Control theory for principled heap sizing

We propose a new, principled approach to adaptive heap sizing based on control theory. We review current state-of-the-art heap sizing mechanisms, as deployed in Jikes RVM and HotSpot. We then formulate heap sizing as a control problem, apply and tune a standard controller algorithm, and evaluate its performance on a set of well-known benchmarks. We find our controller adapts the heap size more responsively than existing mechanisms. This responsiveness allows tighter virtual machine memory footprints while preserving target application throughput, which is ideal for both embedded and utility computing domains. In short, we argue that formal, systematic approaches to memory management should be replacing ad-hoc heuristics as the discipline matures. Control-theoretic heap sizing is one such systematic approach

Framework for Management of Multi-tenant Cloud Environments

© 2018, Springer International Publishing AG, part of Springer Nature. The benefits of using container-based microservices for the development of cloud applications have been widely reported in the literature and are supported by empirical evidence. However, it is also becoming clear that the management of large-scale container-based environments has its challenges. This is particularly true in multi-tenant environments operating across multiple cloud platforms. In this paper, we discuss the challenges of managing container-based environments and review the various initiatives directed towards addressing this problem. We then describe the architecture of the Unicorn Universe Cloud framework and the Unicorn Cloud Control Centre designed to facilitate the management and operation of containerized microservices in multi-tenant cloud environments