Blockchain Based Adaptive Resource Allocation in Cloud Computing

• Main Author: Muruganandam,Sumathi
• Publication Date: 2022
• Other Authors: Natarajan,Vijayaraj, Raj,Raja Soosaimarian Peter, Maharajan,Venkatachalapathy
• Format: Article
• Language: eng
• Source: Brazilian Archives of Biology and Technology
• Download full: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100625
• topic: Resource allocation; Smart contract; Blockchain; Fixed and Variable size Resource Allocation.

Abstract Cloud computing is breakthrough technology with applications in education, industry and research. Implementing a cloud environment, however, depends on virtualization, parallel computing, resource management, service-oriented architecture, and distributed computing. The efficiency of cloud computing depends on effective resource allocation (RA). Conventional RA, from resource requests to price settlement, is handled entirely by cloud service providers (CSPs), which make it difficult for end users (EUs) to view details relating to resource availability and price. Hence, an alternative technique is required to provide an efficient RA between EU’s. In this work, the blockchain (BC) technique is used for RA between the EU’s without the intervention of CSP. The BC provides decentralized and secured communication between the EU’s without any inconsistency. Based on demand, RA is carried out in two different ways, fixed size and variable size. In a fixed size RA, each user utilizes an equal quantity of resources at a particular time but in a variable size RA each user utilizes the varying quantity of resources based on demand. Given that the RA, chosen depends on resource availability and demand, the proposed work employs hybrid RA (Adaptive Resource Allocation) schemes such as fixed-size adaptive RA (FSARA) and variable-sized adaptive RA (VSARA). The simulation results show the comparative analysis of the proposed and existing RA techniques. When compared to existing RA techniques like optimal, greedy and iterative techniques, the proposed technique achieved more 90% customer satisfaction. Depends on priority the allocated resource price is varied in a proposed provides equal profit (50%) to RR and RP. When compared to existing random transmission technique, the proposed technique takes lesser than 8.5% of transmission time. The proposed technique reduces transmission latency, confirmation latency, and response time, while simultaneously increasing throughput and RA scalability without CSP intervention.