Autonomic Self Research Articles

Introduction to ACSOS 2022 Special Issue

This special issue collects extended versions of four of the papers that have received the best scores during the review process of the 3rd IEEE International Conference on Autonomic Computing and Self- Organizing Systems (ACSOS 2022). In this introduction we are going to frame the papers in the general ACSOS context.

A Novel Optimized Case-Based Reasoning Approach With K-Means Clustering and Genetic Algorithm for Predicting Multi-Class Workload Characterization in Autonomic Database and Data Warehouse System

Data management systems are essential elements for any organization which is dealing with large volume of data now a days. Due to increase in data volume, and its complexities, it has become more challenging job for workload management system to maintain its performance. So, there is a need of such a system that can autonomically deal with such complexities with less or without human involvement. Performance of these systems can be improved by making the systems well-aware about the workload entering into the system. The workload of a prevalent typical database and data warehouse system can be characterized into three types that is Online Transaction Processing (OLTP), Decision Support Systems (DSS) and Mixed type of workload. Currently, autonomic characterization of workload into a binary class such as OLTP and DSS is being carried out as reported in the literature, however, characterizing the workload into three types that refers to a multi-class classification problem is relatively a more challenging task. In this study, we propose a novel optimized Case-based Reasoning (CBR) approach based on clustering for autonomically characterizing the workload into multi-class types before entering into the system. We implement four phases of CBR along with case-base generation and map it to the elements of autonomic MAPE-K model. In Retrieve phase, k-means clustering is used for enhancing retrieval efficiency and workload types predictions are made in Reuse phase. Genetic Algorithm is used in Revise and Adapt phase of CBR. Few autonomic self_* characteristics are incorporated to make it autonomic. We performed various experiments and results show that the proposed model outperforms in prediction as compared to existing approaches. We performed post-hoc test for the validation of results in comparison with other machine learning classifiers using the Friedman test that show that the proposed model stands out as the best classifier.

Incorporating autonomicity and trustworthiness aspects for assessing software quality

Autonomic computing covers few self-abilities like self-configuration, self-healing, self-optimization, self-protection, self-adaptability, self-awareness, self-openness etc. in software systems. These self-abilities will lead towards lowering the overall maintenance cost of the software because of minimum level of human intervention. The term Autonomicity refers to the level of autonomic (self) features implemented in the system. The International software quality standard ISO 9126 is now replaced by new software product quality standard ISO/IEC 25010:2011 which defines the framework/model to specify and evaluate the quality of software as a product. However, this does not take into account the self-* features (autonomic aspects) and trust factor of modern day software systems. The present paper proposes here that autonomic characteristics of any system must be considered while assessing the quality of any software product. This autonomic-oriented quality model may be used to assess the software quality in a number of domains. Therefore, a new enhanced software quality model is proposed which considers autonomicity and trustworthiness as a factor of quality.

Some Universal Constructions of Autonomic Systems

Self-* is widely considered as a foundation for autonomic computing. The notion of autonomic systems (ASs) and self-* serves as a basis on which to build our intuition about category of ASs in general. In this paper we will specify ASs and self-* and then move on to consider some universal constructions such as products, coproducts, finite limits and colimits of ASs. All of this material is taken as an investigation of our category, the category of ASs, which we call AS.

On Autonomic Self Healing Architecture for Resiliency in Cyber Physical System

Disturbances to a system are inevitable. Resilience is thus utmost necessary to the system as it has to respond to the stresses and disturbances to keep the system stable. Cyber physical systems are physical engineered systems whose operations are monitored, coordinated, controlled and integrated by a computing communication core which is expected to transform the physical world around us. This paper proposes an autonomic self healing architecture for improving resiliency in cyber physical system using autonomic computing self management properties. This is a layered architecture in which each stage has a mechanism to collect state information of the system and monitor the system behavior, if the performance of the system is degraded in comparison to the normal level, self healing module is activated to facilitate recovery from damaged state and restoring to normal state, thereby achieving resilience in the system.

Workload management: a technology perspective with respect to self-* characteristics

Rapid growth in data, maximum functionality requirements and changing behavior in the database workload tends the workload management to be more complex. Organizations have complex type of workloads that are very difficult to manage by humans and even in some cases this management becomes impossible. Human experts take long time to get sufficient experience so that they can manage the workload efficiently. The versatility in workload due to huge data size and user requirements leads us towards the new challenges. One of the challenges is the identification of the problematic queries and the decision about these, i.e. whether to continue their execution or stop. The other challenge is how to characterize the workload, as the tasks such as configuration, prediction and adoption are fully dependent on the workload characterization. Correct and timely characterization leads managing the workload in an efficient manner and vice versa. In this scenario, our objective is to produce a workload management strategy or framework that is fully adoptive. The paper provides a summary of the structure and achievements of the database tools that exhibit Autonomic Computing or self-* characteristics in workload management. We have categorized the database workload tools to these self-* characteristics and identified their limitations. Finally the paper presents the research done in the database workload management tools with respect to the workload type and Autonomic Computing.

Autonomic Self Healing-Based Load Assessment for Load Division in OKKAM Backbone Cluster

Abstract: Self healing systems are considered as cognation-enabled sub form of fault tolerance system. But our experiments that we report in this paper show that self healing systems can be used for performance optimization, configuration management, access control management and bunch of other functions. The exponential complexity that results from interaction between autonomic systems and users (software and human users) has hindered the deployment and user of intelligent systems for a while now. We show that if that exceptional complexity is converted into self-growing knowledge (policies in our case), can make up for initial development cost of building an intelligent system. In this paper, we report the application of AHSEN (Autonomic Healing-based Self management Engine) to in OKKAM Project infrastructure backbone cluster that mimics the web service based architecture of u-Zone gateway infrastructure. The ‘blind’ load division on per-request bases is not optimal for distributed and performance hungry infrastructure such as OKKAM. The approach adopted assesses the active threads on the virtual machine and does resource estimates for active processes. The availability of a certain server is represented through worker modules at load server. Our simulation results on the OKKAM infrastructure show that the self healing significantly improves the performance and clearly demarcates the logical ambiguities in contemporary designs of self healing infrastructures proposed for large scale computing infrastructures.

Effects of methylphenidate on quality of life in children with both developmental coordination disorder and ADHD

Measurement of health-related quality of life (HRQOL) in attention-deficit-hyperactivity disorder (ADHD) gives a more complete picture of day-to-day functioning and treatment effects than behavioural rating alone. The aim of this pilot study was to investigate the impact of the combined diagnoses of developmental coordination disorder (DCD) and ADHD on HRQOL, and the effectiveness of methylphenidate (MPH) on HRQOL. HRQOL was established using the Dutch-Child-AZL-TNO-Quality-of-Life (DUX-25) and the TNO-AZL-Child-Quality-of-Life (TACQOL) questionnaires, completed by children and parents. HRQOL of these children was compared with that of 23 age- and sex-matched healthy controls. Twenty-three children (21 males, two females; mean age 8 y 6 mo, [SD 3 mo] range 7 y-10 y 8 mo) with ADHD/DCD entered a 4-week, open-label MPH study, after MPH-sensitivity was established, in a double-blind, placebo-controlled trial. In these children's self- and proxy reports, impact of both DCD and ADHD was reflected in lower general well-being (self and proxy report p=0.001) due to lower functioning in motor (selfp=0.026; proxy 0.001), autonomic (self p<0.001; proxy p=0.047), cognitive (self p=0.001; proxy p=0.01), and social (self and proxy p<0.001) domains. HRQOL scores improved in 18 children receiving MPH (p=0.001) versus controls. The ADHD /DCD group also demonstrated a significant improvement in ADHD symptoms (p<0.001) and motor functioning (p<0.001). Additional motor therapy will still be needed in about half of the children with ADHD/DCD receiving MPH, within multimodal treatment including educational and psychosocial assistance.

On Self Managing Autonomous Ubiquitous Networks

The interfaces of computing systems are expanding. This expansion has revealed many new problems that were never considered for conventional software systems. Ubiquitous networks belong to the generation of computing systems that deals with high levels of mobility and ‘interface everywhere’ theme. So they require more resilience. Conventional methods for managing these networks are inappropriate and very costly. Autonomic self management is a promising new concept that aims to (i) increase reliability by designing systems to be self-protecting and self-healing; and (ii) increase autonomy and performance by enabling systems to adapt to changing circumstances, using self-configuring and self-optimizing mechanisms. In this paper we propose that the cost attached with network management can be cut short with increased quality of service and performance. We give the architecture of an Autonomic Management System (AMS) in a smart home scenario, which we plan to implement in our project.

Autonomic self healing and recovery informed by environment knowledge

An important goal of autonomic computing is the development of computing systems that are capable of self healing with a minimum of human intervention. Typically, recovery from even a simple fault will require knowledge of the environment in which a computing system operates. To meet this need, we present an approach to self healing and recovery informed by environment knowledge that combines case based reasoning (CBR) and rule based reasoning. Specifically, CBR is used for fault diagnosis and rule based reasoning for fault remediation, recovery, and referral. We also show how automated information gathering from available sources in a computing system's environment can increase problem solving efficiency and help to reduce the occurrence of service failures. Finally, we demonstrate the approach in an intelligent system for fault management in a local printer network.