FedRec: Trusted rank-based recommender scheme for service provisioning in federated cloud environment

Abstract

The emergence of on-demand service provisioning by Federated Cloud Providers (FCPs) to Cloud Users (CU) has fuelled significant innovations in cloud provisioning models. Owing to the massive traffic, massive CU resource requests are sent to FCPs, and appropriate service recommendations are sent by FCPs. Currently, the Fourth-Generation (4G)-Long Term Evolution (LTE) network faces bottlenecks that affect end-user throughput and latency. Moreover, the data is exchanged among heterogeneous stakeholders, and thus trust is a prime concern. To address these limitations, the paper proposes a Blockchain (BC)-leveraged rank-based recommender scheme, FedRec, to expedite secure and trusted Cloud Service Provisioning (CSP) to the CU through the FCP at the backdrop of base 5G communication service. The scheme operates in three phases. In the first phase, a BC-integrated request-response broker model is formulated between the CU, Cloud Brokers (BR), and the FCP, where a CU service request is forwarded through the BR to different FCPs. For service requests, Anything-as-a-Service (XaaS) is supported by 5G-enhanced Mobile Broadband (eMBB) service. In the next phase, a weighted matching recommender model is proposed at the FCP sites based on a novel Ranking-Based Recommender (RBR) model based on the CU requests. In the final phase, based on the matching recommendations between the CU and the FCP, Smart Contracts (SC) are executed, and resource provisioning data is stored in the Interplanetary File Systems (IPFS) that expedite the block validations. The proposed scheme FedRec is compared in terms of SC evaluation and formal verification. In simulation, FedRec achieves a reduction of 27.55% in chain storage and a transaction throughput of 43.5074 Mbps at 150 blocks. For the IPFS, we have achieved a bandwidth improvement of 17.91%. In the RBR models, the maximum obtained hit ratio is 0.9314 ​at 200 million CU requests, showing an improvement of 1.2% in average servicing latency over non-RBR models and a maximization trade-off of QoE index of 2.7688 at the flow request 1.088 and at granted service price of USD 1.559 million to FCP for provided services. The obtained results indicate the viability of the proposed scheme against traditional approaches.