Collaborative Software Performance Engineering for Enterprise Applications

Müller, Hendrik; Bosse, Sascha; Wirth, Markus; Turowski, Klaus

doi:10.24251/hicss.2017.047

Cited by 3 publications

(18 citation statements)

References 18 publications

(29 reference statements)

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“…Ultimately, additional capacity of the same resource type may not decrease response time to a desired service level. In fact, the performance of services also benefits from certain resource types or architectures that can be integrated by means of performance models [6].…”

Section: Service and Component Capacity Managementmentioning

confidence: 99%

“…Main reasons include a generally high time effort in construction and analysis of analytical models [26] as well as essential expert knowledge about the model itself, the system to be implemented and its dependencies which may not be available [27]. In addition, the credibility of obtained results remains questionable until their validation in later lifecycle phases [6]. Therefore, measurementbased approaches are more frequently used in practice than model-based approaches due to their effectiveness in operation [27].…”

Section: Introductionmentioning

confidence: 99%

“…For this purpose, machine learning techniques can be utilized in order to design a pure black box approach that does not require domain-specific and potentially expensive expert knowledge about the system structure and behavior. In [6], it was shown that predictions of mean response times per business transaction on a cross-organizational data basis can be as accurate as model-based approaches. However, the authors did not demonstrate how to utilize the prediction results in the context of planning and managing the capacity of enterprise applications with respect to costs.…”

Section: Introductionmentioning

confidence: 99%

“…In this article, based on the groundwork of [6], we designed a multi-organizational capacity management process that is particularly beneficial for users of enterprise standard software. While based on the main activities of the process by [37], it integrates specifications from the domain of enterprise applications with regard to their common characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Particularly, costs for enterprise software will grow by 7.1 percent [5]. Hence, IT Service Management (ITSM) frameworks such as ITIL and ISO 20000 embed the task of balancing performance and operational costs into the capacity management process [6]. Since energy costs are a major factor in the total cost of ownership of modern data centers [7], [8], [9], savings can be achieved by avoiding idle resources, which may consume up to 70 percent of their peak power [10].…”

Section: Introductionmentioning

confidence: 99%

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Capacity Management as a Service for Enterprise Standard Software

Müller

Bosse

Turowski

2018

J. Complex Syst. Inform. Model. Q.

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Abstract. Capacity management approaches optimize component utilization from a strong technical perspective. In fact, the quality of involved services is considered implicitly by linking it to resource capacity values. This practice hinders to evaluate design alternatives with respect to given service levels that are expressed in user-centric metrics such as the mean response time for a business transaction. We argue that utilized historical workload traces often contain a variety of performance-related information that allows for the integration of performance prediction techniques through machine learning. Since enterprise applications excessively make use of standard software that is shipped by large software vendors to a wide range of customers, standardized prediction models can be trained and provisioned as part of a capacity management service which we propose in this article. Therefore, we integrate knowledge discovery activities into well-known capacity planning steps, which we adapt to the special characteristics of enterprise applications. Using a realworld example, we demonstrate how prediction models that were trained on a large scale of monitoring data enable cost-efficient measurement-based prediction techniques to be used in early design and redesign phases of planned or running applications. Finally, based on the trained model, we demonstrate how to simulate and analyze future workload scenarios. Using a Pareto approach, we were able to identify cost-effective design alternatives for an enterprise application whose capacity is being managed.

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Section: Service and Component Capacity Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Capacity Management as a Service for Enterprise Standard Software

Müller

Bosse

Turowski

2018

J. Complex Syst. Inform. Model. Q.

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A Homogeneous Actor-Based Monitor Language for Adaptive Behaviour

Clark

Kulkarni²,

Barat³

et al. 2018

Lecture Notes in Computer Science

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This paper describes a structured approach to encoding monitors in an actor language. Within a configuration of actors, each of which publishes a history, a monitor is an independent actor that triggers an action based on patterns occurring in the histories. We define a monitor language based on linear temporal logic and show how it can be homogeneously embedded within an actor language. The approach is demonstrated through a number of examples and evaluated in terms of a real-world actor-based simulation.

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