Self-aware computing in the Angstrom processor

Hoffmann, Henry; Holt, Jon; Kurian, George Thomas; Lau, Eric; Maggio, Martina; Miller, Joel B.; Neuman, Sabrina M.; Sinangil, Mahmut E.; Sinangil, Yildiz; Agarwal, Anant; Chandrakasan, Anantha P.; Devadas, Srinivas

doi:10.1145/2228360.2228409

Cited by 72 publications

(36 citation statements)

References 35 publications

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“…Several recent surveys capture the current state-of-the-art applying control-theory to software applications [5,33], as well as highlighting the main criticisms of early approaches [3]. Examples control delays for web servers [34], manage data centers [35], allocate resources [36][37][38], tune operating systems [39][40][41], minimize energy [42], and coordinate across the system stack [43]. These strategies adapt tunable knobs that can be identified either offline or at runtime [44].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Examples of this trend can be found in control of web servers [34,35], data centers and clusters management [36,37], operating systems [38][39][40], and across the system stack [41].…”

Section: Related Workmentioning

confidence: 99%

Control Theory for Model-based Performance-driven Software Adaptation

Arcelli

Cortellessa

Filieri

et al. 2015

Proceedings of the 11th International ACM SIGSOFT Conference on Quality of Software Architectures

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Self-adaptive techniques have been introduced in the last few years to tackle the growing complexity of software systems, where a major complexity factor leans on their dynamic nature subject to sudden and unpredictable changes that can heavily impact on the software architecture quality. Nonfunctional models, as generated from architectural descriptions of software, have been proven as effective instruments to support designers meeting non-functional requirements since the early architectural phases. However, such models still lack of intrinsic support for adaptable software. Goal of this paper is to extend the modeling capabilities to the case of software adaptation aimed at satisfying performance requirements. In particular, we illustrate how control theory can solve the problem of keeping within pre-defined ranges the indices of a Queueing Network model (such as queue length) through software adaptation actions (such as replacing software services with less resource-demanding ones), while the model is subject to disturbances (such as workload and/or operational profile variations). For this goal we first introduce a library of Modelica components that represent Queueing Network (QN) elements with adaptable parameters (that can be used as knobs for adaptation actions). Then we use such components to build "adaptable" QN models that are subject to disturbances. Finally, in the same framework, we introduce controllers that drive the QN adaptation. We demonstrate the soundness of our approach on a simple representative example in two ways: (i) on one end, we provide a formal proof of controller performance guarantees, and (ii) on the other end we show the sensitivity over time of software adaptation actions to different (types and intensities of) disturbances.

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“…A survey by Zang and Gordon-Ross [15] explores the challenges. Because of these issues, these hardware methods have not found their way to modern processors, and some recent processors, such as Angstrom [16], rely on software to reconfigure their caches.…”

Section: A Background and Motivationmentioning

confidence: 99%

Using an Adaptive HPC Runtime System to Reconfigure the Cache Hierarchy

Totoni

Torrellas

Kalé

2014

SC14: International Conference for High Performance Computing, Networking, Storage and Analysis

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Abstract-The cache hierarchy often consumes a large portion of a processor's energy. To save energy in HPC environments, this paper proposes software-controlled reconfiguration of the cache hierarchy with an adaptive runtime system. Our approach addresses the two major limitations associated with other methods that reconfigure the caches: predicting the application's future and finding the best cache hierarchy configuration. Our approach uses formal language theory to express the application's pattern and help predict its future. Furthermore, it uses the prevalent Single Program Multiple Data (SPMD) model of HPC codes to find the best configuration in parallel quickly. Our experiments using cycle-level simulations indicate that 67% of the cache energy can be saved with only a 2.4% performance penalty on average. Moreover, we demonstrate that, for some applications, switching to a software-controlled reconfigurable streaming buffer configuration can improve performance by up to 30% and save 75% of the cache energy.

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Self-aware computing in the Angstrom processor

Cited by 72 publications

References 35 publications

Automated multi-objective control for self-adaptive software design

Automated multi-objective control for self-adaptive software design

Control Theory for Model-based Performance-driven Software Adaptation

Using an Adaptive HPC Runtime System to Reconfigure the Cache Hierarchy

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