Controlling software applications via resource allocation within the heartbeats framework

Maggio, Martina; Hoffmann, Henry; Santambrogio, Marco D.; Agarwal, Anant; Leva, Alberto

doi:10.1109/cdc.2010.5717893

Cited by 48 publications

(39 citation statements)

References 12 publications

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“…To meet these requirements for handling general and volatile environments, the SEEC decision engine is designed with multiple layers of adaptation as described in [15]. At the lowest-level, SEEC acts as a classical control system, taking feedback, in the form of heartbeats, and using it to tune actuators to meet goals [26]. The classical control system works well given prior knowledge about the application's behavior.…”

Section: Decidementioning

confidence: 99%

“…For example, this interface can be used to describe both operating system-level actions (e.g., allocation of cores to an application [26]) and hardware-level actions (e.g., reconfiguration of the hardware data cache [4]). Given this information, the SEEC runtime system can coordinate adaptation to keep the system on the Pareto optimal curve shown in Figure 2.…”

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: Decidementioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recent surveys capture the current state-of-the-art applying control-theory to software applications [17,46,58]-from controlling web server delays [38], to data service management [10], resource allocation [2,26,27,35], operating systems tuning [30,40,45], and energy management [25,41]. 1 http://www.martinamaggio.com/papers/fse17/ Some of these systems use automata-based formalisms to abstract software's behavior and temporal logic to specify some of its requirements [9,50], while we focus here on discrete-time control, where equation-based models are used to satisfy quantitative software properties.…”

Section: Related Workmentioning

confidence: 99%

Automated control of multiple software goals using multiple actuators

Maggio

Papadopoulos

Filieri

et al. 2017

Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering

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Modern software should satisfy multiple goals simultaneously: it should provide predictable performance, be robust to failures, handle peak loads and deal seamlessly with unexpected conditions and changes in the execution environment. For this to happen, software designs should account for the possibility of runtime changes and provide formal guarantees of the software's behavior. Control theory is one of the possible design drivers for runtime adaptation, but adopting control theoretic principles often requires additional, specialized knowledge. To overcome this limitation, automated methodologies have been proposed to extract the necessary information from experimental data and design a control system for runtime adaptation. These proposals, however, only process one goal at a time, creating a chain of controllers. In this paper, we propose and evaluate the first automated strategy that takes into account multiple goals without separating them into multiple control strategies. Avoiding the separation allows us to tackle a larger class of problems and provide stronger guarantees. We test our methodology's generality with three case studies that demonstrate its broad applicability in meeting performance, reliability, quality, security, and energy goals despite environmental or requirements changes.

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“…An intriguing approach to self-adaptation would be to apply the feed-back approach of control theory to dynamically tuning the software. Initial work in this area can be found in [35,43,53], but no generalized approach has ever been attempted. 4.…”

Section: Discussionmentioning

confidence: 99%

A formal approach to adaptive software: continuous assurance of non-functional requirements

2012

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Abstract. Modern software systems are increasingly requested to be adaptive to changes in the environment in which they are embedded. Moreover, adaptation often needs to be performed automatically, through self-managed reactions enacted by the application at run time. Off-line, human-driven changes should be requested only if self-adaptation cannot be achieved successfully. To support this kind of autonomic behavior, software systems must be empowered by a rich run-time support that can monitor the relevant phenomena of the surrounding environment to detect changes, analyze the data collected to understand the possible consequences of changes, reason about the ability of the application to continue to provide the required service, and finally react if an adaptation is needed. This paper focuses on non-functional requirements, which constitute an essential component of the quality that modern software systems need to exhibit. Although the proposed approach is quite general, it is mainly exemplified in the paper in the context of service-oriented systems, where the quality of service (QoS) is regulated by contractual obligations between the application provider and its clients. We analyze the case where an application, exported as a service, is built as a composition of other services. Non-functional requirements-such as reliability and performance-heavily depend on the environment in which the application is embedded. Thus changes in the environment may ultimately adversely affect QoS satisfaction. We illustrate an approach and support tools that enable a holistic view of the design and run-time management of adaptive software systems. The approach is based on formal (probabilistic) models that are used at design time to reason about dependability of the application in quantitative terms. Models continue to exist at run time to enable continuous verification and detection of changes that require adaptation.

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Controlling software applications via resource allocation within the heartbeats framework

Cited by 48 publications

References 12 publications

Self-aware computing in the Angstrom processor

Self-aware computing in the Angstrom processor

Automated control of multiple software goals using multiple actuators

A formal approach to adaptive software: continuous assurance of non-functional requirements

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