Task scheduling algorithms in Cloud computing systems

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A comparative study on scheduling Algorithms in cloud computing

A comparative study on scheduling Algorithms in cloud computing

D. Task Scheduling: As the number of users of Cloud computing Systems increased, the tasks to be scheduled in Cloud increased proportionally. Therefore, there is a need for better algorithms to schedule tasks on these systems. Algorithms required to schedule tasks are service oriented and differ in different environments. Task Scheduling algorithms in cloud computing aim at minimizing the make span of tasks with minimum resources efficiently. Cloud computing, uses low-power hosts to achieve high usability. The cloud computing refers to a class of systems and applications that employ distributed resources to perform a function in a decentralized manner. Cloud computing is to utilize the computing resources (service nodes) on the network to facilitate the execution of complicated tasks that require large-scale computation. Thus, the selecting nodes for executing a task in the cloud computing must be considered [8]. A task is an activity that uses set of inputs to produce a set of outputs. In Cloud computing, user applications will run on virtual systems where distributed resources are allocated dynamically. Dynamic load-balancing mechanism has to allocate tasks to the processors dynamically as they arrive. Redistribution of tasks has to take place when some processors are overloaded. Every application is completely different in nature and independent where some require more CPU time to compute complex task, and some others may need more memory to store data. Different scheduling algorithms can be used depending on the type of the task to be scheduled. The scheduling algorithms can utilize better executing efficiency and maintain the load balancing of system. The efficiency of the cloud depends on the algorithms used for task scheduling. E. Directed Acyclic Graph Scheduling: Parallel tasks are often represented by a directed
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A Review and Analysis of Task Scheduling Algorithms in Different Cloud Computing Environments

A Review and Analysis of Task Scheduling Algorithms in Different Cloud Computing Environments

Abstract— Task scheduling plays a key role in cloud computing systems. Scheduling in cloud is responsible for selection of best suitable resources for task execution, by taking some static and dynamic parameters and restrictions of tasks’ into consideration. The users’ perspective of efficient scheduling may be based on parameters like task completion time or task execution cost etc. In this paper we are performing comparative study of the different algorithms for their suitability, feasibility, adaptability in the context of cloud scenario, parameters, description, advantages etc.
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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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TASK SCHEDULING IN CLOUD COMPUTING

TASK SCHEDULING IN CLOUD COMPUTING

Abstract: Recently, there has been a dramatic increase in the popularity of cloud computing systems that rent computing resources on-demand, bill on a pay-as-you-go basis, and multiplex many users on the same physical infrastructure. It is a virtual pool of resources which are provided to users via Internet. It gives users virtually unlimited pay-per-use computing resources without the burden of managing the underlying infrastructure. One of the goals is to use the resources efficiently and gain maximum profit. Scheduling is a critical problem in Cloud computing, because a cloud provider has to serve many users in Cloud computing system. So scheduling is the major issue in establishing Cloud computing systems. The scheduling algorithms should order the jobs in a way where balance between improving the performance and quality of service and at the same time maintaining the efficiency and fairness among the jobs. This paper aims at studying various scheduling methods. A good scheduling technique also helps in proper and efficient utilization of the resources. Many scheduling techniques have been developed by the researchers like GA (Genetic Algorithm), PSO (Particle Swarm Optimization), Min-Min, Max-Min, Priority based Job Scheduling Algorithm . This paper reviews certain papers on resource management and job scheduling in cloud computing.
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Enhanced Genetic Algorithm based Task Scheduling in Cloud Computing

Enhanced Genetic Algorithm based Task Scheduling in Cloud Computing

manner. Cloud computing is to utilize the computing resources (service nodes) on the network to facilitate the execution of complicated tasks that require large-scale computation. Thus, the selecting nodes for executing a task in the cloud computing must be considered [6]. A task is an activity that uses set of inputs to produce a set of outputs. In Cloud computing, user applications will run on virtual systems where distributed resources are allocated dynamically. Dynamic load-balancing mechanism has to allocate tasks to the processors dynamically as they arrive. Redistribution of tasks has to take place when some processors are overloaded. Every application is completely different in nature and independent where some require more CPU time to compute complex task, and some others may need more memory to store data. Different scheduling algorithms can be used depending on the type of the task to be scheduled. The scheduling algorithms can utilize better executing efficiency and maintain the load balancing of system. The efficiency of the cloud depends on the algorithms used for task scheduling.
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Efficient Cloud Computing Scheduling: Comparing Classic Algorithms with Generic Algorithm

Efficient Cloud Computing Scheduling: Comparing Classic Algorithms with Generic Algorithm

Scheduling of resources is very important in cloud computing environment. Each user may face with hundreds of virtual resources in executing the task in cloud computing environment. However, allocating the tasks to virtual resources by the user is impossible. Scheduling system controls various tasks in the cloud system to increase the rate of task completion ability of resources and computing power. Cloud computing is highly dependent on virtualization. It can be said that clouds are virtual branches so tasks scheduling in various and heterogeneous physical machines is a crucial task. Scheduling in cloud computing environment has been considered as one of the most important challenges in cloud computing. 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 the efficiency of tasks to decrease the time and to improve resources utilization. In a cloud environment, the user may
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A Brief Review of Scheduling Algorithms in Cloud Computing

A Brief Review of Scheduling Algorithms in Cloud Computing

Abstract— Cloud computing comes in focus development of grid computing, virtualization and web technologies. The cloud computing is a mingle of technologies where a large number of systems are connected in private or public networks. This technology offers dynamically scalable infrastructure for data, file storage, and application. Scheduling is a main task in a cloud computing environment. In cloud computing environment datacenters take care of this task. The selection of a exacting scheduling algorithm depends upon various factors like the parameter to be optimized (cost or time), quality of service to be provided and information available regarding various aspects of job. Workflow applications are the applications which need various sub-tasks to be executed in a particular fashion in order to complete the whole task. Various scheduling algorithms surveyed in this paper. The goal of cloud task scheduling is to achieve high system throughput and to assign various computing resources to applications. The Complexness of scheduling trouble increases with the size of the task and becomes very difficult to solve effectively. Min-Min algorithm is used to lessen the make span of tasks by assuming the task length. Keeping this in mind, cloud providers should achieve user satisfaction
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Optimized task scheduling based on hybrid symbiotic organisms search algorithms for cloud computing environment

Optimized task scheduling based on hybrid symbiotic organisms search algorithms for cloud computing environment

Execution of large scale applications in cloud computing environment is only beneficial, if execution of tasks of the application can be scheduled across compute resources in a manner to achieve a reasonable execution time. To harness the benefits of cloud, task scheduling algorithms play a critical role, task scheduling algorithms assign tasks to compute to meet certain optimization objectives. The common objectives of task scheduling formulations are minimization of financial cost (cost), total execution time (makespan), reliability, security, energy consumption, resource utilization among others. Also, in many task scheduling formulations, user impose certain constraints like budget and deadline. Various QoS optimization techniques have been proposed for distributed systems like clusters and grids. However, these techniques cannot be adapted to cloud environment being an utility based computing platform which is characterized by heterogeneity, dynamism and elasticity. Also, the few works for cloud environment do not either consider the essential characteristics of cloud or the performance of these techniques degrades as the problem size increases. Moreover, cloud based solutions approaches to multi-objective QoS problems are mostly based on weighted sum technique which converts multi-objective formulation to a single objective. However, the assigned weights to each objective may not represent the actual desire of the user and these approaches cannot provide various trade-offs from which the user can choose most suitable option.
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Angel-Agent Based Scheduling for Real Time Tasks in Virtualized Clouds

Angel-Agent Based Scheduling for Real Time Tasks in Virtualized Clouds

The essential concept of cloud computing system is task scheduling. This algorithm aims at reducing the make span of jobs with lowest resources capably. Scheduling algorithms depends on the type of task to be scheduled. The scheduling algorithm gives better executing efficiency and it maintains the load balancing of systems. The cloud efficiency is depends on the task scheduling algorithm. There are different types of task scheduling. Some are cloud service scheduling, user level scheduling, static and dynamic scheduling, heuristic scheduling, real time scheduling, work flow scheduling etc.
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A Survey On Task Scheduling Algorithms In Cloud Computing

A Survey On Task Scheduling Algorithms In Cloud Computing

Cloud computing is basically seen as an evaluation. There are some roots that led to cloud computing. It is because of coming of web services and web service standards that today we can create, package and use powerful services online. So, it becomes the first root. The next and most important reason for evaluation of cloud is grid computing. Grid computing means to collect all the resources which are distributed and get easy access to them. Since different resources have different software configurations, compilers, libraries and runtime environments, there was the need of a technology that addresses these issues. Virtualization technology did the needful to enable hosting of different software applications on a single physical platform. For fair and equal access to resources, grid computing lacked because it considered traditional metrics’ like throughput, waiting time; which failed. Utility computing considered QoS constraints for example deadline and importance of jobs for users to compete for resources. Hardware virtualization allowed virtualization of large scale data centres and other resources (processors, memory, I/O devices) to improve sharing and maintenance. Autonomic computing was studied to improve systems by decreasing human involvement. These were the technologies that led to cloud computing. Hence, cloud
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Scheduling Algorithms in Cloud Computing - An Extensive Survey

Scheduling Algorithms in Cloud Computing - An Extensive Survey

Scientific Workflows Scheduling With Deadline Constraint In The Clouds [1]: cloud computing has been commonly identified as the fundamental of computing paradigm to implement, compute and data intensive business process workflow and scientific workflow application for processing huge amount of scientific data. In cloud computing, there is an one important feature named Multi-tenanted. It provides scalability and economic advantages to final customers and service providers are shares the similar cloud platform. In multi-tenant cloud computing, resource management is becoming one of the biggest tasks because of inherent heterogeneity and resource isolation. In this multi-tenant cloud computing, cloud based workflow scheduling algorithm is used to compute intensive workflow applications. It helps to minimize the complete workflow completion time, tardiness, execution cost of the workflows and it mainly utilize free resources of cloud effectively. Cost of execution of workflow changes depending on the application and on the size of the workflow. In this four-layered workflow scheduling system is introduced. This proposed cloud based workflow scheduling algorithms contrasted with the state-of- the-art algorithm that is First Come First Served, EASY Backfilling and minimum completion time scheduling policies to calculate the performance. Proposed algorithm compared with different scheduling algorithm to highlight the Performance and robustness of the proposed solution. Cloud based workflow scheduling algorithm scheduling performs are best when compared with other in the terms of cost by generating much cheaper schedules.
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Task Scheduling in Big Data   Review, Research: Challenges, and Prospects

Task Scheduling in Big Data Review, Research: Challenges, and Prospects

Chen Men-meng et al. [29] states that the existing task scheduling algorithms for streaming processing fail to handle the links between the streaming tasks and the dynamic nature of the streaming process and the resources. It is essential to consider the status of the available resource and the demand for the resources while scheduling the streaming tasks. In addition to that, it is important to provide priority to the essential network parameters such as bandwidth and latency. Boyang Peng [30] discussed that the streaming tasks should be scheduled close to the network proximity to avoid the network delay which is communicating with each other. Moreover, task scheduling for streaming tasks is extremely difficult than batch jobs because of the nature of continuous and dynamic nature of input data that requires unlimited processing time. Dawei Sun et al. [31] designed a fault tolerant system which is mainly responsible for guaranteeing the deadline in a big data streaming computing. They stated that fault tolerance is an important metric for achieving the quality of service. Their proposed mechanism allocates the tasks based on the fault tolerance and critical path scheduling technique. Their proposed mechanism solves the trade-off between high fault tolerance and low response time for big data streaming jobs.
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SWOT Analysis of Tabu Search and Simulated Annealing Task Scheduling Algorithms in Distributive Environment

SWOT Analysis of Tabu Search and Simulated Annealing Task Scheduling Algorithms in Distributive Environment

SWOT analysis is a most renowned tool for audit and analysis of the overall capability of any element. Basically it is considered that it is used only to judge the business or a venture but it gives the basic capability and weakness of elements in terms of internal and external environments in the term of Strength, Weakness, Threat, and Opportunity. So we have chosen the SWOT Analysis to assess the capability of research work till date in the field of task scheduling in distributive environment. As above described above that lot of work has been purposed by various researcher, so there is need of SWOT analysis of these works. In the next section is related to the extensive SWOT Analysis of few prominent Meta-Heuristic Task Scheduling Techniques.
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Cost Based Task Scheduling in Cloud Computing

Cost Based Task Scheduling in Cloud Computing

Cloud Computing is a way to access, store and manipulate data on internet environment without wasting lots of computer memory of individual systems. This technique decreases the processing burden at the user end. Traditionally, users spend lots of money on hard-disks for a large amount of storage, on processors with high processing speed, on coolants to maintain the temperature etc. But with the help cloud computing, this problem has been solved to a great extent. The organization team of the companies is giving attention to improve the
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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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Optimization Task Scheduling Algorithm in Cloud Computing

Optimization Task Scheduling Algorithm in Cloud Computing

We present this proposed method for extended online scheduling by ORAOLA algorithm. Also here, according to periodic evaluations of the extent of resource utilization, cloud scheduler results in performance optimization of the broker and the difference is that in this case it faces with computational complexities. Generally, in the cloud environment, scheduling virtual machines of V, is performed regardless of previous situations of the system and is merely performed based on current data of the system. Analysis of scheduling behavior shows the effect of previous situation of the system on the load imbalance of the system [6]. We have assumed that existing resources are constant in the physical machines. Therefore, no travel is performed by virtual machines to balance task load and there is no head costs related to Vis on physical machines. Here, scheduling must have the least load on the system by appropriate selection of Vis for the duties. This prepares a context to provide an optimized algorithm using data of the past. Learning automata can model systems with discrete components that their behaviors have known effects on each other. The most important thing is to record experiences of the past and considering them in the future behavior of a system. Therefore, a correlation must be made between two capabilities (A, B) of recording experiences of the past, and optimizing the current behavior, which are provided by LA. When T consists of numerous similar duties, or the current function of T has been iterated for many times in the past, a scheduling template based on scheduling behavior can be resulted by considering tasks loads. If LA would be a learning automata so that β(n) is the tasks added to Tnew queue in the time “n”, and Vf would be the free resources of virtual machines, then the function is defined as F : ∅ ʘ {Tnew, VF} →∅.
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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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Dynamism in Cloud Resource Allocation : A Survey S. Lakshmanan , Dr. D. Ravindran

Dynamism in Cloud Resource Allocation : A Survey S. Lakshmanan , Dr. D. Ravindran

These hotspot mitigation algorithms how much migration to allocate. The hotspot detection component monitoring, proofing engine usages gather on various virtual and physical servers. Unobtrusive Black Box monitoring: It is responsible for each virtual server processor, network, memory, usage, and finds the VM total resource usage. Gray box monitoring: It is used to lightweight monitor the each virtual server. In Linux monitoring demanded the interface together CPU, network, memory usage.Profile Generation: It received the report on the resource from each nucleus. It maintains user history; generate to report on the virtual machine to physical machines. Also maintained the CPU utilization. Network bandwidth, swap rates, memory, service time, drop rate and increment the request rate. Hotspot Detection: It responsible for signaling a need for VM migration with SLA violation. It is performed physical servers with a black box.
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A Review: Metaheuristic Technique in Cloud Computing

A Review: Metaheuristic Technique in Cloud Computing

Load Balancer: This mechanism contains algorithms for mapping virtual machines onto physical machines in a cloud computing environment, for identifying the idle virtual machines and for migrating virtual machines to other physical nodes. Whenever a user submits an application workload into cloud system, one can create a new virtual machine. Now the mapping algorithm of Load balancer will generate a virtual machine placement scheme, assign necessary resources to it and deploy the virtual machine on to the identified physical resource. Load Balancer will have the following three sub modules.
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SECURITY ISSUES AND ALGORITHMS IN CLOUD COMPUTING

SECURITY ISSUES AND ALGORITHMS IN CLOUD COMPUTING

Many organizations today are feeling pressure to reduce IT costs and optimize IT operations. Cloud computing is rapidly emerging as a viable means to create dynamic, rapidly provisioned resources for operating platforms, applications, development environments, storage and backup capabilities, and many more IT functions. A staggering number of security considerations exist that information security professionals need to consider when evaluating the risks of cloud computing. The first fundamental issue is the loss of hands-on control of system, application, and data security. Many of the existing best practice security controls that infosec professionals have come to rely on are not available in cloud environments, stripped down in many ways, or not able to be controlled by security teams. Security professionals must become heavily involved in the development of contract language and Service Level Agreements (SLAs) when doing business with Cloud Service Providers (CSPs). Compliance and auditing concerns are compounded. Control verification and audit reporting
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