Kinesthetics eXtreme: an external infrastructure for monitoring distributed legacy systems

TLDR

Autonomic computing promises self‑configuring, self‑healing, and self‑optimizing systems to reduce complexity and management costs, yet most research targets new software while real‑world organizations still rely on legacy or heterogeneous systems. The authors aim to retrofit autonomic capabilities onto existing legacy or heterogeneous systems externally, without modifying or recompiling the underlying code. They propose a lightweight meta‑architecture implemented as active middleware that attaches a feedback loop for continual monitoring, reconfiguration, and repair, enabling easy adoption across diverse legacy and new systems, as demonstrated in multiple domain experiments.

Abstract

Autonomic computing - self-configuring, self-healing, self-optimizing applications, systems and networks - is widely believed to be a promising solution to ever-increasing system complexity and the spiraling costs of human system management as systems scale to global proportions. Most results to date, however, suggest ways to architect new software constructed from the ground up as autonomic systems, whereas in the real world organizations continue to use stovepipe legacy systems and/or build ''systems of systems'' that draw from a gamut of new and legacy components involving disparate technologies from numerous vendors. Our goal is to retrofit autonomic computing onto such systems, externally, without any need to understand or modify the code, and in many cases even when it is impossible to recompile. We present a meta-architecture implemented as active middleware infrastructure to explicitly add autonomic services via an attached feedback loop that provides continual monitoring and, as needed, reconfiguration and/or repair. Our lightweight design and separation of concerns enables easy adoption of individual components, as well as the full infrastructure, for use with a large variety of legacy, new systems, and systems of systems. We summarize several experiments spanning multiple domains.