Steffen Preissler

Open Service Process Platform 2.0

Service-oriented architectures (SOA) have revolutionized software-engineering over the last years. As a result, many domains have begun to switch to this architecture style. However, to efficiently support different domains from an SOA perspective, several extensions have to be included, e.g., concepts for efficient data transfer or runtime adaptation of processes. On the one hand, our Open Service Process Platform 2.0 is a central SOA infrastructure including several extensions. On the other hand, this platform can be used as the basis for the development of further extensions. The unique features of our platform are: (i) orchestration and execution of processes in an easy way (ii) arbitrary extensibility with regard to simple specialization for various domains, (iii) central infrastructure within an organization, and (iv) full accessibility through Web 2.0 technologies.

Cost-based vectorization of instance-based integration processes

Integration processes are workflow-based integration tasks. The inefficiency of these processes is often caused by low resource utilization and significant waiting times for external systems. With the aim to overcome these problems, we proposed the concept of process vectorization. There, instance-based integration processes are transparently executed with the pipes-and-filters execution model. The term vectorization is used in the sense of processing a sequence (vector) of messages by one standing process. Although it has been shown that process vectorization achieves a significant throughput improvement, this concept has two major drawbacks. First, the theoretical performance of a vectorized integration process mainly depends on the performance of the most cost-intensive operator. Second, the practical performance strongly depends on the number of used threads and thus, on the number of operators. In this paper, we present an advanced optimization approach that addresses the mentioned problems. We generalize the vectorization problem and explain how to vectorize process plans in a cost-based manner taking into account the cost of the single operators in the form of their execution time. Due to the exponential time complexity of the exhaustive computation approach, we also provide a heuristic algorithm with linear time complexity. Furthermore, we explain how to apply the general cost-based vectorization to multiple process plans and we discuss the periodical re-optimization. In conclusion of our evaluation, the message throughput can be significantly increased compared to both the instance-based execution as well as the rule-based vectorized execution.

Cost-Based Vectorization of Instance-Based Integration Processes

The inefficiency of integration processes--as an abstraction of workflow-based integration tasks--is often reasoned by low resource utilization and significant waiting times for external systems. With the aim to overcome these problems, we proposed the concept of process vectorization. There, instance-based integration processes are transparently executed with the pipes-and-filters execution model. Here, the term vectorization is used in the sense of processing a sequence (vector) of messages by one standing process. Although it has been shown that process vectorization achieves a significant throughput improvement, this concept has two major drawbacks. First, the theoretical performance of a vectorized integration process mainly depends on the performance of the most cost-intensive operator. Second, the practical performance strongly depends on the number of available threads. In this paper, we present an advanced optimization approach that addresses the mentioned problems. Therefore, we generalize the vectorization problem and explain how to vectorize process plans in a cost-based manner. Due to the exponential complexity, we provide a heuristic computation approach and formally analyze its optimality. In conclusion of our evaluation, the message throughput can be significantly increased compared to both the instance-based execution as well as the rule-based process vectorization.

BPELDT — Data-Aware Extension for Data-Intensive Service Applications

Aside from business processes, the service-oriented approach —currently realized with Web services and BPEL—should be utilizable for data-intensive applications as well. Fundamentally, data-intensive applications are characterized by (i) a sequence of functional operations processing large amounts of data and (ii) the delivery and transformation of huge data sets between those functional activities. However, for the efficient handling of massive data sets, a significant amount of data infrastructure is required and the predefined ‘by value’ data semantic within the invocation of Web services and BPEL is not well suited for this context. To tackle this problem on the BPEL level, we developed a seamless extension to BPEL—the ‘BPEL data transitions’.

Vectorizing Instance-Based Integration Processes

The inefficiency of integration processes—as an abstraction of workflow-based integration tasks—is often reasoned by low resource utilization and significant waiting times for external systems. Due to the increasing use of integration processes within IT infrastructures, the throughput optimization has high influence on the overall performance of such an infrastructure. In the area of computational engineering, low resource utilization is addressed with vectorization techniques. In this paper, we introduce the concept of vectorization in the context of integration processes in order to achieve a higher degree of parallelism. Here, transactional behavior and serialized execution must be ensured.In conclusion of our evaluation, the message throughput can be significantly increased.

INNOVATIVE PROCESS EXECUTION IN SERVICE-ORIENTED ENVIRONMENTS

Today’s information systems are often built on the foundation of service-oriented environments. Although the fundamental purpose of an information system is the processing of data and information, the service-oriented architecture (SOA) does not treat data as a core first class citizen. Current SOA technologies support neither the explicit modeling of data flows in common business process modeling languages (such as BPMN) nor the usage of specialized data transformation and propagation technologies (for instance ETL-tools) on the process execution layer (BPEL). In this paper, we introduce our data-aware approach on the execution perspective as well as on the modeling perspective of business processes.

An XML-Based Streaming Concept for Business Process Execution

Service-oriented environments are central backbone of todays enterprise workflows. These workflow includes traditional process types like travel booking or order processing as well as data-intensive integration processes like operational business intelligence and data analytics. For the latter process types, current execution semantics and concepts do not scale very well in terms of performance and resource consumption. In this paper, we present a concept for data streaming in business processes that is inspired by the typical execution semantics in data management environments. Therefore, we present a conceptual process and execution model that leverages the idea of stream-based service invocation for a scalable and efficient process execution. In selected results of the evaluation we show, that it outperforms the execution model of current process engines.