Kinesthetics eXtreme: An External Infrastructure for Monitoring Distributed Legacy Systems

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

Retrofitting Autonomic Capabilities onto Legacy Systems

Autonomic computing - self-configuring, self-healing, self-optimizing applications, systems and networks - is a promising solution to ever-increasing system complexity and the spiraling costs of human 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 disparate technologies from numerous vendors. Our goal is to retrofit autonomic computing onto such systems, externally, without any need to understand, modify or even recompile the target system's code. We present an autonomic infrastructure that operates similarly to active middleware, to explicitly add autonomic services to pre-existing systems via continual monitoring and a feedback loop that performs, as needed, reconfiguration and/or repair. Our lightweight design and separation of concerns enables easy adoption of individual components, independent of the rest of the full infrastructure, for use with a large variety of target systems. This work has been validated by several case studies spanning multiple application domains

Wave functions and annihilation widths of heavy quarkonia

Within the framework of nonrelativistic quark-antiquark Cornell potential model formalism, we study the annihilation of heavy quarkonia. We determine their annihilation widths resulting into $\gamma\gamma$, $gg$, $3\gamma$, $3g$ and $\gamma gg$ and compare our findings with the available theoretical results and experimental data. We also provide the charge radii and absolute square of radial Schr\"odinger wave function at zero quark-antiquark separation.Comment: 2 figures, 6 table

An Approach to Autonomizing Legacy Systems

Adding adaptation capabilities to existing distributed systems is a major concern. The question addressed here is how to retrofit existing systems with self-healing, adaptation and/or self management capabilities. The problem is obviously intensified for 'systems of systems' composed of components, whether new or legacy, that may have been developed by different vendors, mixing and matching COTS and 'open source' components. This system composition model is expected to be increasingly common in high performance computing. The usual approach is to train technicians to understand the complexities of these components and their connections, including performance tuning parameters, so that they can then manually monitor and reconfigure the system as needed. We envision instead attaching a 'standard' feedback loop infrastructure to existing distributed systems for the purposes of continual monitoring and dynamically adapting their activities and performance. (This approach can also be applied to 'new' systems, as an alternative to 'building in' adaptation facilities, but we do not address that here.) Our proposed infrastructure consists of multiple layers with the objectives of probing, measuring and reporting of activity and state within the execution of the legacy system among its components and connectors; gauging, analysis and interpretation of the reported events; and possible feedback to focus the probes and gauges to drill deeper, or when necessary - direct but automatic reconfiguration of the running system

Patient-reported outcome measure for children and young people with amelogenesis imperfecta

Background: Amelogenesis imperfecta (AI) is a genetic enamel defect that can affect both the primary and permanent dentition. It has a range of clinical phenotypes, and children and young people often present with challenging oral health needs. Patient-reported outcome measures (PROMs) can identify key patient concerns. Methods: This was a multi-centre service evaluation across several specialist paediatric dentistry services in the UK. A PROM questionnaire was created with clinician and patient input, through peer review with the national AI Clinical Excellence Network, as well as piloting the PROM with ten children and young people with AI. The final PROM questionnaire was distributed to all patients with AI attending each unit between January and March 2020. Results: Sixty children and young people (aged 5-17 years) across four specialist units participated, with 72% reporting that they 'often' or 'sometimes' experienced pain or sensitivity and 76% reporting that they 'often' or 'sometimes' felt unhappy with the way their teeth look. Of the patients who were post-treatment, 81% indicated that they were happy with their teeth, compared to just 41% of patients who were mid-treatment and 33% of patients who were pre-treatment. Conclusion: Children and young people with AI experience a range of issues related to their function and psychosocial wellbeing. This simple PROM demonstrates the range of issues this group of patients face, and could be used to monitor an individual's progress to ensure that treatment is planned to address the patient's individual concerns and needs

Covering problems in edge- and node-weighted graphs

This paper discusses the graph covering problem in which a set of edges in an edge- and node-weighted graph is chosen to satisfy some covering constraints while minimizing the sum of the weights. In this problem, because of the large integrality gap of a natural linear programming (LP) relaxation, LP rounding algorithms based on the relaxation yield poor performance. Here we propose a stronger LP relaxation for the graph covering problem. The proposed relaxation is applied to designing primal-dual algorithms for two fundamental graph covering problems: the prize-collecting edge dominating set problem and the multicut problem in trees. Our algorithms are an exact polynomial-time algorithm for the former problem, and a 2-approximation algorithm for the latter problem, respectively. These results match the currently known best results for purely edge-weighted graphs.Comment: To appear in SWAT 201

Parameter estimation in spatially extended systems: The Karhunen-Loeve and Galerkin multiple shooting approach

Parameter estimation for spatiotemporal dynamics for coupled map lattices and continuous time domain systems is shown using a combination of multiple shooting, Karhunen-Loeve decomposition and Galerkin's projection methodologies. The resulting advantages in estimating parameters have been studied and discussed for chaotic and turbulent dynamics using small amounts of data from subsystems, availability of only scalar and noisy time series data, effects of space-time parameter variations, and in the presence of multiple time-scales.Comment: 11 pages, 5 figures, 4 Tables Corresponding Author - V. Ravi Kumar, e-mail address: [email protected]