Top PDF Scheduling the Tasks in Cloud Computing to Enhance the System Performance

Scheduling the Tasks in Cloud Computing to Enhance the System Performance

Scheduling the Tasks in Cloud Computing to Enhance the System Performance

Cloud Computing is a crucial ingredient of progressed computing systems. Computing concepts, technology and architectures have developed and tightened in the last decades. Many aspects are subject to technological evolution and revolution. Cloud Computing is a computing technology that is rapidly strengthening itself as the next step in the development and deployment of enhancing the number of distributed application.Cloud computing is a kind of supercomputing in an Inter connected mode. It is a type of shared foundation, which simply puts the huge system pools together by using various means: virtualization, distribution etc. It gives users a plenty of storage, networking and computing resources in the cloud computing environment over Internet. Users can put a lot of information and also he can access lot of information.
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Evolutionary Algorithm Based Multi-Objective Tasks Scheduling Algorithm in Cloud Computing

Evolutionary Algorithm Based Multi-Objective Tasks Scheduling Algorithm in Cloud Computing

In today’s world, cloud computing is the hottest emerging area in field of Information technology [1]. Cloud computing is a service based, on-demand, pay per use model consisting of an inter-connected and virtualizes resources delivered over internet. In the cloud platform, task scheduling is the most important concern that aims to ensure that user’s requirement are properly and correctly satisfied by cloud infrastructure. Basically, scheduling is the process of mapping or assigning task to the available resources after looking the characteristics of task. An efficient scheduling mechanism should meet user’s requirement and helps service provider to provide good quality of service, so as to enhance the overall system performance.
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Efficient QoS Based Tasks Scheduling using Multi-Objective Optimization for Cloud Computing

Efficient QoS Based Tasks Scheduling using Multi-Objective Optimization for Cloud Computing

In today‟s world, cloud computing is the hottest emerging area in field of Information technology [1]. Cloud computing is a service based, on-demand, pay per use model consisting of an inter-connected and virtualizes resources delivered over internet. In the cloud platform, task scheduling is the most important concern that aims to ensure that user‟s requirement are properly and correctly satisfied by cloud infracture. Basically, scheduling is the process of mapping or assigning task to the available resources after looking the characteristics of task. An efficient scheduling mechanism should meet user‟s requirement and helps service provider to provide good quality of service, so as to enhance the overall system performance.
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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

Task scheduling is the process of allocating the resources to the particular job in specific time. The objective of scheduling is to maximize the resource utilization. Minimizing the waiting time is the goal of scheduling. A betterscheduling algorithm yields better system performance. In the cloud there are numerous and distinct resources available. The cost of performing tasks in cloud depends on which resources are being used so the scheduling in a cloud environment is different from the traditional scheduling. In a cloud computing environment task scheduling is a biggest and challenging issue. Task scheduling problem is an NP-Complete problem. A lot of heuristic scheduling algorithms hasbeen introduced, but more improvement is needed to make the system faster and more responsive. The traditional scheduling algorithms like FCFS, SJF, RR, Min-Min, and Max-Min algorithms are not the much better solution to scheduling problems with cloud computing. So we need the better solution to this heuristic problem.
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Scheduling of Heterogeneous Tasks in Cloud Computing using Multi Queue (MQ) Approach

Scheduling of Heterogeneous Tasks in Cloud Computing using Multi Queue (MQ) Approach

For scheduling the task, round robin algorithm was mainly used and its performance is highly dependent on Quantum size. There is a problem of performance degradation with respect to average waiting time (AWT), average turnaround time (ATT) and number of context switches (NCS) occurring in round robin scheduling algorithm. To overcome this problem, an improved Dynamic Round Robin Scheduling Algorithm Based on a Variant Quantum Time was developed by Ahmed Alsheikhy, et al. [7]. The main aim of this algorithm was to improve the overall system performance and maximize the throughput of system with minimization of average waiting time, average turnaround time and number of context switching. By choosing large quantum size, maximum number of task completed their execution with minimization of overhead occurring during context switching.
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Fault tolerant based hyper heuristic algorithm for task scheduling in 
		cloud 

Fault tolerant based hyper heuristic algorithm for task scheduling in cloud 

Cloud computing is one of the recent emerging technologies for providing classy services by means of the Internet based on the requirements of the users. Numerous efficient scheduling algorithms are required to make a valuable use of terrific capabilities of the cloud environment. The aim of task scheduling is to schedule the tasks within the given deadline to achieve minimum makespan. The heuristics scheduling algorithm schedules the tasks on cloud by multiple iterations. The diversity revealing and improvement revealing operators are used to determine which low-level heuristic is to be used for finding enhanced solutions in task scheduling. The heuristics task scheduling should be fault tolerant to overcome the failure of tasks. The proposed Fault Tolerant Based Hyper Heuristic Algorithm (FT-HHA) provides fault tolerance in task scheduling by using task replication and task resubmission. FT-HHA schedules the tasks within the given deadline even in the occurrence of failures. The experiments were carried out in a simulated cloud computing environment by scheduling tasks in the existence of malfunction which are generated randomly.
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Evaluation of Tasks Allocation, Scheduling and Ranking Techniques in a Cloud Computing Environment

Evaluation of Tasks Allocation, Scheduling and Ranking Techniques in a Cloud Computing Environment

less management and interactions with service providers. Cloud computing may enhance the availability of IT resources, and provides many advantages over other computing techniques. Users can utilize the IT infrastructure with Pay-per-Use-On-Demand mode; as this may benefit and reduce the cost for buying the vacant physical resources. For these various reasons, organizations desire to shift towards IT solutions, that contain cloud computing, since they just need to pay for the resources on consumption basis. Also, organizations can easily satisfy the requirements of varying markets to confirm that they are responding to customers’ needs [1]. Cloud computing emerged as a business necessity, being described by the idea of just using the infrastructure without managing it. Although this idea appeared initially in the academic area, but recently, it was transferred into industry by companies like Microsoft, Amazon, Google, Yahoo! and Salesforce.com. So, it is easier for new beginners to enter the market, as the cost of the infrastructure is diminished. Also, developers can focus on the business value instead on the initial budget. The clients of commercial clouds have the ability to rent computing power (virtual machines) or storage space (virtual space) dynamically, depending on the needs of their business. With the discovery of this technology, users can reach heavy applications through lightweight portable devices, like mobile phones, PCs and PDAs. Clouds form the new direction in the evolution of the distributed systems, as the predecessor of cloud was the grid. The user does not need special knowledge or expertise to manage the infrastructure of clouds; as it offers only abstraction. Also, it can be implemented as a service of an Internet with high scalability, higher throughput, quality of service and high computing power. Cloud computing suppliers deliver common online business applications, which are reached from servers via web browser [2].
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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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Priority Of Overflow Tasks To Increase Performance Of Mobile In Cloud Computing

Priority Of Overflow Tasks To Increase Performance Of Mobile In Cloud Computing

The pressure between asset hungry applications, for example, confront apperception, characteristic dialect preparing, intelligent gaming, and increased credibility, and asset and vitality compelled portable creations represents a considerable test for present and future versatile stage advancement. Portable distributed computing, where versatile contraptions [8] can offload some computational occupations to the cloud is imagined as a promising way to deal with address such a test . The attributes of portable creations and remote system makes the usage of versatile distributed computing more astounded than stationary mists.
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An Improved Priority Based VM Placement Approach for the Cloud Environment

An Improved Priority Based VM Placement Approach for the Cloud Environment

In this paper [10], they proposed aim of load balancing as “to remove overload of any of the resources, maximize throughput, and minimize response time”. According to G. Shobana et al. for achieving these we need to do load balancing properly. In the paper they introduced the preemptive task scheduling algorithm that almost abbreviate make span which observe honeybee’s foraging behavior. Aim of this algorithm is to maximize throughput and minimize latency by priority of the tasks. In this paper [11], author proposed an load balancing approach for the cloud. According to this approach important role of algorithm to provide useful mechanisms for appointing the client’s request to usable cloud nodes. These way wish to increase the cloud performance and bring efficient services for cloud user. In this paper they investigate dissolve issue of different load balancing algorithm and task scheduling in cloud computing. They hash out and comparison these algorithm to offer an overview of the latest approaches in this field.
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Greedy scheduling of tasks with time constraints for energy-efficient cloud-computing data centers

Greedy scheduling of tasks with time constraints for energy-efficient cloud-computing data centers

In this paper, we formulated the task assignment for a data center as an integer programming optimization problem and proved the average task response time is bounded with an optimized number of active servers. We proposed a greedy task-scheduling scheme, the most- efficient-server-first scheduling, to reduce energy con- sumption of data center servers. The proposed MESF scheduling scheme schedules tasks to the most energy- efficient servers of a data center. This scheme minimizes the average task response time and, at the same time, min- imizes the server-related energy expenditure. We showed that the system using MESF is weakly stable under i.i.d. task arrivals with an exponential distribution. We eval- uated and compared the performance of the proposed scheme with that of a random-based task-scheduling scheme using Matlab simulation. Our simulation results show that a data center using the proposed MESF task- scheduling scheme saves on average over 70 times that of a data center using a random-based task-scheduling scheme. The proposed scheme saves energy at the cost of longer task response times, albeit within the maximum constraint.
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Abstract: Task scheduling plays the key role systems in cloud computing. Scheduling of tasks cannot be

Abstract: Task scheduling plays the key role systems in cloud computing. Scheduling of tasks cannot be

Cloud computing is a new computing mode. It is similar to utility computing which involves a large number of computers connected through communication n/w. Cloud computing is trend to provide service as resources including hardware, software, network etc. Every service is provided over network that require high speed of network and persistence connection where its services are distributed over the network according to architecture and geo-location. It is based on pay as you go model, means it depends on matrices like usability, durability, cost, load etc. So that consumer does not need to buy any hardware, software etc. The main goal of cloud computing is to achieve higher throughput, availability, scalability, consistency guarantees, and usability, fault tolerance etc. used distributed resources [1]. Cloud computing resources should able to solve large scale of computation problems. Cloud computing uses characteristics of Client–server model, Grid computing, Peer-to-peer, Mainframe computer, Utility computing to provide better services like gaming, tons of computation, message passing, network etc. Cloud computing has an advantage of delivered a flexible, very high performance, pay-as-you-go, on-demand service. Operators should guarantee to the subscribers and stick to the Service Level Agreement. Google adopts Map-Reduce scheduling mechanism scheduling algorithms are relatively simple (First fit etc.). FIFO, default algorithm performs not so well for short jobs. Besides, Facebook proposes fair share scheduler; Yahoo raises computation ability scheduler. However, these scheduling algorithms cannot work out a better scheduling scheme. In fact, tasks scheduling in cloud is a NP complement problem with time limit. That is to say, it is seldom impossible to search out a reasonable solution in polynomial time. To improve performance of cloud computing, efficient task scheduling and resource management is required.
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BAT ALGORITHM FOR ROUGH SET ATTRIBUTE REDUCTION

BAT ALGORITHM FOR ROUGH SET ATTRIBUTE REDUCTION

Resources in cloud computing environment are heterogeneous. To integrate the resources effectively and schedule users’ tasks is the key problem. The goal of resource scheduling is to coordinate various resources and manage them reasonably, optimizing resource scheduling according to task demands submitted by users. Resource scheduling consider- ing QoS constraints from the aspect of user and system load balance can make both of them be satisfied. Quality service is a measure of cloud users’ needs [8], QoS model is a extending vector, it can be described from many aspects such as complete time, cost, extension, throughput etc. They estimate QoS from different aspects. In this paper, multiple QoS constrains can be divided into three parts, they are complete time, cost and load balance. The front two parts are constraint indexes of user, the third one is system constraint index.
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MULTIPLE VIRTUAL MACHINE IN A CUMULATIVE SERVER USING XEN BALLOON DRIVER BY IMPLEMENTING REAL TIME TASK ORIENTED TASK SCHEDULING ALGORITHM

MULTIPLE VIRTUAL MACHINE IN A CUMULATIVE SERVER USING XEN BALLOON DRIVER BY IMPLEMENTING REAL TIME TASK ORIENTED TASK SCHEDULING ALGORITHM

Cloud is like a virtual supercomputer. However, we need to consider about many conditions such as network status and resource status because the members of Cloud are connected by networks. Cloud is also a heterogeneous system. Organize independent tasks are more complicated. In order to utilize the power of Cloud computing, we need a dynamic job scheduling algorithm to assign jobs to resources. This paper focuses on the efficient job scheduling considering the completion time of jobs in a Cloud environment.
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Hybrid Starling Social Spider Algorithm for Energy and Load Aware Task Scheduling in Cloud Computing

Hybrid Starling Social Spider Algorithm for Energy and Load Aware Task Scheduling in Cloud Computing

Abstract: The efficiency of the cloud-based systems is greatly relying on the task scheduling algorithm which affects the performance parameters such as makespan, response time, degree of imbalance and cost. In recent years, the energy efficiency is also considered as another challenging issue which affects the efficiency of cloud computing systems. This paper proposes a Hybrid Starling Social Spider Algorithm (Starling-SSA) for Energy and Load Aware Task Scheduling in cloud computing. The Starling-SSA is designed as a hybrid algorithm inspired by the intelligent behavior of social spider and the collective response behavior of starling birds. The foraging behavior of spider is implemented to identify the best VMs for the given task with minimum makespan and degree of imbalance. In addition to this, the distance factor is incorporated inspired by starling flock distance in order identify the closeness of VM pairs and avoids the VMs that are far away, thereby VMs can be limited during the searching process. This will greatly reduce energy consumption by taking only VMs that are belongs to the distance factor. The performance metrics such as makespan, degree of imbalance and energy efficiency are evaluated against the existing algorithms such as EATS, CBAT and HC-ACO. The results presents a significant improvements when comparing to the existing algorithms.
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A Survey Of The Results Of The Qos Parameters In The Job Scheduling To Allocate Virtual Machines In The Cloud

A Survey Of The Results Of The Qos Parameters In The Job Scheduling To Allocate Virtual Machines In The Cloud

Job scheduling model and other models of such algorithms are the burgers. The algorithm is a combination of neural network models and defects Berger and Berger, on behalf of the model. In this work, jobs are based on various parameters such as bandwidth, memory, time of completion and exploitation of resources. Users neural network classification tasks to be transferred. The neural network consists of an input layer, hidden layer and output layer. With the help of hidden layers, businesses with resources equal weight. The performance of the system using the efficient use of bandwidth, reducing the completion time, which in turn improves the use of resources has been improved [3].
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Tasks Distribution Strategy based on Cluster in MWfSCC

Tasks Distribution Strategy based on Cluster in MWfSCC

Cloud computing technology offers a new way to enable massive amounts of data sets to work together, since it supplies a pool of abstracted, virtualized, dynamically-scalable, highly available, and configurable and reconfigurable computing resources (e.g., networks, servers, storage, applications, data) [1]. However, as solving problems becomes more and more complex, especially the exponential growth of data sets, the data movements is a challenge for the sake of network bandwidth limitation. In this way, migrating workflow is an emerging technology that applies mobile agent technology to workflow management, which deploys an agent (called CMI) to fulfill several tasks by migrating to other computers (called work machines) [2-4]. By taking advantage of cloud computing technology, migrating workflow system based on cloud computing paradigm (MWfSCC) could gain a wider utilization. MWfSCC obtains so many advantages in system performance, such as easy interaction, intelligent decision making, especially in distributed, dynamic and unpredictable environment [5-6].
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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 Efficient Fully Dynamic Algorithm to Optimize the Task Scheduling Activities in Cloud Systems

An Efficient Fully Dynamic Algorithm to Optimize the Task Scheduling Activities in Cloud Systems

Abstract— The model of cloud computing has been evolved as a very popular and interesting model that concentrates on the on demand services very efficiently whenever they are needed by cloud terminals. In a quite different computing style, the delivery of massively scalable resources to the end users/clients/customers is carried out ‘as a service’ within the cloud systems using internet as a communication channel. In the cloud systems, the selection of the best suitable resources for the execution of arriving tasks is decided by the used scheduling strategies after considering some static and dynamic behaviour and the restrictions applied on them. From the users’ point of view, some other issues like task execution cost or task completion time may become essential parameters for deciding the scheduling algorithm efficient. The service providers always try to provide the resources in optimized manner so that the utilization of resources may be optimum and the resource potential might be left minimum. This paper proposes an efficient scheduling algorithm by which these big challenges may be addressed efficiently in the cloud systems. The arriving tasks would be bound after measuring them on the basis of their requirement like minimum task execution cost or minimum task completion time and their priority levels. The selection of the resources would be made on the basis of greedy approach based task constraints. This proposed work has been implemented, tested, validated and verified on a simulator. Results Show the effectiveness and correctness of the proposed framework and come out with a significant improvement over sequential scheduling algorithms.
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Load Balancing in Heterogeneous Cloud Environments by Using PROMETHEE Method

Load Balancing in Heterogeneous Cloud Environments by Using PROMETHEE Method

We suggest that load balancing in cloud computing can be formulated as a multi-criteria decision making (MCDM) problem and then use PROMETHEE decision making algorithm to solve it. The proposed algorithm uses resources' information to compute the desired criteria (load balancing) and solve the problem. Then it directs the Abstract: Efficient Scheduling of tasks in a cloud environment improves resources utilization thereby meeting users' requirements. One of the most important objectives of a scheduling algorithm in cloud environment is a balanced load distribution over various resources for enhancing the overall performance of the cloud. Such a scheduling is complex in nature due to the dynamicity of resources and incoming application specifications. In this paper, we employ PROMETHEE decision making model to design a scheduling algorithm, called PROMETHEE Load Balancing (PLB).This paper formulates the load balancing issue as a multi-criteria decision making problem and aims to achieve well-balanced load across virtual machines for maximizing the overall throughput of the cloud. Extensive simulation results in CloudSim environment show that the proposed algorithm outperforms existing algorithms in terms of load balancing index (LBI), VM load variation, makespan, average execution time and waiting time.
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