scholarly journals Mitigating impact of short-term overload on multi-cloud web applications through geographical load balancing

2017 ◽  
Vol 29 (12) ◽  
pp. e4126 ◽  
Author(s):  
Chenhao Qu ◽  
Rodrigo Neves Calheiros ◽  
Rajkumar Buyya
Keyword(s):  
Load Balancing ◽  
Web Applications ◽  
Short Term ◽  
Multi Cloud

A Cost-Efficient Multi-cloud Orchestrator for Benchmarking Containerized Web-Applications

2019 ◽  
pp. 407-423
Author(s):  
Devki Nandan Jha ◽  
Zhenyu Wen ◽  
Yinhao Li ◽  
Michael Nee ◽  
Maciej Koutny ◽  
...  
Keyword(s):  
Web Applications ◽  
Cost Efficient ◽  
Multi Cloud

Renewable-aware geographical load balancing of web applications for sustainable data centers

2017 ◽  
Vol 83 ◽  
pp. 155-168 ◽  
Author(s):  
Adel Nadjaran Toosi ◽  
Chenhao Qu ◽  
Marcos Dias de Assunção ◽  
Rajkumar Buyya
Keyword(s):  
Load Balancing ◽  
Data Centers ◽  
Web Applications

Dynamic Load Balancing Using Actual Workload Traces Based on Central Processing Unit Temperatures

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
Yusuke Nakajo ◽  
Jayati Athavale ◽  
Minami Yoda ◽  
Yogendra Joshi ◽  
Hiroaki Nishi
Keyword(s):  
Load Balancing ◽  
Dynamic Load ◽  
Data Centers ◽  
Web Applications ◽  
Experimental Studies ◽  
Central Processing Unit ◽  
Dynamic Load Balancing ◽  
Processing Unit ◽  
Central Processing ◽  
Processing Times

Abstract With the rapid growth in demand for distributed computing, data centers are a critical physical component of the “cloud.” Recent studies show that the energy consumption of data centers for both cooling and computing keeps increasing, and the growth in server power densities makes it ever more challenging to keep the servers below their maximum operating temperature. This paper presents a new dynamic load-balancing approach based on individual server central processing unit (CPU) temperatures. In this approach, a load balancer assigns a task in real time to a server based on the objective to keep the CPU temperatures below a maximum value. Experimental studies are conducted in a single rack based on production workload traces of Google clusters. This study also compares the performance of this method with two other load balancing approaches, Round Robin, and a CPU utilization-based method in terms of temperature distributions, local fan rotation speeds, system loads, and server processing times. Furthermore, we investigate how the effect of the proposed load balancing changes with different assumed applications run on servers. The results indicate that this new method can more effectively reduce both server CPU temperatures and local fan rotation speed in a rack especially for the most of web applications.

A novel cooperative resource provisioning strategy for Multi-Cloud load balancing

2021 ◽  
Author(s):  
Bo Zhang ◽  
Zeng Zeng ◽  
Xiupeng Shi ◽  
Jianxi Yang ◽  
Bharadwaj Veeravalli ◽  
...  
Keyword(s):  
Load Balancing ◽  
Resource Provisioning ◽  
Multi Cloud

MPSO

2017 ◽  
Vol 8 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Subhadarshini Mohanty ◽  
Prashanta Kumar Patra ◽  
Subasish Mohapatra ◽  
Mitrabinda Ray
Keyword(s):  
Particle Swarm Optimization ◽  
Load Balancing ◽  
Resource Utilization ◽  
Web Applications ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Cloud Environment ◽  
Swarm Optimization ◽  
Load Balancing Algorithm

Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better Quality of Service (QOS). It is a prominent area of research as well as challenging to allocate the resources with changeable capacities and functionality. In this paper, a load balancing algorithm using Multi Particle Swarm Optimization (MPSO) has been developed by utilizing the benefits of particle swarm optimization (PSO) algorithm. Proposed approach aims to minimize the task overhead and maximize the resource utilization in a homogenous cloud environment. Performance comparisons are made with Genetic Algorithm (GA), Multi GA, PSO and other popular algorithms on different measures like makespan calculation and resource utilization.

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