Federated Geo-Distributed Clouds: Optimizing Resource Allocation Based on Request Type Using Autonomous and Multi-objective Resource Sharing Model

Due to the problems exist in non-geographic federated clouds, the geographic ones are considered. Nev-ertheless, the approaches that have already been proposed to allocate resources across the geographical federated clouds have two basic problems that we will address in this article: (1) Lack of proper distribu-tion of user requests leading to increases file transfer volume and cost, as well as response time to user requests, (2) Lack of appropriate resource sharing among requests due to: (1) the use of a centralized DC and (2) considering the satisfaction of single objective which case (1) suffers the problem of single-point of failure and case (2) raises an obstacle for the situations need considering multi conflicting objectives. Concerning the problem of one, it should be said that as federal DCs are distributed globally in the geographic clouds, the cost of file transfer between DCs in these clouds is more focused than the concen-trated ones. Since there has been no work in this field in the geo-distributed federated clouds, we have presented a new scheduling mechanism based on hypervolume for the distribution of applications that leads to increasing service quality and reducing file transfer cost. Concerning the problem of two, the previous solutions in the geographic federated clouds have focused on a centralized resource sharing with single objective (increase of the cloud service provider (CSP) profit). These solutions not only just consider the CSP profitability, but, because of the possibility of failure of central broker of resource-sharing, suffer the single-point of failure. In this paper, we propose a new, autonomic and peer-to-peer multi-objective resource sharing approach that considers objectives: (1) enhancing the CSP's profit, (2) decreasing the network latency and (3) decreasing file transfer traffic and (3) increasing fairness in CSPs' profit. The techniques presented in this paper are evaluated by extensive experiments using real workloads. To validate the proposed method, we have extended the CloudSim tool. The results of our experiments show the increase of performance in the scheduling and resource-sharing objectives among which the main objectives of average rate of success, profit and execution time were enhanced 8.5%, 15.47% and 25.84%, respectively compared with previous studies. (C) 2021 Elsevier Inc. All rights reserved.