Software as a Service (SaaS) is a cloud service model where users obtain software access rights by paying subscription fees. Due to the diversity of business operations, users exhibit significant variations in the online access rates for different software. Consequently, there are variations in the cloud computing resources consumed by different software applications. To avoid the risk of violating Service Level Agreements (SLAs) and incurring penalty payments, SaaS operators optimize the computational resource allocation for various software applications and impose subscription limits on each category of software. Considering SLA constraints, this paper formulates a resource-constrained nonlinear integer programming model with the objective of maximizing revenue. Due to the computational complexity of the model, which falls into the class of NP-hard problems, this paper proposes a Q-learning-particle swarm optimization (PSO) hybrid algorithm for its solution. This algorithm embeds Q-learning into PSO to dynamically adjust PSO parameters, thereby avoiding the issues of local optima and low computational efficiency associated with direct PSO application. Simulation experiments validate the effectiveness of the model and algorithm in different scenarios. The results indicate that the algorithm can achieve higher revenue for subscription limits and resource allocation with superior solving efficiency under the condition of limited cloud computing resources. Specifically, in scenarios with significant demand fluctuations, operators should aim to reduce the resource contention ratio of software. This can be achieved by provisioning an ample amount of virtual machine resources and enforcing strict subscription limits to ensure the quality of service, consequently reducing penalty payments. Conversely, in scenarios with minimal demand fluctuations, operators have the flexibility to increase the resource contention ratio of software. By relaxing subscription limits, they can seize a larger market share, thus realizing revenue maximization.

[2024-03-11] Accepted Paper /doc/2023.10.0542/v1