Dionysis Athanasopoulos

Dionysis Athanasopoulos, Dewei Liu

Given that cloud servers are usually remotely located from the devices of mobile apps, the end-users of the apps can face delays. The Fog has been introduced to augment the apps with machines located at the network edge close to the end-users. However, edge machines are usually resource constrained. Thus, the execution of online data-analytics on edge machines may not be feasible if the time complexity of the data-analytics algorithm is high. To overcome this, multiple instances of the back-end should be deployed on edge and remote machines. In this case, the research question is how the switching of the app among the instances of the back-end can be dynamically decided based on the response time of the service instances. To answer this, we contribute an AI approach that trains machine-learning models of the response time of service instances. Our approach extends a back-end as a service into an AI self-back-end as a service that self-decides at runtime the right edge/remote instance that achieves the lowest response-time. We evaluate the accuracy and the efficiency of our approach by using real-word machine-learning datasets on an existing auction app.

Yutao Ma, Xiao Geng, Jian Wang et al.

Recent years have witnessed the rapid development of service-oriented computing technologies. The boom of Web services increases software developers' selection burden in developing new service-based systems such as mashups. Timely recommending appropriate component services for developers to build new mashups has become a fundamental problem in service-oriented software engineering. Existing service recommendation approaches are mainly designed for mashup development in the single-round scenario. It is hard for them to effectively update recommendation results according to developers' requirements and behaviours (e.g. instant service selection). To address this issue, the authors propose a service bundle recommendation framework based on deep learning, DLISR, which aims to capture the interactions among the target mashup to build, selected (component) services, and the following service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending a candidate service. The authors also design two separate models for learning interactions from the perspectives of content and invocation history, respectively, and a hybrid model called HISR. Experiments on a real-world dataset indicate that HISR can outperform several state-of-the-art service recommendation methods to develop new mashups iteratively.