GA-Based Customer-Conscious Resource Allocation and Task Scheduling in Multi-cloud Computing

Jena, Tamanna; Mohanty, Jnyana Ranjan

doi:10.1007/s13369-017-2766-x

Cited by 43 publications

(30 citation statements)

References 27 publications

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“…But it is not good for solution space in GA. The existing system on the GA scheduling not consider the cost as well as total completion time [1].…”

Section: Challenges In Existing Systemmentioning

confidence: 99%

“…Another proposed algorithm in this survey paper is shortest job first scheduling algorithm. The shortest job first scheduling algorithm is one of the algorithms which have the minimum execution time, its process having the smallest CPU burst gets a higher priority and execute at the first by CPU [1]. The remaining job or task in the pool are wait for the execution during this time.…”

Section: Figure 3 Resource Allocation Principlementioning

confidence: 99%

“…Multicloud computing technique provides the resource of various capacities to customer. The cloud computing is popular this day withe the need for in demand and large scale computing, public cloud provider such as Amazon Google, Microsoft or Rackspace offers access to their reliable infrastructural or directly to the virtual resource [1]. In a multicloud environment the task or job scheduler adaptable their scheduling strategy to the ever-changing situation and the various type of task.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comprehensive Study on Task Scheduling Strategies in Multicloud Environment

Patil¹,

Gade²,

Rewatkar³

2019

JESA

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The task scheduling in multicloud computing environment is complicated task. The cloud computing has increase tremendous popularity both in academia and business because of its on demand service over internet to the various customer. The task scheduling problem is N-P (Non deterministic polynomial) completeness problem. Task scheduling in cloud computing is a best-known problem that has been paid attention. This is again more challenging, specially for multicloud computing environment. This survey paper presents the Shortest Job First algorithm, Round Robin algorithm and Genetic Algorithm for task scheduling in multicloud computing. The round robin algorithm provides the fair allocation and square allocation of resource to the task. It provides the accurate result in finding solutions to large scale optimization problems, by Genetic algorithms such as task scheduling and it is also helpful. A good task and resource scheduling mechanism must satisfy the QoS requirement of the user and at the same time make an efficient utilization of resources. This survey paper presents an algorithm which tries to achieve application high availability, and minimum makespan, minimum response time and completion time.

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“…But it is not good for solution space in GA. The existing system on the GA scheduling not consider the cost as well as total completion time [1].…”

Section: Challenges In Existing Systemmentioning

confidence: 99%

Section: Figure 3 Resource Allocation Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive Study on Task Scheduling Strategies in Multicloud Environment

Patil¹,

Gade²,

Rewatkar³

2019

JESA

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“…Tamanna Jena, et al [1] used GA to find out best task-VM pair which will results in improvement of makespan and throughput. Regular FCFS policy is ignored by implementing shortest job first policy.…”

Section: Related Workmentioning

confidence: 99%

“…Whenever it has been stated that it has infinite resources it actually doesn't mean infinite it has some limitations and from the perspectives of cloud service providers efforts are usually made to minimize resource utilization, particularly in case of peak time. Multi-tenancy, on-demand services and any service-any time are the features which makes cloud computing even more happening [1]. Maintaining these huge amount resources while providing guarantee of services is a tedious task.…”

Section: Introductionmentioning

confidence: 99%

Adaptive League Championship Algorithm (ALCA) for Independent Task Scheduling in Cloud Computing

Gade¹,

Mundukur²,

Thakare³

2019

ISI

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Scheduling is a heart of cloud computing as without appropriate scheduling it is impossible to get the desired results. Primary focus of this article is to focus on minimization of makespan, minimum utilization of resources and make the cloud services economic for an independant task. Out of the various task scheduling strategies, in last few years meta-heuristic algorithms have gained recognition in successful operation of task scheduling algorithms. League Championship based Algorithm (LCA) is fascinated from sports leagues through which best team/task in this case can be find out for scheduling. Task scheduling using Adaptive League Championship Algorithm (ALCA) is employed in this article and thereby it shrinks makespan, cloud utilization and cost. ALCA is implemented with cloudsim simulator using java as a programming tool and scheduling has followed the non-premptive approach. Implementation of ALCA results reducation in makespan by 32.95 %, 20.99 % and 7.29 % against customary Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Global League Championship Algorithm (GBLCA) respectively. ALCA also reduces significantly cloud utilization value and improves economy of scale. ALCA may serve as preferred choice for cloud broker as it proved to be multipurpose in the area of makespan, resource utilization and economy.

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Hybrid Electro search beetle optimization based task scheduling and game theory SOA based resource allocation in multi cloud computing

Sreelatha,

Reddy,

Hanafiah

et al. 2024

Softw Pract Exp

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The most complicated process in multi‐cloud computing is resource allocation, as it needs to cope with a number of configurations and constraints of cloud providers and customers. At the time of resource allocation, the centralized cloud broker monitors the virtual machines (VM) status, scheduling process, and fitness. However, VM scheduling is found tedious and has received huge attention in business, academia, and research. This enhances the demand for both task scheduling and resource allocation in a multi‐cloud environment. To bridge the gap between the consumer requirement and server infrastructure, a joint optimization‐based resource allocation and task scheduling concept is analyzed in the proposed framework. The first phase introduces the task scheduling mechanism, which uses Hybrid Electro Search and Beetle Swarm Optimization to determine the optimal task for specific VMs. The optimal selection procedure is done by analyzing a multi‐cloud environment's makespan, energy, cost, and throughput parameters. In the second step, an Adaptive Game Theory‐based Seagull optimization approach performs several rounds of reassignment iteratively to minimize the variation in the expected completion time, consequently decreasing high energy consumption and load balancing. The experimental analysis for the proposed model is implemented using Python. The proposed methodology is shown to achieve cheaper costs, shorter waiting times, improved resource allocation, and efficient load balancing. Finally, a comparative analysis is performed with some hybrid optimization models, which illustrate the efficiency of the proposed hybrid optimization model.

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GA-Based Customer-Conscious Resource Allocation and Task Scheduling in Multi-cloud Computing

Cited by 43 publications

References 27 publications

Comprehensive Study on Task Scheduling Strategies in Multicloud Environment

Comprehensive Study on Task Scheduling Strategies in Multicloud Environment

Adaptive League Championship Algorithm (ALCA) for Independent Task Scheduling in Cloud Computing

Hybrid Electro search beetle optimization based task scheduling and game theory SOA based resource allocation in multi cloud computing

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