K42

Krieger, Orran; Auslander, Marc; Rosenburg, Bryan S.; Wisniewski, Robert W.; Xenidis, Jimi; Silva, Dilma Da; Ostrowski, M.; Appavoo, Jonathan; Butrico, Maria; Mergen, Mark; Waterland, Amos; Uhlig, Volkmar

doi:10.1145/1217935.1217949

Cited by 67 publications

(8 citation statements)

References 19 publications

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“…For example, companies like IBM [25] developed the IBM Touchpoint Simulator and the K42 Operating System [26]. Oracle produced the Automatic Workload Repository [27] and Intel the RAS Technologies for Enterprise [28].…”

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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“…Although the Ftrace circular buffer available in the kernel version we started with (version 2.6.28) was deemed to be somewhat lock-heavy [18] with impact on performance, there have since been various attempts to replace it with a wait-free alternative [18,19]. Wait-free FIFO buffers [20,21] are difficult to prove correct and are prone to subtle race-conditions and errors, which is why their adoption into the mainline Linux kernel has been slow.…”

Section: Extracting Signaturesmentioning

confidence: 99%

Fmeter: Extracting Indexable Low-Level System Signatures by Counting Kernel Function Calls

Marian¹,

Weatherspoon²,

Lee³

et al. 2012

Lecture Notes in Computer Science

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Abstract. System monitoring tools serve to provide operators and developers with an insight into system execution and an understanding of system behavior under a variety of scenarios. Many system abnormalities leave a significant impact on the system execution which may arise out of performance issues, bugs, or errors. Having the ability to quantify and search such behavior in the system execution history can facilitate new ways of looking at problems. For example, operators may use clustering to group and visualize similar system behaviors. We propose a monitoring system that extracts formal, indexable, low-level system signatures using the classical vector space model from the field of information retrieval and text mining. We drive an analogy between the representation of kernel function invocations with terms within text documents. This parallel allows us to automatically index, store, and later retrieve and compare the system signatures. As with information retrieval, the key insight is that we need not rely on the semantic information in a document. Instead, we consider only the statistical properties of the terms belonging to the document (and to the corpus), which enables us to provide both an efficient way to extract signatures at runtime and to analyze the signatures using statistical formal methods. We have built a prototype in Linux, Fmeter, which extracts such low-level system signatures by recording all kernel function invocations. We show that the signatures are naturally amenable to formal processing with statistical methods like clustering and supervised machine learning.

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“…This work includes research on monitoring GRID, cloud computing, and web services [Vadhiyar and Dongarra 2005, Buisson et al 2005, Reinecke and Wolter 2008, single-and multi-core architectures [Sprunt 2002b, Kumar et al 2003, Intel Inc. 2006, Azimi et al 2005, and complex software systems and operating systems [Caporuscio et al 2005, De Rose and Reed 1999, Cascaval et al 2006, Krieger et al 2006, Wisniewski and Rosenburg 2003, Tamches and Miller 1999. Considerable work has been done using tracing to understand kernel performance (e.g.,K42 [Wisniewski and Rosenburg 2003], KernInst [Tamches and Miller 1999]) and application behavior (e.g.,Paradyn [Miller et al 1995], SvPablo [De Rose and Reed 1999]).…”

Section: Related Workmentioning

confidence: 99%

“…Major companies such as IBM [IBM Inc. 2009] (IBM Touchpoint Simulator, the K42 Operating System [Krieger et al 2006 [Intel Inc. 2005]) have all invested effort in adaptive computing.…”

Section: Related Workmentioning

confidence: 99%

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Application heartbeats for software performance and health

et al. 2010

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Adaptive, or self-aware, computing has been proposed as one method to help application programmers confront the growing complexity of multicore software development. However, existing approaches to adaptive systems are largely ad hoc and often do not manage to incorporate the true performance goals of the applications they are designed to support. This paper presents an enabling technology for adaptive computing systems: Application Heartbeats. The Application Heartbeats framework provides a simple, standard programming interface that applications can use to indicate their performance and system software (and hardware) can use to query an application's performance. Several experiments demonstrate the simplicity and efficacy of the Application Heartbeat approach. First the PARSEC benchmark suite is instrumented with Application Heartbeats to show the broad applicability of the interface. Then, an adaptive H.264 encoder is developed to show how applications might use Application Heartbeats internally. Next, an external resource scheduler is developed which assigns cores to an application based on its performance as specified with Application Heartbeats. Finally, the adaptive H.264 encoder is used to illustrate how Application Heartbeats can aid fault tolerance.

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K42

Cited by 67 publications

References 19 publications

Automated multi-objective control for self-adaptive software design

Automated multi-objective control for self-adaptive software design

Fmeter: Extracting Indexable Low-Level System Signatures by Counting Kernel Function Calls

Application heartbeats for software performance and health

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