Toward a Configurable Thing Composition Language for the SIoT

With the advent of the Social Internet-of-Things (SIoT), which builds upon the success of IoT, different IoT devices engage in relationships to achieve common goals calling for a composition language that would dictate who will do what, when, and where.Since the number of service interactions may become overwhelming the composition plan may have many alternatives. For that reason, variability is an important concern for IoT and SIoT. A configurable composition model could provide a consolidated view of multiple variants of composition plans. It promotes the reuse of proven practices by providing analysts with a generic modeling approach from which to derive individual composition plan models. Unfortunately, the scope of existing notations for configurable composition modeling is restricted or even non-existent in the IoT domain.In this paper, we propose a variability awareness IoT service Composition Framework. The new Framework handles both configurable and classical IoT service composition. We represent the new primitives, the configurable composition language, and algorithms of customization. Finally we demonstrate the features of the framework through a proof of concept application

Feature placement algorithms for high-variability applications in cloud environments

While the use of cloud computing is on the rise, many obstacles to its adoption remain. One of the weaknesses of current cloud offerings is the difficulty of developing highly customizable applications while retaining the increased scalability and lower cost offered by the multi-tenant nature of cloud applications. In this paper we describe a Software Product Line Engineering (SPLE) approach to the modelling and deployment of customizable Software as a Service (SaaS) applications. Afterwards we define a formal feature placement problem to manage these applications, and compare several heuristic approaches to solve the problem. The scalability and performance of the algorithms is investigated in detail. Our experiments show that the heuristics scale and perform well for systems with a reasonable load

Requirements and Tools for Variability Management

Explicit and software-supported Business Process Management has become the core infrastructure of any medium and large organization that has a need to be efficient and effective. The number of processes of a single organization can be very high, furthermore, they might be very similar, be in need of momentary change, or evolve frequently. If the ad-hoc adaptation and customization of processes is currently the dominant way, it clearly is not the best. In fact, providing tools for supporting the explicit management of variation in processes (due to customization or evolution needs) has a profound impact on the overall life-cycle of processes in organizations. Additionally, with the increasing adoption of Service-Oriented Architectures, the infrastructure to support automatic reconfiguration and adaptation of business process is solid. In this paper, after defining variability in business process management, we consider the requirements for explicit variation handling for (service based) business process systems. eGovernment serves as an illustrative example of reuse. In this case study, all local municipalities need to implement the same general legal process while adapting it to the local business practices and IT infrastructure needs. Finally, an evaluation of existing tools for explicit variability management is provided with respect to the requirements identified.

A planning approach to the automated synthesis of template-based process models

The design-time specification of flexible processes can be time-consuming and error-prone, due to the high number of tasks involved and their context-dependent nature. Such processes frequently suffer from potential interference among their constituents, since resources are usually shared by the process participants and it is difficult to foresee all the potential tasks interactions in advance. Concurrent tasks may not be independent from each other (e.g., they could operate on the same data at the same time), resulting in incorrect outcomes. To tackle these issues, we propose an approach for the automated synthesis of a library of template-based process models that achieve goals in dynamic and partially specified environments. The approach is based on a declarative problem definition and partial-order planning algorithms for template generation. The resulting templates guarantee sound concurrency in the execution of their activities and are reusable in a variety of partially specified contextual environments. As running example, a disaster response scenario is given. The approach is backed by a formal model and has been tested in experiment

Development of an Extended Product Lifecycle Management through Service Oriented Architecture.

Organised by: Cranfield UniversityThe aim of this work is to define new business opportunities through the concept of Extended Product Lifecycle Management (ExtPLM), analysing its potential implementation within a Service Oriented Architecture. ExtPLM merges the concepts of Extended Product, Avatar and PLM. It aims at allowing a closer interaction between enterprises and their customers, who are integrated in all phases of the life cycle, creating new technical functionalities and services, improving both the practical (e.g. improving usage, improving safety, allowing predictive maintenance) and the emotional side (e.g. extreme customization) of the product.Mori Seiki – The Machine Tool Company; BAE Systems; S4T – Support Service Solutions: Strategy and Transitio

Variety Management in Assemble-to-Order Supply Chains

Assemble-to-order refers to a supply chain strategy in which products are not assembled until customer order arrives. It is based on the so-called form postponement that is to hold components at a generic form and to delay the point of product differentiation. The performance of an assem-ble-to-order supply chain depends on two main dimensions, which are responsiveness and achievement level of scale economies. Responsiveness refers to the capability of fulfilling customer requirements in a fast-paced manner, whereas the achievement of scale economies reflects the degree of operations efficiency. Assemble-to-order supply chains induce high product variety, which has adverse effects on performance. We use demand volumes as a proxy for scale economies and lead times as a proxy for responsiveness. A matrix that consists of both dimensions can be defined, in which we distinguish between short/long lead times and low/high demand volumes. This matrix is called performance matrix. On the other hand, the consequence that results from product variety is a high demand variability of end products, which also affects the demand variability of components. An analysis of component demand variability enables one to identify the components with low/high demand variability. These components can further be classified into supplied and in-house made components. Thus, a second matrix (called component matrix) with two dimensions, namely variability (low/high) and supply source (in-house/supplier) can be defined. Due to the supply source dimension in the component matrix, the supply chain perspective is also taken into ac-count. The combination of both matrixes into a single one provides the performance/component matrix for assemble-to-order supply chains. To use the final matrix, it is necessary to compute lead times, demand volumes and demand variability of the supplied and in-house made components. By plotting the components in the matrix, one can determine the problems induced by variety. In order to improve the performance of the assemble-to-order supply chain, the implementation of variety management strategies is necessary. The identified strategies are: commonality, component families, modularity, and platforms. Based on the performance/component matrix, we discuss how these strategies or a combination of them can contribute to derive recommendations that aim to alleviate variety impacts on the as-semble-to-order supply chain.Assemble-to-order; Supply Chain Management; Variety Management