A Comparative Look at Adaptive Memory Management in Virtual Machines

Memory management plays a vital role in modern virtual machines. Both system- and language-level VMs manage memory to give the illusion of a unbounded allocation space although the underlying physical resources are limited. One of the main challenges for memory management is the range of dynamic characteristics of the workloads. Researchers have developed a large body of work using different mechanisms and dynamic decision making to specialize the memory management system to specific workloads. This design can be considered as a control loop where sensors are monitored, decisions are made and actions are performed by actuators. Nevertheless as is common in systems research, improvement in one property is accomplished at the expense of some other property. In this work we survey different techniques for adaptive memory management expressed as a control loop. We propose to analyse memory management in virtual machines using three seemingly orthogonal characteristics: responsiveness (R), comprehensiveness (C) and intricateness (I). We then present the details of an extensible classification framework which emphasizes the tradeoffs of different approaches. Using this framework, some representative state of the art systems are evaluated showing inherent tensions between R, C and I.