An Overview of Quality of Service with Load Balancing in Cloud Computing Environment

2020 ◽  
pp. 63-76
Author(s):  
Tazein Azmat ◽  
Vijay Kumar Dwivedi
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Load Balancing ◽  
Computing Environment ◽  
Cloud Computing Environment

scholarly journals Service Level Agreement Violation Preventive Task Scheduling for Quality of Service Delivery in Cloud Computing Environment

2020 ◽  
Vol 178 ◽  
pp. 375-385
Author(s):  
Ismail Zahraddeen Yakubu ◽  
Zainab Aliyu Musa ◽  
Lele Muhammed ◽  
Badamasi Ja’afaru ◽  
Fatima Shittu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Service Delivery ◽  
Task Scheduling ◽  
Service Level Agreement ◽  
Service Level ◽  
Computing Environment ◽  
Cloud Computing Environment

Sharing VM Resources With Using Prediction of Future User Requests for an Efficient Load Balancing in Cloud Computing Environment

2021 ◽  
Vol 13 (2) ◽  
pp. 37-64
Author(s):  
Mousa Elrotub ◽  
Ahmed Bali ◽  
Abdelouahed Gherbi
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Performance Metrics ◽  
Virtual Machines ◽  
Machine Learning Techniques ◽  
Data Repository ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Learning Techniques

The problem of balancing user requests in cloud computing is becoming more serious due to the variation of workloads. Load balancing and allocation processes still need more optimizing methodologies and models to improve performance and increase the quality of service. This article describes a solution to balance user workload efficiently by proposing a model that allows each virtual machine (VM) to maximize the serving number of requests based on its capacity. The model measures VMs' capacity as a percentage and maps groups of user requests to appropriate active virtual machines. Finding the expected patterns from a big data repository, such as log data, and using some machine learning techniques can make the prediction more efficiently. The work is implemented and evaluated using some performance metrics, and the results are compared with other research. The evaluation shows the efficiency of the proposed approach in distributing user workload and improving results.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

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