Autonomic Cluster Management System (ACMS): A Demonstration of Autonomic Principles at Work

Cluster computing, whereby a large number of simple processors or nodes are combined together to apparently function as a single powerful computer, has emerged as a research area in its own right. The approach offers a relatively inexpensive means of achieving significant computational capabilities for high-performance computing applications, while simultaneously affording the ability to. increase that capability simply by adding more (inexpensive) processors. However, the task of manually managing and con.guring a cluster quickly becomes impossible as the cluster grows in size. Autonomic computing is a relatively new approach to managing complex systems that can potentially solve many of the problems inherent in cluster management. We describe the development of a prototype Automatic Cluster Management System (ACMS) that exploits autonomic properties in automating cluster management

Autonomic Systems

An autonomic system is defined as self-configuring, self-optimizing, self-healing, and self-protecting. We implemented the Autonomic Cluster Management System (ACMS), a low overhead Java application designed to manage and load balance a cluster, while working at NASA GSFC. The ACMS is a mobile multi-agent system in which each agent is designed to fulfill a specific role. The agents collaborate and coordinate their activities in order to achieve system management goals. The ACMS is scalable and extensible to facilitate future development

Система управления кластером для комплексов семейства «ИНПАРКОМ»

Современные операционные системы не содержат собственных средств по управлению ресурсами и координации заданий вычислительного кластера. Поэтому разработчиками кластерных комплексов семейства «Инпарком» создана система управления кластером ACMS. Рассмотрены предпосылки, архитектура и функциональность этой системы, а также особенности реализации.Modern operating systems do not have means to control cluster resources and manage parallel jobs. So developers of INPARCOM HPC - cluster complex developed a cluster management system ACMS. Background, architecture, functionality and design features are described in this paper

Towards an Autonomic Cluster Management System (ACMS) with Reflex Autonomicity

Cluster computing, whereby a large number of simple processors or nodes are combined together to apparently function as a single powerful computer, has emerged as a research area in its own right. The approach offers a relatively inexpensive means of providing a fault-tolerant environment and achieving significant computational capabilities for high-performance computing applications. However, the task of manually managing and configuring a cluster quickly becomes daunting as the cluster grows in size. Autonomic computing, with its vision to provide self-management, can potentially solve many of the problems inherent in cluster management. We describe the development of a prototype Autonomic Cluster Management System (ACMS) that exploits autonomic properties in automating cluster management and its evolution to include reflex reactions via pulse monitoring

СИСТЕМА УПРАВЛЕНИЯ КЛАСТЕРОМ ДЛЯ КОМПЛЕКСОВ СЕМЕЙСТВА «ИНПАРКОМ»

Современные операционные системы не содержат собственных средств по управлению ресурсами и координации заданий вычислительного кластера. Поэтому разработчиками кластерных комплексов семейства «Инпарком» создана система управления кластером ACMS. Рассмотрены предпосылки, архитектура и функциональность этой системы, а также особенности реализации.\ud Modern operating systems do not have means to control cluster resources and manage parallel jobs. So developers of INPARCOM HPC cluster complex developed a cluster management system ACMS. Background, architecture, functionality and design features are described in this paper. \u

Active Learning for Text Classification

Text classification approaches are used extensively to solve real-world challenges. The success or failure of text classification systems hangs on the datasets used to train them, without a good dataset it is impossible to build a quality system. This thesis examines the applicability of active learning in text classification for the rapid and economical creation of labelled training data. Four main contributions are made in this thesis. First, we present two novel selection strategies to choose the most informative examples for manually labelling. One is an approach using an advanced aggregated confidence measurement instead of the direct output of classifiers to measure the confidence of the prediction and choose the examples with least confidence for querying. The other is a simple but effective exploration guided active learning selection strategy which uses only the notions of density and diversity, based on similarity, in its selection strategy. Second, we propose new methods of using deterministic clustering algorithms to help bootstrap the active learning process. We first illustrate the problems of using non-deterministic clustering for selecting initial training sets, showing how non-deterministic clustering methods can result in inconsistent behaviour in the active learning process. We then compare various deterministic clustering techniques and commonly used non-deterministic ones, and show that deterministic clustering algorithms are as good as non-deterministic clustering algorithms at selecting initial training examples for the active learning process. More importantly, we show that the use of deterministic approaches stabilises the active learning process. Our third direction is in the area of visualising the active learning process. We demonstrate the use of an existing visualisation technique in understanding active learning selection strategies to show that a better understanding of selection strategies can be achieved with the help of visualisation techniques. Finally, to evaluate the practicality and usefulness of active learning as a general dataset labelling methodology, it is desirable that actively labelled dataset can be reused more widely instead of being only limited to some particular classifier. We compare the reusability of popular active learning methods for text classification and identify the best classifiers to use in active learning for text classification. This thesis is concerned using active learning methods to label large unlabelled textual datasets. Our domain of interest is text classification, but most of the methods proposed are quite general and so are applicable to other domains having large collections of data with high dimensionality

Investigating the impact of supply chain technologies within automative supplier clusters

Organisations are constantly expected to be more competitive while working in an environment in which time and cost are limited, thereby preventing such organisations from taking the time required to be responsive. The supply chain provides a critical linkage between various organisations which should seek collective opportunities to improve performance. It is, therefore, important that organisations understand that conventional knowledge and methods will not serve unless there is a concerted focus on improvement of organisational performance toward fulfilling increased expectations, not just maintaining that which is comfortable. A more sustainable approach may be the introduction of supply chain best practice. An optimal supply chain is one that continuously strives to reduce unnecessary cost and eliminate waste, thereby increasing the percentage of time that may be devoted to value-adding activities. Supply chain technology principles were assessed and the application thereof, sought to understand its efficiency and effectiveness. This study was intended to identify supply chain cost dimensions with a focus on the optimal use of supply chain technology. Within the current supply chain context, the use of Information and Communication Technology (ICT) was explored to identify opportunities. A supply chain audit tool (SCAT) was developed which had proven to be an effective tool to analyse it’s logistics functions. Implementation of remedial tools through the SCAT could result in a leaner, cost optimal and more value-adding process. The result of conducting individual organisational improvements is expected to result in an overall improvement in the total supply chain. These supply chain cost drivers were rooted in cost, quality, safety and product performance. Recommendations on further improvements were also offered

Temporal bone phantom for decoupled cochlear implant electrode insertion force measurement

In research on cochlear implants, preclinical testing of newly developed electrode arrays and surgical tools is an essential procedure, which requires the availability of a suitable testing environment. For this purpose, human temporal bone specimens are most realistic, but their availability is limited and additional parameters such as insertion forces are hardly measurable. Therefore, the aim of this study was to develop a temporal bone phantom with realistic anatomical structures for intracochlear force measurement. The temporal bone was segmented from CBCT data of a human cadaver head. The segmented model was 3D printed with an additional artificial skin layer to enable the simulated use of surgical instruments such as a self-retaining retractor. A mechanically decoupled artificial cochlear model was realistically positioned within the temporal bone and was furthermore attached to a force sensor. The usability of the phantom was evaluated by performing automated EA insertions using an automated hydraulic insertion device. The experiments showed that the insertion forces within the cochlea could be measured without interferences from surrounding structures. Moreover, the artificial skin provided a rigid interface for the insertion tool. The new phantom is a realistic testing and training platform for cochlear implant electrode insertions with the advantage of measureable insertion forces

IXIAM: ISA EXtension for Integrated Accelerator Management

During the last few years, hardware accelerators have been gaining popularity thanks to their ability to achieve higher performance and efficiency than classic general-purpose solutions. They are fundamentally shaping the current generations of Systems-on-Chip (SoCs), which are becoming increasingly heterogeneous. However, despite their widespread use, a standard, general solution to manage them while providing speed and consistency has not yet been found. Common methodologies rely on OS mediation and a mix of user-space and kernel-space drivers, which can be inefficient, especially for fine-grained tasks. This paper addresses these sources of inefficiencies by proposing an ISA eXtension for Integrated Accelerator Management (IXIAM), a cost-effective HW-SW framework to control a wide variety of accelerators in a standard way, and directly from the cores. The proposed instructions include reservation, work offloading, data transfer, and synchronization. They can be wrapped in a high-level software API or even integrated into a compiler. IXIAM features also a user-space interrupt mechanism to signal events directly to the user process. We implement it as a RISC-V extension in the gem5 simulator and demonstrate detailed support for complex accelerators, as well as the ability to specify sequences of memory transfers and computations directly from the ISA and with significantly lower overhead than driver-based schemes. IXIAM provides a performance advantage that is more evident for small and medium workloads, reaching around 90x in the best case. This way, we enlarge the set of workloads that would benefit from hardware acceleration