Top PDF 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

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

Task Scheduling plays a key role in the Cloud Computing system. Scheduling of the task cannot be done on basis of single criteria. It is regulations and rules that can term as an agreement between users and providers of cloud. This agreement is nothing else simply the quality of service that the user or client requires. Providing good quality of the services to the users or clients according to the agreement it is a decisive task for the providers at the same time there is large number of tasks running at the side of provider’s. The task scheduling problem can be viewed or seen as the finding or searching an optimal mapping of set of subtasks of different tasks over the available set of the resources (e.g. processors/computer machines) hence can be achieved or attained the desired goals for tasks. Scheduling is a method or procedure by which threads, processes and data flows are given access to system resources. Scheduling is the fundamental operating system function, almost all computer resources are scheduled before use. The idea of multiprogramming is relatively simple, if a job is waiting for an I/O request, then the CPU switches from that job to another job, so that it always busy in multiprogramming[3].
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Centralized Software Based Task Delegation System for Teams of MNC Company

Centralized Software Based Task Delegation System for Teams of MNC Company

ABSTRACT: Cloud computing plays an significant role on achieving better performance and high system utilization. The goal of a task delegation system is to perform task allocation, task scheduling in project efficiently and to implement it in the cloud as the project is distributed over different countries. The existing system involves manual integration of data and management of different-different processes, so it takes more time. The proposed system integrates system with human resource information, client management and project management at one place. It will reduce overall project cost, time, manual effort and ensures project completion in time. It uses the resources efficiently and allowing the employers to complete the particular task in the given period of time. Software projects are people- intensive activity and require employees with different skills, so task allocation scheduling are crucial phases in project. Task allocation is prepared by the Event based scheduler where the tasks are delegated to the employees based on their skills and availability and Tasks are assigned to the corresponding employee automatically as per the schedule . Using Event based scheduler, the different level of tasks are assigned automatically to the corresponding employees based on their efficiency. The proposed system calculate the efficiency of the employee using working hours based on which the task is allocated.
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Modified Flower Pollination based Task Scheduling in Cloud Environment using Virtual Machine Migration

Modified Flower Pollination based Task Scheduling in Cloud Environment using Virtual Machine Migration

High performance Distributed computing systems are the trending approaches for scientific and computational based intensive applications. Cloud computing is one of the high performance computing paradigm in which a shared pool of resources like storage, computation etc. are used as a service through internet access. The key challenges of cloud computing is scheduling of tasks to proper resources as well as efficiently utilization of the resources also. Task scheduling is based on the Quality of service parameters which can be cost, time, deadline, energy etc. Different users have different parameters for Quality of service. Some users want cost effective scheduling others required deadline based task execution. In cloud computing, cloud broker has all the information about the availability of resources. It uses the
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A Review paper on CPU Scheduling in Cloud Environment

A Review paper on CPU Scheduling in Cloud Environment

Shridhar Domanal, Ram Mohana Reddy Guddeti, and Rajkumar Buyya (2016) [18] wrote a paper. In this paper, they proposed a novel hybrid Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation of resources to these tasks should be handled in an intelligent manner. In this proposed work, we allocate the tasks to the virtual machines in an efficient manner using Modified Particle Swarm Optimization algorithm and then allocation / management of resources (CPU and Memory), as demanded by the tasks, is handled by proposed HYBRID Bio-Inspired algorithm (Modified PSO + Modified CSO). Experimental results demonstrate that our proposed HYBRID algorithm outperforms peer research and benchmark algorithms (ACO, MPSO, CSO, RR and Exact algorithm based on branch-and- bound technique) in terms of efficient utilization of the cloud resources, improved reliability and reduced average response time.
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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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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

while scheduling the tasks to cloud resources[11]. During the schedule of user tasks, load and energy of each resource needs to be realized and decisions can be taken to assign a task to the best VM so that the overall makespan, degree of imbalance and energy consumption is minimized[12]. Thus, makespan optimization with load balancing is a critical issue in achieving high performance in cloud computing. Additionally, reducing the energy consumption is also an important parameter that needs to be considered while assigning the task to the resources since it sustains the whole cloud computing system with green ambience[13]. The task scheduling policies plays an important role on reducing energy consumption in order to allocate tasks on distributed VMs. In recent studies carried out by [14] have estimated in average of 55% of energy utilized by the computing system in cloud computing. Thus, green computing is also an important issue for ensuring the sustainability of cloud computing system in terms of cost and environment safety. This paper proposes a hybrid algorithm for energy efficient load balance aware task scheduling in cloud computing inspired by social spider’s foraging behavior and starling birds flock behavior. The social spider algorithm is an algorithm inspired by foraging behavior of social spiders has been adopted by [15] for global optimization. It is an intelligent exploitation of foraging strategies present in the spider’s web. This swarm intelligence helps the spiders in the population to find the food sources which is hybridized with the intelligent flocking behavior[16] of starling birds as major component for scheduling the cloud user tasks in energy efficient way. The flocks of starling birds are an interesting group behavior where simple rules of interaction among the birds sufficient to produce the collective behavior. The starling flock intelligence is incorporated to the social spider foraging in order to achieve energy efficient task scheduling in cloud computing with balanced load on VMs[17]. The remainder of this paper is organized as follows. Section II describes the various research study related to improve the energy efficiency of task scheduling cloud computing and Section III presents the background concepts of proposed system. Section IV describes the task scheduling model and detailed procedure of proposed system. Section V provides the simulation results and performance evaluation with existing techniques.
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Enhanced Genetic Algorithm based Task Scheduling in Cloud Computing

Enhanced Genetic Algorithm based Task Scheduling in Cloud Computing

Resource Scheduling became complicated task in cloud computing as required resources are limited and the number of users increase day by day so it is most important to manage these resources in such a way that resources are properly utilize and the waiting time for resources is decreases[7]. For proper scheduling of resources many algorithm are available as well as methods in cloud computing. Optimum resource scheduling help both user as well as service provider cloud provider get benefits in term of efficient resource management which in turn provides more resources to allocate user without postponing or declining user request for resources, cloud user get benefit in term of money proper utilization of money is done. Resource scheduling is the basic and key process for clouds in Infrastructure as a Service (IaaS) as the need of the request processing is must in the cloud. Every server has limited resources so jobs/requests needs to be scheduled. Each application in the cloud computing is designed as a business processes including a set of abstract processes. To allocate the resources to the tasks there need to schedule of the resources as well as tasks coming to the resources. There need to be a Service Level Agreements (SLAs) for Quality of Service (QoS).
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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

ABSTRACT: The success of cloud computing makes an increasing number of real-time applications such as signal processing and weather forecasting run in the cloud. Meanwhile, scheduling for real-time tasks is playing an essential role for a cloud provider to maintain its quality of service and enhance the system’s performance, for a given set of jobs the general scheduling problem asks for an order according to which the jobs are to be executed such that various constraints are satisfied. Typically, a job is characterized by its execution time, ready time, deadline and resource requirements. Specifically, the execution of a job cannot begin until the execution of all its predecessors is completed. In this paper, we devise a novel agent-based scheduling mechanism in cloud computing environment to allocate real- time tasks and dynamically provision resources. On the basis of the bidirectional announcement-bidding mechanism, we propose an agent-based dynamic scheduling algorithm named ANGEL for real-time, independent and a periodic tasks in clouds. Extensive experiments are conducted on Cloud Sim platform by injecting random synthetic workloads and the workloads from the last version of the Google cloud trace logs to evaluate the performance of our ANGEL. The experimental results indicate that ANGEL can efficiently solve the real-time task scheduling problem in virtualized clouds.
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Scheduling of Processors in Cloud: A Review

Scheduling of Processors in Cloud: A Review

Shridhar Domanal, Ram Mohana Reddy Guddeti, and Rajkumar Buyya (2016) [19] wrote a paper. In this paper, they proposed a novel hybrid Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation of resources to these tasks should be handled in an intelligent manner. In this proposed work, we allocate the tasks to the virtual machines in an efficient manner using Modified Particle Swarm Optimization algorithm and then allocation / management of resources (CPU and Memory), as demanded by the tasks, is handled by proposed HYBRID Bio-Inspired algorithm (Modified PSO + Modified CSO). Experimental results demonstrate that our proposed HYBRID algorithm outperforms peer research and benchmark algorithms (ACO, MPSO, CSO, RR and Exact algorithm based on branch-and-bound technique) in terms of efficient utilization of the cloud resources, improved reliability and reduced average response time.
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Cost Based Task Scheduling in Cloud Computing

Cost Based Task Scheduling in Cloud Computing

As cloud computing technique frees the user from the overhead cost of hardware, but still some cost factors are always involved and these cost factors are is comparatively very low as they are charged according to the services requested by the end user. For example, if a user requests for any task, then the cost is charged according to the resource required for accomplish on of the task, time of acquisition, turnaround time, I/O cost, the cost of resources etc. [5]. As each task is totally different from the other task so it is required to compute the cost of every individual task uniquely when it is requested. Different task results in the different cost factor.
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A Survey On Task Scheduling Algorithms In Cloud Computing

A Survey On Task Scheduling Algorithms In Cloud Computing

IAAS- Infrastructure As A Service. Under infrastructure as a service, the virtualised resources, computation, storage, communication, networking and networking services(e.g firewall) are provided on demand. IaaS examples: Google compute engine, Microsoft Azure, Cisco metapod and Amazon web services mainly offers IAAS. PAAS- Platform As A Service. To make the cloud easily programmable, it can be called as platform as a service. Developers can create, deploy applications without knowing how many processors or how much data those applications will need. So instead of installing the libraries, software which will in turn fill up your system's space, you just need the internet connection to develop and host, whatever is needed all at a single cloud platform. PAAS examples: Google app engine provides an environment to host and develop web applications in specific languages- Python or Java. SAAS- Software As A Service. Web portal is required to access services at this
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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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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 Algorithm using in Cloud Sim

Cost Based Algorithm using in Cloud Sim

Paul Martinaitis, Craig Patten and Andrew Wendelborn et 2009[3] Current activity in our project includes development of a template stream-aware scheduling component. The motivation for developing such a template as the basis for future work is that we need a more sophisticated mechanism than the default Grid bus round robin scheduler, which operates in a queue-oriented manner, iterating over the jobs in the queue. Such an arrangement is reasonable in the usual Grid bus environment, where jobs are independent. However, in a stream computation context, each job corresponds to a component which is almost certainly linked to at least one other component. It is therefore highly desirable that stream-aware schedulers are able to absorb the entire body of information regarding a stream.
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An IoT based task scheduling optimization scheme considering the deadline and cost aware scientific workflow for cloud computing

An IoT based task scheduling optimization scheme considering the deadline and cost aware scientific workflow for cloud computing

In recent years, increasing research has focussed on the characteristics of the cloud environment, such as indefinite quantity, heterogeneity, performance vari- ation, and acquisition delay of VMs, and few of these characteristics have been fully considered in previous studies. Mao et al. [28] present a new auto-scaling mechanism named scaling-consolidation-scheduling (SCS) to allocate all workflow tasks to the most cost- efficient VMs. The researchers consider the character- istics of VMs, such as performance variation and ac- quisition delay, but ignore the data transfer time between tasks, which will affect the completion time and total execution cost of the workflow. Rodriguez et al. [29] propose a static cost-minimization and deadline-constrained algorithm for workflow schedul- ing in a cloud environment. Based on the main char- acteristics of IaaS, the researchers merge and model both resource provisioning and scheduling as an optimization problem and use PSO to generate a so- lution that minimizes the lease cost of VMs before the deadline. However, the index of resources used to encode particles does not include much information about the type of VMs, so the algorithm cannot easily produce the best global optimal solution when parti- cles move to the individual best solution. Poola et al. [30] propose robustness-cost-time (RCT), robustness- time-cost (RTC), and weighted algorithms to schedule workflow tasks on heterogeneous resources in the cloud. These algorithms based on partial critical paths (PCPs) provide a robust and fault-tolerant schedule while minimizing the total makespan and cost. More- over, the three policies have different objectives, ro- bustness, time, and cost, and each objective has different priorities in each algorithm. However, these algorithms schedule the entire tasks of PCPs on VMs and may affect the makespan of the workflow because the performance variation of a VM will delay the completion time of all the tasks assigned to it, thereby affecting the start time of the tasks of the other PCPs. Sahni et al. [31] propose a dynamic cost-
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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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Scheduling the Tasks in Cloud Computing to Enhance the System Performance

Scheduling the Tasks in Cloud Computing to Enhance the System Performance

ABSTRACT: Wireless Cloud computing transfers the data and computing resources through the internet on the basis of its usage. We can automatically update our software by using this facility. We can use only the space required for the server which reduces the memory usage. Task scheduling is the main problem in cloud computing which reduces the system performance. Scheduling introduces a set of policies to control the order of the 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. To enhance the system performance, there is a need of an efficient task-scheduling algorithm. Existing task- scheduling algorithms focus on task resource requirements, CPU memory, execution time and execution cost. However, these do not consider bandwidth of virtual machine. In this paper, we introduce an efficient task scheduling algorithm, which presents divisible task scheduling by considering bandwidth of virtual machine. By this, we can allocate the workflow based on the availability of space in the virtual machine. Our proposed task-scheduling algorithm uses a nonlinear programming model for divisible task scheduling, which assigns the correct number of tasks to each virtual machine. In this research paper we presented an algorithm for efficient execution of task which gives better performance compared to other traditional scheduling algorithm.
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A Survey On Workflow Task Scheduling Using Intelligent Water Droplets In Cloud Computing

A Survey On Workflow Task Scheduling Using Intelligent Water Droplets In Cloud Computing

In this paper the authors analyze the various algorithms that have been used for task scheduling on resources in cloud computing environments. Scheduling is an important activity in multi-tasking systems to manage resources, minimize completion time and increase performance of systems. To implement task scheduling, any of the above discussed methods can be used to get optimistic scheduling. The benefits of using these methods is to get the optimal solution in minimum time. Future work in this field can include application of newer optimization methods like modified IWD, etc, which may be able to provide better and faster results.
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Cloud Scheduling   A Survey

Cloud Scheduling A Survey

Service-orientation is the reference model for Cloud computing systems. It is the logical way of organizing software systems to provide end users or other entities distributed over the network with services through published and discoverable interfaces. It introduces two important fundamental concepts, Quality of Service (QoS) and Software as a Service (SaaS) for Cloud computing. Quality of Service identifies response time, security attributes, transactional integrity, reliability, scalability, and availability as performance metrics to evaluate the behavior of a service from different perspectives. The SaaS approach allows the delivery of complex business processes and transactions as a service, while allowing applications to be composed on the fly and services to be reused from everywhere by anybody.
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Apply AHP for Resource Allocation Problem in Cloud

Apply AHP for Resource Allocation Problem in Cloud

namic resource allocation is also a challenge in cloud computing. A complex mathematical decision model as- sociated with the selection of cloud computing services in a multi-source scenario is presented [7]. PaaS by eva- luating ranking framework solutions according to the needs is required and also proposed a set of benchmarking algorithms that can help determine the suitable PaaS provider based on different resources and applications re- quirement [8]. When many users make requests for cloud resources at the same time, then how these requested and other resources will be allocated to users to get resources. This is a challenging task in cloud computing. Consider the following (hypothetical) example: an application that needs to define technical specifications for its cloud applications with various criteria that are important to them. When several applications are offered by dif- ferent vendors, the selection of particular application becomes a key issue [9]. It involves analysis of selection parameters and attributes of applications. As multiple criteria are involved in decision making, it is a multi- criteria decision-making (MCDM) problem. Being a problem involving multi-criteria and different application, it can’t be solved with mere assessment. The assessment may work fine, only when the selected parameters and attributes of applications are few. During the selection process, generally the characteristics are ranked or priori- tized. The prioritization includes deciding the weights of parameters. While considering the weights of parame- ters, it is quite likely that the user’s assessment may be based towards the key factors only. This may lead to im- proper priority and incorrect weights being assigned to the parameters. To make an appropriate decision, it is necessary to have a quantifiable value rather than subjective opinions. To deal with this problem we have pro- posed analytical hierarchy process which is widely accepted by the experts. AHP brings an ability to judge the consistency in the analysis process and helps to reduce deviations. Continuation of this paper includes AHP se- lection parameters based on literature study, methodology adopted and application of AHP to the problem fol- lowed by a conclusion.
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