The internet of things (IoT) attracts great interest in many application domains concerned with monitoring and control of physical phenomena. However, application development is still one of the main hurdles to a wide adoption of IoT technology. Application development is done at a low level, very close to the operating system and requires programmers to focus on low-level system issues. The underlying APIs can be very complicated and the amount of data collected can be huge. This can be very hard to deal with as a developer. In this paper, we present a runtime model based approach to IoT application development. First, the manageability of sensor devices is abstracted as runtime models that are automatically connected with the corresponding systems. Second, a customized model is constructed according to a personalized application scenario and the synchronization between the customized model and sensor device runtime models is ensured through model transformation. Thus, all the application logic can be carried out by executing programs on the customized model. An experiment on a real-world application scenario demonstrates the feasibility, effectiveness, and benefits of the new approach to IoT application development.

Service computing promotes a large number of web-delivered services, including web services, APIs and data feeds. Composing data, functionalities and even UI from these web-delivered services into a single web application, usually called service mashup, becomes a popular web development paradigm. The web-delivered services can be modeled as mashup components, while the development of mashup actually yields a set of inter-connected mashup components. The growing popularity of mashup components enriches functionality and user experiences, while the possible connections among components are complex and difficult to mashup developers, who might be non-professional programmers or even end-users, as actions over one component may have potential impacts on another. This paper proposes a novel approach for recommending developers in terms of navigation and completion of mashup components with a large-scale components repository. From data-driven perspective, we model the relationships between mashup components by a generic layered-graph model. Developers are allowed to select some initial components as starting point, while a graph-based algorithm recommends how to navigate to potentially relevant mashup components and complete the relevant mashup application. We experimentally demonstrate the efficiency and effectiveness of our approach for rapid mashup construction.

The convergence of Services Computing and Web 2.0 gains a large space of opportunities to compose “situational” web applications from web-delivered services. However, the large number of services and the complexity of composition constraints make manual composition difficult to application developers, who might be non-professional programmers or even end-users. This paper presents a systematic data-driven approach to assisting situational application development. We first propose a technique to extract useful information from multiple sources to abstract service capabilities with a set tags. This supports intuitive expression of user's desired composition goals by simple queries, without having to know underlying technical details. A planning technique then exploits composition solutions which can constitute the desired goals, even with some potential new interesting composition opportunities. A browser-based tool facilitates visual and iterative refinement of composition solutions, to finally come up with the satisfying outputs. A series of experiments demonstrate the efficiency and effectiveness of our approach.

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System management is becoming increasingly complex and brings high costs, especially with the advent of cloud computing. Cloud computing involves numerous platforms often of virtual machines (VMs) and middleware has to be managed to make the whole system work cost-effectively after an application is deployed. In order to reduce management costs, in particular for the manual activities, many computer programs have been developed remove or reduce the complexity and difficulty of system mamnagement. These programs are usually hard-coded in languages like Java and C++, which bring enough capability and flexibility but also cause high programming effort and cost. This paper proposes an architecture for developing management programs in a simple but powerful way. First of all, the manageability of a given platform (via APIs, configuration files, and scripts) is abstracted as a runtime model of the platform’s software architecture, which can automatically and immediately propagate any observable runtime changes of the target platforms to the corresponding architecture models, and vice versa. The management programs are developed using modeling languages, instead of those relatively low-level programming languages. Architecture-level management programs bring many advantages related to performance, interoperability, reusability, and simplicity. An experiment on a real-world cloud deployment and comparisonwith traditional programming language approaches demonstrate the feasibility, effectiveness, and benefits of the new model based approach for management program development.