Improving resource utilization in a heterogeneous cloud environment

2012 ◽  
Author(s):  
Hsin-Yu Shih ◽  
Jenq-Shiou Leu
Keyword(s):  
Resource Utilization ◽  
Cloud Environment ◽  
Heterogeneous Cloud

Improved Scheduling Procedure for Intensify Resource Utilization in Heterogeneous Cloud Environment

2018 ◽  
Vol 6 (5) ◽  
pp. 304-308
Author(s):  
Lovejoban Preet Singh ◽  
◽  
◽  
Anil Kumar
Keyword(s):  
Resource Utilization ◽  
Cloud Environment ◽  
Heterogeneous Cloud

Research on Energy-Saving Algorithms with Migration Energy Consumption in Heterogeneous Cloud Environment

2014 ◽  
Vol 986-987 ◽  
pp. 1383-1386
Author(s):  
Zhen Xing Yang ◽  
He Guo ◽  
Yu Long Yu ◽  
Yu Xin Wang
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Saving ◽  
Large Scale ◽  
Electrical Energy ◽  
Cloud Environment ◽  
Migration Energy ◽  
Live Migration ◽  
Large Scale Data ◽  
Heterogeneous Cloud

Cloud computing is a new emerging paradigm which delivers an infrastructure, platform and software as services in a pay-as-you-go model. However, with the development of cloud computing, the large-scale data centers consume huge amounts of electrical energy resulting in high operational costs and environment problem. Nevertheless, existing energy-saving algorithms based on live migration don’t consider the migration energy consumption, and most of which are designed for homogeneous cloud environment. In this paper, we take the first step to model energy consumption in heterogeneous cloud environment with migration energy consumption. Based on this energy model, we design energy-saving Best fit decreasing (ESBFD) algorithm and energy-saving first fit decreasing (ESFFD) algorithm. We further provide results of several experiments using traces from PlanetLab in CloudSim. The experiments show that the proposed algorithms can effectively reduce the energy consumption of data center in the heterogeneous cloud environment compared to existing algorithms like NEA, DVFS, ST (Single Threshold) and DT (Double Threshold).

scholarly journals Task scheduling in heterogeneous cloud environment using mean grey wolf optimization algorithm

ICT Express ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 110-114 ◽  
Author(s):  
Gobalakrishnan Natesan ◽  
Arun Chokkalingam
Keyword(s):  
Task Scheduling ◽  
Optimization Algorithm ◽  
Grey Wolf ◽  
Cloud Environment ◽  
Grey Wolf Optimization ◽  
Heterogeneous Cloud

Profiling-Based Effective Resource Utilization in Cloud Environment Using Divide and Conquer Method

2018 ◽  
pp. 495-508 ◽  
Author(s):  
Namra Bhadreshkumar Shah ◽  
Neel Dhananjaybhai Shah ◽  
Jitendra Bhatia ◽  
Harshal Trivedi
Keyword(s):  
Resource Utilization ◽  
Divide And Conquer ◽  
Cloud Environment

A Proficient Approach for Load Balancing in Cloud Computing-Join Minimum Loaded Queue

2020 ◽  
Vol 11 (1) ◽  
pp. 12-36
Author(s):  
Minakshi Sharma ◽  
Rajneesh Kumar ◽  
Anurag Jain
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Resource Utilization ◽  
Cloud Service ◽  
Cloud Environment ◽  
Cloud Service Provider ◽  
Qos Parameters ◽  
Load Balancing Algorithm

Cloud load balancing is done to persist the services in the cloud environment along with quality of service (QoS) parameters. An efficient load balancing algorithm should be based on better optimization of these QoS parameters which results in efficient scheduling. Most of the load balancing algorithms which exist consider response time or resource utilization constraints but an efficient algorithm must consider both perspectives from the user side and cloud service provider side. This article presents a load balancing strategy that efficiently allocates tasks to virtualized resources to get maximum resource utilization in minimum response time. The proposed approach, join minimum loaded queue (JMLQ), is based on the existing join idle queue (JIQ) model that has been modified by replacing idle servers in the I-queues with servers having one task in execution list. The results of simulation in CloudSim verify that the proposed approach efficiently maximizes resource utilization by reducing the response time in comparison to its other variants.

A Survey on Load Balancing Algorithms for Cloud Environment

2019 ◽  
Vol 16 (2) ◽  
pp. 764-767
Author(s):  
P. Chitra ◽  
Karthika D. Renuka ◽  
K. Senathipathi ◽  
S. Deepika ◽  
R. Geethamani
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Comparative Analysis ◽  
Load Balancing ◽  
Resource Utilization ◽  
The Internet ◽  
Cloud Environment ◽  
Web Based ◽  
Qos Metrics

Cloud computing is the cutting edge technology in the information field to provide services to the users over the internet through web–based tools and applications. One of the major aspects of cloud computing is load balancing. Challenges like Quality of service (QoS) metrics and resource utilization can be improved by balancing the load in cloud environment. Specific scheduling criteria can be applied using load balancing for users prioritization. This paper surveys different load balancing algorithms. The approaches that are existing are discussed and analyzed to provide fair load balancing and also a comparative analysis was presented for the performance of the existing different load balancing schemes.

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