On the Use of Online Analytic Performance Models, in Self-Managing and Self-Organizing Computer Systems

Menascé, Daniel A.; Bennani, Mohamed N.; Ruan, Honglei

doi:10.1007/11428589_9

Cited by 55 publications

(49 citation statements)

References 14 publications

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“…For example, Hellerstein et al [HDPT04] and Lu et al [LAL + 03] described dynamic regression models in the context of autonomic computing and self-optimization. Menascé and Bennani [MB03] used QNM as predictive models for avoiding bottleneck saturation and for online capacity sizing. Ghanbari et al [GSLI11] used dynamically tuned layered queuing models, which are software specific versions of QNMs, for online performance problem determination and mitigation in cloud computing.…”

Section: Classifying Assurance Methods According To Non-functional Crmentioning

confidence: 99%

Using Models at Runtime to Address Assurance for Self-Adaptive Systems

et al. 2014

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Abstract.A self-adaptive software system modifies its behavior at runtime in response to changes within the system or in its execution environment. The fulfillment of the system requirements needs to be guaranteed even in the presence of adverse conditions and adaptations. Thus, a key challenge for self-adaptive software systems is assurance. Traditionally, confidence in the correctness of a system is gained through a variety of activities and processes performed at development time, such as design analysis and testing. In the presence of selfadaptation, however, some of the assurance tasks may need to be performed at runtime. This need calls for the development of techniques that enable continuous assurance throughout the software life cycle. Fundamental to the development of runtime assurance techniques is research into the use of models at runtime (M@RT). This chapter explores the state of the art for using M@RT to address the assurance of self-adaptive software systems. It defines what information can be captured by M@RT, specifically for the purpose of assurance, and puts this definition into the context of existing work. We then outline key research challenges for assurance at runtime and characterize assurance methods. The chapter concludes with an exploration of selected application areas where M@RT could provide significant benefits beyond existing assurance techniques for adaptive systems.

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Section: Classifying Assurance Methods According To Non-functional Crmentioning

confidence: 99%

Using Models at Runtime to Address Assurance for Self-Adaptive Systems

et al. 2014

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“…An autonomous, dynamic (re-)configuration requires advanced feedback control mechanisms to monitor the system state and crucial system metrics, and also to take appropriate actions. One approach is to use controllers that periodically try to determine the best configuration for the system by combining analytic performance models with combinatorial search techniques (Menascé and Bennani 2003). If the desired state of the network entities can be described using policies, they can be monitored and configured using NESTOR, an architecture for network selfmanagement and organization (Yemini et al 2000).…”

Section: Related Workmentioning

confidence: 99%

Securing Industrial Control Systems through Autonomous Hardening

Chapas

Hristova

Locher

et al. 2014

2nd International Symposium for ICS &Amp; SCADA Cyber Security Research 2014

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The secure and reliable operation of industrial control systems is becoming more and more challenging due to the increasing size, complexity, and heterogeneity of such systems. A constant change of requirements and responsibilities results in an increased frequency of configuration and topological changes, which renders a manual verification of system security infeasible. Thus, there is a need for automatic mechanisms that allows a system to uphold a desired level of security autonomously. In this paper, we present a framework that enables a system to harden itself periodically, i.e., the framework ensures that each device complies to a security baseline tailored to the device's functionality and capability. The evaluation of our implementation shows that the framework effectively and efficiently corrects any deviations from the desired state at each networked device and thereby guarantees that the overall system remains compliant to pre-defined security policies. Moreover, the scalability tests conducted in a cloud infrastructure indicate that the framework is suitable for fairly large networks, with hundreds of individual devices, which makes it suitable for a wide range of practical control systems.

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“…The work by Menascé and Bennani (2003) describes creating autonomic computing models for computer systems based on the notion of queuing network (QN) models (Menascé & Bennani, 2003). Much like computer systems, an ED has many shared resources (e.g., beds, X-ray machines, doctors, and nurses).…”

Section: Model-driven Implementation In the Edmentioning

confidence: 99%

An Autonomic Computing Framework for Self-Managed Emergency Departments

Almomen

Menascé

2011

Proceedings of the International Conference on Health Informatics

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Abstract:The delivery of cost-effective and quality Emergency Department (ED) services remains an important and ongoing challenge for the healthcare industry. ED overcrowding has become a common problem in hospitals around the world, threatening the safety of patients who rely on timely emergency treatment. Despite numerous advances in medical procedures and technologies, EDs continue to experience overcrowding problems. The combination of increased demand and diminished resources makes optimizing emergency departments a difficult problem for healthcare decision makers. We examine this problem by applying an autonomic computing framework for self-managed emergency departments to maintain optimal Quality of Service (QoS) during its operation. Our work has potential implications in guiding a hospital's effort to optimize their emergency department system.

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On the Use of Online Analytic Performance Models, in Self-Managing and Self-Organizing Computer Systems

Cited by 55 publications

References 14 publications

Using Models at Runtime to Address Assurance for Self-Adaptive Systems

Using Models at Runtime to Address Assurance for Self-Adaptive Systems

Securing Industrial Control Systems through Autonomous Hardening

An Autonomic Computing Framework for Self-Managed Emergency Departments

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