Cloud Computing and Scheduling

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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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A comparative study on scheduling Algorithms in cloud computing

A comparative study on scheduling Algorithms in cloud computing

of a task This scheduling enhances the execution of the system and makes traverse of all the tasks. It has better load balance of nodes. A Community Cloud Oriented Workflow System Framework and its Scheduling Strategy can bolster the quick cooperation component with high productivity. Aggregated– DAG scheduling for work stream augmentation in Heterogeneous Cloud Computing algorithm minimizes make traverse, by conglomerating numerous occupations utilizing good scheduling, and a close ideal throughput can be accomplished. Tending to Resource Management in Grids through Network Aware Meta Scheduling. In Advance algorithm lets the system make rescheduling of undertakings already scheduled similarly as a BoT. To do that, the jobs are rescheduled by its begin time rather than by its arrival time. Consequently, the reallocation of those tasks will make less discontinuity into assets. In light of the above examination it can be inferred that influence traverse to can be diminished by gathering the assignments. Since cloud computing systems have a high level of capriciousness as for asset accessibility in future as the cloud size increases, there is a requirement for better scheduling algorithms.
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A Dynamic Scheduling Scheme for Cloud Computing

A Dynamic Scheduling Scheme for Cloud Computing

Abstract— Cloud computing, the concept of accessing the resources being stored at some remote location, using them for performing some task required by the user, who may be the end user or the cloud user, rather than the data been stored at the personal computer or a hardware device which is handheld. Cloud computing, the name by itself has got a reach among both the technical and non-technical users, is an emerging area of growth these days. Users have started to migrate from using the normal computing environments to the cloud computing environments. In the same time it has become mandatory to serve the user a better service on the newly developing environment. It is becoming compulsory to provide a reliable environment with all kind of flexibilities to the user. In this paper, a dynamic scheduling scheme for cloud computing is discussed. Considering the resource provisioning as the main issue to be address, the scheduling is being considered as the context. Here we use a dynamic scheduling scheme considering the makespan as the metric. Algorithms like min-min and round robin are compared with respect to the proposed scheme. A real time scenario based model named Berger model [15] is taken in account of comparison with the existing conventional algorithms. By considering and evaluating all the above given constraints and strategies, a makespan based dynamic scheduling scheme for cloud computing is being proposed.
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A Review on Task Scheduling in Cloud Computing

A Review on Task Scheduling in Cloud Computing

R. Ramesh Kannan, et.al [6] proposed that cloud computing latest technology gives the excellent possibilities to solve a systematic difficulties and many queries that is used to finish the work economically. In present invention the work get neglected due to users quality of services and it combine with elasticity and heterogeneity as various principles in computing assets. In this paper, the resource provisioning and scheduling strategy for systematic workflows on infrastructure as a service (IAAS) cloud were presented. In this strategy, wise use two algorithms namely meta – heuristic optimization technique and particle swarm optimization (PSO), which aims to reduce overall execution cost and fulfilling the consumer defined deadline. Thus it advances better than the current state – of –the – art algorithms.
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A Survey On Task Scheduling Algorithms In Cloud Computing

A Survey On Task Scheduling Algorithms In Cloud Computing

There are many parameters based on which algorithms have been proposed. These parameters if considered improve the utilization of cloud resources. Scheduling in case of mobile cloud computing is well researched. Task scheduler model [1] for mobile cloud computing is one of the algorithm that focused on reducing energy consumption and monetary cost in case of deployment in public cloud and energy consumption parameter in case of deploying in private cloud. Most algorithms consider CPU and memory as important resource; the proposed heuristic approach [2] takes bandwidth to load tasks to resources as constraint. Here each task is processed before actual allocation. The algorithm effectively utilizes memory, bandwidth and CPU when compared with the existing algorithms and gives 50% less response time. . For scheduling in heterogeneous cloud environment, a map reduce scheduling algorithm [3] was proposed that considered job deadline unlike other map reduce
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Optimized Task Scheduling in Cloud Computing: A Survey

Optimized Task Scheduling in Cloud Computing: A Survey

Today’s age is the age of technology. Technology is growing at a totally speedy charge, every and the whole lot is getting connected. Cloud computing has attracted a whole lot interest these days from each enterprise and academia. However, the size and surprisingly dynamic nature of cloud utility imposes enormous new demanding situations to useful resource management. Thus, efficient aid scheduling schemes is still a task. As a new computing version, cloud computing has converted the IT industry with its developing utility and popularization. Though cloud computing gives considerable opportunities, those are many undertaking faces in its improvement process. This research, introduces Task Scheduling strategies and Load Balancing techniques to improve the cloud assets. With the immense growing business areas, distributed computing has all the earmarks of being the main alternative to meet their extending needs. A cloud supplier initially builds up a processing framework called cloud, where a couple of virtual machines are interconnected through this; the provider shapes the undertaking of the customers. Distributed computing is certifiably not a respectful model to offer the customer to a typical pool of configurable processing assets that can be promptly given and discharged low care effort or administration will consider the particular errand planning [7] of better execution registering approaches.
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Survey of Task Scheduling Algorithms in Cloud Computing

Survey of Task Scheduling Algorithms in Cloud Computing

 Rajiv Ranjan and Rajkumar Buyya Hosting Internet-based application services. These applications have different composition, configuration, and deployment requirements. The simulation framework has the following novel features: (i)support for modelling and instantiation of large scale Cloud computing infrastructure, including data centers on a single physical computing node and java virtual machine; (ii) a self-contained platform for modelling data centers, service brokers, scheduling, and allocations policies; (iii) availability of virtualization engine, which aids in creation and management of multiple, independent, and co-hosted virtualized services on a data center node.[7]
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Implementation of Improved Multi Queue Job Scheduling Algorithm (IMQJSA) for Load Balancing in Cloud Computing

Implementation of Improved Multi Queue Job Scheduling Algorithm (IMQJSA) for Load Balancing in Cloud Computing

Cloud computing term is used for the delivery of hosted services over the internet. The main advantage to utilize this technology is to pay as you go. On the basis of rental charges user will use cloud service by demanding from the cloud service provider. The dependency of cloud computing is based on the main three parameters viz. abstraction, encapsulation and isolation of the resources on the demand basis. Several machines are in general connected with cloud data centers and the distributed nature approach is used by implementing any cloud strategy on cloud servers. Majorly, grid computing is work behind it. The main purpose to utilize this is it consumes less power of energy when especially operates on a large scale and can be easily managed by the utilization of centralized server approach. That ultimately results show the overall energy saving [3]. There are some important factors studied by the authors who are very important and more helpful to create an ambiance for cloud. For implementing ambiance MATLAB tools are used by the authors in this research paper. In this research paper, authors considered main two things first one is effective job scheduling[4] when come in multi-queue at a same time on cloud server and the second one is proper utilization of resources. This paper proposes an enhanced multi-queue job scheduling strategy that helps to provide a facility of effective job scheduling by load balancing [10] without any delay. The
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Cloud Computing Online Scheduling

Cloud Computing Online Scheduling

Abstract: - Cloud computing has gained a lot of attention to be used as a computing model for a variety of application domains. Task scheduling is the fundamental issue in this environment. To utilize cloud efficiently, a good task scheduling algorithm is needed to assign tasks to resources in cloud. Cloud task can be divided into two categories such as on-line mode service and the batch mode service. In this paper, online cloud task scheduling based on virtual machine adaptive fault tolerance and load balancing using ant colony algorithm is proposed. The main contribution of this work is that load balancing factor is added and the system tolerates the faults by tacking the decision on the basis of reliability of the virtual machines in scheduling process. The proposed scheduling strategy was simulated using the Cloudsim toolkit package. Experimental results show that the proposed algorithm achieved the better load balance than Join-shortest-queue (JSQ) and Modified Ant Colony Optimization (MACO) algorithms.
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A Survey on Workflow Scheduling in Cloud Computing Environment

A Survey on Workflow Scheduling in Cloud Computing Environment

M.E Scholar, Dept. of Computer Science and Engineering, NITTTR, Panjab University, Chandigarh, India 1 Assistant Professor, Dept. of Computer Science and Engineering, NITTTR, Panjab University, Chandigarh, India 2 ABSTRACT: Cloud computing is a practical approach which allows central resources to be served anywhere anytime on pay per basis. The fundamentals of cloud computing are based on principle of reusability of Information technology capabilities. Still there are many challenges in cloud computing. One of them is scheduling. To balance the load in cloud systems, the resources and workload must be scheduled in a good fashion so that maximum use of available resources can be made. There are various scheduling algorithms used by the load balancer to schedule the tasks that are running on any virtual machine. In this paper, we discuss the various scheduling algorithms that improve the utilization of resources allocated to the tasks and maximize the resource utilization.
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Scheduling Algorithms in Cloud Computing

Scheduling Algorithms in Cloud Computing

Cloud computing is an evolving technology. Cloud computing delivers infrastructure, platform, and software that are made available as subscription-based services in a pay-as-you-go model to consumers. In Cloud Computing the term Cloud is used for the service provider, which holds all types of resources for storage, computing etc. The cloud definition provided by the National Institute of Standards and Technology (NIST).The NIST cloud computing definition:”Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.[3]The reliability and performance of cloud services depends up on various factors like scheduling of tasks. Scheduling can be done at task level or resource level or workflow level. [7]
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An Improved Cpu Scheduling Approach For Cloud Computing Environment

An Improved Cpu Scheduling Approach For Cloud Computing Environment

Job Scheduling is used to allocate certain jobs to particular resources in particular time. In cloud computing, job- scheduling problem is a biggest and challenging issue. Hence the job scheduler should be dynamic. Job scheduling in cloud computing is mainly focuses to improve the efficient utilization of resource that is bandwidth, memory and reduction in completion time. An efficient job scheduling strategy must aim to yield less response time so that the execution of submitted jobs takes place within a possible minimum time and there will be an occurrence of in-time where resources are reallocated. Because of this, less rejection of jobs takes place and more number of jobs can be submitted to the cloud by the clients which ultimately show increasing results in accelerating the business performance of the cloud.
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A Brief Review of Scheduling Algorithms in Cloud Computing

A Brief Review of Scheduling Algorithms in Cloud Computing

Lovejit Singh et al (2013). Cloud Computing refers to a paradigm whereby services are offered by internet using pay as you go model. Services are deployed in data centers and the pool of data centers is together referred to as “Cloud”. Data centers make use of scheduling techniques to optimally allocate resources to diverse jobs. Different scenarios require different scheduling algorithms. The selection of a specific scheduling algorithm depends upon various factors like the parameter to be optimized (cost or time), eminence of service to be provided and information available concerning various aspects of job. Workflow applications are the applications which involve various sub-tasks to be executed in a particular fashion in order to complete the entire task. These tasks have parent child association. The parent task needs to be executed before its child task.
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Task Scheduling in Cloud Computing: Review

Task Scheduling in Cloud Computing: Review

The author of this paper proposed the approach which is known as improved cost-based scheduling algorithm. The main objective of his work is to schedule groups of task in cloud computing platform, where resources are having different resource costs and different computation performance. When grouping of jobs is done, communication between jobs and resources optimizes computation/communication ratio. This algorithm measured performance of computation and cost of resources. This also increased the execution of tasks / transfer of data between tasks ratio by combining various tasks during execution. The process of combining task is usually done by after analysing the capability of different available resource and its processing. CloudSim has been used for performing the simulation and the inputs of the simulation are: average MI of tasks, granularity size of tasks, total number of tasks and task overhead time. Result of his work shows that for this particular algorithm time taken to complete tasks after grouping of tasks is very less as compared to when grouping is not done.
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Priority Based Job Scheduling Techniques In Cloud Computing: A Systematic Review

Priority Based Job Scheduling Techniques In Cloud Computing: A Systematic Review

3) A Priority based Job Scheduling Algorithm in Cloud Computing: Shamsollah Ghanbari, Mohamed Othman [1] presented a novel approach of job scheduling in cloud computing by using mathematical statistics. This algorithm considers the priority of jobs for scheduling and named as priority based job scheduling algorithm. It is based on multiple criteria decision making model. A pairwise comparison based on multiple criteria and multiple attributes method was first developed by Thomas Saaty [13] in 1980 and named as Analytical Hierarchy Process (AHP). Consistent Comparison Matrix is the foundation of AHP, so to use the concept of AHP comparison matrices are computed according to the attributes and criteria’s accessibilities. In this algorithm, each job requests a resource with determined priority. So comparison matrices of each jobs according to resources accessibilities is computed and also comparison matrix of resources is computed. For each of the comparison matrices priority vectors (vector of weights) are computed and finally a normal matrix of all jobs is computed named as Δ. Likewise, normal matrix of all resources is also computed and name of that matrix is γ. The next step of the algorithm is to compute Priority Vector of S (PVS), where S is set of jobs. PVS is calculated by multiplying matrix Δ with matrix γ. The final step of the algorithm is to choose the job with maximum calculated priority, so a suitable resource is allocated to that job. The list of jobs is updated and the scheduling process continues till all the jobs are scheduled to suitable resource. Experimental results indicate that the algorithm has reasonable complexity. Also there are several issues related to this algorithm such as complexity, consistency and finish time.
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Efficient Cloud Computing Scheduling: Comparing Classic Algorithms with Generic Algorithm

Efficient Cloud Computing Scheduling: Comparing Classic Algorithms with Generic Algorithm

One of the most important indexes of using cloud services in that this technology is far from the user. In cloud computing systems, computing resources are presented as virtual machines. In such a scenario, scheduling algorithm plays a very important role because the purpose of scheduling is tasks efficiency so that time is reduced, and resources utilization can be improved. A user may use hundreds of computing resources in a cloud environment, so it is not possible to perform scheduling manually. This can be done by using classic algorithms whose results have been studied and compared with our proposed algorithm, genetic algorithm. Selecting an appropriate and efficient algorithm for resources scheduling is required due to dynamic feature of resources and various requests of users in cloud technology to increase efficiency. In this research, our purpose is to perform and obtain an optimal scheduling by using genetic algorithm to reach the main purpose of finding an optimum scheduling to execute tasks graph in a multi-processor structure so that total execution time or ending time of the last work unit is minimized.
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Scheduling Algorithms in Cloud Computing - An Extensive Survey

Scheduling Algorithms in Cloud Computing - An Extensive Survey

Temporal Task Scheduling For Profit Maximization In Hybrid Clouds [4]: As cloud computing is becoming increasingly popular, consumers’ tasks around the world arrive in cloud data centers. Scheduling tasks while assuring the service delay bound of delay-tolerant tasks. A challenging problem is the aperiodicity of arrival tasks and how to dynamically schedule all arrival tasks given the fact that the capacity of a private cloud provider is limited. In Previous works, an admission control to intelligently refuse some of arrival tasks. Although, this will reduce the throughput of a private cloud, and affect revenue loss. The problem of how to increase the profit of a private cloud in hybrid clouds while assuring the service delay bound of delay-tolerant tasks. So a profit maximization algorithm(PMA) to find out the temporal variation of prices in hybrid clouds. The temporal task scheduling contributes by PMA can automatically schedule all arrival tasks to execute in private and public clouds. The sub problem in each iteration of the profit maximization algorithm (PMA) clarified by the proposed hybrid heuristic optimization algorithm, parallel annealing particle swarm optimization (SAPSO). Finally, the proposed method can greatly increase the throughput and the profit of a private cloud and energy aware scheduling comprise of five sub algorithm. Initially, the virtual machine selection algorithm is used to implement cost utility idea to direct task to their correct virtual machines (VM) types. Next, two tasks are merged by using merging methods to minimize cost of execution and energy consumption. In last task slack algorithm is used to save the energy by DVFS techniques. In other word, sequence tasks merging, parallel tasks merging and VM reuse algorithms will minimize the economic cost of workflow is energetic. In addition, sequence tasks merging, the parallel tasks merging,
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Efficient Virtual Machine Scheduling in Cloud Computing

Efficient Virtual Machine Scheduling in Cloud Computing

Abstract - Due to rapid increase in use of Cloud Computing, moving of more and more applications on cloud and demand of clients for more services and better results, load balancing in Cloud has become a very interesting and important research area. VM Scheduling is essential for efficient operations in distributed environments. In cloud computing the load balancing concept broadly classify in three stages as Data Centre Selection, Virtual Machine Scheduling and Task scheduling at particular data centre. Many algorithms were suggested to provide efficient mechanisms and algorithms for assigning the client’s requests to available Cloud nodes. In this paper, we explained different algorithms and techniques proposed for Virtual Machine Scheduling either at single data centre or multiple data center. Also infers their characteristics to resolve the issue of efficient Virtual Machine Management in Cloud Computing. We discuss and compare these algorithms and techniques in regards of various performance matrices to provide an overview of the latest approaches in the field.
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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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Optimization Task Scheduling Algorithm in Cloud Computing

Optimization Task Scheduling Algorithm in Cloud Computing

I n cloud computing, IaaS approach is to increase the efficiency and utilization of resource, equipment and existing networks [1]. In this approach, it is attempted to manage request executions so that operational costs such as energy consumption in datacenters, and costs of networks would be reduced [2]. Cloud computing by numerous virtual machines on some data centers increase capability of response to requests. Here, managing virtual machines and physical resources, besides scheduling policies of tasks, is a significant issue. An inappropriate scheduling may involve numerous resource for a series of requests while, an optimized scheduling with less resources and better management give the same response. In cloud computing there are many serial request of users at the same times and conditions is causing similar condition for cloud [3]. Iteration of similar events indicates that a learning algorithm is able to provide suitable efficiency in such conditions. Appling intelligent methods based on learning to the cloud, is increasing in the field of optimization of tasks scheduling and resource allocation [4]. In this research, we will attempt to present a method based on scheduling information of the past of cloud so that it would be
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