AHP-based Adaptive Resource Selection for Cognitive Platform in Cloud Gaming Service

Cloud gaming service enables offloading heavy video-processing tasks up to the cloud server so that simple computers or mobile devices can be eligible to run sophisticated games, but on the expense of high network communications. In this regard, the adequate network utilization must be realized for delivering good gaming experiences to the game players. This necessitates a cognitive platform, which is capable of modifying its multimedia quality requirement in response to the network constraint, and notifying the cloud gaming server for updating the corresponded workload. In this regard, the Analytic Hierarchy Process (AHP) method has been proposed to deploy at the cognitive platform for cloud gaming service to select an optimal resource allocation strategy that satisfies various multimedia requirements and energy-awareness. Experiment results can confirm that the proposed method is flexible to enhance the capability of cloud gaming service in term of more efficient cloud gaming resource utilization, particularly during heavy-congested periods, while players’ quality of gaming experience can be still maintained under the mandate of intelligent agent on the player devices

The Computing Fleet: Managing Microservices-based Applications on the Computing Continuum

In this paper we propose the concept of "Computing Fleet" as an abstract entity representing groups of heterogeneous, distributed, and dynamic infrastructure elements across the Computing Continuum (covering the Edge- Fog-Cloud computing paradigms). In the process of using fleets, stakeholders obtain the virtual resources from the fleet, deploy software applications to the fleet, and control the data flow, without worrying about what devices are used in the fleet, how they are connected, and when they may join and exit the fleet. We propose a three-layer reference architecture for the Computing Fleet capturing key elements for designing and operating fleets. We discuss key aspects related to the management of microservices-based applications on the Computing Fleet and propose an approach for deployment and orchestration of microservices-based applications on fleets. Furthermore, we present a software prototype as a preliminary evaluation of the Computing Fleet concept in a concrete Cloud- Edge scenario related to remote patients monitoring.acceptedVersio

Cloud-Edge Orchestration for the Internet-of-Things: Architecture and AI-Powered Data Processing

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordThe Internet-of-Things (IoT) has been deeply penetrated into a wide range of important and critical sectors, including smart city, water, transportation, manufacturing and smart factory. Massive data are being acquired from a fast growing number of IoT devices. Efficient data processing is a necessity to meet diversified and stringent requirements of many emerging IoT applications. Due to the constrained computation and storage resources, IoT devices have resorted to the powerful cloud computing to process their data. However, centralised and remote cloud computing may introduce unacceptable communication delay since its physical location is far away from IoT devices. Edge cloud has been introduced to overcome this issue by moving the cloud in closer proximity to IoT devices. The orchestration and cooperation between the cloud and the edge provides a crucial computing architecture for IoT applications. Artificial intelligence (AI) is a powerful tool to enable the intelligent orchestration in this architecture. This paper first introduces such a kind of computing architecture from the perspective of IoT applications. It then investigates the state-of-the-art proposals on AI-powered cloud-edge orchestration for the IoT. Finally, a list of potential research challenges and open issues is provided and discussed, which can provide useful resources for carrying out future research in this area.Engineering and Physical Sciences Research Council (EPSRC