An Economic Aspect of Device-to-Device Assisted Offloading in Cellular Networks

Traffic offloading via device-to-device (D2D) communications has been proposed to alleviate the traffic burden on base stations (BSs) and to improve the spectral and energy efficiency of cellular networks. The success of D2D communications relies on the willingness of users to share contents. In this paper, we study the economic aspect of traffic offloading via content sharing among multiple devices and propose an incentive framework for D2D assisted offloading. In the proposed incentive framework, the operator improves its overall profit, defined as the network economic efficiency (ECE), by encouraging users to act as D2D transmitters (D2D-Txs) which broadcast their popular contents to nearby users. We analytically characterize D2D assisted offloading in cellular networks for two operating modes: 1) underlay mode and 2) overlay mode. We model the optimization of network ECE as a two-stage Stackelberg game, considering the densities of cellular users and D2D-Tx’s, the operator’s incentives and the popularity of contents. The closedform expressions of network ECE for both underlay and overlay modes of D2D communications are obtained. Numerical results show that the achievable network ECE of the proposed incentive D2D assisted offloading network can be significantly improved with respect to the conventional cellular networks where the D2D communications are disabled

An Economic Aspect of Device-to-Device Assisted Offloading in Cellular Networks

Traffic offloading via device-to-device (D2D) communications has been proposed to alleviate the traffic burden on base stations (BSs) and to improve the spectral and energy efficiency of cellular networks. The success of D2D communications relies on the willingness of users to share contents. In this paper, we study the economic aspect of traffic offloading via content sharing among multiple devices and propose an incentive framework for D2D assisted offloading. In the proposed incentive framework, the operator improves its overall profit, defined as the network economic efficiency (ECE), by encouraging users to act as D2D transmitters (D2D-Txs) which broadcast their popular contents to nearby users. We analytically characterize D2D assisted offloading in cellular networks for two operating modes: 1) underlay mode and 2) overlay mode. We model the optimization of network ECE as a two-stage Stackelberg game, considering the densities of cellular users and D2D-Tx’s, the operator’s incentives and the popularity of contents. The closedform expressions of network ECE for both underlay and overlay modes of D2D communications are obtained. Numerical results show that the achievable network ECE of the proposed incentive D2D assisted offloading network can be significantly improved with respect to the conventional cellular networks where the D2D communications are disabled

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

A Repeated Auction Model for Load-Aware Dynamic Resource Allocation in Multi-Access Edge Computing

Multi-access edge computing (MEC) is one of the enabling technologies for high-performance computing at the edge of the 6 G networks, supporting high data rates and ultra-low service latency. Although MEC is a remedy to meet the growing demand for computation-intensive applications, the scarcity of resources at the MEC servers degrades its performance. Hence, effective resource management is essential; nevertheless, state-of-the-art research lacks efficient economic models to support the exponential growth of the MEC-enabled applications market. We focus on designing a MEC offloading service market based on a repeated auction model with multiple resource sellers (e.g., network operators and service providers) that compete to sell their computing resources to the offloading users. We design a computationally-efficient modified Generalized Second Price (GSP)-based algorithm that decides on pricing and resource allocation by considering the dynamic offloading requests arrival and the servers' computational workloads. Besides, we propose adaptive best-response bidding strategies for the resource sellers, satisfying the symmetric Nash equilibrium (SNE) and individual rationality properties. Finally, via intensive numerical results, we show the effectiveness of our proposed resource allocation mechanism.Comment: 17 pages, 11 figure