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Personal Sign In. For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy. Email Address. Sign In. A Uniform Programming Abstraction for Effecting Autonomic Adaptations onto Software Systems Abstract: Most general-purpose work towards autonomic or self-managing systems has emphasized the front end of the feedback control loop, with some also concerned with controlling the back end enactment of runtime adaptations u t usually employing an effector technology peculiar to one type of target system.

While completely generic "one size fits all" effector technologies seem implausible, we propose a general-purpose programming model and interaction layer that abstracts away from the peculiarities of target-specific effectors, enabling a uniform approach to controlling and coordinating the low-level execution of reconfigurations, repairs, micro-reboots, etc.

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Autonomic Wireless Sensor Networks: A Systematic Literature Review

To tackle the complexity of autonomic computing systems it is crucial to provide methods supporting their systematic and principled development. Using the PSCEL language, autonomic systems can be described in terms of the constituent components and their reciprocal interactions.

The computational behaviour of components is defined in a procedural style, by the programming constructs, while the adaptation logic is defined in a declarative style, by the policing constructs. In this paper we introduce a suite of practical software tools for programming and policing autonomic computing systems in PSCEL. Specifically, we integrate a Java-based runtime environment, supporting the execution of programming constructs, with the code corresponding to the policing ones.

Usability and potentialities of the approach are illustrated by means of a robot swarm case study. Unable to display preview. Download preview PDF.

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programming abstractions for autonomic computing

Kephart, J. Margheri, A. De Nicola, R. Dastani, M. Ashley-Rollman, M. In: IROS, pp. Khakpour, N. Lanese, I. In: Wirsing, M.

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Technical report, Univ. Masi, M. In: Barthe, G. ESSoS LNCS, vol. Damianou, N. In: Sloman, M.

programming abstractions for autonomic computing

Serbedzija, N.Delicato, Paulo F. Autonomic computing AC is a promising approach to meet basic requirements in the design of wireless sensor networks WSNsand its principles can be applied to efficiently manage nodes operation and optimize network resources.

Middleware for WSNs supports the implementation and basic operation of such networks. In this systematic literature review SLR we aim to provide an overview of existing WSN middleware systems that address autonomic properties. Another goal is finding out which interactions and behavior can be automated in WSN components.

We drew the following main conclusions from the SLR results: i the selected studies address WSN concerns according to the self- properties of AC, namely, self-configuration, self-healing, self-optimization, and self-protection; ii the selected studies use different approaches for managing the dynamic behavior of middleware systems for WSN, such as policy-based reasoning, context-based reasoning, feedback control loops, mobile agents, model transformations, and code generation.

Finally, we identified a lack of comprehensive system architecture designs that support the autonomy of sensor networking. Wireless sensor networks WSNs consist of networks composed of devices equipped with sensing, processing, storage, and wireless communication capabilities. Each node of the network can have several sensing units, which are able to perform measurements of physical variables, such as temperature, luminosity, humidity, and vibration [ 1 ].

The nodes in a WSN have limited computing resources and are usually powered by batteries; thus energy saving is a key issue in these networks in order to prolong their operational lifetime. WSN nodes operate collaboratively, extracting environmental data, performing the same simple processing, and transmitting them to one or more exit points of the network often called sink nodesto be analyzed and further processed.

There is currently a wide range of applications for WSN, ranging from environmental monitoring to structural damage detection. The quality of a WSN application depends not only on how well it has been designed and implemented but also on how well it can deal with problems and events at runtime [ 2 ].

Therefore, such networks should have an autonomous behavior and be able to tolerate several types of failures, such as faulty nodes or hardware physical malfunction e. In other words, WSN should be able to self-manage those failures and to dynamically self-adapt to the environment [ 3 ]. The first WSN applications had simple requirements that did not demand complex software infrastructures. Typically, WSNs were designed to meet the needs of a single target application usually of a single user, who was also the infrastructure owner.

However, with the rapid evolution in this field combined with the increasing complexity of sensors and applications, the need of specific middleware platforms for these networks has risen [ 4 ]. A WSN middleware is layered software that lies between application code and the communication infrastructure providing, via well-defined interfaces, a set of services that may be configured to facilitate the application development and its execution in an efficient way for a distributed environment [ 5 ].

Thus, the main goal of a middleware is to enable the interaction and the communication between distributed components, hiding from application developers the complexity of the underlying hardware and network platforms, and freeing them from explicit manipulation of protocols and infrastructure services.

WSN middleware should provide generic services for applications based on sensing and additionally consider application-specific needs and the inherent features of WSN nodes, such as the nodes limited resources of energy, memory, and CPU and the dynamic execution context.

Middleware systems developed until today e. In order to grant autonomic behavior, individual components of any autonomic system should foresee the following set of functionalities, also known as self- properties [ 36 ]: self-configuration, self-healing, self-optimization, and self-protection. Self-configuration is the ability of a system to adapt itself to the environment, changing according to high-level policies, aligned with business goals and defined by system administrators.

Self-healing is the ability of a system to recover after a disturbance and to minimize interruptions to maintain the software available for the user, even in the presence of individual failure of components. And self-protection is the ability to predict, detect, recognize, and protect from malicious attacks and unplanned cascade failures. These properties are the essence of autonomic computing.

According to [ 36 ], autonomic computing AC is the capacity of an infrastructure for adapting itself according to policies and business goals. A highlighted approach to develop autonomic systems is the architecture for AC proposed by IBM [ 37 ] that defines an abstract framework for self-managing IT systems. In this framework, an autonomic system is a collection of autonomic elements. Each element consists of an autonomic manager and a managed resource.GrammaTech's research in autonomic computing is focused on the creation of systems that can monitorprotectand defend themselves without human intervention.

A number of GrammaTech technologies work together under this umbrella to enable complex systems to police themselves automatically. Our research in autonomic computing extends and advances — as well as makes use of — our innovations in software assurance and application security hardening. In the Cyber Grand Challenge, teams from around the world began the competition to develop a security system capable of automatically defending against cyber-attacks as fast as they are launched.

GrammaTech was part of a smaller group of 7 teams selected to receive funding from DARPA to develop automated network defense technology for the challenge, and advanced to become part of a group of 7 finalists, all of whom competed to win the competition at DEF CON in Las Vegas.

The Cyber Grand Challenge was aimed at solving a major cyber-security issue that we are starting to face with alarming frequency — the reliance on expert programmers to uncover and repair weaknesses in an attacked system.

programming abstractions for autonomic computing

Repairing weaknesses only after the system has been attacked, and after hackers have fully taken advantage of these weaknesses to steal data or otherwise impact processes, is dangerous for any system. GrammaTech worked to solve this problem, while collaborating with the University of Virginia. Our system provided automatic and adaptive protection of a network service implemented as an x86 binary and automatically evaluate network defenses by generating proofs of vulnerability.

The system included breakthrough technology for automated analysis, repair, and protection of binaries and an autonomous cyber reasoning component that dynamically adapts, adjusting resource allocation in response to evolving circumstances. The complexity of modern computer systems has grown to the point of stressing human ability to understand their behavior completely. The sheer number of software components and the myriad interactions between them that are present on a single desktop computer presents a difficult security challenge that continues to confound modern protection technologies.

Every day, new exploits are created that take advantage of obscure combinations of software bugs and unexpected behavior to sidestep existing defenses. Response is slow, requiring human effort to diagnose and develop new counter-measures to each new threat.

GrammaTech envisions a new paradigm for building system security that endows the computer itself with the tools to diagnose new attacks, reason about their impact to the system, and implement countermeasures in an automatic fashion. Autonomy-oriented computation offers the potential to allow complex computer systems to police themselves, detecting intrusion, performing self-healing, and directly countering cyber threats.

Computer security is critical to both national security as well as the private sector. This technology will provide next-generation technology for creating autonomic systems capable of detecting and closing breaches in security in an automated fashion. Such autonomic applications monitor for deviations from expected behavior, assess mission health, and react to preserve objectives — raising alerts, initiating recovery processes, or shutting the system down.

Most systems are just implemented in code. A central problem in this approach to cyber security is defining a precise model that lets the monitor distinguish good behavior from bad. When the implementation is complete, there is no higher-level description of correct behavior that can serve as a model. The essence of GrammaTech's approach is to help programmers define the models while they are coding. All rights reserved.

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Search form Search. Current Research Research in Autonomic Computing GrammaTech's research in autonomic computing is focused on the creation of systems that can monitorprotectand defend themselves without human intervention.

Cyber Grand Challenge. Sponsored by: The U. Office of Naval Research.

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On Programming and Policing Autonomic Computing Systems

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