Despite the growing adoption of blockchains, their isolated architectures hinder seamless cross-chain communication, challenging applications that rely on integrated blockchain infrastructures, notably Blockchain-based Information Systems (BISs). Achieving interoperability while preserving privacy and regulatory compliance remains a core challenge, particularly when separate organizations operate different blockchain platforms and tokenized value must move across them without exposing transaction links that may reveal business relationships or payment behavior. Existing interoperability solutions often incur high computational overhead and rely on protocol-specific assumptions, limiting their applicability across heterogeneous blockchains. We introduce zkPACT, a privacy-preserving framework for compliant cross-chain token transfers across heterogeneous blockchains. Our framework combines Zero-Knowledge Proofs (ZKPs), oracle networks, and off-chain batching to support scalable transfers. It employs a coordinated oracle model in which validators process cross-chain burn events, while a rotating aggregator updates the shared off-chain Merkle tree after reaching consensus, enabling private and efficient token claims. To improve scalability and reduce gas costs, zkPACT batches claim requests off-chain and then submits a single succinct proof to the smart contract. To ensure validator accountability, the framework enforces an incentive mechanism and dynamic slashing. We also integrate a Know Your Customer (KYC) mechanism that enables users to demonstrate compliance without revealing sensitive data, preserving privacy and accountability in the event of abuse. We present a proof-of-concept implementation of zkPACT that achieves up to 95% lower gas costs and up to 94% lower off-chain memory usage than a non-batching approach, demonstrating its suitability for private, scalable cross-chain token transfers.

The need for low latency, bandwidth efficiency, and privacy has driven the deployment of distributed applications to the network edge. However, edge environments introduce concrete challenges such as limited infrastructure control, constrained connectivity due to NAT or firewalls, and the heterogeneity of devices and network conditions. This paper introduces a software-defined overlay networking (SDON) middleware that addresses these issues by simplifying the development and deployment of edge applications through centralized control and dynamic overlay management. SDON allows applications to define high-level requirements, such as node and link characteristics and the network topology. These requirements are translated into device-specific configurations and enforced across suitable edge devices. We implemented our SDON middleware as a fully functional software and evaluated it in two edge computing use cases: i) routing for video streaming across middleboxed edge devices and ii) computation offloading on heterogeneous edge devices. Our results show that deployments via SDON, with centrally enforced optimizations, improve application performance by reducing mean streaming latency by 20 % and computation times by 22 %.

Blockchains have become increasingly important in recent years and have expanded their applicability to many domains beyond finance and cryptocurrencies. This adoption has particularly increased with the introduction of smart contracts, which are immutable, user-defined programs directly deployed on blockchain networks. However, many scenarios require business transactions to simultaneously access smart contracts on multiple, possibly heterogeneous blockchain networks while ensuring the atomicity and isolation of these transactions, which is not natively supported by current blockchain systems. Therefore, in this work, we introduce the Transactional Cross-Chain Smart Contract Invocation (TCCSCI) approach, which supports such distributed business transactions while ensuring their global atomicity and serializability. The approach introduces the concept of Resource Manager Smart Contracts (RMSCs), and 2PC for Blockchains (2PC4BC), a client-driven Atomic Commit Protocol (ACP) specialized for blockchain-based distributed transactions. We validate our approach using a prototypical implementation, evaluate its introduced overhead, and prove its correctness.

Second-generation blockchains like Ethereum allow users to execute smart contracts. Usually, blockchains charge gas fees for deploying and invocating smart contracts. These costs can be significant and even render some use cases non-economical. Therefore, optimizing smart contracts regarding gas costs is a significant achievement, and several approaches have already been presented. However, existing methods of gas cost minimization are often based on rule-based code optimization techniques, which can perform only a subset of possible optimizations and cannot detect outlying and uncommon code patterns.Therefore, this paper discusses using machine learning methods to detect a more cost-efficient version of a Solidity smart contract. This approach trains a Siamese neural network to detect the similarity between a contract and its optimized version, providing the basis for informing the user about existing optimizing patterns. We evaluate our approach using a repository of 30,432 Solidity smart contracts.

Federated Learning (FL) has gained prominence as a method for facilitating collaborative and privacy-preserving model training across multiple heterogeneous devices in recent years. In most approaches, the clients are closely deployed to the data source. However, as FL systems are implemented in the industry, multiple platform options can be considered in the design phase.In this paper, we present a novel approach for deploying FL clients to multiple locations considering a multi-platform strategy with cloud, fog, and edge resources. We provide a FL architecture that integrates mechanisms for building cohorts of similar clients and a client selection algorithm for optimizing the performance of all clients with respect to energy consumption, model performance, and FL completion time.We evaluate seven deployment strategies in three scenarios given a real-world use case from the electronics industry and heterogeneous hardware capabilities. Our results show that our approach can improve model performance by up to 15%, while energy consumption and completion time converge to the relatively best deployment.

Payment Channel Networks (PCNs) are a promising approach to overcome scalability issues of blockchains. To achieve efficient payments in PCNs, it is necessary to route transactions between a payer and a payee. Especially in large-scale PCNs, multi-hop routing becomes necessary, since transactions need to be relayed by nodes. For this, a scalable routing algorithm is needed, which fits the individual objectives of PCN users. In this article, we study whether routing protocols from the field of Wireless Sensor Networks can be applied in PCNs. To this end, we first derive requirements for routing in PCNs, select suitable approaches, and analyze to which degrees they perform well and meet the requirements. We adapt selected protocols and evaluate them with regard to the lengths of payment paths, fees, and success ratio.

The closed architecture of prevailing blockchain systems renders the usage of this technology mostly infeasible for a wide range of real-world problems. Most blockchains trap users and applications in their isolated space without the possibility of cooperating or switching to other blockchains. Therefore, blockchains need additional mechanisms for seamless communication and arbitrary data exchange between each other and external systems. Unfortunately, current approaches for cross-blockchain communication are resource-intensive or require additional blockchains or tailored solutions depending on the applied consensus mechanisms of the connected blockchains. Therefore, we propose an oracle with an off-chain aggregation mechanism based on Zero-Knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs) to facilitate cross-blockchain communication. The oracle queries data from another blockchain and applies a rollup-like mechanism to move state and computation off-chain. The zkOracle contract only expects the transferred data, an updated state root, and proof of the correct execution of the aggregation mechanism. The proposed solution only requires constant 378 kgas to submit data on the Ethereum blockchain and is primarily independent of the underlying technology of the queried blockchains.

Cloud services have been well integrated into various sectors of contemporary societies such as transportation, healthcare, and work. However, the energy sector has made little progress in offering cloud services to energy consumers. In some cases, even basic features (such as energy consumption monitoring and visualization of households) are still unattainable. To foster innovation in cloud-based energy services, governments implement regulations that moderate access to energy data via the energy providers’ platforms. Despite these regulations, cloud service providers may still struggle to aggregate energy data because each energy provider may be using different procedures, authentication mechanisms, and data semantics. Consequently, aggregating energy data at scale becomes challenging, as it requires achieving compatibility with diverse platforms. To address this challenge, we propose a cloud-based framework that handles all interactions with energy providers and prepares the data for further processing by services. Additionally, we analyze the socioeconomic impact of our approach, and we outline novel use cases that may emerge due to the proposed framework.

In industrial settings, traditional centralized ap-proaches for training AI models can be insufficient due to limited training data. Industrial Federated Learning (IFL) offers a promising solution by enabling collaborative training across multiple industrial devices, while keeping data on-premises. In this paper, we propose a novel approach for supporting the development, deployment, integration and execution of IFL solutions. The proposed method provides a lifecycle management of FL artifacts and supports FL as a Service (FlaaS). This enables the extensibility and customizability of FL-based edge applications in industrial settings. Additionally, we introduce a federated clustering algorithm that we have integrated into a condition monitoring app running on client locations to evaluate the proposed lifecycle management. We run two scenarios with four and 33 clients using real-world time series data from industrial pumps. Our results show the applicability of the implemented lifecycle management and demonstrates that privacy-preserving approaches compete well with privacy-disclosing ones.

Monitoring of batch production processes is complex and existing solutions do not offer good performance in providing real-time feedback about the state of the process. Therefore, we introduce an AI system that monitors a fermentation process and detects deviations from the normal process execution directly on the edge and provides real-time feedback to the operator, allowing intervention before the process gets out of control. We analyze the accuracy of the novel AI-based approach by carrying out several experiments and compare the outcome with statistical methods as a baseline. The experiments show that the AI-based approach performs significantly better at detecting anomalies in a fermentation process than the statistical methods.

Fog computing enables the execution of IoT applications on compute nodes which reside both in the cloud and at the edge of the network. To achieve this, most fog computing systems route the IoT data on a path which starts at the data source, and goes through various edge and cloud nodes. Each node on this path may accept the data if there are available resources to process this data locally. Otherwise, the data is forwarded to the next node on path. Notably, when the data is forwarded (rather than accepted), the communication latency increases by the delay to reach the next node. To avoid this, we propose a routing mechanism which maintains a history of all nodes that have accepted data of each context in the past. By processing this history, our mechanism sends the data directly to the closest node that tends to accept data of the same context. This lowers the forwarding by nodes on path, and can reduce the communication latency. We evaluate this approach using both prototype- and simulation-based experiments which show reduced communication latency (by up to 23 percent) and lower number of hops traveled (by up to 73 percent), compared to a state-of-the-art method.

In recent years cloud storage services have gained much attention and become a commodity to companies and private users. Nevertheless, cloud storage services have some limitations, especially related to privacy, latency, and availability. In this work we propose a distributed storage system which tackles the major limitations of classical cloud storage services. To this end, we design and implement a storage system which combines classical cloud storage services with the approach of fog computing by using resources at the edge of the network. At the core of our system lies a placement strategy which distributes the data to different storage components. Our implementation is based on well-established methods and techniques from information theory and cryptography. Our empirical analysis shows that our system preserves privacy, provides low latency, and offers high availability. Most notably, we reduce the latency by up to 42% in Upload Mode and even by up to 76% in Download Mode compared to a cloud-only solution.

Various performance benefits such as low latency and high bandwidth have turned fog computing into a well-accepted extension of the cloud computing paradigm. Many fog computing systems have been proposed so far, consisting of distributed compute nodes which are often organized hierarchically in layers. To achieve low latency, these systems commonly rely on the assumption that the nodes of adjacent layers reside close to each other. However, this assumption may not hold in fog computing systems that span over large geographical areas, due to the wide distribution of the nodes. To avoid relying on this assumption, in this paper we design distributed algorithms whereby the compute nodes measure the network proximity to each other, and self-organize into a hierarchical or a flat structure accordingly. Moreover, we implement these algorithms on geographically distributed compute nodes, and we experiment with image processing and smart city use cases. Our results show that compared to alternative methods, the proposed algorithms decrease the communication latency of latency-sensitive processes by 27%–43%, and increase the available network bandwidth by 36%–86%. Furthermore, we analyze the scalability of our algorithms, and we show that a flat structure (i.e., without layers) scales better than the commonly used layered hierarchy due to generating less overhead when the size of the system grows.

Business Process Management Systems (BPMS) need to be able to take into account the fluctuating demand for computational resources during the execution of business process activities. Today, BPMS rely on the leasing and releasing of virtual machines (VMs) on cloud resources, which leads to a rather coarse-grained allocation of computational resources. This may result in an increase in the execution cost, flexibility restrictions, and a negative impact on the Quality of Service. In order to overcome these drawbacks, we introduce the Vienna Platform for Elastic Processes on Containers (ViePEP-C). ViePEP-C is an elastic BPMS that uses containers instead of VMs for the execution of business process activities on cloud resources, leading to a more fine-grained execution environment. To achieve this, ViePEP-C offers cloud controller, monitoring and business process execution functionalities and provides a platform for different resource and task scheduling algorithms. To evaluate the benefits of ViePEP-C, we further present a resource and task scheduling algorithm and show that, by using containers as execution environment, the execution cost can be decreased by over 20 percent (compared to a state-of-the-art VM-based scheduling algorithm) while considering a high service level.

Low latency and on demand resource availability enable fog computing to host industrial applications in a cloud like manner. One industrial domain which stands to benefit from the advantages of fog computing is robotics. However, the challenges in developing and implementing a fog-based robotic system are manifold. To illustrate this, in this paper we discuss a system involving robots and robot cells at a factory level, and then highlight the main building blocks necessary for achieving such functionality in a fog-based system. Further, we elaborate on the challenges in implementing such an architecture, with emphasis on resource virtualization, memory interference management, real-time communication and the system scalability, dependability and safety. We then discuss the challenges from a system perspective where all these aspects are interrelated.

Since the proliferation of fog computing, various distributed architectures have been proposed to extend the cloud to the edge of the network. However, so far there exists no study that compares different fog computing architectures, and produces quantitative results in order to examine the efficiency of each architecture for different use cases. Such a study could provide guidelines for selecting an appropriate distributed architecture for fog computing while taking into account the requirements of the final applications. To bridge this gap in the literature, we create a unified system model which is able to represent the basic architectures commonly used for fog computing, i.e., hierarchical and flat. Furthermore, we design algorithms that can be used for creating fog computing systems that follow these architectures, and we perform various experiments that focus on communication latency and bandwidth utilization. Notably, our results show that for applications that do not have a dependency on the cloud, i.e., no resource-demanding tasks are involved, the hierarchical architecture reduces the communication latency by 13% compared to the flat. However, for applications that also include resource-demanding tasks, the flat architecture reduces the communication latency by 16% compared to the hierarchical.

Edge computing describes a paradigm for combining computational resources at the edge of the network with the cloud. Even though complementing the cloud with these resources provides benefits, e.g., low latency, it also introduces new challenges to the operational staff. Such challenges can be: deciding if the applications should be placed in the cloud or at the edge, and monitoring them at runtime to ensure that all the application requirements are met. This becomes more challenging when using microservices due to the complexity of the resulting placement problem. To mitigate such concerns, we introduce an automatic deployment framework along with a prototype implementation, called D-DAD. This framework provides a transparent (to the operational staff) way to deploy applications with respect to all their requirements-including the non-functional-using mechanisms for monitoring and adapting the deployments to the available resources in a cloud-edge environment. For evaluating our framework, we provide results from a series of experiments which show how the adaptation mechanism meets the application requirements, including a ~90% reduction of CPU utilization violations, compared to using only the local resources.

Elastic data stream processing enables applications to query and analyze streams of real time data. This is commonly facilitated by processing the flow of the data streams using a collection of stream processing operators which are placed in the cloud. However, the cloud follows a centralized approach which is prone to high latency delay. For avoiding this delay, we leverage on the fog computing paradigm which extends the cloud to the edge of the network.In order to design a stream processing solution for the fog, we first formulate an optimization problem for the placement of stream processing operators, which is tailored to fog computing environments. Then, we build a plugin (for stream processing frameworks) which solves the optimization problem periodically in order to support the dynamic resources of the fog. We evaluate this approach by performing experiments on an OpenStack testbed. The results show that our plugin reduces the response time and the cost by 31.5% and 8.8% respectively, compared to optimizing the placement of operators only upon initialization.

The emergence of fog computing has led to the design of multi-layer fog computing models which are organized hierarchically. These models commonly dictate the hierarchical structure to all the participating compute nodes. However, organizing the compute nodes by adding customized connections that do not abide by the hierarchical approach, may result in improved performance due to the network’s properties i.e., latency or bandwidth between the nodes. For this reason, in this paper we propose an alternative to the hierarchical approach, which is the self-organizing compute nodes. These nodes organize themselves into a flat model which leverages on the network’s properties to provide improved performance. The results of the evaluation show that this approach reduces bandwidth utilization (~30%) by using optimized messaging instead of direct messaging. Furthermore, we show that following a flat model, enables the design of mechanisms for fault tolerance which has been mostly neglected in existing hierarchical models.

Elastic distributed stream processing systems are able to dynamically adapt to changes in the workload. Often, these systems react to the rate of incoming data, or to the level of resource utilization, by scaling up or down. The goal is to optimize the system's resource usage, thereby reducing its operational cost. However, such scaling operations consume resources on their own, introducing a certain overhead of resource usage, and therefore cost, for every scaling operation. In addition, migrations caused by scaling operations inevitably lead to brief processing gaps. Therefore, an excessive number of scaling operations should be avoided. We approach this problem by preventing unnecessary scaling operations and over-compensating reactions to short-term changes in the workload. This allows to maintain elasticity, while also minimizing the incurred overhead cost of scaling operations. To achieve this, we use advanced filtering techniques from the field of signal processing to pre-process raw system measurements, thus mitigating superfluous scaling operations. We perform a real-world testbed evaluation verifying the effects, and provide a break-even cost analysis to show the economic feasibility of our approach.

Cloud manufacturing supports companies to create cross-organizational elastic process landscapes with flexible and scalable processes. In recent years, the Business Process Model and Notation language gained interest in the cloud manufacturing domain not only as a modeling language but also as a base for process enactment. During this enactment of manufacturing processes, accurate knowledge about when a machine is needed is of great importance. While such planning is simple in a static production environment, it becomes a challenging task in an elastic cloud manufacturing environment with a variety of flexible and scalable processes. Hence, we propose an approach that performs enactment simulations before an actual manufacturing process is carried out. The goal is to reserve manufacturing machines at the time at which they will be needed in a machine reservation timetable. By discussing different use case scenarios, we further elaborate on the benefits of our approach.

Fog computing provides a decentralized approach to data processing and resource provisioning in the Internet of Things (IoT). Particular challenges of adopting fog-based computational resources are the adherence to geographical distribution of IoT data sources, the delay sensitivity of IoT services, and the potentially very large amounts of data emitted and consumed by IoT devices. Despite existing foundations, research on fog computing is still at its very beginning. A major research question is how to exploit the ubiquitous presence of small and cheap computing devices at the edge of the network in order to successfully execute IoT services. Therefore, in this paper, we study the placement of IoT services on fog resources, taking into account their QoS requirements. We show that our optimization model prevents QoS violations and leads to 35% less cost of execution if compared to a purely cloud-based approach.

Continuous Integration (CI) has become a common practice in both industrial and open-source software development. While CI has evidently improved aspects of the software development process, errors during CI builds pose a threat to development efficiency. As an increasing amount of time goes into fixing such errors, failing builds can significantly impair the development process and become very costly. We perform an indepth analysis of build failures in CI environments. Our approach links repository commits to data of corresponding CI builds. Using data from 14 open-source Java projects, we first identify 14 common error categories. Besides test failures, which are by far the most common error category (up to >80% per project), we also identify noisy build data, e.g., induced by transient Git interaction errors, or general infrastructure flakiness. Second, we analyze which factors impact the build results, taking into account general process and specific CI metrics. Our results indicate that process metrics have a significant impact on the build outcome in 8 of the 14 projects on average, but the strongest influencing factor across all projects is overall stability in the recent build history. For 10 projects, more than 50% (up to 80%) of all failed builds follow a previous build failure. Moreover, the fail ratio of the last k=10 builds has a significant impact on build results for all projects in our dataset.

The Internet of Things (IoT) leads to an ever-growing presence of ubiquitous networked computing devices in public, business, and private spaces. These devices do not simply act as sensors, but feature computational, storage, and networking resources. Being located at the edge of the network, these resources can be exploited to execute IoT applications in a distributed manner. This concept is known as fog computing. While the theoretical foundations of fog computing are already established, there is a lack of resource provisioning approaches to enable the exploitation of fog-based computational resources. To resolve this shortcoming, we present a conceptual fog computing framework. Then, we model the service placement problem for IoT applications over fog resources as an optimization problem, which explicitly considers the heterogeneity of applications and resources in terms of Quality of Service attributes. Finally, we propose a genetic algorithm as a problem resolution heuristic and show, through experiments, that the service execution can achieve a reduction of network communication delays when the genetic algorithm is used, and a better utilization of fog resources when the exact optimization method is applied.

The use of cloud-based storage systems for storing data is a popular alternative to local storage systems. Beside several benefits of cloud-based storages, there are also downsides like vendor lock-in or unavailability. Moreover, the selection of the best fitting storage solution can be a tedious and cumbersome task and the storage requirements may change over time. In this paper, we formulate a system model that uses multiple cloud-based services to realize a redundant and cost-efficient storage. Within this system model, we formulate a local and a global optimization problem that considers historical data access information and predefined quality of service requirements to select a cost-efficient storage solution. Furthermore, we present a heuristic optimization approach for the global optimization. Extensive evaluations show the benefits of our work in comparison with a baseline that follows a state-of-the-art approach. We show that our solutions save up to 30% of the cumulative cost in comparison with the baseline.

Emerging trends like Big Data and the Internet of Things pose new challenges to established data stream processing engines. Especially, with the advent of the Internet of Things, the data that has to be processed can become very large. Since companies usually aim for cost efficiency, engines need to support resource elasticity to minimize the operational cost while maintaining real-time processing capabilities. In the work at hand, we propose and realize the distributed Platform for Elastic Stream Processing (PESP). An extensive evaluation demonstrates the practical feasibility and efficiency of the system design. The evaluation shows that PESP is able to reduce cost by 20% with minimal effects on the Quality of Service in comparison to an over-provisioning baseline. Compared to an under-provisioning baseline, PESP allows a Quality of Service improvement of 72%.

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.

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.

Contemporary cloud providers offer out-of-the-box auto-scaling solutions. However, defining a non-trivial scaling behavior that goes beyond the feature set provided by existing solutions is still challenging. In this paper we present SPEEDL, a declarative and extensible domain-specific language that simplifies the creation of elastic scaling behavior on top of IaaS clouds. SPEEDL simplifies the creation of event-driven policies for resource management (How many resources, and what resource types, are needed?), as well as task mapping (Which tasks should be handled by which resources?). Based on a dataset of real-life scaling policies, we demonstrate that SPEEDL can cover most scaling behaviors real-life developers want to express, and that the resulting SPEEDL policies are at the same time substantially more compact, easier to read, and less error-prone than the same behavior expressed via a general-purpose programming language.

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.

The problem of selecting services from a set of functionally appropriate ones under Quality of Service constraints - the Service Selection Problem - is well-recognized in the literature based on deterministic parameters. However, Quality of Service may rather follow a stochastic distribution and, thus, may change at runtime. In order to cope with differing Quality of Service, we present a heuristic approach for efficiently addressing the Service Selection Problem in conjunction with stochastic Quality of Service attributes. Accounting for penalty cost which accrue due to Quality of Service violations, our approach reduces the impact of stochastic Quality of Service behavior on total cost significantly.

Modeling collaboration processes is a challenging task. Existing modeling approaches are not capable of expressing the unpredictable, non-routine nature of human collaboration, which is influenced by the social context of involved collaborators. We propose a modeling approach which considers collaboration processes as the evolution of a network of collaborative documents along with a social network of collaborators. Our modeling approach, accompanied by a graphical notation and formalization, allows to capture the influence of complex social structures formed by collaborators, and therefore facilitates such activities as the discovery of socially coherent teams, social hubs, or unbiased experts. We demonstrate the applicability and expressiveness of our approach and notation, and discuss their strengths and weaknesses.

Especially in large companies, business process landscapes may be made up from thousands of different process definitions and instances. As a result, a Business Process Management System (BPMS) needs to be able to handle the concurrent execution of a very large number of workflow steps. Many of these workflow steps may be resource-intensive, leading to ever-changing requirements regarding the needed computing resources to execute them. Using Cloud technologies, it is possible to allocate workflow steps to resources obtained on demand from Cloud platform providers. However, current BPMS do not feature the means to make use of Cloud resources in order to execute workflows. This work presents an approach to automatically lease and release Cloud resources for workflow executions based on knowledge about the current and future process landscape. This approach to self-adaptive resource allocation for elastic process execution is implemented as part of ViePEP, a research BPMS able to handle workflow executions in the Cloud.

Within the last years, the availability of mobile Internet connections has been increased significantly. Nevertheless, the impact of this ubiquitous Web access has not arrived at road user information systems with a few exceptions for navigation systems or traffic monitoring. Even those services that are available today are very limited, as they mainly use GPS or TMC information combined with Web-based maps. Making full use of the Internet availability can enable a whole range of new services and apps for road users, helping them to make their journey safer, more comfortable, and more environmental friendly.

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.

Contrary to simple Web content, standard Web services do not offer their clients the possibility to use cached information without the risk that it may be out-of-date. This feature has not been worth its costs in realistic Web service usage scenarios until now. However, its absence may pose restrictions and impede possible benefits in a future scenario, where mediators are both willing and able to effectively minimize the amount of wirelessly transmitted data in the Internet of Services. This paper describes how developments in the Internet of Services start to motivate the automatic enablement of safe (i.e., always up-to-date) client-side caching for Web services. It presents our solution for generically adding this feature to any Web service, and, based on new experiments, reveals the limits beyond which the approach can offer significant benefits.

As wireless sensor nodes are mostly battery- powered, energy-efficient operation is a necessity to use their confined energy budget optimally. This is especially true in the logistics domain, where timely and accurate monitoring of containers is required, while the cost pressure is high. Thus, besides the need for energy efficiency, wireless sensor network deployments in logistics require cost efficiency as well. As data transmission represents the most expensive operation in terms of energy consumption and monetary costs, we present a concept for the local determination of transmission relevance in this paper. By omitting irrelevant events from transmission, the amount of data to transmit is effectively reduced. Our approach employs concepts from the business economics sector and is based on the use of scoresheets, which evaluate information on a wireless sensor node to decide whether they are "worth" transmitting or not. Thus, a scoresheet-based approach provides a viable solution for local filtering to realize energy-and cost-efficient operation of a wireless sensor network while maintaining the benefits of data fidelity and real-time event notifications.

In recent years, a number of approaches to semantic Web service matchmaking have been proposed. Most of these proposals are based on discrete and thus relatively coarse Degrees of Match (DoMs). However, different basic assumptions regarding the generalization and specialization of semantic concepts in ontologies and their subsequent rating in matchmaking exist. Hence, most matchmakers are only properly suitable if these assumptions are met. In this paper, we present an approach for mapping subsumption reasoning-based DoMs to a continuous scale. Instead of determining the numerical equivalents of the formerly discrete DoMs manually, these values are automatically derived using a linear regression model. This permits not only easy combination with other numerical similarity measures, but also allows to adapt matchmaking to different basic assumptions. These notions are implemented and tested in LOG4SWS.KOM-a matchmaker for SAWSDL that provides very good evaluation results with respect to Information Retrieval metrics such as precision and recall.

Performance evaluation and execution management of service-oriented workflows became quite important in order to avoid performance degradation. Performance measurement is crucial to ensure that workflow execution remains feasible and that SLA violations due to overload are avoided. Network calculus as a well-known system theory for deterministic queuing systems can be used to describe the worst-case performance behavior of a workflow in order to plan workflow control in advance. Concerning business processes with high repetition rates the workflow controller has to be able to serve all incoming requests with an optimal composition of Web services. Thus, this paper presents a formal worst-case calculation model using the concepts of network calculus. Furthermore, optimization problems based on the worst-case scenario are introduced in order to minimize the worst-case delay and to maximize the throughput of the Web services invoked with minimal costs.