Dieter Schuller

Plug-and-Play Virtual Factories

Although virtual factories are a well-established approach to managing distributed, cross-organizational manufacturing processes, corresponding end-to-end IT support is still lacking. Service-oriented virtual factories use and extend well-known methods and technologies from service-oriented computing, the Internet of Things, and business process management to establish, manage, monitor, and adapt virtual factories in a plug-and-play-like fashion.

Maximizing Cloud Provider Profit from Equilibrium Price Auctions

Auctioning constitutes a market-driven scheme for the allocation of cloud-based computing capacities. It is practically applied today in the context of Infrastructure as a Service offers, specifically, virtual machines. However, the maximization of auction profits poses a challenging task for the cloud provider, because it involves the concurrent determination of equilibrium prices and distribution of virtual machine instances to the underlying physical hosts in the data center. In the work at hand, we propose an optimal approach, based on linear programming, as well as a heuristic approach to tackle this Equilibrium Price Auction Allocation Problem (EPAAP). Through an evaluation based on realistic data, we show the practical applicability and benefits of our contributions. Specifically, we find that the heuristic approach reduces the average computation time to solve an EPAAP by more than 99.9%, but still maintains a favorable average solution quality of 96.7% in terms of cloud provider profit, compared to the optimal approach.

Cost-Driven Optimization of Complex Service-Based Workflows for Stochastic QoS Parameters

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.

Towards Process Support for Cloud Manufacturing

Due to increasing competitive pressure, manufacturing companies need to support flexible and scalable business processes - both on the shop floor and in their enterprise software systems. Cloud manufacturing is a recent approach to realize real-world manufacturing processes by applying well-known basic concepts from the field of Cloud computing to this domain. To implement Cloud manufacturing, it is necessary to model, enact and monitor according manufacturing processes and virtualize the single process steps. So far, Business Process Management Systems do not explicitly support Cloud manufacturing. This paper analyzes requirements regarding process enactment for Cloud manufacturing and provides a concept for an according software framework.

Adaptive matchmaking for RESTful services based on hRESTS and MicroWSMO

Matchmaking -- i.e., the task of finding functionally suitable service offers based on a service request -- has only been addressed in the context of WS-* Web services. However, RESTful services are gaining increasing attraction and have been adopted by major companies, thus increasing the need for suitable matchmaking solutions. This paper introduces XAM4SWS, an adaptive matchmaker for semantic Web services that supports multiple service description formats, including hRESTS and MicroWSMO for RESTful services. XAM4SWS adapts existing methodologies from WS-* matchmaking and extends them through the inclusion of REST-specific service features. A prototypical implementation of the matchmaker is evaluated with respect to multiple information retrieval metrics using an adapted semantic Web service test collection.

Optimization of complex qos-aware service compositions

In Service-oriented Architectures, business processes can be realized by composing loosely coupled services. The problem of QoS-aware service composition is widely recognized in the literature. Existing approaches on computing an optimal solution to this problem tackle structured business processes, i.e., business processes which are composed of XOR-block, AND-block, and repeat loop orchestration components. As of yet, OR-block and unstructured orchestration components have not been sufficiently considered in the context of QoS-aware service composition. The work at hand addresses this shortcoming. An approach for computing an optimal solution to the service composition problem is proposed considering the structured orchestration components, such as AND/XOR/OR-block and repeat loop, as well as unstructured orchestration components.

Concurrent negotiations in cloud-based systems

Utilizing cloud-based services, consumers gain a high level of flexibility, but they cannot obtain individual Quality of Service guarantees or request service compositions according to their specific business needs. Therefore, appropriate mechanisms for an automated negotiation of Quality of Service parameters are required that do not only consider the individual business objectives and strategies of the negotiation partners involved, but do also account for the dependencies between the different services and service tiers in cloud computing. This enables enterprises to increase the quality and flexibility of their business processes and lays the foundation for market-based complex service provisioning. In this paper, we present one such negotiation approach and evaluate the application of different negotiation strategies.

Cost-Efficient Scheduling of Elastic Processes in Hybrid Clouds

Cloud computing is becoming increasingly important for executing business processes. This development contributes to a novel class of Business Process Management Systems, called eBPMS, that inherit elasticity from cloud computing. The aim of eBPMS is to improve the efficiency of process enactment, in particular regarding scalability and cost-efficiency. However, there is hardly any research that investigates scheduling for eBPMS so far. Against this background, we design an elastic scheduling approach for eBPMS and a corresponding formal problem definition in order to evaluate its data transfer capabilities -- this is especially important for hybrid cloud environments. Through extensive evaluations, we are able to show that our approach reduces the total cost by a considerable share.

QoS-Aware Service Composition for Complex Workflows

In Service-oriented Architectures, business processes are realized by composing loosely coupled services. With the ongoing success of Web services technologies, the invocation of Web services goes beyond enterprises’ borders. So, besides implementing required services within one enterprise, services can be purchased from external service providers. With a growing number and variety of services, enterprises can choose between functionally equivalent services at different cost and quality levels. The paper at hand addresses this service selection problem considering complex workflow patterns. Aggregation functions for different quality of service parameters are proposed and a linear optimization problem is formulated, which can be solved using integer linear programming techniques or by applying heuristic solutions.

Optimization of Complex Elastic Processes

Business Process Management is a matter of great importance in different industries and application areas. In many cases, it involves the execution of resource-intensive tasks in terms of computing power such as CPU and RAM. Due to the emergence of Cloud computing, theoretically unlimited resources can be used for the enactment of business processes. These Cloud resources render several challenges for Business Process Management Systems to ensure a predefined Quality of Service level during Cloud-based process enactment. Therefore, new solutions for process scheduling and resource allocation are required to tackle these challenges. Within this paper, we present a novel approach to schedule business processes and optimize the used Cloud-based computational resources in a cost-efficient way, thus realizing so-called elastic processes. For that, we specify the Service Instance Placement Problem, i.e., an optimization model which defines the setting of how service instances are scheduled among resources. Through extensive evaluations we show the benefits of our contributions and compare the novel approach against a baseline which follows an ad hoc approach.