RACE: QoI-Aware Strategic Resource Allocation for Provisioning Se-aaS

• Published in: IEEE transactions on services computing 2022-05, Vol.15 (3), p.1540-1550
• Main Authors: Misra, Sudip, Schober, Robert, Chakraborty, Aishwariya
• Format: Article
• Language: English
• Subjects: Cloud computing; Computer architecture; Economics; End users; Game theory; Maintenance costs; Maintenance engineering; Nodes; Profitability; Profits; Provisioning; Quality of service; Resource allocation; Resource management; Se-aaS; Sensor-cloud; Sensors; User requirements; User satisfaction; Utility theory; Wireless sensor networks
• ISSN: 1939-1374; 2372-0204
• DOI: 10.1109/TSC.2020.3001078

In this paper, the problem of ensuring profitability for multiple sensor-owners in sensor-cloud, while satisfying the service requirements of end-users, is studied. In traditional sensor-cloud, Sensor-Cloud Service Provider (SCSP) solely dictates the service provisioning process. However, the SCSP cannot always ensure high profits for sensor-owners, who incur significant maintenance costs for their sensor-nodes. Contrarily, it is highly essential to meet the Quality-of-Information (QoI) requirements of end-users to ensure their service satisfaction. Existing works proposed a few node allocation schemes which neither consider the cost incurred by sensor-owners nor the QoI of sensed-data in sensor-cloud. To address this problem, a strategic resource allocation scheme, named RACE, is proposed, which introduces the participation of sensor-owners in the node allocation process. First, utility theory is used to calculate the optimum number of nodes to be allocated for a service. Thereafter, single leader multiple followers Stackelberg game is formulated to decide the number of nodes to be contributed by each sensor-owner and the price to be charged. Simulation-based experimental results reveal that, using RACE, the profits of the sensor-owners and those of the SCSP increase by 86.11-89.26 percent and 41.95-80.82 percent, respectively, as compared to the existing benchmark schemes, while considering that each sensor-node is capable of serving multiple applications simultaneously. Moreover, service availability in sensor-cloud increases by 31.70-96.96 percent using RACE.