Scheduling virtual machines and containers: A comparative review of techniques, performance, and future trends

Abstract

Virtual machines (VMs) and containers underpin modern cloud infrastructures through distinct virtualization mechanisms, yet their scheduling strategies remain theoretically disconnected. Existing studies often address each paradigm in isolation, overlooking shared system-level challenges and conceptual overlaps. In this survey, we offers a virtualization-agnostic perspective by introducing a layered abstraction of scheduling, encompassing task dispatch, instance placement, and resource provisioning. We use this taxonomy to systematically compare five popular scheduling paradigms – mathematical modeling, heuristics, meta-heuristics, machine learning, and hybrids – on VM and container domains. Instead of enumerating techniques, our comparison emphasizes methodological convergence, indicating shared trade-offs guided by deployment granularity, orchestration flexibility, and performance isolation.

By projecting scheduling classes onto workload sensitivities, we provide context-specific guidance that connects theoretical research to practical configurations. Further, the research explains high-level algorithmic trends which indicate a shift away from isolated techniques to integrated orchestration systems. In this comparative viewpoint, the paper redefines VM and container scheduling as structurally equivalent problems with equivalent constraints and objectives. The outcomes are designed to help system designers and researchers design scheduling solutions that are both platform-adaptive and workload-responsive in heterogeneous computing systems.