Optimising infrastructure as a service provider revenue through customer satisfaction and efficient resource provisioning in cloud computing

With limited resources, it is quite challenging for cloud providers to meet dynamic and massive customers' demands. Higher utilisation or refusing any service level agreement (SLA) may lead to penalties which play a crucial role in the cloud business. Overutilisation of resources, instead of maximizing the revenue, may lead to a decrease in revenue due to the SLA violations. Various studies have been conducted to investigate these issues; however, there is still room for improvement. In this study, the authors proposed a model to address the resource scalability and SLA violation issues by hiring external resources at low prices. However, in contrast to a federated cloud, the proposed model allows a provider to hire resources from any external provider with flexible terms and price. They designed algorithms to optimise providers' revenue by taking into account different parameters, including resource utilization, customer satisfaction, SLA violation, and prices. Simulation result shows that the proposed model is efficient in handling massive demands, and improves revenue generation and customer satisfaction. Offering joint pricing on customers' choice and outsourcing the extra workload to external resources leads to revenue maximization. Hiring external resources earns external revenue as well as it maximizes the total revenue.

[1]  Sarbani Roy,et al.  Toward maximization of profit and quality of cloud federation: solution to cloud federation formation problem , 2018, The Journal of Supercomputing.

[2]  Elizabeth Chang,et al.  Risk-based framework for SLA violation abatement from the cloud service provider's perspective , 2018, Comput. J..

[3]  R. Rajeswara Rao,et al.  Locality-Load-Prediction Aware Multi-Objective Task Scheduling in the Heterogeneous Cloud Environment , 2017 .

[4]  Raquel A. Asaka,et al.  Factors Influencing Customer Satisfaction in Software as a Service (SaaS): Proposal of a System of Performance Indicators , 2017, IEEE Latin America Transactions.

[5]  Djamal Zeghlache,et al.  Mathematical Programming Approach for Revenue Maximization in Cloud Federations , 2017, IEEE Transactions on Cloud Computing.

[6]  Shiwen Mao,et al.  A survey of multimedia big data , 2018, China Communications.

[7]  Zhu Han,et al.  Resource Management in Cloud Networking Using Economic Analysis and Pricing Models: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[8]  Tingwen Huang,et al.  Cloud Computing Service: The Caseof Large Matrix Determinant Computation , 2015, IEEE Transactions on Services Computing.

[9]  Muriati Mukhtar,et al.  A combinatorial double auction resource allocation model in cloud computing , 2016, Inf. Sci..

[10]  Kenli Li,et al.  Profit Maximization for Cloud Brokers in Cloud Computing , 2019, IEEE Transactions on Parallel and Distributed Systems.

[11]  Kenli Li,et al.  Customer-Satisfaction-Aware Optimal Multiserver Configuration for Profit Maximization in Cloud Computing , 2017, IEEE Transactions on Sustainable Computing.

[12]  Rajkumar Buyya,et al.  Priority-Aware VM Allocation and Network Bandwidth Provisioning in Software-Defined Networking (SDN)-Enabled Clouds , 2019, IEEE Transactions on Sustainable Computing.

[13]  Rajkumar Buyya,et al.  Revenue Maximization with Optimal Capacity Control in Infrastructure as a Service Cloud Markets , 2015, IEEE Transactions on Cloud Computing.

[14]  Xiaofei Wang,et al.  A Fairness-Aware Pricing Methodology for Revenue Enhancement in Service Cloud Infrastructure , 2017, IEEE Systems Journal.