Abstract
Every software in the universe requires maintenance and management during its life cycle. The manual management of software is costly and sometimes error-prone. The other solution is autonomic computing that induces self-management capabilities, “self-*services”, in software systems with the help of autonomic managers. The design quality of a self-management capability affects the computing infrastructure regarding processing load, the memory requirement, data channel demand and performance of perturbation restore. It is critical to assess the design quality of a self-management capability to determine its effect over the computing infrastructure when it gets invoke against some anomaly or perturbation. Moreover, there are two possible host environments for an autonomic manager to offer a self-management capability as a self-* service: the local environment and the cloud environment. A criterion is needed to decide which environment is more suitable and cost-effective to run the service. However, the literature lacks in the assessment of the design quality metrics on self-management capabilities and the suitability and cost-effectiveness of the execution environment. In this work, we have proposed a suite of design quality metrics to determine the design quality of self-management capabilities. We validate the proposed metrics with a stock trade & forecasting system that was designed as an autonomic computing system with self-management capabilities. The proposed metrics were applied to define functions that identify the suitable and cost-effective execution environment for the self-* service. The results proved that these metrics are useful in determining the design quality, suitability, and cost-effectiveness of a self-* capability for an autonomic computing system. The proposed metrics can be used to compare differently designed autonomic solutions for complexity, efficiency, performance, understandability, and maintainability.
Highlights
During the life cycle of software, a significant amount of human effort is required to control and manage the software
To validate the design quality metrics, a case study of stock trading & forecasting was developed with self-management capabilities using the principle of autonomic computing
The results of experiments on the case study self-management capability (SMC) proved that the design quality metrics are useful to measure perturbation resolving complexity, perturbation resolving time, perturbation resolving data load, memory cost incurred and data channel requirements of an SMC
Summary
During the life cycle of software, a significant amount of human effort is required to control and manage the software. In the case of complex IT systems, an increased number of skilled IT professionals are required for configurations, installations, maintenance, and operation of these systems [1]. Autonomic software management facilitates in minimizing the IT budget by reducing human efforts required. To install, maintain and operate an IT system. By shifting the human task of software controlling to just policy and rules defining, autonomic computing automates the manual task of management and introduces self-management capabilities like self-configuration, self-healing, self-optimization, selfprotection, and others. These are collectively termed as self-* capabilities [2]. The motivation behind this work is that the design quality of a self-management capability (SMC) can be used to indicate how an SMC will affect the computing infrastructure in terms of processing load, the memory requirement, data
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