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[15] M. Korupolu, A. Singh, and B. Bamba, “Coupled Placement in Modern Data Centers,” in 2009 IEEE Int. Symp. on Parallel & Distributed Processing, 2009. [16] S. Zaman and D. Grosu, “Efficient Bidding for Virtual Machine Instances in Clouds,” in 2011 IEEE Int. Conf. on Cloud Computing, 2011. [17] W. Lin, G. Lin, and H. Wei, “Dynamic Auction Mechanism for Cloud Resource Allocation,” in 10th IEEE/ACM Int. Conf. on Cluster, Cloud and Grid Computing, 2010. [18] A. Özer and C. Özturan, “An Auction Based Mathematical Model and Heuristics for Resource Co-Allocation Problem in Grids and Clouds,” in 5th Int. Conf. on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009.
Cost-driven Optimization of Complex Service-based Workflows for Stochastic QoS Parameters Dieter Schuller, Ulrich Lampe, Julian Eckert, <strong>Ralf</strong> Steinmetz Multimedia Communications <strong>Lab</strong> (KOM) Technische Universität Darmstadt Darmstadt, Germany Email: {firstname.lastname}@KOM.tu-darmstadt.de Abstract—The challenge of optimally selecting services from a set of functionally appropriate ones under Quality of Service (QoS) constraints – the Service Selection Problem – has been extensively addressed in the literature based on deterministic parameters. In practice, however, Quality of Service QoS parameters rather follow a stochastic distribution. In the work at hand, we present an integrated approach which addresses the Service Selection Problem for complex workflows in conjunction with stochastic Quality of Service parameters. Accounting for penalty cost which accrue due to Quality of Service violations, our approach reduces the impact of stochastic QoS behavior on total cost significantly. Keywords-Service Selection, Stochastic Quality of Service, Optimization, Simulation I. INTRODUCTION The selection of services from a set of appropriate ones that are able to provide the required functionality and thereby best meeting cost and Quality of Service (QoS) requirements – the Service Selection Problem (SSP) – is widely recognized in the literature, e.g., [1]–[4]. The optimization of the SSP is thereby based on deterministic QoS values. A solution to the SSP describes an execution plan, i.e., an assignment of services to certain tasks of a workflow, which satisfy the mentioned cost and QoS constraints. But QoS, e.g., the response time of a service or its availability, is not always deterministic in reality. Due to network latency or server load, response times of services may change dynamically. I.e., when the execution of the computed execution plan actually takes place, the perceived QoS might differ from the expected QoS which has previously been used for the calculation of the execution plan. Thus, although having computed an optimal solution to the SSP during design time which satisfies the constraints, it still is possible that these constraints are violated during runtime. The work at hand addresses this issue. Based on a service broker scenario, which is presented in Section II, we describe how QoS violations due to stochastic QoS behavior negatively impacts total cost. In order to account for this impact of stochastic QoS parameters, we present an integrated approach comprising an optimization, a simulation, and an adaptation step. Stefan Schulte Distributed Systems Group Vienna University of Technology Vienna, Austria Email: s.schulte@infosys.tuwien.ac.at During the optimization step, we compute an optimal solution to the SSP, i.e., an optimal execution plan, satisfying the QoS constraints based on the deterministic QoS values denoted by the respective service providers. In the simulation step, we observe the expected runtime behavior of the computed execution plan in terms of QoS. This way, we can assess potentially occurring constraint violations. We thereby assume that violating QoS constraints is penalized, i.e., penalty fees become due in addition to service invocation cost. According to the results of the simulation, we apply a greedy adaptation heuristic in order to reduce the impact of potentially occurring constraint violations. The remainder of this work is structured as follows. In Section II, we present a motivating scenario, which will be used throughout the paper. In Section III, we describe our solution to the SSP, which is based on our previous work in [5]. The applied simulation approach is presented in Section IV. Based on the simulation results, we apply our greedy adaptation heuristic which is described in Section V and evaluated in Section VI. Finally, after having distinguished our approach from related work in Section VII, we draw conclusions and discuss future work in Section VIII. II. SCENARIO In this section, we present a scenario, which is used as an example in the work at hand in order to illustrate the impact of stochastic QoS parameters. The application of our approach is not limited to this scenario. Imagine a service broker who receives requests from its customers. Paying the broker a fixed amount of money, the customers require certain tasks and workflows, respectively, to be executed. For this, they provide the broker with a document which specifies the required tasks from a functional perspective and indicates the ordering of the tasks, i.e., the structure of the workflow. One of the broker’s customers asks for instance for the workflow in Figure 1. The process steps P Si thereby indicate the tasks which have to be accomplished. Each task can be executed by a single service.
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Prof. Dr. Wolfgang König, Prof. Dr.-Ing. Ralf ... - E-Finance Lab

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