On personalized cloud service provisioning for mobile users using adaptive and context-aware service composition

Abstract

Cloud service providers typically compose their services from a number of elementary services, which are developed in-house or built by third-party providers. Personalization of composite services in mobile environments is an interesting and challenging issue to address, given the opportunity to factor-in diverse user preferences and the plethora of mobile devices at use in multiple contexts. This work proposes a framework to address personalization in mobile cloud-service provisioning. Service personalization and adaptation may be considered at different levels, including the user profile, the mobile device in use, the context of the user and the composition specification. The user's mobile device and external services are typical sources of context information, used in our proposed algorithm to elicit context-aware services. The selection process is guided by quality-of-context criteria that combine cloudservice provider requirements and user preferences. Hence, the paper proposes an integrated framework for enhancing personalized mobile cloud-services, based on a composition approach that adapts context information using a common model of service metadata specification.

A Resource-aware Service Discovery Architecture for Ad-hoc Mobile Cloud

“…To satisfy this requirement existing solutions have mainly adopted what can be described as a "user-centric" approach: adapting services requests to return relevant services based on the knowledge about the user's profile [23] which includes user preferences [9], current activities, physical location, and surrounding objects [24]. However, Badidi [10] has argued in support of the need for a more comprehensive approach towards service personalization. According to the author, in the context of MCC, service personalization needs to be driven by three components of context informanation, namely, "the user's functional needs", "the device in use", and "the user's context".…”

“…First, the explosive growth in the number of mobile devices. As indicated by Badidi et al in [10], the global mobile device user-base has proliferated, with a corresponding increase in the number of MCC subscribers. In fact, as at the beginning of the last decade, the Cisco Internet Business Solutions Group (IBSG) as cited in [11], already maintained that an excess of 80% of the world's population had access to diverse mobile devices.…”

On the Challenge of Service Recommendation to Mobile Users in Smart Cities: Context and Architecture

“…These concepts are identified based on two points: (1) the need to properly describe the mobility of users, issue that has been little discussed in literature works. And (2) the main related works on mobile context modeling in smart cities (Boudaa et al, 2018, Gutowski et al, 2017, Ntalasha et al, 2016, Vahdat-Nejad et al, 2019, Badidi et al, 2019, Aguilar et al, 2018.…”

“…Vahdat, et al (2019) have cited the importance of considering the history of activities performed by the user (the mobility history) to recommend personalized services. Badidi, et al (2019) propose an integrated framework for personalized mobile-cloud services recommendation. The work defines an architecture for the framework which has a client side on mobile devices responsible for locale adaptation of the recommended services and a backend cloud-based side for service delivery, composition and context adaptation.…”

“…This makes the context definition step necessary where it is essential to sort the information that can be obtained on the user and his environment and identify which one is relevant for mobile service recommendation. There are several works (Boudaa et al, 2018, Gutowski et al, 2017, Ntalasha et al, 2016, Vahdat-Nejad et al, 2019, Badidi et al, 2019, Aguilar et al, 2018 that have focused on defining the context of mobile users in a smart city and on the designing of context models to show context parameters that influence the service recommendation process. However, these models are in most cases general and do not describe well the mobility of users in the city.…”