Grouping algorithms for scalable self-monitoring distributed systems
Benjamin Satzger, Theo Ungerer
Abstract
Benjamin Satzger, Theo Ungerer
Abstract
The growing complexity of distributed systems demands for new ways of control. Future systems should be able to adapt dynamically to the current conditions of their environ-ment. They should be characterised by so-called self-x prop-erties like self-configuring, self-healing, self-optimising, self-protecting, and context-aware. For the incorporation of such features typically monitoring components provide the neces-sary information about the system’s state. In this paper we propose three algorithms which allow a distributed system to install monitoring relations among its components. This serves as a basis to build scalable distributed systems with self-x features and to achieve a self-monitoring capability. Evaluation measurements have been conducted to compare the proposed algorithms.
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The growing complexity of distributed systems demands for new ways of control. Future systems should be able to adapt dynamically to the current conditions of their environ-ment. They should be characterised by so-called self-x prop-erties like self-configuring, self-healing, self-optimising, self-protecting, and context-aware. For the incorporation of such features typically monitoring components provide the neces-sary information about the system’s state. In this paper we propose three algorithms which allow a distributed system to install monitoring relations among its components. This serves as a basis to build scalable distributed systems with self-x features and to achieve a self-monitoring capability. Evaluation measurements have been conducted to compare the proposed algorithms.
Key concepts: Computer science, Scalability, Distributed computing, Distributed algorithm, Context (archaeology), State (computer science), Algorithm, Database