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Improving Quality-of-Service in Cloud/Fog Computing through Efficient Resource Allocation.

Samson Busuyi Akintoye¹, Antoine Bagula²

¹ISAT Laboratory, Department of Computer Science, University of the Western Cape, Bellville 7535, South Africa. 3515640@myuwc.ac.za.

Sensors (Basel, Switzerland)

|March 16, 2019

Summary

This study addresses cloud computing challenges by proposing new algorithms for task allocation and virtual machine placement. The solutions effectively improve Quality-of-Service and reduce allocation costs in cloud/fog environments.

Keywords:

CloudSim CloudSim and hungarian algorithm cloud computing data center fog computing genetic algorithm greedy heuristics virtual machine

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Area of Science:

Computer Science
Cloud Computing
Resource Management

Background:

Enterprise applications are increasingly migrating to cloud environments.
Quality-of-Service (QoS) management is a critical challenge in cloud computing, involving resource allocation.
Optimizing resource allocation is vital, especially in cloud infrastructures using lightweight devices.

Purpose of the Study:

To formulate and present task allocation and virtual machine placement problems in a unified cloud/fog computing environment.
To propose novel algorithmic solutions for these complex resource management challenges.

Main Methods:

Developed a specific algorithmic solution for task allocation.
Implemented a Genetic Algorithm Based Virtual Machine Placement (GABP) approach.
Evaluated solutions within a single cloud/fog computing environment.

Main Results:

The proposed task allocation and virtual machine placement solutions demonstrated improved Quality-of-Service.
A significant reduction in allocation cost was observed in the cloud/fog computing environment.
Experimental results validate the effectiveness of the developed algorithms.

Conclusions:

The study successfully addressed key challenges in cloud/fog resource management.
The proposed methods offer a viable approach to enhance QoS and optimize costs.
This work contributes to efficient resource allocation strategies in distributed computing environments.