Abstract
Cloud computing is an emerging technology undergoing various challenges that integrate parallel and distributed computing together. In the multi-tenant environment cloud applications can be utilized as a service. User request are enormous and therefore the attributes to be concerned about are scalability, reliability, and resource availability and server response. Utilization of software, platform and infrastructure increases in this environment paving way for resource consumption. This scenario arises various types of issues through collision, traffic jam, data loss, request dropout and delay in response. The past research provides solutions for aspects like scalability, resource allocation, scheduling, load balancing and optimized request and response handling, resource management through virtualization. The process of virtualization and migration of environment is difficult. The cost for allocating VM for a single user is less. The paper proposed a novel scheduling approach for handling unlimited incoming request with quality of service through energy and throughput. The allocated resource focus on maintaining incoming job request, request for dispatch to the server and an acknowledgement for the receipt of response. The paper provides resource allocation methodology through scheduling approaches called integrating of AI techniques namely Genetic Algorithms (GA) and Artificial Neural Networks (ANN). The property of the request is analyzed and priorityis applied for scheduling the request using resource allocation.
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30 May 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-03982-y
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-03982-y"
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Geetha, R., Parthasarathy, V. RETRACTED ARTICLE: An advanced artificial intelligence technique for resource allocation by investigating and scheduling parallel-distributed request/response handling. J Ambient Intell Human Comput 12, 6899–6909 (2021). https://doi.org/10.1007/s12652-020-02334-y
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DOI: https://doi.org/10.1007/s12652-020-02334-y