PAC-MEN: Personal Autonomic Computing Monitoring Environments

The overall goal of this research is to improve the `environment awareness' aspect of personal autonomic computing. Personal Computing offers unique challenges for self-management due to its multiequipment, multi-situation, and multi-user nature. The aim is to develop a support architecture for multiplatform working, based on autonomic computing concepts and techniques. Of particular interest is collaboration among personal systems to take a shared responsibility for environment awareness. Concepts mirroring human mechanisms, such as 'reflex reactions' and the use of 'vital signs' to assess operational health, are used in designing and implementing the personal computing architecture. A proof of concept self-healing tool is considered and lessons learned used for the requirements specification of the community-based environment awareness prototype environment---PACMEN (Personal Autonomic Computing Monitor ENvironment)

Personal Autonomic Computing Reflex Reactions and Self-Healing

The overall goal of this research is to improve theself-awareness and environment-awareness aspect of personal au-tonomic computing (PAC) to facilitate self-managing capabilitiessuch as self-healing. Personal computing offers unique challengesfor self-management due to its multiequipment, multisituation, andmultiuser nature. The aim is to develop a support architecture formultiplatform working, based on autonomic computing conceptsand techniques. Of particular interest is collaboration among per-sonal systems to take a shared responsibility for self-awareness andenvironment awareness. Concepts mirroring human mechanisms,such as reflex reactions and the use ofvital signsto assess oper-ational health, are used in designing and implementing the PACarchitecture. As proof of concept, this was implemented as a self-healing tool utilizing a pulse monitor and a vital signs health moni-tor within the autonomic manager. This type of functionality opensnew opportunities to provide self-configuring, self-optimizing, andself-protecting, as well as self-healing autonomic capabilities topersonal computing

A classification of emerging and traditional grid systems

The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings. However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area

Incorporating prediction models in the SelfLet framework: a plugin approach

A complex pervasive system is typically composed of many cooperating \emph{nodes}, running on machines with different capabilities, and pervasively distributed across the environment. These systems pose several new challenges such as the need for the nodes to manage autonomously and dynamically in order to adapt to changes detected in the environment. To address the above issue, a number of autonomic frameworks has been proposed. These usually offer either predefined self-management policies or programmatic mechanisms for creating new policies at design time. From a more theoretical perspective, some works propose the adoption of prediction models as a way to anticipate the evolution of the system and to make timely decisions. In this context, our aim is to experiment with the integration of prediction models within a specific autonomic framework in order to assess the feasibility of such integration in a setting where the characteristics of dynamicity, decentralization, and cooperation among nodes are important. We extend an existing infrastructure called \emph{SelfLets} in order to make it ready to host various prediction models that can be dynamically plugged and unplugged in the various component nodes, thus enabling a wide range of predictions to be performed. Also, we show in a simple example how the system works when adopting a specific prediction model from the literature

Modeling adaptation with a tuple-based coordination language

In recent years, it has been argued that systems and applications, in order to deal with their increasing complexity, should be able to adapt their behavior according to new requirements or environment conditions. In this paper, we present a preliminary investigation aiming at studying how coordination languages and formal methods can contribute to a better understanding, implementation and usage of the mechanisms and techniques for adaptation currently proposed in the literature. Our study relies on the formal coordination language Klaim as a common framework for modeling some adaptation techniques, namely the MAPE-K loop, aspect- and context-oriented programming

Semantic-based policy engineering for autonomic systems

This paper presents some important directions in the use of ontology-based semantics in achieving the vision of Autonomic Communications. We examine the requirements of Autonomic Communication with a focus on the demanding needs of ubiquitous computing environments, with an emphasis on the requirements shared with Autonomic Computing. We observe that ontologies provide a strong mechanism for addressing the heterogeneity in user task requirements, managed resources, services and context. We then present two complimentary approaches that exploit ontology-based knowledge in support of autonomic communications: service-oriented models for policy engineering and dynamic semantic queries using content-based networks. The paper concludes with a discussion of the major research challenges such approaches raise