Multi-objective optimization of IT service availability and costs

Bosse, Sascha; Splieth, Matthias; Turowski, Klaus

doi:10.1016/j.ress.2015.11.004

Cited by 26 publications

(17 citation statements)

References 65 publications

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“…The cloud OS primarily manages the operation of one or more virtual machines within a virtualized environment, while the management server executes the cloud services in the operating system. P s = P Hw × P OS × P Mg (6) where P Hw , P OS and P Mg is the hardware, operating system, and management server availability (reliability), respectively. Equation 7 computes the availability for the main node.…”

Section: Availability Models For the Base Cloud Environmentmentioning

confidence: 99%

“…However, providing cloud services according to the customer needs and specific constraints remains a challenge. Parameters such as reliability, capacity-oriented availability and cost are relevant factors in the negotiation of such services [6,10]. Therefore, an efficient and accurate assessment of cloud infrastructures considering availability, reliability and cost requirements is fundamental in allowing customers to identify a cloud infrastructure that suits their needs and preferences.…”

Section: Introductionmentioning

confidence: 99%

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Decision making in cloud environments: an approach based on multiple-criteria decision analysis and stochastic models

et al. 2018

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Cloud computing is a paradigm that provides services through the Internet. The paradigm has been influenced by previously available technologies (for example cluster, peer-to-peer, and grid computing) and has now been adopted by almost all large organizations. Companies such as Google, Amazon, Microsoft and Facebook have made significant investments in cloud computing, and now provide services with high levels of dependability. The efficient and accurate assessment of cloud-based infrastructure is fundamental in guaranteeing both business continuity and uninterrupted public services, as much as is possible. This paper presents an approach for selecting cloud computing infrastructures, in terms of dependability and cost that best suits both company and customer needs. We use stochastic models to calculate dependability-related metrics for different cloud infrastructures. We then use a Multiple-Criteria Decision-Making (MCDM) method to rank the best cloud infrastructures, taking customer service constraints such as reliability, downtime, and cost into consideration. A case study demonstrates the practicability and usefulness of the proposed approach.

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Section: Availability Models For the Base Cloud Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decision making in cloud environments: an approach based on multiple-criteria decision analysis and stochastic models

et al. 2018

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“…Consequently, all design alternatives with minimized costs and maximized performance need to be identified, leading to a multi-objective optimization problem. Such problems can be addressed by a Pareto approach as done, e.g., in [62] for optimizing performance and costs and in [63] for optimizing service availability and costs. On this basis, design decisions can be made, resulting in a capacity plan.…”

Section: Activities Of the Capacity Management Consumermentioning

confidence: 99%

Capacity Management as a Service for Enterprise Standard Software

Müller

Bosse

Turowski

2017

CSIMQ

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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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“…Lee [12] analyzed the steady-state availability of a simple parallel 1+1 redundancy model with one active and one standby component. Bosse et al [2] estimated the availability of a redundancy model with imperfect switchovers and interrupted repairs by using a Petri net Monte Carlo simulation. Kuo and Ke [10] and Lee [13] studied the steady-state availability of series systems with switching failures, interrupted repairs, and generally distributed repair time.…”

Section: Introductionmentioning

confidence: 99%

Availability analysis of series redundancy models with imperfect switchover and interrupted repairs

Shim¹,

Ryu²,

Lee³

2017

EiN

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Article citation info: Shim J, Ryu h, Lee y. Availability analysis of series redundancy models with imperfect switchover and interrupted repairs. eksploatacja i Niezawodnosc -maintenance and Reliability 2017; 19 (4): 640-649, http://dx.doi.org/10.17531/ein.2017.4.19 IntroductionThe availability of a system is defined as the probability that the system is operational at a point in time [16,25]. High availability is becoming a must in various domains such as telecommunication networks, power plants, and industrial and manufacturing systems [5, 16,27]. During the past years, many efforts have been made to improve system availability.Redundancy is a common approach to improve system availability [6]. The redundancy service can offer different levels of availability depending on its redundancy model. Availability Management Framework [16] defines the following four redundancy models: 2N, N+M, N-way, and N-way active. The 2N redundancy model ensures one standby replica for each component in active mode. The active components execute the service, while the standby components are ready to take over the active role if the active components fail. The N+M redundancy model extends the 2N redundancy by allowing more than two components to be active or standby. In the N+M redundancy, N represents the number of components in active mode and M represents the number of components in standby. The N-way redundancy model extends the N+M redundancy allowing a component to be simultaneously active and standby for different services. Lastly, the N-way active redundancy model differs from the 2N, N+M, and N-way redundancy, as it does not support standby service assignments, but allows a service to be assigned active to several components [16].The availability analysis of a redundancy model is based on analyzing the various states that the model undergoes during its lifespan [21]. The analysis mainly focuses on capturing the failures that cause the system to switch to a faulty state and the repairs that shift the system back to a healthy state [6]. Since the occurrence of failures is erratic by nature, stochastic models have been used to conduct the availability analysis [20]. Markovian models have been extensively used for this purpose because of their expressiveness and their capability of capturing the complexity of real systems [1,[23][24][25]. One of the major problems of using Markovian models is that a large number of states are required to represent the model accurately [1]. As an alternative, Kanso et al. [6] various redundancy models and evaluated the availability by using the Stochastic Petri Net Package (SPNP). Kim et al. [9] analyzed the networking service availability of 2N redundancy model with nonstop forwarding by using the SPNP. The analytic-numeric methods of SPNP provide the capabilities of solving the Markovian SRNs but fail for non-Markovian SRNs. Actually there is no reason to assume Markov property in modeling of repairable systems [11]. Recently, modeling and analysis of repairable systems with general rep...

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Multi-objective optimization of IT service availability and costs

Cited by 26 publications

References 65 publications

Decision making in cloud environments: an approach based on multiple-criteria decision analysis and stochastic models

Decision making in cloud environments: an approach based on multiple-criteria decision analysis and stochastic models

Capacity Management as a Service for Enterprise Standard Software

Availability analysis of series redundancy models with imperfect switchover and interrupted repairs

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