Creating robust software through self-adaptation

Laddaga, Robert

doi:10.1109/mis.1999.769879

Cited by 60 publications

(29 citation statements)

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“…SEEC makes it easy to create self-adaptive, or autonomic, computing systems which can alter their behavior to meet multiple goals and automatically adapt to environmental changes. Like all self-adaptive systems, SEEC is characterized by the presence of an observe-decide-act, or ODA, loop [18,23]. The Angstrom system continuously monitors its goals (observe) and available resources (actions) using a decision engine to determine how best to use resources to meet goals (decide) given the current state of the system.…”

Section: Introductionmentioning

confidence: 99%

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Self-aware computing in the Angstrom processor

Hoffmann¹,

Holt²,

Kurian³

et al. 2012

Proceedings of the 49th Annual Design Automation Conference

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Addressing the challenges of extreme scale computing requires holistic design of new programming models and systems that support those models. This paper discusses the Angstrom processor, which is designed to support a new Self-aware Computing (SEEC) model. In SEEC, applications explicitly state goals, while other systems components provide actions that the SEEC runtime system can use to meet those goals. Angstrom supports this model by exposing sensors and adaptations that traditionally would be managed independently by hardware. This exposure allows SEEC to coordinate hardware actions with actions specified by other parts of the system, and allows the SEEC runtime system to meet application goals while reducing costs (e.g., power consumption).

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Section: Introductionmentioning

confidence: 99%

“…Self-aware, or autonomic, computing has been proposed as one method to deal with the rising complexity of computer systems [18,23], and adaptive systems have been implemented in both hardware [2,5,10,11] and software [32]. One limitation of these approaches is that they typically do not support adaptation as a first-class object.…”

Section: Introductionmentioning

confidence: 99%

Self-aware computing in the Angstrom processor

Hoffmann¹,

Holt²,

Kurian³

et al. 2012

Proceedings of the 49th Annual Design Automation Conference

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show abstract

“…Such a specification must include all input data sets, which is impossible, in general, for embedded sensorimotor applications. Self-adaptive software, however, modifies its behavior based on observed progress toward goals as the system state evolves at run-time [8]. Current research in self-adaptive software draws from two traditions, namely control theoretic and planning.…”

Section: Related Workmentioning

confidence: 99%

A Fault-Tolerant Distributed Vision System Architecture for Object Tracking in a Smart Room

Karuppiah¹,

Zhu²,

Shenoy³

et al. 2001

Lecture Notes in Computer Science

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Abstract. In recent years, distributed computer vision has gained a lot of attention within the computer vision community for applications such as video surveillance and object tracking. The collective information gathered by multiple cameras that are strategically placed has many advantages. For example, aggregation of information from multiple viewpoints reduces the uncertainty about the scene. Further, there is no single point of failure, thus the system as a whole could continue to perform the task at hand. However, the advantages arising out of such cooperation can be realized only by timely sharing of the information between them. This paper discusses the design of a distributed vision system that enables several heterogeneous sensors with different processing rates to exchange information in a timely manner in order to achieve a common goal, say tracking of multiple human subjects and mobile robots in an indoor smart environment. In our fault-tolerant distributed vision system, a resource manager manages individual cameras and buffers the time-stamped object candidates received from them. A User Agent with a given task specification approaches the resource manager, first for knowing the available resources (cameras) and later for receiving the object candidates from the resources of its interest. Thus the resource manager acts as a proxy between the user agents and cameras, thereby freeing the cameras to do dedicated feature detection and extraction only. In such a scenario, many failures are possible. For example, one of the cameras may have a hardware failure or it may lose the target, which moved away from its field of view. In this context, important issues such as failure detection and handling, synchronization of data from multiple sensors and sensor reconfiguration by view planning are discussed in the paper. Experimental results with real scene images will be given.

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“…Adaptive software detects such changes in the operating environment, determines how to respond, and implements the response with an actuation phase. Many methodologies have arisen for designing and implementing such adaptive or autonomic software systems [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, using control theory requires (1) understanding all the actuators, or software components that can be changed during runtime, (2) developing a suitable mathematical model relating these components to observable feedback, and (3) implementing a control strategy based on the model and the desired behavior [6]. While the first point is typically easy for the software developer, the other two items require deep background in control science and might be not applicable for all the software adaptation problems [6].…”

Section: Introductionmentioning

confidence: 99%

Automated multi-objective control for self-adaptive software design

Filieri

Hoffmann

Maggio

2015

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

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While software is becoming more complex everyday, the requirements on its behavior are not getting any easier to satisfy. An application should offer a certain quality of service, adapt to the current environmental conditions and withstand runtime variations that were simply unpredictable during the design phase. To tackle this complexity, control theory has been proposed as a technique for managing software's dynamic behavior, obviating the need for human intervention. Control-theoretical solutions, however, are either tailored for the specific application or do not handle the complexity of multiple interacting components and multiple goals. In this paper, we develop an automated control synthesis methodology that takes, as input, the configurable software components (or knobs) and the goals to be achieved. Our approach automatically constructs a control system that manages the specified knobs and guarantees the goals are met. These claims are backed up by experimental studies on three different software applications, where we show how the proposed automated approach handles the complexity of multiple knobs and objectives.

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Creating robust software through self-adaptation

Cited by 60 publications

References 0 publications

Self-aware computing in the Angstrom processor

Self-aware computing in the Angstrom processor

A Fault-Tolerant Distributed Vision System Architecture for Object Tracking in a Smart Room

Automated multi-objective control for self-adaptive software design

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