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

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 c...

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Bibliographic Details
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
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Summary: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.
ISSN:1939-1374
2372-0204
DOI:10.1109/TSC.2020.3001078