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Enhanced Genetic Algorithm based Task Scheduling in Cloud Computing

Enhanced Genetic Algorithm based Task Scheduling in Cloud Computing

Cloud computing is basically internet based computing while software, information and shared resources are provided to devices and computers on demand, like electricity grid. With the fusion of network technology and traditional computing technology such as distributed computing parallel computing, grid computing a cloud computing product is formed. Task scheduling is the major concern in the field of cloud computing. As the use of cloud computing increases, the burden on the cloud network also increases. So, it’s the duty of the scheduler to make cloud efficient to solve client's tasks. This work focuses on the same to achieve the objective of optimized task scheduling where improved genetic algorithm is proposed. Genetic algorithm is artificial intelligent based soft computing technique to optimize the process. Here in this work, genetic algorithm is enhanced using new fitness function based on mean and grand mean values. This optimization can be implemented on both ends, for job scheduling and resource scheduling. This will schedule the whole process and optimize as much as possible. The results analysis also proves the cloud system’s increased efficiency for task scheduling.
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A Review on Task Scheduling in Cloud Computing

A Review on Task Scheduling in Cloud Computing

Abstract— Cloud computing, rising in information technology (IT) industry suitable due to its mounting performance, directness, economical services compared to existing online computing and storage process. Cloud computing provides a enormous storage for facts and rapid computing services over the internet. Cloud computing refers to the relief of computing resources in excess of the Internet. Cloud computing is a form of distributed computing where services being provided by distant vendors through internet providing high piece gain to the users and also providing benefits to the Cloud Service Provider (CSP).To attain this goal many problems have to be faced. Task Scheduling is one of them which be disturbed with pointed for optimal (or near-optimal) real-time and predictive schedules subject to a number of constraints". This survey is done on various task scheduling categories of various task of scheduling algorithms.
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Parallel Particle Swarm Optimization for Task Scheduling in Cloud Computing

Parallel Particle Swarm Optimization for Task Scheduling in Cloud Computing

ABSTRACT : Cloud computing is the internet based computing where sources are accessed via online. These services have the ability to extend the provisioning of resources based on users demand. The user applications are submitted to the virtual machines for processing. So the mapping of user tasks to virtual machines plays a major role in efficient provisioning of resources. The task scheduling problem can‟t be solved in polynomial time. So for solving this kind of problem, the heuristics algorithms like particle swarm optimization are used. To minimize the execution time of particle swarm optimization, the parallel version of the algorithm is used.
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Time and Resource Efficient Task Scheduling in Cloud Computing Environment

Time and Resource Efficient Task Scheduling in Cloud Computing Environment

Task Scheduling is a critical problem in Cloud computing, because a cloud provider has to serve many users. So scheduling is the major issue in establishing Cloud computing systems. Job Scheduling of cloud computing refers to dispatch the computing tasks to resource pooling between different resource users according to certain rules of resource use under a given cloud circumstances. Resource management and job scheduling are the key technologies of cloud computing that plays a vital role in an efficient cloud resource management [4]. In cloud environment, huge number of tasks is executed simultaneously; an effective Task Scheduling is required to gain better performance of the cloud system. Various Cloud-based Task Scheduling algorithms are available that schedule the user’s task to resources for execution. Due to the novelty of Cloud Computing, traditional scheduling algorithms cannot satisfy the cloud’s needs, the researchers are trying to modify traditional algorithms that can fulfil the cloud requirements like rapid elasticity, resource pooling and on-demand self- service[5].
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Optimized Task Scheduling in Cloud Computing: A Survey

Optimized Task Scheduling in Cloud Computing: A Survey

Abstract: The increase of cloud computing is so exponential that it offers facts connection between special structures and devices. Due to this boom in connectivity and rapid utilization cloud network desires a statistics grid or computing grid comprising of different type of processing gadgets to perform the query this is despatched to the cloud network. This work provides a review on optimized undertaking scheduling in cloud computing environment. The main element of cloud computing is offering desirable response time for end users, that affords a primary impediment in achievement of cloud computing. All components should coordinate to deal with this mission. This can be handled through a suitable Task scheduling algorithm. So, there's a need of efficient mission scheduling method in implementation of cloud computing surroundings. Due to boom in era and increase in range of statistics facilities the venture dealing with ability of each information centres is foremost concern.
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A Review on Task Model and Task Scheduling in Cloud Computing

A Review on Task Model and Task Scheduling in Cloud Computing

When we are using cloud computing technology, we have to face a lot of new challenges. The main problem occur in cloud computing is task scheduling. So, the main aim of the scheduling is to utilize more and more resources, so that makespan get reduced. A comparative study of different scheduling methods ahs been explained above. A number of researchers has researched to provide a better solution for task scheduling.In this paper a number of scheduling algorithm of cloud environment based on different parameters like objective, scheduling factors their advantages and disadvantages has been discussed.
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Task Scheduling in Cloud Computing: Review

Task Scheduling in Cloud Computing: Review

Task scheduling is one of the most famous problems in cloud computing so; there is always a chance of modification of previously completed work in this particular field. The researchers at their own time performed their work according to their knowledge space and after some time their work had been carried out some other people. During scheduling they had considered various techniques and applied constraints but as the cloud computing is too vast that they had not been able to capture all aspects at the same time but they mentioned these facts that there is a chance of modification of algorithms and which part has to be modified.
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Particle Swarm Optimization Based Task Scheduling for Cloud Computing

Particle Swarm Optimization Based Task Scheduling for Cloud Computing

Abstract—Task scheduling is the important requirement in cloud computing, whole cloud computing facilities efficiency is dependent on task scheduling. In cloud computing, task scheduling by considering different parameters like make span, cost, scalability, time, reliability, availability, resource utilization, etc. it does the allocation of best suitable resources to the task that is to be executed. The main motive of research is to propose a new algorithm using particle swarm optimization technique which has better and less response time and do better task scheduling on VM’s. Using GA the problem of complexity and convergence increases the response time and does not balances the load among the VM’s. So, we introduce a new algorithm using particle swarm optimization that resolves these problems. The objective is to test the proposed approach through simulator and do the comparative analysis with genetic algorithm based approach on the following parameter (a) Makespan (b) Efficient utilization of VM and Do the comparison on above mentioned parameters in various scenarios.
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A survey for task scheduling in cloud computing

A survey for task scheduling in cloud computing

concept of cloud architecture and compares cloud computing with grid computingand aimed to pinpoint the challenges and issues of cloud computing and identified several challenges from the cloud computing adoption perspective. However, security and privacy issues present a strong barrier for users to adapt into cloud computing systems. Gajender Pal et al. (2014), provides a better understanding of the cloud computing and identifies important research issues in this burgeoning area of computer science. On demand or on pay per use of resource such as: network, storage and server these all facilities are provided by cloud computing through internet is called cloud computing. Although, cloud computing is facilitating the Information Technology industry, the research and development in this arena is yet to be satisfactory. GE Junwei and YUAN Yongsheng presents a genetic algorithm consider total task completion time, average task completion time and cost constraint. Compared with algorithm that only consider cost constraint (CGA) and adaptive algorithm that only consider total task completion time by the simulation experiment. Amit Agarwal and Saloni Jain (2014), presented a Generalized Priority algorithm for efficient execution of task and comparison with FCFS and Round Robin Scheduling. Algorithm should be tested in cloud Sim toolkit and result shows that it gives better performance compared to other traditional scheduling algorithm. Cloud is developing day by day and faces many challenges, one of them is scheduling. Scheduling refers to a set of policies to control the order of work to be performed by a computer system. A good scheduler adapts its scheduling strategy according to the changing environment and the type of task. Ekta Rani and Harpreet Kaur (Ekta Rani, 2017), followeda Raven Roosting Optimization Algorithm (RRO) is followed to light on the load balancing for task scheduling problems solution in cloud environment. Heterogeneity of birds, insects enroll in roosting. In raven Roosting, Roosts are information centers or can say servers and scrounge feature of common ravens inspired to solve problems. This technique is good enough to handle number of overloaded tasks transfer on Virtual Machines (VMs) by determining the availability of VMs capacity. Raven Roosting Optimization (RRO) random allocation of VMs to Cloudlets results huge change in makespan with respect to VM to which allocated.
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An Improved Task Scheduling algorithm Based on PSO for Cloud Computing

An Improved Task Scheduling algorithm Based on PSO for Cloud Computing

In [6] Virtual Machine-Based Task Scheduling Algorithm in a Cloud Computing Environment is proposed. This paper introduces a Greedy Particle Swarm Optimization (G&PSO) based algorithm to solve the task scheduling problem. It uses a greedy algorithm to quickly solve the initial particle value of a PSO derived from a virtual machine-based cloud platform. Initial solution found by using Greedy method does not yield good results. The updating threshold value will not find the best value of gbest as it depends on the initial solution of Greedy method.
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TASA: A New Task Scheduling Algorithm in Cloud Computing

TASA: A New Task Scheduling Algorithm in Cloud Computing

Abstract — Cloud computing refers to services that run in a distributed network and are accessible through common internet protocols. It merges a lot of physical resources and offers them to users as services according to service level agreement. Therefore, resource management alongside with task tim has direct influence on cloud networks’ performance and efficiency. Presenting a proper scheduling method can lead to efficiency of resources by decreasing response time and costs. This paper studies the existing approaches of task scheduling and resource allocation in cloud infrastructures and assessment of their advantages and disadvantages. Afterwards, a compound algorithm is presented in order to allocate tasks to resources properly and decrease runtime. The proposed algorithm is built according to conditions of compounding Min- min and Sufferage algorithms. In the proposed algorithm, task allocation between machines takes place alternatively and with continuous change of scheduling algorithms. The main idea of the proposed algorithm is to concentrate on the number of tasks instead of the existing resources. The simulation results reveal that the proposed algorithm can achieve higher performance in decreasing response time.
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Study of Task Scheduling Algorithms in the          Cloud Computing Environment: A Review

Study of Task Scheduling Algorithms in the Cloud Computing Environment: A Review

Abstract— Cloud computing is a recent advancement in the internet world .The internet world has been revolutionized by this provision of shared resources. Cloud service providers compete for scalability of virtualized resources dynamically. The performance and efficiency of cloud computing services always depend upon the performance of the user tasks submitted to the cloud system. Cloud services performance can be significantly improved by scheduling the user tasks. The cost emerging from data transfers between resources as well as execution costs must also be taken into consideration while optimizing system efficiency in scheduling. Moving applications to a cloud computing environment trigger the need for scheduling as it enables the utilization of various cloud services to facilitate execution. Service provider’s goal is to utilize the assets effectively and increase benefit. This makes task scheduling as a core and challenging issue in cloud computing. It is the process of mapping task to the available resource. This paper presents a detailed study of various task scheduling methods existing for the cloud environment.
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An Analysis of Priority, Length, and Deadline Based Task Scheduling Algorithms in Cloud Computing

An Analysis of Priority, Length, and Deadline Based Task Scheduling Algorithms in Cloud Computing

Abstract - The cloud computing can be simply stated as delivery of computing environment where different resources are delivered as a service to the customer or multiple tenants over the internet. The task scheduling mainly focuses on enhancing the efficient utilization of resources and hence reduction in task completion time. Task scheduling is used to allocate certain tasks to particular resources at a particular time instance. Many different techniques have been presented to solve the problems of scheduling of numerous tasks. Task scheduling improves the efficient utilization of resource and yields less response time so that the execution of submitted tasks takes place within a possible minimum time. This paper discusses the analysis of priority, length and deadline based task scheduling algorithms used in cloud computing.
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A Survey on Task Scheduling For Parallel Workloads in the Cloud Computing System

A Survey on Task Scheduling For Parallel Workloads in the Cloud Computing System

ABSTRACT: Cloud computing is a computing paradigm where applications, data, memory, bandwidth and IT services are provided over the Internet. Cloud computing is based on pay per usage model. Cloud service providers provide virtual resources to the cloud users. The ultimate goal of cloud service providers is to gain maximum profit and use resources efficiently. Scheduling refers to a set of policies to control the order of work to be performed by a system. Task scheduling plays vital role in cloud computing system to manage heavy load or traffic. Efficient task scheduling improves resource utilization, response time and also meets user requirements. In this paper, Survey on various task scheduling methods for parallel workloads is made.
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Title: A REVIEW ON TASK SCHEDULING IN MOBILE CLOUD COMPUTING ENVIRONMENT

Title: A REVIEW ON TASK SCHEDULING IN MOBILE CLOUD COMPUTING ENVIRONMENT

ABSTRACT: Cloud computing is a recent and upcoming technology which includes various areas. Due to some inherent defects of mobile devices, such as limited battery energy, insufficient storage space, mobile applications are confronted with many challenges in mobility management, quality of service (QoS) insurance, energy management and security issues, which has stimulated the emergence of many computing paradigms, such as Mobile Cloud Computing (MCC), Fog Computing, etc. Mostly one network application can be decomposed into fine-grained tasks which consist of sequential tasks and parallel tasks. With the assistance of mobile cloud computing, some tasks could be offloaded to the cloud for speeding up executions and saving energy. Maintaining energy conservation the efficiency of energy has become a major problem with increased usage of devices consuming more energy due to MCC paradigms allow to offload some tasks to the cloud for execution. To manage this problem task are schedule in both at the mobile device and in the mobile cloud. Task scheduling is taken as the factor to reduce consumption of energy. Tasks can be assigned and scheduled based on the algorithms and so energy can be conserved.
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Enhanced Max-min Task Scheduling Algorithm
in Cloud Computing

Enhanced Max-min Task Scheduling Algorithm in Cloud Computing

Cloud Computing is the use of computing resources (Hardware and Software) that are delivered as a service over a network (typically the internet). It supplies a high performance computing based on protocols which allow shared computation and storage over long distances. In cloud computing, there are many tasks requires to be executed by the available resources to achieve best performance, minimal total time for completion, shortest response time, utilization of resources etc. Because of these different intentions, we need to design, develop, propose a scheduling algorithm to outperform appropriate allocation map of tasks on resources. A unique modification of Improved Max-min task scheduling algorithm is proposed. The algorithm is built based on comprehensive study of the impact of Improved Max-min task scheduling algorithm in cloud computing. Improved Max-min is based on the expected execution time instead of completion time as a selection basis. Enhanced (Proposed) Max-min is also based on the expected execution time instead of completion time as a selection basis but the only difference is that Improved Max-min algorithm assign task with Maximum execution time (Largest Task) to resource produces Minimum completion time (Slowest Resource) while Enhanced Max-min assign task with average execution time (average or Nearest greater than average Task) to resource produces Minimum completion time (Slowest Resource).
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Survey of Task Scheduling Algorithms in Cloud Computing

Survey of Task Scheduling Algorithms in Cloud Computing

ABSTRACT: Cloud computing means storing and accessing data and programs over the internet Instead of our computer’s hard drive. There are number of issues define in task scheduling, such as performance, cost, execution time, security and privacy. In these types the main drawback is allocation time is large. This can be overcome with the help of environment types i.e. Static and Dynamic allocation of VMs. Working on the existing QoS based algorithm which is integrated with the improved shortest job Task grouping algorithm i.e. tasks are assigned to the resources in the groups.
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A Survey On Various Types Of Task Scheduling   Algorithm In Cloud Computing Environment

A Survey On Various Types Of Task Scheduling Algorithm In Cloud Computing Environment

Abstract: Now a days, Cloud computing has become an significant and most popular computing model that usually supports on demand servic es. Cloud Computing provides its services on pay-as-you-go basis .By using cloud computing resources expeditiously and by reducing in managing time and cost and increasing the outcome of the project is the main idea of cloud service provider. Therefore, using effective clo ud scheduling algorithms is still main concern in cloud computing. Task scheduling is a pivotal part in the field of the cloud environment. In task scheduling user requests for certain task, then tasks are scheduled to certain resources at a specific exemplification of time. Basically task scheduling mainly f ocuses to diminish the make span and lengthen the resource utilization. Task scheduling is an Non Polynomial-Complete problem. There are lots of subsisting trail-and-error techniques for task scheduling till now but more amelioration and rectification is needed for better execution and t o increase the efficiency of task scheduling till now, there is no combined study of task scheduling mechanism in cloud computing which describes its parameter, pros, cons, algorithm. This paper mainly emphasis on explaining Comparison on different Task scheduling algorithm in cloud computing adaptive
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Cost Based Task Scheduling in Cloud Computing

Cost Based Task Scheduling in Cloud Computing

designed by various researchers which address the problem of task scheduling effectively and efficiently. With the passage of time and continuous research, almost every factor which increases the performance of the cloud, has been considered minutely, but cost was the only factor which has not been taken seriously. Cost plays an important role for the organization because at the end which matters the most to set up any business, is money. To measure the cost, there is need to work upon some parameters like the cost of resources, CPU time, turnaround time etc. In this paper, an algorithm has been proposed which takes care of the cost of these primary factors and the overall cost of the activity. ABC has been considered to be the best technique to schedule the task with the consideration of cost but there is always hope of more improvement. The implementation of the algorithm of ABC has been compared with the new algorithm, which has been proposed in this paper.
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TASK SCHEDULING IN CLOUD COMPUTING

TASK SCHEDULING IN CLOUD COMPUTING

A new Cloud scheduler based on Ant Colony Optimization is the one presented by Cristian Mateos and et.al [3]. The goal of our scheduler is to minimize the weighted flowtime of a set of PSE jobs, while also minimizing Makespan when using a Cloud. In the ACO algorithm, the load is calculated on each host taking into account the CPU utilization made by all the VMs that are executing on each host. This metric is useful for an ant to choose the least loaded host to allocate its VM.Parameter Sweep Experiments (PSE) is a type of numerical simulation that involves running a large number of independent jobs and typically requires a lot of computing power. These jobs must be efficiently processed in the different computing resources of a distributed environment such as the ones provided by Cloud. Consequently, job scheduling in this context indeed plays a fundamental role. In this algorithm, Makespan and flowtime are evaluated. Evaluation results of this metrics show that ACO performance better than two other (Random and Best effort) algorithms.
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