Scheduling Method Based on Backfill Strategy for Multiple DAGs in Cloud Computing

2019 ◽  
pp. 278-290
Author(s):  
Zhidan Hu ◽  
Hengzhou Ye ◽  
Tianmeizi Cao
Keyword(s):  
Cloud Computing ◽  
Scheduling Method

Efficient on Demand Dynamic Availability-Distribution-Span Scheduling and Load Balancing Scheme for Cloud Computing

2016 ◽  
Vol 13 (10) ◽  
pp. 7655-7660 ◽  
Author(s):  
V Jeyakrishnan ◽  
P Sengottuvelan
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Resource Utilization ◽  
Scheduling Algorithms ◽  
Time Factor ◽  
Cloud Environment ◽  
On Demand ◽  
Availability Factor ◽  
Scheduling Method

The problem of load balancing in cloud environment has been approached by different scheduling algorithms. Still the performance of cloud environment has not been met to the point and to overcome these issues, we propose a naval ADS (Availability-Distribution-Span) Scheduling method to perform load balancing as well as scheduling the resources of cloud environment. The method performs scheduling and load balancing in on demand nature and takes dynamic actions to fulfill the request of users. At the time of request, the method identifies set of resources required by the process and computes Availability Factor, Distributional Factor and Span Time factor for each of the resource available. Based on all these factors computed, the method schedules the requests to be processed in least span time. The proposed method produces efficient result on scheduling as well as load balancing to improve the performance of resource utilization in the cloud environment.

scholarly journals Multi-objective Prediction based Task Scheduling Method in Cloud Computing

2019 ◽  
Vol 8 (4) ◽  
pp. 9388-9394 ◽  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Job Scheduling ◽  
Sigmoid Function ◽  
Service Model ◽  
Multi Objective ◽  
Computing Paradigm ◽  
Optimal Node ◽  
Scheduling Method

Cloud Computing is Internet based computing where one can store and access their personal resources from any computer through Internet. Cloud Computing is a simple pay-per-utilize consumer-provider service model. Cloud is nothing but large pool of easily accessible and usable virtual resources. Task (Job) scheduling is always a noteworthy issue in any computing paradigm. Due to the availability of finite resources and time variant nature of incoming tasks it is very challenging to schedule a new task accurately and assign requested resources to cloud user. Traditional task scheduling techniques are improper for cloud computing as cloud computing is based on virtualization technology with disseminated nature. Cloud computing brings in new challenges for task scheduling due to heterogeneity in hardware capabilities, on-demand service model, pay-per-utilize model and guarantee to meet Quality of Service (QoS). This has motivated us to generate multi-objective methods for task scheduling. In this research paper we have presented multi-objective prediction based task scheduling method in cloud computing to improve load balancing in order to satisfy cloud consumers dynamically changing needs and also to benefit cloud providers for effective resource management. Basically our method gives low probability value for not capable and overloaded nodes. To achieve the same we have used sigmoid function and Euclidean distance. Our major goal is to predict optimal node for task scheduling which satisfies objectives like resource utilization and load balancing with accuracy.

scholarly journals Scheduling Method of Data-Intensive Applications in Cloud Computing Environments

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xiong Fu ◽  
Yeliang Cang ◽  
Xinxin Zhu ◽  
Song Deng
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Completion Time ◽  
Network Performance ◽  
Communication Delay ◽  
Data Intensive ◽  
Related Data ◽  
Scheduling Method ◽  
Placement Algorithm ◽  
User Tasks

The virtualization of cloud computing improves the utilization of resources and energy. And a cloud user can deploy his/her own applications and related data on a pay-as-you-go basis. The communications between an application and a data storage node, as well as within the application, have a great impact on the execution efficiency of the application. The locations of subtasks of an application and the data that transferred between the subtasks are the main reason why communication delay exists. The communication delay can affect the completion time of the application. In this paper, we take into account the data transmission time and communications between subtasks and propose a heuristic optimal virtual machine (VM) placement algorithm. Related simulations demonstrate that this algorithm can reduce the completion time of user tasks and ensure the feasibility and effectiveness of the overall network performance of applications when running in a cloud computing environment.

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