Short-term performance management by priority-based queueing

Markl, Christian; Hühn, Oliver; Bichler, Martin

doi:10.1007/s11761-010-0066-3

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Information system quality measures have been addressed in service science (Choi et al 2009), software assurance (Markl et al 2010), Enterprise Resource Planning (ERP) systems (Dwyer 2006), Application Service Providers (ASP) (Kim 2010) and Healthcare (Winter 2009). …”

Section: Intrinsic Measures Of Information System Qualitymentioning

confidence: 99%

Extending the IT service quality measurement framework through a systematic literature review

Lepmets¹,

Cater-Steel

Gacenga

et al. 2012

J Serv Sci Res

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Section: Intrinsic Measures Of Information System Qualitymentioning

confidence: 99%

Extending the IT service quality measurement framework through a systematic literature review

Lepmets¹,

Cater-Steel

Gacenga

et al. 2012

J Serv Sci Res

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“…As shown by [9] and discussed in Fig. 1, this problem is challenging, as there is a combinatorial problem to be solved as services are shared amongst workflows types.…”

Section: A Controlled Flows In Service Networkmentioning

confidence: 99%

Autonomic Prioritization of Enterprise Transactions Based on Bid-Price Controls

Setzer

Markl

2013

IEEE Trans. Netw. Serv. Manage.

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We study the business-driven prioritization of workflows in enterprise transaction processing systems. Today, IT services are increasingly shared amongst workflows, and static prioritization is applied during phases of high system workload to mitigate the business impact of service queuing and resulting workflow response-time increase. Static prioritization rules can only be optimal for one particular workflow definition and demand mix scenario, while in practice workflow definitions are continuously re-engineered and demand varies over time. We propose a technique to automatically adapt workflow priorities aimed at minimizing overload-related costs resulting from workflow delays. We continuously align maximum service buffer lengths and bid-prices reflecting current marginal opportunity costs of invoking a shared service. New prioritization is derived by comparing expected costs when queuing a workflow with its current opportunity costs, computed as the sum of bid-prices of invoked services. Simulation outcomes based on industry data illustrate that the approach dominates standard queuing disciplines as well as static prioritization.

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Using matrix approximation for high-dimensional discrete optimization problems: Server consolidation based on cyclic time-series data

Setzer

Bichler

2013

European Journal of Operational Research

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We consider the assignment of enterprise applications in virtual machines to physical servers, also known as server consolidation problem. Data center operators try to minimize the number of servers, but at the same time provide sufficient computing resources at each point in time. While historical workload data would allow for accurate workload forecasting and optimal allocation of enterprise applications to servers, the volume of data and the large number of resulting capacity constraints in a mathematical problem formulation renders this task impossible for any but small instances. We use singular value decomposition (SVD) to extract significant features from a large constraint matrix and provide a new geometric interpretation of these features, which allows for allocating large sets of applications efficiently to physical servers with this new formulation. While SVD is typically applied for analytical purposes like time series decomposition, noise filtering, or clustering, in this paper features are used to transform the original allocation problem in a low-dimensional integer program with only the extracted features in a much smaller constraint matrix. We evaluate the approach using workload data from a large Data center and show that it leads to high solution quality, but at the same time allows for solving considerably larger problem instances than what would be possible without data reduction and model transform. The overall approach can also be applied to other large integer problems, as they can be found in applications of multiple or multi-dimensional knapsack or bin packing problems.

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Short-term performance management by priority-based queueing

Cited by 4 publications

References 23 publications

Extending the IT service quality measurement framework through a systematic literature review

Extending the IT service quality measurement framework through a systematic literature review

Autonomic Prioritization of Enterprise Transactions Based on Bid-Price Controls

Using matrix approximation for high-dimensional discrete optimization problems: Server consolidation based on cyclic time-series data

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