Achieving Resource Isolation in Multi-Tenant Cloud Platforms Without Sacrificing Performance

Multi-tenant cloud (MTC) platforms have become the cornerstone of modern distributed computing infrastructure, enabling diverse workloads from independent clients to coexist on shared physical hardware while each tenant perceives a logically dedicated environment. The central engineering challenge in these environments is achieving robust resource isolation (RI) without incurring performance penalties that undermine the economic and operational advantages of consolidation. This paper provides a comprehensive review of mechanisms, scheduling policies, and architectural innovations that address the isolation-performance trade-off across compute, memory, network, and storage domains. We survey hardware-assisted virtualization techniques, container-based sandboxing, software-defined networking (SDN) overlays, and disaggregated storage architectures, analyzing how each approach positions itself on the spectrum between isolation fidelity and execution efficiency. We further examine the growing role of machine learning (ML) in dynamic resource management, the unique demands imposed by emerging workload classes such as serverless functions and artificial intelligence (AI) training pipelines, and the microarchitectural threat landscape shaped by speculative execution vulnerabilities.