Resource Scalability and Security Using Entropy Based Adaptive Krill Herd Optimization for Auto Scaling in Cloud

Cloud Computing has changed the way we are thinking about computer security and the way how corporations organize their internal processes. Therefore the Cloud computing is a new paradigm to convey computing architecture and assistance in acquiring the chances and difficulties in the region of distr...

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Bibliographic Details
Published in:Wireless personal communications 2021, Vol.119 (1), p.791-813
Main Authors: Rahumath, Anver Shahabdeen, Natarajan, Mohanasundaram, Malangai, Abdul Rahiman
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Cloud computing
Communications Engineering
Computer Communication Networks
Cybersecurity
Engineering
Entropy
Krill
Networks
Optimization
Resource allocation
Resource management
Scaling
Signal,Image and Speech Processing
Trustworthiness
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Summary:Cloud Computing has changed the way we are thinking about computer security and the way how corporations organize their internal processes. Therefore the Cloud computing is a new paradigm to convey computing architecture and assistance in acquiring the chances and difficulties in the region of distributed resources management. Resource scalability and security are the two major issues under Infrastructure as a Service (IaaS) of resource allocation. In this manner, the Entropy-based Adaptive Krill herd optimization for auto-scaling in the cloud is proposed. Here, auto-scaling is a significant cloud computing feature under IaaS, which is utilized to dynamically assign computational resources to applications to coordinate their present loads absolutely, in this way removing resources that would diversely stay idle and waste power. In the first stage, the task is monitored by determining the trust-based anomaly detection objectives such as Frequency Value, Trust Hypothesis Statistics, trust factor value, and trust policy. At that point, the given task is scheduled to find the task status. Then it is scaled using the execution time and workload calculation. After that, the scaled data is optimized utilizing the entropy-based krill herd algorithm. At long last, the comparisons of the proposed and existing methods are evaluated.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-021-08238-0