Towards an Analysis of Load Balancing Algorithms to Enhance Efficient Management of Cloud Data Centres

2016 ◽  
pp. 143-155 ◽  
Author(s):  
J. Prassanna ◽  
P. Ajit Jadhav ◽  
V. Neelanarayanan
Keyword(s):  
Load Balancing ◽  
Cloud Data ◽  
Efficient Management ◽  
Data Centres

scholarly journals A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Dalia Abdulkareem Shafiq ◽  
NZ Jhanjhi ◽  
Azween Abdullah ◽  
Mohammed A AlZain
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Load Balancing Algorithm ◽  
Data Centres

Regression Analysis of Predictions and Forecasts of Cloud Data Centre KPIs Using the Boosted Decision Tree Algorithm

2021 ◽  
Author(s):  
Thomas Weripuo Gyeera
Keyword(s):  
Decision Tree ◽  
Heterogeneous Computing ◽  
Learning Algorithm ◽  
High Volume ◽  
Dynamic Allocation ◽  
Test Bed ◽  
Cloud Data ◽  
On Demand ◽  
Boosted Decision Tree ◽  
Data Centres

<div>The National Institute of Standards and Technology defines the fundamental characteristics of cloud computing as: on-demand computing, offered via the network, using pooled resources, with rapid elastic scaling and metered charging. The rapid dynamic allocation and release of resources on demand to meet heterogeneous computing needs is particularly challenging for data centres, which process a huge amount of data characterised by its high volume, velocity, variety and veracity (4Vs model). Data centres seek to regulate this by monitoring and adaptation, typically reacting to service failures after the fact. We present a real cloud test bed with the capabilities of proactively monitoring and gathering cloud resource information for making predictions and forecasts. This contrasts with the state-of-the-art reactive monitoring of cloud data centres. We argue that the behavioural patterns and Key Performance Indicators (KPIs) characterizing virtualized servers, networks, and database applications can best be studied and analysed with predictive models. Specifically, we applied the Boosted Decision Tree machine learning algorithm in making future predictions on the KPIs of a cloud server and virtual infrastructure network, yielding an R-Square of 0.9991 at a 0.2 learning rate. This predictive framework is beneficial for making short- and long-term predictions for cloud resources.</div>

scholarly journals Evaluating Modern Data Centre Transport Protocols in OMNeT++/INET

10.29007/h27x ◽  
2019 ◽  
Author(s):  
Mohammed Alasmar ◽  
George Parisis
Keyword(s):  
Load Balancing ◽  
Simulation Model ◽  
Network Topology ◽  
Transport Protocol ◽  
Data Transport ◽  
Transport Protocols ◽  
Data Centre ◽  
Evaluation Platform ◽  
Data Centres

In this paper we present our work towards an evaluation platform for data centre transport protocols. We developed a simulation model for NDP1, a modern data transport protocol in data centres, a FatTree network topology and per-packet ECMP load balancing. We also developed a data centre environment that can be used to evaluate and compare data transport protocols, usch as NDP and TCP. We describe how we integrated our model with the INET Framework and present example simulations to showcase the workings of the developed framework. For that, we ran a comprehensive set of experiments and studied different components and parameters of the developed models.

scholarly journals A LIGHTWEIGHT AUTHENTICATION TECHNIQUE FOR SECURE COMMUNICATION OF EDGE/FOG DATA-CENTERS

2020 ◽  
Vol 2 (1) ◽  
pp. 76-81
Author(s):  
Muktar Yahuza ◽  
Yamani Idna Bin Idris ◽  
Ainuddin Wahid Bin Abdul Wahab ◽  
Mahdi A. Musa ◽  
Adamu Abdullahi Garba
Keyword(s):  
Load Balancing ◽  
Secure Communication ◽  
Secret Key ◽  
End User ◽  
Resource Constrained ◽  
Key Escrow ◽  
Academic Researchers ◽  
Cloud Datacenter ◽  
Lightweight Authentication ◽  
Data Centres

Edge computing has significantly enhanced the capabilities of cloud computing. Edge data-centres are used for storing data of the end-user devices. Secure communication between the legitimate edge data-centres during the load balancing process has attracted industrial and academic researchers. Recently, Puthal et al. have proposed a technique for authenticating edge datacenters to enable secure load balancing. However, the resource-constraint nature of the edge data-centres is ignored. The scheme is characterized by complex computation and memory intensive cryptographic protocol. It is also vulnerable to key escrow attack because the secret key used for encrypting and decrypting of the communicated messages is been created by the trusted cloud datacenter. Additionally, the key sharing phase of their algorithm is complex. Therefore, to address the highlighted challenges, this paper proposed a lightweight key escrow-less authentication algorithm that will ensure secure communication of resource-constrained edge data-centres during the load balancing process. The security capability of the proposed scheme has been formally evaluated using the automatic cryptographic analytical tool ProVerif. The relatively low computation and communication costs of the proposed scheme compared to the benchmark schemes proved that it is lightweight, thus suitable for resource-constrained edge datacenters.      

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).

Dynamic Task Assignment with Load Balancing in Cloud Platform

2016 ◽  
pp. 363-385
Author(s):  
Subhadarshini Mohanty ◽  
Prashant Kumar Patra ◽  
Subasish Mohapatra
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
User Satisfaction ◽  
Task Assignment ◽  
Heterogeneous Data ◽  
Hybrid Technique ◽  
Cloud Platform ◽  
Dynamic Task ◽  
Data Centres ◽  
Task Assignment Problem

Load balancing is one of the major issue in cloud computing. Load balancing helps in achieving maximum resource utilization and user satisfaction. This mechanism transparently transfer load from heavily loaded process to under loaded process. In this paper we have proposed a hybrid technique for solving task assignment problem in cloud platform. PSO based heuristic has been developed to schedule random task in heterogeneous data centres. Here we have also used variants of Particle Swarm Optimization(PSO) which gives better result than PSO and other heuristics for load balancing in cloud computing environment.

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