A multi-objective load balancing algorithm for virtual machine placement in cloud data centers based on machine learning

Computing ◽  
2020 ◽  
Vol 102 (9) ◽  
pp. 2049-2072 ◽  
Author(s):  
Arezoo Ghasemi ◽  
Abolfazl Toroghi Haghighat
Keyword(s):  
Machine Learning ◽  
Load Balancing ◽  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machine Placement ◽  
Cloud Data ◽  
Multi Objective ◽  
Cloud Data Centers ◽  
Load Balancing Algorithm

scholarly journals A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers

2020 ◽  
Vol 128 ◽  
pp. 106390
Author(s):  
Ennio Torre ◽  
Juan J. Durillo ◽  
Vincenzo de Maio ◽  
Prateek Agrawal ◽  
Shajulin Benedict ◽  
...  
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machine Placement ◽  
Cloud Data ◽  
Multi Objective ◽  
Cloud Data Centers

scholarly journals AN ENHANCED TASK ALLOCATION STRATEGY IN CLOUD ENVIRONMENT

2017 ◽  
Vol 16 (6) ◽  
pp. 6953-6961
Author(s):  
Kavita Redishettywar ◽  
Prof. Rafik Juber Thekiya
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Data Centers ◽  
Job Scheduling ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Load Balancing Algorithm

Cloud computing is a vigorous technology by which a user can get software, application, operating system and hardware as a service without actually possessing it and paying only according to the usage. Cloud Computing is a hot topic of research for the researchers these days. With the rapid growth of Interne technology cloud computing have become main source of computing for small as well big IT companies. In the cloud computing milieu the cloud data centers and the users of the cloud-computing are globally situated, therefore it is a big challenge for cloud data centers to efficiently handle the requests which are coming from millions of users and service them in an efficient manner. Load balancing ensures that no single node will be overloaded and used to distribute workload among multiple nodes. It helps to improve system performance and proper utilization of resources. We propose an improved load balancing algorithm for job scheduling in the cloud environment using K-Means clustering of cloudlets and virtual machines in the cloud environment. All the cloudlets given by the user are divided into 3 clusters depending upon client’s priority, cost and instruction length of the cloudlet. The virtual machines inside the datacenter hosts are also grouped into multiple clusters depending upon virtual machine capacity in terms of processor, memory, and bandwidth. Sorting is applied at both the ends to reduce the latency. Multiple number of experiments have been conducted by taking different configurations of cloudlets and virtual machine. Various parameters like waiting time, execution time, turnaround time and the usage cost have been computed inside the cloudsim environment to demonstrate the results. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.

Energy-Efficient Virtualized Scheduling and Load Balancing Algorithm in Cloud Data Centers

2021 ◽  
Vol 11 (3) ◽  
pp. 34-48
Author(s):  
J. K. Jeevitha ◽  
Athisha G.
Keyword(s):  
Quality Of Service ◽  
Load Balancing ◽  
Energy Efficient ◽  
Data Centers ◽  
Scheduling Algorithm ◽  
Cloud Data ◽  
The Third ◽  
Cloud Data Centers ◽  
Load Balancing Algorithm

To scale back the energy consumption, this paper proposed three algorithms: The first one is identifying the load balancing factors and redistribute the load. The second one is finding out the most suitable server to assigning the task to the server, achieved by most efficient first fit algorithm (MEFFA), and the third algorithm is processing the task in the server in an efficient way by energy efficient virtual round robin (EEVRR) scheduling algorithm with FAT tree topology architecture. This EEVRR algorithm improves the quality of service via sending the task scheduling performance and cutting the delay in cloud data centers. It increases the energy efficiency by achieving the quality of service (QOS).

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