Top PDF HTSCC A Hybrid Task Scheduling Algorithm in Cloud Computing Environment

HTSCC A Hybrid Task Scheduling Algorithm in Cloud Computing Environment

HTSCC A Hybrid Task Scheduling Algorithm in Cloud Computing Environment

The work in this paper presents a hybrid task scheduling algorithm to ensure distribution of the tasks through VMs in an efficient way to increase resource utilization and decrease makespan of the application in the cloud computing environment. The proposed HTSCC algorithm makes use of the advantages of the GA and PSO algorithms in order to maximize resource utilization and minimize makespan. Simulation results of the HTSCC show that the makespan can be enhanced by about 31.32% and 22.36% while resource utilization is enhanced by about 23.17% and 19.6% compared to the GA and PSO respectively. In the future work, the proposed algorithm will be tested over dynamic workflow applications where the user can change the parameters of the workflow task during the runtime. Moreover, the proposed algorithm will be enhanced to address multi- objective metrics such as cost, speed up and efficiency.
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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

problem have been studied in [11] and proposed an energy efficient approach based on PSO for task scheduling in cloud computing. The energy consumption taken into an account with fitness in order to guide the scheduling and VM allocation policies in order to tackle the problem of local convergence also. The comparison results show that the proposed algorithm better than the traditional approaches but it is found that the algorithm fails to treat the scalability. As mentioned in[21] more research is focusing on “Green Cloud” by reducing the energy consumption in cloud computing environment. The authors have presented a DENS algorithm for energy efficient task scheduling. They have taken the network awareness as a key part to take decisions in assigning the tasks and utilized the feedback channel information to limit the computing servers. Their results shown few improvements in minimizing the energy consumption but fails to tackle the problem of load balancing. Another research work [22] have presented an approach called e-STAB for task scheduling with reduced energy consumption. They have considered the traffic load balancing strategy across the cloud data centers Their results show improvements in communication delays and congestion control. The research work done by [23] have proposed CBAT algorithm for scheduling the user task in cloud computing. The authors have designed the algorithm with array concept to represent the candidate solution and formulated the fitness correlated with makespan and energy consumption. The simulation results show significant improvement in reducing the makespan and few improvements were noted in reducing the energy consumption. A dynamic resource allocation strategy was presented in [24] for scheduling independent tasks where the priority order is used to perform pre emptiable task execution and adjust the scheduling policies dynamically based the feedback information. The proposed algorithm has shown significant improvements in energy consumption and makespan optimization. A hybrid task scheduling algorithm was proposed in [25] using a combination of cultural algorithm and the intelligent behavior of ant colony. The objective of the proposed algorithm is to reduce the makespan while minimizing the energy consumption by make using the benefits of both algorithms. The proposed hybrid algorithm shows significant improvement in makespan optimization and minimization of energy consumption. However, the algorithm fails in dynamic cloud computing environments due to static computation model.
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Efficient Task Scheduling Over Cloud Computing with An Improved Firefly Algorithm

Efficient Task Scheduling Over Cloud Computing with An Improved Firefly Algorithm

IJEDR1602271 International Journal of Engineering Development and Research (www.ijedr.org) 1518 Scheduling is one of the most important tasks in the cloud computing environment. In this thesis we have implemented Ant Colony Optimization and Adaptive firefly and tested the results of the algorithm on cloudsim-3.0 by varying the configuration of virtual machines. After running the algorithm for different sets of jobs given to cloudsim-3.0 we are able to conclude that the results of Adaptive firefly are quite better as ACO. As the total time taken by the Adaptive firefly is less as compared to the earlier one, hence the number of jobs send over the cloud will execute faster as compared to other. So, by adding the new parameter 𝛼to the algorithm of firefly, taking extra virtual machines configuration and cloudlets/jobs status parameters to modify the equation of 𝛼, 𝛽, &𝛾 and hence Intensity of the firefly to make it Adaptive firefly, we achieved the better results.
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Time and Resource Efficient Task Scheduling in Cloud Computing Environment

Time and Resource Efficient Task Scheduling in Cloud Computing Environment

Abstract - Cloud computing has become a new age technology that has got huge potentials in enterprises and markets. Cloud Computing has Large Scale Distributed Infrastructure which is accessible and scalable infrastructure. Cloud computing provides a pay as you go model in which the user has to pay for the services he uses. However one of the major challenges in cloud computing is related to optimizing the resources being allocated. Because of the uniqueness of the model, resource allocation should be performed with the objective of minimizing the costs associated with it. This optimized use of cloud can only be done by efficient and effective algorithm to select the best resources. In this paper, the Task Based allocation of resources is used to minimize the makespan of the cloud system and also to increase the resource utilization. The simulation is done using CloudSim and results show that TBA algorithm reduces the makespan, execution time and cost as compared to Random Algorithm and FCFS algorithm.
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A Review Work On Task Scheduling In Cloud Computing Using Genetic Algorithm

A Review Work On Task Scheduling In Cloud Computing Using Genetic Algorithm

Abstract: Cloud computing is one of the upcoming latest new computing paradigm where applications and data services are provided over the Internet. Today‘s most of the business organizations and educational institutions use cloud environment. The Task management is the key role in cloud computing systems task scheduling problems are main which relate to the efficiency of the whole cloud computing facilities. Scheduling in cloud means selection of best suitable resources for task execution. A task scheduler in Cloud computing has to satisfy cloud users with the agreed QoS and improve profits of cloud providers. There are a mass of researches on the issue of scheduling in cloud computing most of them, however are about workflow and job scheduling. The scheduling entails the selection of the services and the appropriate start time for each workflow. In this paper we mainly focus on different types of workflow scheduling algorithms. The main focus is to study various problems, issues and types of scheduling based on the genetic algorithm for cloud workflows.
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TASK SCHEDULING IN CLOUD COMPUTING

TASK SCHEDULING IN CLOUD COMPUTING

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

TASK SCHEDULING IN CLOUD COMPUTING

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

To overcome the challenges of fitness assignment problem, new efforts have been reported to use techniques for solving multi-objective task scheduling problems efficiently. These techniques are based on using multiple populations for multiple objectives for solving multi-objective problems where each population optimize one objective (Zhan et al., 2013). Each population is optimized using existing optimization algorithm. Yao et al. (2016) proposed endocrine-based co-evolutionary multi-swarm multi-objective algorithm to find optimal trade-offs solutions between energy consumption, makespan, and cost. The proposed strategy adopted multi-swarm optimization strategy where each swarm corresponds to one objective and PSO is used to optimize each objective. A novel competition and cooperation strategy is designed to avoid swarms getting trapped in local optima. Similarly, Li et al. (2015a) presents co- evolutionary multi-swarm PSO algorithm to obtain optimal trade-off solutions between makespan and cost. Learning between the particles is enhanced using renumber strategy (Li et al., 2015b). However, the proposed techniques can not scale well since the efficiency of PSO algorithms is challenged by local optima entrapment and imbalance between local and global search. Moreover, efficiently exchanging information between swarms and avoidances of local Pareto Fronts are still challenging issues with co- evolutionary multi-swarm multi-objective task scheduling approaches.
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SAKTHI: Scheduling Algorithm K to Hybrid in Cloud Computing

SAKTHI: Scheduling Algorithm K to Hybrid in Cloud Computing

The main objective of the scheduling algorithms in cloud environment is to utilize the resources properly while managing the load between the resources so that to get the minimum execution time. Here we mainly discuss three scheduling algorithm First come first serve, Round robin scheduling and new scheduling approach is generalized priority algorithm. Hybrid scheduling achieves better throughput and good performance.

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LABTS: a Learning Automata-Based Task Scheduling algorithm in cloud computing

LABTS: a Learning Automata-Based Task Scheduling algorithm in cloud computing

Scheduling is the problem of mapping a set of tasks to a set of distributed resources. Thus many researchers have made significant efforts to provide effective solutions to solve this problem. The problem of scheduling in a simpler case is a hybrid optimization problem that can be considered as the bin packing problem, in which tasks are items needed to be packaged and the virtual machines are bins with different capacities [20]. Due to the complexity of the scheduling problem, solving it by using complete search methods may not be suitable because it will be expensive in operation counts and thus time [21]. Thus, by considering some parameters in allocating tasks to virtual machines, different scheduling methods including meta-heuristic algorithms and the swarm intelligence algorithm have been introduced in the literature. A new parallel bi-objective hybrid genetic algorithm has been proposed in [13], reducing the makespan as well as consumed energy. Two models: island parallel model and the multi-start parallel model are investigated in this paper. The dynamic voltage scaling (DVS) is used to minimize the energy consumption. Yu et al. use the Genetic Algorithm to optimize cost and execution time by considering deadline and budget[22]. Ramezani et al. use the multi- objective PSO algorithm (MOPSO) to minimize execution time, transfer time and the cost of the tasks in scheduling.[23]. Netjinda et al. aimed to optimize the cost of purchasing the IaaS in order to execute the workflow in the specified deadline. In the proposed system, the number of purchase distances, instance types, purchasing options, and task scheduling are the main constraints in the optimization process. In this paper, particles swarm optimization augmented with a variable neighborhood search technique have been used to find the configurations of purchasing options with optimal cost and budget to meet task requirements, which shows excellent results in terms of overall cost and fitness convergence compared to other algorithms [24]. Parthasarathy et al. presented a scheduling algorithm which is oppositional-GSO algorithm using heuristic search methods in cloud computing environment. In this paper, a population that contains a group of members are generated with their respective jobs and the fitness are calculated for each member. Based on the fitness, different operations such as producer operation, scrounger operation, ranger operation and oppositional operation are applied to generate the best schedule [25].
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A Survey On Various Task Scheduling Algorithm in cloud Environment

A Survey On Various Task Scheduling Algorithm in cloud Environment

There are different types of scheduling algorithm used to solve scheduling problems, most of applied in the cloud environment with suitable verification. Various algorithms has been proposed by researches to allocate and schedule in the cloud environment [11][4]. Jobs are schedule by user need. New scheduling strategies require to be proposed to overcome the problems posed by network properties between user and resource[1]. The main advantages of scheduling algorithm obtain a high performance and proper utilization resources. Job scheduling are the key technologies of cloud computing plays a vital role in an efficient use resource Management[5]. The main examples of scheduling algorithms are FCFS,Round Robin, Min-Min ,and Max- Min algorithm.
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Study of Task Scheduling Algorithms in the          Cloud Computing Environment: A Review

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

the basis of different parameters so that it enhances the overall cloud performance. A task may be related to entering data, processing, accessing software, or storage functions. The data center specifies tasks according to the service-level agreement and demanded services. In the process, the users submit their jobs to the cloud scheduler. The cloud scheduler probes the cloud information service for acquiring the status of available resources and their properties and hence allocating the different tasks onto diverse resources as per the task specifications. Cloud Scheduler will designate multiple user tasks to many virtual machines. A Good scheduler always selects the virtual machines in an optimal way. A good scheduling algorithm improves the CPU utilization, turnaround time and combined throughput. Task scheduling can be implemented based on various parameters in distinct ways. They can be allocated statically at compile time or allocated dynamically at runtime.
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Enhanced Genetic Algorithm based Task Scheduling in Cloud Computing

Enhanced Genetic Algorithm based Task Scheduling in Cloud Computing

achieve high resource utilization for having maximum revenue, on the other hand, users try to minimize the expenses, while keeping the requirement regarding the performance. Due to lack of information shared between the resources, it is tuff to allocate recourses in mutual optimal way. More over variability of the environment and ever- increasing heterogeneity gives even bigger challenges for both parties. Cloud computing is recently a booming area and has been emerging as a commercial reality in the information technology domain. However the technology is still not fully developed. There are still some areas that are needs to be focused on [4].These areas can be broadly classified into two domains:
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Task Scheduling using Hybrid PSO in cloud based environment

Task Scheduling using Hybrid PSO in cloud based environment

Figure 5 indicates that PSO&GA algorithm has better load balancing overall performance as compared with the G&PSO algorithm. usually, using more virtual machines does no longer imply acquiring a better result, as configuring every virtual device often consumes extra machine sources and eventually results in a increase in the basic system overall performance. because of the predicament of the physical hardware within the widespread host and community bandwidth, the number of virtual machines assigned to a single host ought to be set to no greater than 10 to obtain the first-class system overall performance. To similarly verify the performance of the proposed G&PSO algorithm of load balancing in virtual machines, the variety of virtual machines turned into improved from 5 to 10 and their processing competencies updated to 500, 600, 700, 800, 900, 1000, 550, 650, 750, and 850. The variety of duties remained unchanged, and those responsibilities are assigned to each virtual device proven in Figs. 5 and 6. In Figs.5 and 6, the simulation effects for each large- scale and small-scale mission scheduling are proven. When the usage of the proposed algorithm the number of tasks assigned to each virtual machine remains toward the mean cost and the device load remains balanced. In conclusion, the proposed G&PSO algorithm achieves the desires of shorter mission of completion time and a greater balanced virtual machine load; the comprehensive efficiency of the cloud computing platform has therefore been advanced.
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Pso-Based Task Scheduling Algorithm Using Adaptive Load Balancing Approach For Cloud Computing Environment

Pso-Based Task Scheduling Algorithm Using Adaptive Load Balancing Approach For Cloud Computing Environment

CLOUD computing has become inspiring technology due to its powerful architecture to perform large scale and complex computing. It simplifies appropriate on-demand network access to a shared unlimited number of computing resources such as CPUs, memory and storage [1] [2]. These resources are quickly provisioned and released with the least management effort. The most critical services offer by cloud computing are reliability, scalability, elasticity and high availability, which make it a highly complex and large distributed system. To provide these facilities for processing of massive amount of data, cloud computing services are categorized as infrastructure, platform and software as service [3] [5-7]. These services are made available to customers based on the pay as use model [8-10]. As masses of users post their computing tasks on the cloud system, task scheduling methods plays a essential part in cloud computing environments [1][4][11]. The scheduling mechanism can perform dynamically provisioning, allocation and balancing of users demand to available resources [3] [12] [13]. Therefore, the scheduling of tasks deals to reduce makespan and maximize resource utilization [12]. In this paper, we emphasis on minimizing the makespan and maximizing the throughput. We achieve this by using a meta-heuristics concept called Particle Swarm Optimization (PSO) and Adaptive Load Balancing (ALB) approach [14]. The benefit of using an adaptive scheduling algorithm with the PSO technique is, it handles over-loaded and under-loaded condition concurrently. Load balancing method guarantees load balancing of the system by measuring the load on each virtual machine and then relocating the task according to the status of each virtual machine based on the deadlines of the tasks.
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Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

Among these studies, one describes an introductory lesson that builds on student familiarity with Google Docs to illustrate the concepts of Infrastructure, Software, Data, and Platform as a Service (Frydenberg, 2011). The study found that while many first-year college students have heard of the term Cloud Computing and used Google Docs, a popular web- based office suite of applications for collaboration, their knowledge of how the Cloud is used in a business environment is limited (Frydenberg, 2011).

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Improved Genetic and Memetic based Task Scheduling in Cloud Computing Environment

Improved Genetic and Memetic based Task Scheduling in Cloud Computing Environment

The genetic algorithm is not well suited for fine-tuning structures which are close to optimal solution. The memetic algorithms can be viewed as a marriage between a population-based global technique and a local search made by each of the individuals. They are a special kind of genetic algorithms with a local hill climbing. Like genetic algorithms, memetic Algorithms are a population-based approach. They have shown that they are orders of magnitude faster than traditional genetic Algorithms for some problem domains. In a memetic algorithm the population is initialized at random or using a heuristic. Then, each individual makes local search to improve its fitness. To form a new population for the next generation, higher quality individuals are selected. The selection phase is identical inform to that used in the classical genetic algorithm selection phase. Once two parents have been selected, their chromosomes are combined and the classical operators of crossover are applied to generate new individuals. The latter are enhanced using a local search technique. The role of local search in memetic algorithms is to locate the local optimum more efficiently then the genetic algorithm. Figure 3 explains the generic implementation of memetic algorithm.
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Deadline Constrained Task Scheduling with Load Balancing in Cloud Computing Environment

Deadline Constrained Task Scheduling with Load Balancing in Cloud Computing Environment

An attempt has been made to compare the performance of these two algorithms for the purpose of scheduling. Let us consider, three different resources and five different tasks. Table A presents the processing speed of the resources and Table B presents the size of all task. Considering these data, execution_time and expected completion_time of the tasks are calculated using Min-Min algorithm as shown in Table C. The struck out figures indicate that those resources are not allocated to the corresponding row task rather that task is assigned to the resource that is left out. Table C presents the resultant schedule according to Min-Min algorithm.
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Enhanced Max-min Task Scheduling Algorithm
in Cloud Computing

Enhanced Max-min Task Scheduling Algorithm in Cloud Computing

Cloud Computing is getting advanced day by day. Cloud service providers are willing to provide services using large scale cloud environment with cost effectiveness. Also, there are some popular large scaled applications like social- networking and e-commerce. These applications can benefit to minimize the costs using cloud computing. Cloud computing is considered as internet based computing service provided by various infrastructure providers on an on- demand basis, so that cloud is subject to Quality of Service(QoS), Load Balance(LB) and other constraints which have direct effect on user consumption of resources controlled by cloud infrastructure [1] [2].
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Survey on Scheduling Algorithm for Deadline Obligated Task in cloud computing

Survey on Scheduling Algorithm for Deadline Obligated Task in cloud computing

ABSTRACT: Cloud computing is parallel and distributed system and delivery of IT services via the Internet and everyone can access their documents and application from anywhere and at anytime. Cloud computing is a distributed paradigm and it gives a remedy for solving huge scale problem. Scheduling the task is a challenging in the cloud environment and fulfilling performance, minimum execution time, shortest response time. Dynamic scheduling is used at the time of allocating a task to VM and gives deadline to the task. There are different factor considered in scheduling algorithm for load balancing, system throughput, system reliability.In cloud computing performance evaluation of algorithm is important and survey on various scheduling algorithm.
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