Quality of Service Challenges and Solutions in Multi-Tenant Community Clouds: A Comprehensive Survey
Abstract
Community cloud computing has emerged as an effective deployment model for organizations that share common security policies, governance frameworks, and regulatory requirements while benefiting from collaborative resource sharing. By enabling multiple organizations to utilize a common cloud infrastructure, community clouds improve resource utilization, reduce operational costs, and facilitate secure collaboration. The integration of multi-tenancy further enhances infrastructure efficiency by allowing multiple tenants to share computing resources through logical isolation mechanisms implemented using virtualization and containerization technologies. However, the shared nature of these environments introduces significant challenges in maintaining consistent Quality of Service (QoS), including resource contention, workload interference, heterogeneous application demands, noisy-neighbor effects, and stringent Service Level Agreement (SLA) requirements. Consequently, effective QoS management has become a critical requirement for ensuring reliable, scalable, and fair service delivery in multi-tenant community cloud environments. This survey presents a comprehensive review of QoS management techniques developed for multi-tenant community clouds. It examines the evolution of community cloud computing, multi-tenancy models, virtualization technologies, and key QoS attributes such as response time, throughput, availability, reliability, scalability, latency, energy efficiency, fairness, and SLA compliance. Furthermore, the survey critically reviews recent advances in resource scheduling, dynamic resource allocation, load balancing, container orchestration, Software-Defined Networking (SDN), and Artificial Intelligence (AI)-based cloud optimization. Existing approaches are comparatively analyzed to identify their strengths, limitations, and applicability across different cloud scenarios. Finally, emerging research directions, including autonomous cloud management, edge–cloud integration, explainable artificial intelligence, and sustainability-aware resource management, are discussed to highlight future opportunities for developing adaptive, intelligent, and scalable QoS frameworks for next-generation community cloud infrastructures.
