Minimum Completion Time for Power-Aware Scheduling in Cloud Computing

2011 ◽  
Author(s):  
Nawfal A. Mehdi ◽  
Ali Mamat ◽  
Ali Amer ◽  
Ziyad T. Abdul-Mehdi
Keyword(s):  
Cloud Computing ◽  
Completion Time ◽  
Minimum Completion Time ◽  
Power Aware Scheduling

Efficient task scheduling on virtual machine in cloud computing environment

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahfooz Alam ◽  
Mahak ◽  
Raza Abbas Haidri ◽  
Dileep Kumar Yadav
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Virtual Machine ◽  
Task Allocation ◽  
Completion Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Content Type ◽  
Minimum Completion Time

Purpose Cloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization. Design/methodology/approach Cloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing. Findings To evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time. Originality/value The simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.

Resource Provisioning Based Scheduling Framework for Execution of Virtual Machine in Heterogeneous Environment in Cloud Computing

2020 ◽  
Vol 37 ◽  
pp. 59-68
Author(s):  
Maheta Ashish ◽  
Samrat V.O. Khanna
Keyword(s):  
Cloud Computing ◽  
System Performance ◽  
Completion Time ◽  
Resource Provisioning ◽  
Resource Manager ◽  
Cloud Resource ◽  
Cpu Utilization ◽  
Dynamic Resource ◽  
Minimum Completion Time

Cloud computing is provides resource allocation which facilitates the cloud resource provider responsible to the cloud consumers. The main objective of resource manager is to assign the dynamic resource to the task in the execution and measures response time, execution cost, resource utilization and system performance. The resource manager is optimizing the resource and measure the completion time for assign resource. The resource manager is also measure to execute the resource in the optimal way to complete the task in minimum completion time. The virtualization is techniques mandatory to allocate the dynamic resource depends on the users need. There are also green computing techniques involved for enhanced the no of server. The skewness is basically used to enhance the quality of service using the various parameters. The proposed algorithms are considered to allocate the cloud resource as per the users requirement. The advantage of proposed algorithm is to view the analysis of cpu utilization and also reduced the memory usage.

scholarly journals Resource-Aware Min-Min (RAMM) Algorithm for Resource Allocation in Cloud Computing Environment

2019 ◽  
Vol 8 (3) ◽  
pp. 1863-1870 ◽  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Utilization ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Cloud Resource ◽  
Tasks Scheduling ◽  
Minimum Completion Time ◽  
Resource Aware ◽  
Time Resource

Resource allocation (RA) is a significant aspect of Cloud Computing. The Cloud resource manager is responsible to assign available resources to the tasks for execution in an effective way that improves system performance, reduce response time, lessen makespan and utilize resources efficiently. To fulfil these objectives, an effective Tasks Scheduling algorithm is required. The standard Max-Min and Min-Min Task Scheduling algorithms are not able to produce better makespan and effective resource utilization. In this paper, a Resource-Aware Min-Min (RAMM) Algorithm is proposed based on basic Min-Min algorithm. The proposed RAMM Algorithm selects shortest execution time task and assigns it to the resource which takes shortest completion time. If minimum completion time resource is busy, then the RAMM Algorithm selects next minimum completion time resource to reduce waiting time of the task and improve resource utilization. The experiment results show that the proposed RAMM Algorithm produces better makespan and load balance than Max-Min, Min-Min and improved Max-Min Algorithms.

Cloud Computing in Three Phase Scheduling Algorithm Based on Service Quality

2014 ◽  
Vol 687-691 ◽  
pp. 2862-2866
Author(s):  
Yong Long Zhuang ◽  
Xiao Lan Weng ◽  
Xiang He Wei
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Service Quality ◽  
Network Topology ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Hierarchical Network ◽  
Three Phase ◽  
Simulation Results ◽  
Minimum Completion Time

Assurance issues for cloud computing’s service quality; propose a hierarchical network topology on the basis of the three-stage scheduling algorithm. The algorithm ensures that each work to be performed can be assigned to the appropriate resources, and can effectively improve the load of each node and reduce the waste of resources. FCFS, SJF, OLB and Min-Min (MM) scheduling algorithm’s simulation results show that the algorithm can not only make the work get the minimum completion time, but also the service node operation achieve the load balancing.

scholarly journals Max-Min Processors Scheduling

2021 ◽  
Vol 50 (1) ◽  
pp. 5-12
Author(s):  
Hani Alquhayz ◽  
Mahdi Jemmali
Keyword(s):  
Literature Review ◽  
Knapsack Problem ◽  
Completion Time ◽  
Research Work ◽  
Parallel Processors ◽  
Experimental Results ◽  
Running Time ◽  
Similar Performance ◽  
Minimum Completion Time ◽  
Randomization Method

This paper focuses on the maximization of the minimum completion time on identical parallel processors. The objective of this maximization is to ensure fair distribution. Let a set of jobs to be assigned to several identical parallel processors. This problem is shown as NP-hard. The research work of this paper is based essentially on the comparison of the proposed heuristics with others cited in the literature review. Our heuristics are developed using essentially the randomization method and the iterative utilization of the knapsack problem to solve the studied problem. Heuristics are assessed by several instances represented in the experimental results. The results show that the knapsack based heuristic gives almost a similar performance than heuristic in a literature review but in better running time.  

scholarly journals Workflow Scheduling Based on Mobile Cloud Computing Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fanghai Gong
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Completion Time ◽  
Mobile Cloud Computing ◽  
Task Completion ◽  
Workflow Scheduling ◽  
Mobile Cloud ◽  
Task Completion Time ◽  
Computing Machine

In recent years, cloud workflow task scheduling has always been an important research topic in the business world. Cloud workflow task scheduling means that the workflow tasks submitted by users are allocated to appropriate computing resources for execution, and the corresponding fees are paid in real time according to the usage of resources. For most ordinary users, they are mainly concerned with the two service quality indicators of workflow task completion time and execution cost. Therefore, how cloud service providers design a scheduling algorithm to optimize task completion time and cost is a very important issue. This paper proposes research on workflow scheduling based on mobile cloud computing machine learning, and this paper conducts research by using literature research methods, experimental analysis methods, and other methods. This article has deeply studied mobile cloud computing, machine learning, task scheduling, and other related theories, and a workflow task scheduling system model was established based on mobile cloud computing machine learning from different algorithms used in processing task completion time, task service costs, task scheduling, and resource usage The situation and the influence of different tasks on the experimental results are analyzed in many aspects. The algorithm in this paper speeds up the scheduling time by about 7% under a different number of tasks and reduces the scheduling cost by about 2% compared with other algorithms. The algorithm in this paper has been obviously optimized in time scheduling and task scheduling.

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