing is an online primarily based computing. This computing paradigm has increased the employment of network wherever the potential of 1 node may be used by alternative node. Cloud provides services on demand to distributive resources like info, servers, software, infrastructure etc. in pay as you go basis. Load reconciliation is one amongst the vexing problems in distributed atmosphere. Resources of service supplier have to be compelled to balance the load of shopper request. Totally different load reconciliation algorithms are planned so as to manage the resources of service supplier with efficiency and effectively. This paper presents a comparison of assorted policies used for load reconciliation.
LOAD BALANCING ALGORITHM TO IMPROVE RESPONSE TIME ON CLOUD COMPUTINGijccsa
Load balancing techniques in cloud computing can be applied at different levels. There are two main
levels: load balancing on physical server and load balancing on virtual servers. Load balancing on a
physical server is policy of allocating physical servers to virtual machines. And load balancing on virtual
machines is a policy of allocating resources from physical server to virtual machines for tasks or
applications running on them. Depending on the requests of the user on cloud computing is SaaS (Software
as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service) that has a proper load
balancing policy. When receiving the task, the cloud data center will have to allocate these tasks efficiently
so that the response time is minimized to avoid congestion. Load balancing should also be performed
between different datacenters in the cloud to ensure minimum transfer time. In this paper, we propose a
virtual machine-level load balancing algorithm that aims to improve the average response time and
average processing time of the system in the cloud environment. The proposed algorithm is compared to the
algorithms of Avoid Deadlocks [5], Maxmin [6], Throttled [8] and the results show that our algorithms
have optimized response times.
Dynamic Three Stages Task Scheduling Algorithm on Cloud Computing
Naglaa Sayed Abdelrehem, Fathi Ahmed Amer, Imane Aly Saroit,
Department of Information Technology, Faculty of Computer and Artificial Intelligence, Cairo University, Cairo, Egypt.
Information Technology Disaster Recovery Plans (DRPs) are becoming an essential component for any organization with IT infrastructure. However, DRPs varies in performance and cost; therefore, based on requirements and resources, an organization can design their DRP. Typically, DRPs depends on data and/or system replication, data needs to be backed up frequently, and a plan to restore the system to running state within the allowed time. Hence, DRP designer must know the needed business requirements in terms of recovery time objective (RTO) and recovery point objective (RPO). Then, the appropriate technical requirements will be set. At the same time, the cost factor can play a role in choosing the appropriate DRP. The industry has a widely accepted seven-tier system of how DRP can be designed. In this work, we design and implement a software tool that can simulate the IT DPR systems and therefore help designers to design, optimize, and test their design before it is physically implemented. This tool will run a simulated system with DRP specific design and the designer can exercise with the system to show it’s RTO, RPO, and cost that can significantly improve DRP design.
Comparative Analysis of Various Grid Based Scheduling Algorithmsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Scheduling Divisible Jobs to Optimize the Computation and Energy Costsinventionjournals
ABSTRACT : The important challenge in cloud computing environment is to design a scheduling strategy to handle jobs, and to process them in a heterogeneous environment with shared data centers. In this paper, we attempt to investigate a new analytical framework model that enables an existing private cloud data-center for scheduling jobs and minimizing the overall computation and energy cost together. Our model is based on Divisible Load Theory (DLT) model to derive closed-form solution for the load fractions to be assigned to each machines considering computation and energy cost. Our analysis also attempts to schedule the jobs such a way that cloud provider can gain maximum benefit for his service and Quality of Service (QoS) requirement user’s job. Finally, we quantify the performance of the strategies via rigorous simulation studies.
Job Resource Ratio Based Priority Driven Scheduling in Cloud Computingijsrd.com
Cloud Computing is an emerging technology in the area of parallel and distributed computing. Clouds consist of a collection of virtualized resources, which include both computational and storage facilities that can be provisioned on demand, depending on the users' needs. Job scheduling is one of the major activities performed in all the computing environments. Cloud computing is one the upcoming latest technology which is developing drastically. To efficiently increase the working of cloud computing environments, job scheduling is one the tasks performed in order to gain maximum profit. In this paper we proposed a new scheduling algorithm based on priority and that priority is based on ratio of job and resource. To calculate priority of job we use analytical hierarchy process. In this paper we also compare result with other algorithm like First come first serve and round robin algorithms.
Stochastic Scheduling Algorithm for Distributed Cloud Networks using Heuristi...Eswar Publications
Rule based heuristic scheduling algorithms in real time and cloud computing Systems employ for resource or task scheduling since they are suitable to implement for NP-complete problems. However, they are simple but there is much room to improve these algorithms. This study presents a heuristic scheduling algorithm, called High performance hyper-heuristic scheduling algorithm (HHSA) using detection operator, to find better scheduling solutions for real and cloud computing systems. The two operators - diversity detection and
improvement detection operators - are employed in this algorithm to determine the timing to determine the heuristic algorithm.. These two are employed to dynamically determine a low level heuristic that can be used to find better solution. To evaluate the performance of this method, authors examined the above method with several scheduling algorithms and results prove that Hyper Heuristic Scheduling Algorithm can
significantly decrease the makespan of task scheduling when compared with all other scheduling algorithms.
A novel high-performance hyper-heuristic algorithm is proposed for scheduling on cloud computing systems to
reduce the makespan. This algorithm can be applied to both sequence dependent and sequence independent scheduling problems.
Dynamic Scheduling of Data Using Genetic Algorithm in Cloud Computingijcoa
Cloud Computing is the utilization of pool of resources for remote users through internet that can be easily accessible, scalable and utilization of resources. To attain maximum utilization of resources the tasks need to be scheduled. The problem in scheduling is allocating the correct resources to the arrived tasks. Dynamic scheduling is that the task arrival is uncertain at run time and allocating resources are tedious as several tasks arrive at the same time. To avoid this scheduling problem, Genetic Algorithm is used. Genetic algorithm is a heuristic method that deals with the natural selection of solution from all possible solutions. Using genetic algorithm the tasks are scheduled according to the computation and memory usage. The tasks are scheduled dynamically. The execution time is reduced by parallel processing. The scheduled data is stored in cloud. By using GA to obtain global optimization.
Cloud computing is an emerging technology. It process huge amount of data so scheduling mechanism
works as a vital role in the cloud computing. Thus my protocol is designed to minimize the switching time,
improve the resource utilization and also improve the server performance and throughput. This method or
protocol is based on scheduling the jobs in the cloud and to solve the drawbacks in the existing protocols.
Here we assign the priority to the job which gives better performance to the computer and try my best to
minimize the waiting time and switching time. Best effort has been made to manage the scheduling of jobs
for solving drawbacks of existing protocols and also improvise the efficiency and throughput of the server.
A Review on Scheduling in Cloud Computingijujournal
Cloud computing is the requirement based on clients that this computing which provides software,
infrastructure and platform as a service as per pay for use norm. The scheduling main goal is to achieve
the accuracy and correctness on task completion. The scheduling in cloud environment which enables the
various cloud services to help framework implementation. Thus the far reaching way of different type of
scheduling algorithms in cloud computing environment surveyed which includes the workflow scheduling
and grid scheduling. The survey gives an elaborate idea about grid, cloud, workflow scheduling to
minimize the energy cost, efficiency and throughput of the system.
A HYPER-HEURISTIC METHOD FOR SCHEDULING THEJOBS IN CLOUD ENVIRONMENTieijjournal1
Currently cloud computing has turned into a promising technology and has become a great key for
satisfying a flexible service oriented , online provision and storage of computing resources and user’s
information in lesser expense with dynamism framework on pay per use basis.In this technology Job
Scheduling Problem is acritical issue. For well-organizedmanaging and handling resources,
administrations, scheduling plays a vital role. This paper shares out the improved Hyper- Heuristic
Scheduling Approach to schedule resources, by taking account of computation time and makespan with two
detection operators. Operators are used to select the low-level heuristics automatically. Conditional
Revealing Algorithm (CRA)idea is applied for finding the job failures while allocating the resources. We
believe that proposed hyper-heuristic achieve better results than other individual heuristics
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
Virtual Machine Migration and Allocation in Cloud Computing: A Reviewijtsrd
Cloud computing is an emerging computing technology that maintains computational resources on large data centers and accessed through internet, rather than on local computers. VM migration provides the capability to balance the load, system maintenance, etc. Virtualization technology gives power to cloud computing. The virtual machine migration techniques can be divided into two categories that is pre copy and post copy approach. The process to move running applications or VMs from one physical machine to another is known as VM migration. In migration process the processor state, storage, memory and network connection are moved from one host to another.. Two important performance metrics are downtime and total migration time that the users care about most, because these metrics deals with service degradation and the time during which the service is unavailable. This paper focus on the analysis of live VM migration Techniques in cloud computing. Khushbu Singh Chandel | Dr. Avinash Sharma "Virtual Machine Migration and Allocation in Cloud Computing: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29556.pdfPaper URL: https://www.ijtsrd.com/computer-science/computer-network/29556/virtual-machine-migration-and-allocation-in-cloud-computing-a-review/khushbu-singh-chandel
Locality Sim : Cloud Simulator with Data Localityneirew J
Cloud Computing (CC) is a model for enabling on-demand access to a shared pool of configurable
computing resources. Testing and evaluating the performance of the cloud environment for allocating,
provisioning, scheduling, and data allocation policy have great attention to be achieved. Therefore, using
cloud simulator would save time and money, and provide a flexible environment to evaluate new research
work. Unfortunately, the current simulators (e.g., CloudSim, NetworkCloudSim, GreenCloud, etc..) deal
with the data as for size only without any consideration about the data allocation policy and locality. On
the other hand, the NetworkCloudSim simulator is considered one of the most common used simulators
because it includes different modules which support needed functions to a simulated cloud environment,
and it could be extended to include new extra modules. According to work in this paper, the
NetworkCloudSim simulator has been extended and modified to support data locality. The modified
simulator is called LocalitySim. The accuracy of the proposed LocalitySim simulator has been proved by
building a mathematical model. Also, the proposed simulator has been used to test the performance of the
three-tire data center as a case study with considering the data locality feature.
LOCALITY SIM: CLOUD SIMULATOR WITH DATA LOCALITY ijccsa
Cloud Computing (CC) is a model for enabling on-demand access to a shared pool of configurable computing resources. Testing and evaluating the performance of the cloud environment for allocating, provisioning, scheduling, and data allocation policy have great attention to be achieved. Therefore, using cloud simulator would save time and money, and provide a flexible environment to evaluate new research
work. Unfortunately, the current simulators (e.g., CloudSim, NetworkCloudSim, GreenCloud, etc..) deal with the data as for size only without any consideration about the data allocation policy and locality. On the other hand, the NetworkCloudSim simulator is considered one of the most common used simulators because it includes different modules which support needed functions to a simulated cloud environment,
and it could be extended to include new extra modules. According to work in this paper, the NetworkCloudSim simulator has been extended and modified to support data locality. The modified simulator is called LocalitySim. The accuracy of the proposed LocalitySim simulator has been proved by building a mathematical model. Also, the proposed simulator has been used to test the performance of the
three-tire data center as a case study with considering the data locality feature
Load Balancing Algorithm to Improve Response Time on Cloud Computingneirew J
Load balancing techniques in cloud computing can be applied at different levels. There are two main
levels: load balancing on physical server and load balancing on virtual servers. Load balancing on a
physical server is policy of allocating physical servers to virtual machines. And load balancing on virtual
machines is a policy of allocating resources from physical server to virtual machines for tasks or
applications running on them. Depending on the requests of the user on cloud computing is SaaS (Software
as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service) that has a proper load
balancing policy. When receiving the task, the cloud data center will have to allocate these tasks efficiently
so that the response time is minimized to avoid congestion. Load balancing should also be performed
between different datacenters in the cloud to ensure minimum transfer time. In this paper, we propose a
virtual machine-level load balancing algorithm that aims to improve the average response time and
average processing time of the system in the cloud environment. The proposed algorithm is compared to the
algorithms of Avoid Deadlocks [5], Maxmin [6], Throttled [8] and the results show that our algorithms
have optimized response times.
Dynamic Cloud Partitioning and Load Balancing in Cloud Shyam Hajare
Cloud computing is the emerging and transformational paradigm in the field of information technology. It mostly focuses in providing various services on demand and resource allocation and secure data storage are some of them. To store huge amount of data and accessing data from such metadata is new challenge. Distributing and balancing of the load over a cloud using cloud partitioning can ease the situation. Implementing load balancing by considering static as well as dynamic parameters can improve the performance cloud service provider and can improve the user satisfaction. Implementation the model can provide dynamic way of resource selection de-pending upon different situation of cloud environment at the time of accessing cloud provisions based on cloud partitioning. This model can provide effective load balancing algorithm over the cloud environment, better refresh time methods and better load status evaluation methods.
A load balancing strategy for reducing data loss risk on cloud using remodif...IJECEIAES
Cloud computing always deals with new problems to fulfill the demand of the challenging organizations around the whole world. Reducing response time without the risk of data loss is a very critical issue for the user requests on cloud computing. Load balancing ensures quick response of virtual machine (VM), proper usage of VMs, throughput, and minimal cost of VMs. This paper introduces a re-modified throttled algorithm (RTMA) that reduces the risk of data hampering and data loss considering the availability of VM which increases system’s performance. Response time of virtual machines have been considered in our work, so that when migration process is running, data will not be overflowed in the VMs. Thus, the data migration process becomes high and reliable. We have completed the overall simulation of our proposed algorithm on the cloud analyst tool and successfully reduced the risk of data loss as well as maintains the response time.
Cost-Efficient Task Scheduling with Ant Colony Algorithm for Executing Large ...Editor IJCATR
The aim of cloud computing is to share a large number of resources and pieces of equipment to compute and store knowledge and information for great scientific sources. Therefore, the scheduling algorithm is regarded as one of the most important challenges and problems in the cloud. To solve the task scheduling problem in this study, the ant colony optimization (ACO) algorithm was adapted from social theories with a fair and accurate resource allocation approach based on machine performance and capacity. This study was intended to decrease the runtime and executive costs. It was also meant to optimize the use of machines and reduce their idle time. Finally, the proposed method was compared with Berger and greedy algorithms. The simulation results indicate that the proposed algorithm reduced the makespan and executive cost when tasks were added. It also increased fairness and load balancing. Moreover, it made the optimal use of machines possible and increased user satisfaction. According to evaluations, the proposed algorithm improved the makespan by 80%.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
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Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Optimized Assignment of Independent Task for Improving Resources Performance ...Ricardo014
Grid computing has emerged from category of distributed and parallel computing where the heterogeneous resources from different network are used simultaneously to solve a particular problem that need huge amount of
resources. Potential of Grid computing depends on my issues such as security of resources, heterogeneity of resources, fault tolerance & resource discovery and job scheduling. Scheduling is one of the core steps to
efficiently exploit the capabilities of heterogeneous distributed computing resources and is an NP-complete problem. To achieve the promising potential of grid computing, an effective and efficient job scheduling algorithm is
proposed, which will optimized two important criteria to improve the performance of resources i.e. makespan time & resource utilization. With this, we have classified various tasks scheduling heuristic in grid on the basis of
their characteristics.
Optimized Assignment of Independent Task for Improving Resources Performance ...ijgca
Grid computing has emerged from category of distributed and parallel computing where the heterogeneous resources from different network are used simultaneously to solve a particular problem that need huge amount of resources. Potential of Grid computing depends on my issues such as security of resources, heterogeneity of resources, fault tolerance & resource discovery and job scheduling. Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing resources and is an NP-complete problem. To achieve the promising potential of grid computing, an effective and efficient job scheduling algorithm is proposed, which will optimized two important criteria to improve the performance of resources i.e. makespan time & resource utilization. With this, we have classified various tasks scheduling heuristic in grid on the basis of their characteristics.
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VIRTUAL MACHINE SCHEDULING IN CLOUD COMPUTING ENVIRONMENT
1. International Journal of Managing Public Sector Information and Communication Technologies (IJMPICT),
Vol. 6, No. 4, December 2015
DOI : 10.5121/ijmpict.2015.6401 1
VIRTUAL MACHINE SCHEDULING IN CLOUD
COMPUTING ENVIRONMENT
Yousef Tohidirad1
, Siamak Abdezadeh2
, Zahed Soltani aliabadi3
, Abdolsalam
Azizi4
and Mohammad Moradi5
1
Department of Computer Engineering, Electronic Branch, Islamic Azad University,
Tehran, Iran
2
Department of Computer, Boukan Branch, Islamic Azad University, Boukan, Iran
3,4
Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia,
Iran
5
Department of Computer, baneh Branch, Islamic Azad University, baneh, Iran
ABSTRACT
Cloud computing is an upcoming technology in dispersed computing facilitating paying for each model as
for each user demand and need. Cloud incorporates a set of virtual machine which comprises both storage
and computational facility. The fundamental goal of cloud computing is to offer effective access to isolated
and geographically circulated resources. Cloud is growing every day and experiences numerous problems
such as scheduling. Scheduling means a collection of policies to regulate the order of task to be executed
by a computer system. An excellent scheduler derives its scheduling plan in accordance with the type of
work and the varying environment. This research paper demonstrates a generalized precedence algorithm
for effective performance of work and contrast with Round Robin and FCFS Scheduling. Algorithm needs
to be tested within CloudSim toolkit and outcome illustrates that it provide good presentation compared
some customary scheduling algorithm.
KEYWORDS
CloudSim, Virtual Machine, Cloud Computing, Scheduling, FCFS Scheduling
1. INTRODUCTION
To Use cloud computing technology users need just to take a regular PC, high speed internet
connection and a good browser and connect to their cloud. Two main reasons for using cloud
computing is to maximize performance and minimize costs [1, 2]. Cloud computing reduce heavy
hardware costs for companies. For example, we don’t need to buy a high capacity hard disks and
advanced processors. Furthermore, there is no need for physical storage space but only pay for
rent and put the information on the store tool and access our data. A cloud computing system is
also faster boot and setup because in that case computers have fewer programs and process that
will load into memory. The performance of this computer compared with other computing
systems is optimized with maximum performance [1, 3]. One of these methods to reduce the
overall cost of server consolidation is virtualization [1, 4] which is the most widely used method
in cloud computing as a cloud computing infrastructure [1]. Cloud computing focuses on creation
of virtualization, grid computing and web technologies. It is defined as internet centered
computing that provides software as service SaaS, platform as service PaaS, and infrastructure as
a service (ISaaS). Software app is made accessible by the cloud provider in SaaS. In IaaS the
computing infrastructure is offered as a service to the client in the shape of Virtual Machine
(VM). In PaaS an app creation platform is offered as a service to the developer to generate a web
based app [5]. All these services are made accessible on contribution basis through pay-as-you-
2. International Journal of Managing Public Sector Information and Communication Technologies (IJMPICT),
Vol. 6, No. 4, December 2015
2
use model to clients, in spite of their locality. Cloud computing is still under development stage
and characterize several concerns out of different challengers in cloud scheduling plays incredibly
vital role in identifying the efficient implementation [6].
Scheduling means to establish policies to regulate the sequence of task to be executed using a
processor system. There have been different forms of scheduling algorithm available in the
dispersed computing system, and work scheduling is part on them. The key benefit of job
scheduling algorithm is to attain a great execution computing and the most excellent system all
through. Scheduling similarly manages the existing computer memory and better scheduling plan
provides utmost application of resource [7].
Including important features for scheduling algorithms are distribution and planning of resources,
in section two the joint approach and framework to offer them. In the third section of the article,
mentioned Methods of planning algorithms and the simulation algorithm. Also in In the third
section, Is proposed overall prioritization algorithm for better performance and compare it with
the FCFS scheduling algorithm and Clearly presented and compared the effective performance of
the Prioritization algorithm against algorithm listed is shown. Conclusions will be discussed in
section Four.
2. PROPOSED FAME WORK AND METHODOLOGY
Resource distribution and scheduling of resources remains significant feature that impacts the
concert of a networking, analogous cloud computing and distributed computing [8]. Numerous
researchers have suggested a variety of algorithms for distributing, scaling and scheduling the
resources effectively in a cloud scheduling course can be produced into stages that is to say:
• Resource determining and filtering – Datacenter agent identifies resources available in
within the network and gathers status data associated to them.
• Resource assortment – aim resources are chosen based on particular factors of resource
and task. This is referred as deciding stage [5].
• Task submission – in this stage, job is submitted to the selected source.
According to this description, Tabel 1 and figure 1 shows the Result comparison of available
algorithms in Different datacenters.
TABLE 1. Result comparison of available algorithms [9]
Algorithms Datacenter_0 Datacenter_1 Datacenter_2
Algorithm to select host
with minimum PE’s
7179.2 debt 7179.2 debt 1025.6 debt
Dynamic VM allocation
with Clustering 5128 debt 5128 debt 3076.8 debt
3. International Journal of Managing Public Sector Information and Communication Technologies (IJMPICT),
Vol. 6, No. 4, December 2015
3
Figure 1. Performance comparison of the Proposed Algorithm having allocation Algorithm by similar input
parameter [9]
3. METHODS OF ALGORITHM SCHEDULING
A) First come first serve (FCFS): It is for parallel processing and targets the resource having the
least waiting line up time and is chosen for the received job. The Sim toolkit backs FCFS
scheduling plan for interior scheduling tasks. Distribution of app-specified VMs to Hosts within a
Cloud-based datacenter is the work of the virtual machine stipulated element [7]. The default
policy adopted by the VM stipulated is a simple policy that distributes a VM in FCFS method.
The limitations of First come first serve is that it is non preventative. The shortest errands which
are based at the back of the line-up must wait for long errands at the front to complete. It is
turnaround and reaction is fairly minimal [7].
B) Round Robin Scheduling (RS): RS algorithm targets on the equality. It uses the loop as it
queue to store tasks. Each task within the queue has similar implementation time and it will be
implemented as a result. If the task cannot be finished during its moment, it will be retained back
to the line-up waiting for the succeeding turn [10]. The benefit of Round Robin algorithm is that
each task will be implemented as a result and they do not have to be waited for the preceding one
to be finished. But is the weight is discovered to be heavy, Round Robin will take longer to finish
all the tasks. RR scheduling strategy will be supported by CloudSim toolkit for interior
scheduling of tasks. The disadvantage of Round Robin is that the prime work takes sufficient
period to for completion [11, 12].
C) Generalized Priority Algorithm: Client classify in accordance with the consumer demand. You
should classify the limitation such as memory, size, bandwidth scheduling and others. It is the
recommended strategy; the jobs are originally prioritized depending on their size in a way that
one with the largest size has largest score [10, 11, 12]. The virtual machine is similarly prioritized
in accordance to their MIPS quality in a way that one with highest MIPS ranks the highest.
Therefore, the main factor for ranking jobs is their size and MIPS for VM. This policy is doing
well than RR and FCFS scheduling [5].
4. International Journal of Managing Public Sector Information and Communication Technologies (IJMPICT),
Vol. 6, No. 4, December 2015
4
According to the method described, figure 2 shows Structure of reservation cluster-based cloud
computing approach.
Figure 2. Structure of reservation cluster-based cloud computing approach [2]
3.1. Algorithm Solutions
The algorithm shown in Figure 3, stores every appropriate Virtual Machine within VM List.
Figure 3. Storing every appropriate Virtual Machine within VM List
3.2. Various steps of algorithm solution
Step 1 – Generate VM to unlike Datacenter in accordance to computational strength of
physical/host server in provisions of its prize CPU, CPU speed, storage and memory.
Step 2 – Distribute cloudlet size in accordance to computational strength
Prev 99
Push first vertex
While stck 6 = Empty do
Get unvisited vertex adjacent close to
stack top
If no adjacent vertex then
If prev = 6 StackTop then
Copy all stack contents to VM List
End if
Else
Mark the node as visited
Push close vertex
End if
End while
5. International Journal of Managing Public Sector Information and Communication Technologies (IJMPICT),
Vol. 6, No. 4, December 2015
5
Step 3 – VM weight balance retain and index table of VMs, currently VM comprise zero
distribution [10].
Step 4 – Cloudlet bound in accordance to MIPS and particular length.
Step 5 – Largest length of cloudlet acquire largest MIPS of VM.
Step 6 – Datacentre agents sends the application to the virtual machine.
Step 7 – Update the obtainable resource.
According to the steps described, table 2 shows List of parameters are sorted according to
memory required and table 3 Available memory after allocation of processes to virtual machines.
TABLE 2. List of parameters are sorted according to memory required [13]
User
ID
Arrival
Process
Time
Deadline
I/O
request
Memory
request
Type of
service
6 82 7 93 4 1102 0
3 91 8 109 3 1143 1
5 17 8 27 1 1476 0
2 97 10 113 4 4541 0
9 16 2 24 4 10893 1
4 93 7 107 3 12554 1
7 38 8 53 0 15670 0
8 64 8 79 1 21749 1
10 22 8 34 2 22369 1
1 9 3 18 4 30875 0
TABLE 3. Available memory after allocation of processes to virtual machines [13]
Cloud No.
Arrival Virtual machine number related with cloud
1 2 3 4 5 6 7 8
1 12847 3776 10249 9629 1123 3200 3200 3200
2 3200 3200 3200 3200 3200 3200 3200 3200
3 3200 3200 3200 3200 3200 3200 3200 3200
4 3200 3200 3200 3200 3200 3200 3200 3200
5 3200 3200 3200 3200 3200 3200 3200 3200
6 3200 3200 3200 3200 3200 3200 3200 3200
3.3. Experiment and Evaluation
So as to confirm the algorithm, experiment on core (TM) i5 computer 2.6 GHz, Cloud Sim 3.0.3
simulator and Windows 7 platform. The Cloud Sim toolkit maintains reproduction of cloud
system elements for example virtual machines, data centers, scheduling, hosts and resource
provisioning strategies [7]. A tool kit is the use which opens the probability of assessing the
theory before software development within an environment where you can duplicate tests
available [11]. For comparison evaluation, tables are used to demonstrate implemented
generalized priority algorithm, FCFS, and Round Robin methodology.
6. International Journal of Managing Public Sector Information and Communication Technologies (IJMPICT),
Vol. 6, No. 4, December 2015
6
CONCLUSIONS
Scheduling is one of the greatest vital jobs in cloud computing background. Scheduling is very
much necessary to improve the server and resource utilization also to increases the performance
of the computer. The different scheduling algorithm methods discussed above effectively
schedules the computational jobs in cloud background. Priority remains a significant matter of
task scheduling in cloud backgrounds. The experiment is conducted for unreliable number of
VMs workload outlines. The experiment carried out is proportionate to Round Robin and FCFS
algorithm.
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