An empirical study of modeling self-management capabilities in autonomic systems using case-based reasoning

Autonomic systems promise to inject self-managing capabilities in software systems. The major objectives of autonomic computing are to minimize human intervention and to enable a seamless self-adaptive behavior in the software systems. To achieve self-managing behavior, various methods have been exploited in past. Case-based reasoning (CBR) is a problem solving paradigm of artificial intelligence which exploits past experience, stored in the form of problem–solution pairs. We have applied CBR based modeling approach to achieve autonomicity in software systems. The proposed algorithms have been described and CBR implementation on externalization and internalization architectures of autonomic systems using two case studies RUBiS and Autonomic Forest Fire Application (AFFA) have been shown. The study highlights the effect of 10 different similarity measures, the role of adaptation and the effect of changing nearest neighborhood cardinality for a CBR solution cycle in autonomic managers. The results presented in this paper show that the proposed CBR based autonomic model exhibits 90–98% accuracy in diagnosing the problem and planning the solution.     

Seven software engineering principles for autonomic computing development

The complexity of modern middleware and software solutions is growing at an exponential rate. Only self-managing, or autonomic computing technology can reasonably stem the confusion this complexity brings to bear on human administrators. While much has been published on “architecture” and “function” for producing such systems, little has been written about the engineering of self-managing systems as a distinct paradigm. In this short article we suggest a set of software engineering principles for engineering of autonomic systems that should guide the planning of autonomic systems and their interfaces, with the intent to guide the thinking of R&D organizations pursuing the development of autonomic computing capability.     

Hierarchical case-based reasoning integrating case-based anddecompositional problem-solving techniques for plant-control softwaredesign

Case based reasoning (CBR) is an artificial intelligence technique that emphasises the role of past experience during future problem solving. New problems are solved by retrieving and adapting the solutions to similar problems, solutions that have been stored and indexed for future reuse as cases in a case-base. The power of CBR is severely curtailed if problem solving is limited to the retrieval and adaptation of a single case, so most CBR systems dealing with complex problem solving tasks have to use multiple cases. The paper describes and evaluates the technique of hierarchical case based reasoning, which allows complex problems to be solved by reusing multiple cases at various levels of abstraction. The technique is described in the context of Deja Vu, a CBR system aimed at automating plant-control software design     

Case-based reasoning systems: from automation to decision-aidingand stimulation

Over the past decade, case-based reasoning (CBR) has emerged as a major research area within the artificial intelligence research field due to both its widespread usage by humans and its appeal as a methodology for building intelligent systems. Conventional CBR systems have been largely designed as automated problem-solvers for producing a solution to a given problem by adapting the solution to a similar, previously solved problem. Such systems have had limited success in real-world applications. More recently, there has been a search for new paradigms and directions for increasing the utility of CBR systems for decision support. The paper focuses on the synergism between the research areas of CBR and decision support systems (DSSs). A conceptual framework for DSSs is presented and used to develop a taxonomy of three different types of CBR systems: 1) conventional, 2) decision-aiding, and 3) stimulative. The major characteristics of each type of CBR system are explained with a particular focus on decision-aiding and stimulative CBR systems. The research implications of the evolution in the design of CBR systems from automation toward decision-aiding and stimulation are also explored     

Agents and Service-Oriented Computing for Autonomic Computing: A Research Agenda

Autonomic computing is the solution proposed to cope with the complexity of today's computing environments. Self-management, an important element of autonomic computing, is also characteristic of single and multiagent systems, as well as systems based on service-oriented architectures. Combining these technologies can be profitable for all — in particular, for the development of autonomic computing systems.     

Self-managing software

Software has become pervasive. Despite this success and expansion into daily life, there have, of course, been a number of software-related disasters and near-disasters. Software failures have resulted in giving cancer patients excessive (and lethal) doses of radiation, loss of aircraft and spacecraft, and disclosures of private financial information. We continue to push software to the limits, in many cases using it where failure would be catastrophic, and where many organizations are spending as much as 33 to 50 percent of the total cost of ownership of their computing and communication systems to avoid software failure. Many practitioners believe that self-managing software can potentially ensure safer, more reliable, and cost-effective computer systems. Creating software systems that are self-directed, self-governing, and self-adapting has been the focus of development in autonomic computing, autonomic communications, pervasive computing, organic computing, and adaptive computing.     

具有自主计算特征的新型网格体系结构研究

网格环境的复杂性和动态性迫切需要自主计算范型的支持。提出了一种具有自主计算特征的新型网格体系结构。网格的自我管理功能通过自主网格服务层、多Agent自主协同层和表达用户需求的政策层来完成。为了更好的实现Agent间的自主协同,提出了构造多Agent相似-关联树的算法。模拟环境下实现的网格原型系统的运行表明,符合该体系结构的网格系统具有一定程度的自配置、自优化、自保护和自愈合的能力。     

The vision of autonomic computing

A 2001 IBM manifesto observed that a looming software complexity crisis -caused by applications and environments that number into the tens of millions of lines of code - threatened to halt progress in computing. The manifesto noted the almost impossible difficulty of managing current and planned computing systems, which require integrating several heterogeneous environments into corporate-wide computing systems that extend into the Internet. Autonomic computing, perhaps the most attractive approach to solving this problem, creates systems that can manage themselves when given high-level objectives from administrators. Systems manage themselves according to an administrator's goals. New components integrate as effortlessly as a new cell establishes itself in the human body. These ideas are not science fiction, but elements of the grand challenge to create self-managing computing systems.     

Towards Software Architecture and Mechanisms for Improving Runtime Variability of Internetware System

As an emerging software paradigm, Internetware is proposed to handle openness, dynamism of software systems in the context of Internet, which implies that such software systems typically have runtime variability that can be improved dynamically to handle various or even unexpected changes of requirements and open environment. Though many progresses of Internetware software technologies have been made to support the adaptation, evolution, context-awareness, etc. of Internetware, how to construct Internetware systems with the ability to improve their runtime variability is still a great challenge in the literature of software engineering. In this paper, we propose software architecture and mechanisms for Internetware systems to support the improvement of their runtime variability by combining software variability and autonomic computing techniques. The Internetware system is organized as three levels that are consist of variable autonomic elements and Internetware entities, and architecture of these software entities is defined and discussed respectively. Moreover, we put forward a series of runtime mechanisms based on these levels, including module selection, intermediator and horizontal management, to realize operations upon the variation points and variants in software architectures and thus achieve the improvement of runtime variability. We develop a sample of Personal Data Resource Network to depict the requirements and scenario of improving runtime variability, and further study the case based on our proposed approach to show its effectiveness and applicability.     

Case-based reasoning for interpretation of data from non-destructive testing

Non-destructive testing (NDT) is a name for a range of methods and procedures used to determine fitness of industrial products for further use. The use of NDT testing techniques results in data in the form of signals, images, or sequences of these, which have to be analysed in order to determine if they contain any indications of defects in the inspected objects. This analysis is often quite complex. In the past, systems have been built which used neural networks (and other statistical classifiers) as well as expert systems to interpret NDT data; however, successful uses of these systems in inspection practice are rare. This article presents how the case-based reasoning methodology (where interpretation of new data is based on previous data-interpretation cases) can be used to tackle the problem of NDT data interpretation. The article presents the characteristics of CBR, which make it an interesting alternative to statistical classifiers and to expert systems. Suitability of CBR for NDT data interpretation is illustrated based on examples of two applications: a CBR system for ultrasonic rail inspection and a CBR system for eddy-current inspection of heat exchangers.     

A unified logical model for CBR‐based e‐commerce systems

This article examines new issues resulting from applying case‐based reasoning (CBR) in e‐commerce and proposes a unified logical model for CBR‐based e‐commerce systems (CECS) that consists of three cycles and covers almost all activities of applying CBR in e‐commerce. This article also decomposes case adaptation into problem adaptation and solution adaptation, which not only improves the understanding of case adaptation in the traditional CBR, but also facilitates the refinement of activity of CBR in e‐commerce and intelligent support for e‐commerce. It then investigates CBR‐based product negotiation. This article thus gives insight into how to use CBR in e‐commerce and how to improve the understanding of CBR with its applications in e‐commerce from a logical viewpoint. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 29–46, 2005.     

A logical foundation for the case‐based reasoning cycle

Case‐based reasoning (CBR) has drawn considerable attention in artificial intelligence (AI) fields with many successful applications in systems such as e‐commerce and multiagent systems. For the moment, research and development of CBR basically follows the traditional process model of CBR, i.e., the R⁴ model and problem space model introduced in 1994 and 1996, respectively. However, there has been no logical analysis for this popular CBR model. This article will fill this gap by providing a unified logical foundation for the CBR cycle. The proposed approach is based on an integration of traditional mathematical logic, fuzzy logic, and similarity‐based reasoning. At the same time, we examine the CBR cycle from the knowledge‐based (KB) viewpoint. The proposed logical approach can facilitate research and development of CBR. © 2003 Wiley Periodicals, Inc.     

An overview of case-based reasoning applications in drilling engineering

Application-oriented research in the area of case-based reasoning has moved mature research results into practical applications. This paper presents an overview of different applications of case-based reasoning (CBR) in petroleum engineering, with focus on the drilling process, based on a survey and comparative evaluation of different applications. The numbers of papers, research groups, and experimental systems are indicative of the importance, need, and growth of CBR in different industries. A clear growing trend has been seen in the oil and gas industry over the last 5–10 years. In this paper we present the evolving story of CBR applied to problems in drilling engineering. We show that drilling engineering is an application domain in which the systematic storage and situation-triggered reuse of past concrete experiences provide significant support to drilling personnel at various levels. Some CBR systems have been successfully deployed in operational settings. With increased understanding of the complexity of drilling operations and continuous development of CBR and combined methods, the future potential is significantly higher.     

Adaptive reflex autonomicity for real-time systems

It may appear that for software systems that require strict real-time behavior, the idea of incorporating self-management (and specifically concepts from Autonomic Computing) may add the burden of excessive additional functionality and overhead. However, our experience is that, not only does real-time software benefit from autonomicity, but also the Autonomic Computing initiative (like other initiatives aiming at self-management) requires the expertise of the real-time community in order to achieve its overarching vision. In particular, there are emerging classes of real-time systems for which incorporation of self-management is absolutely essential in order to implement all of the requirements of the system, and in particular the timing requirements.     

Fulfilling the Vision of Autonomic Computing

Efforts since 2001 to design self-managing systems have yielded many impressive achievements, yet the original vision of autonomic computing remains unfulfilled. Researchers must develop a comprehensive systems engineering approach to create effective solutions for next-generation enterprise and sensor systems.     

自主计算概念模型与实现方法

自主计算是一个新兴的热点研究领域,旨在通过"技术管理技术"的手段隐藏系统管理复杂性,建立用户可指导的、状态觉察的和自适应的计算机系统.目前,自主计算的研究仍处于起步阶段,尚缺乏系统而成熟的理论体系.在阐明自主计算概念的基础上,提出一个自主计算概念模型.该模型刻画了自主元素和自主计算系统的基本工作机制和原理.以该模型为依据,概括性地提出了两类分别基于知识模型和数学模型的自主计算系统,分析它们的优点和不足.最后,给出了自主计算研究展望.     

Coordinating self-sizing and self-repair managers for multi-tier systems

Computing systems have become more and more distributed and heterogeneous, making their manual administration difficult and error-prone. The Autonomic Computing approach has been proposed to overcome this issue, by automating the administration of computing systems with the help of control loops called autonomic managers. Many research works have investigated the automation of the administration functions of computing systems and today many autonomic managers are available. However the existing autonomic manages are mostly specialized in the management of few administration concerns such as self-repair which handles server failures, and self-sizing which deals with dynamic server allocation. This makes necessary the coexistence of multiple autonomic managers for a complete system management. The coexistence of several such managers is required to handle multiple concerns, yet requires coordination mechanisms to avoid incoherent administration decisions. We investigate the use of control techniques for the design of coordination controllers, for which we exercise synchronous programming that provide formal semantics, and discrete controller synthesis to automate the construction of the controller. The paper details an application of the latter approach for the design of a coordination controller to orchestrate the execution of four self-repair and two self-sizing managers that address the availability and performance of a multi-tier replication-based system. We evaluate and demonstrate the benefits of our coordination solution by executing the RUBiS Benchmark web application.     

Autonomic computing

Autonomic computing (AC) has as its vision the creation of self-managing systems to address todays concerns of complexity and total cost of ownership while meeting tomorrows needs for pervasive and ubiquitous computation and communication. This paper reports on the latest autonomic systems research and technologies to influence the industry; it looks behind AC, summarising what it is, the current state-of-the-art research, related work and initiatives, highlights research and technology transfer issues and concludes with further and recommended reading.     

An architectural co-synthesis algorithm for energy-aware Network-on-Chip design

Network-on-Chip (NoC) has been proposed to overcome the complex on-chip communication problem of System-on-Chip (SoC) design in deep sub-micron. A complete NoC design contains exploration on both hardware and software architectures. The hardware architecture includes the selection of Processing Elements (PEs) with multiple types and their topology. The software architecture contains allocating tasks to PEs, scheduling of tasks and their communications. To find the best hardware design for the target tasks, both hardware and software architectures need to be considered simultaneously. Previous works on NoC design have concentrated on solving only one or two design parameters at a time. In this paper, we propose a hardware–software co-synthesis algorithm for a heterogeneous NoC architecture. The design goal is to minimize energy consumption while meeting the real-time requirements commonly seen in embedded applications. The proposed algorithm is based on Simulated-Annealing (SA). To compare the solution quality and efficiency of the proposed algorithm, we also implement the branch-and-bound and iterative algorithm to solve the hardware–software co-synthesis problem of a heterogeneous NoC. With the given synthetic task sets, the experimental results show that the proposed SA-based algorithm achieves near-optimal solution in a reasonable time, while the branch-and-bound algorithm takes a very long time to find the optimal solution, and the iterative algorithm fails to achieve good solution quality. When applying the co-synthesis algorithms to a real-world application with PE library that has little variation in PE performance and energy consumption, the iterative algorithm achieves solution quality comparable to that of the proposed SA-based algorithm.