A stochastic processing network is a system that takes materials of various kinds as inputs, and uses processing resources to produce other materials as outputs. Such a network provides a powerful abstraction of a wide range of real world, complex systems, including semiconductor wafer fabrication facilities, networks of data switches, and large-scale call centers. Key performance measures of a stochastic processing network include throughput, cycle time, and holding cost. The network performance can dramatically be affected by the choice of operational policies. We propose a family of operational policies called maximum pressure policies. The maximum pressure policies are attractive in that their implementation uses minimal state information of the network. The deployment of a resource (server) is decided based on the queue lengths in its serviceable buffers and the queue lengths in their immediate downstream buffers. In particular, the decision does not use arrival rate information that is often difficult or impossible to estimate reliably. We prove that a maximum pressure policy can maximize throughput for a general class of stochastic processing networks. We also establish an asymptotic optimality of maximum pressure policies for stochastic processing networks with a unique bottleneck. The optimality is in terms of minimizing workload process. A key step in the proof of the asymptotic optimality is to show that the network processes under maximum pressure policies exhibit a state space collapse.

Large-scale software development companies delivering market-driven products have introduced agile methodologies as the way of working to a big extent. Even though there are many benefits with an agile way of working, problems occur when scaling agile because of the increased complexity. One explicit problem area is to evolve deep product knowledge, which is a domain specific knowledge that cannot be developed anywhere else but at the specific workplace. This research aims to identify impediments for developing domain specific knowledge and provide solutions to overcome these challenges in order to optimize knowledge growth and throughput. The result of the research shows that impediments occur in four different categories, based on a framework for knowledge sharing drivers. These are people-related, task-related, structure-related and technology-related. The challenging element with knowledge growth is to integrate the training into the feature development process, without affecting the feature throughput negatively. The research also shows that by increasing the knowledge sharing, the competence level of the whole organization can be increased, and thereby be beneficial from many perspectives, such as feature-throughput and code quality.

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.

The popularity of the wireless networks is so great that we will soon reach the point where most of the devices work based on that, but new challenges in wireless channel access will be created with these increasingly widespread wireless communications. Multi-channel CSMA protocols have been designed to enhance the throughput of the next generation wireless networks compared to single-channel protocols. However, their performance analysis still needs careful considerations. In this thesis, a set of techniques are proposed to model and analyse the CSMA protocols in terms of channel sensing and channel access. In that respect, the performance analysis of un-slotted multi-channel CSMA protocols is studied through considering the hidden terminals. In the modelling phase, important parameters such as shadowing and path loss impairments are being considered. Following that, due to the high importance of spectrum sensing in CSMA protocols, the Double-Threshold Energy Detector (DTED) is thoroughly investigated in this thesis. An iterative algorithm is also proposed to determine optimum values of detection parameters in a sensing-throughput problem formulation. Vehicle-to-Roadside (V2R) communication, as a part of Intelligent Transportation System (ITS), over multi-channel wireless networks is also modelled and analysed in this thesis. In this respect, through proposing a novel mathematical model, the connectivity level which an arbitrary vehicle experiences during its packet transmission with a RSU is also investigated.

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.