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

Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges

In recent years, blockchain has gained widespread attention as an emerging technology for decentralization, transparency, and immutability in advancing online activities over public networks. As an essential market process, auctions have been well studied and applied in many business fields due to their efficiency and contributions to fair trade. Complementary features between blockchain and auction models trigger a great potential for research and innovation. On the one hand, the decentralized nature of blockchain can provide a trustworthy, secure, and cost-effective mechanism to manage the auction process; on the other hand, auction models can be utilized to design incentive and consensus protocols in blockchain architectures. These opportunities have attracted enormous research and innovation activities in both academia and industry; however, there is a lack of an in-depth review of existing solutions and achievements. In this paper, we conduct a comprehensive state-of-the-art survey of these two research topics. We review the existing solutions for integrating blockchain and auction models, with some application-oriented taxonomies generated. Additionally, we highlight some open research challenges and future directions towards integrated blockchain-auction models

Socially Trusted Collaborative Edge Computing in Ultra Dense Networks

Small cell base stations (SBSs) endowed with cloud-like computing capabilities are considered as a key enabler of edge computing (EC), which provides ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. However, due to the limited computation resources of an individual SBS, providing computation services of high quality to its users faces significant challenges when it is overloaded with an excessive amount of computation workload. In this paper, we propose collaborative edge computing among SBSs by forming SBS coalitions to share computation resources with each other, thereby accommodating more computation workload in the edge system and reducing reliance on the remote cloud. A novel SBS coalition formation algorithm is developed based on the coalitional game theory to cope with various new challenges in small-cell-based edge systems, including the co-provisioning of radio access and computing services, cooperation incentives, and potential security risks. To address these challenges, the proposed method (1) allows collaboration at both the user-SBS association stage and the SBS peer offloading stage by exploiting the ultra dense deployment of SBSs, (2) develops a payment-based incentive mechanism that implements proportionally fair utility division to form stable SBS coalitions, and (3) builds a social trust network for managing security risks among SBSs due to collaboration. Systematic simulations in practical scenarios are carried out to evaluate the efficacy and performance of the proposed method, which shows that tremendous edge computing performance improvement can be achieved.Comment: arXiv admin note: text overlap with arXiv:1010.4501 by other author

Truthful Computation Offloading Mechanisms for Edge Computing

Edge computing (EC) is a promising paradigm providing a distributed computing solution for users at the edge of the network. Preserving satisfactory quality of experience (QoE) for users when offloading their computation to EC is a non-trivial problem. Computation offloading in EC requires jointly optimizing access points (APs) allocation and edge service placement for users, which is computationally intractable due to its combinatorial nature. Moreover, users are self-interested, and they can misreport their preferences leading to inefficient resource allocation and network congestion. In this paper, we tackle this problem and design a novel mechanism based on algorithmic mechanism design to implement a system equilibrium. Our mechanism assigns a proper pair of AP and edge server along with a service price for each new joining user maximizing the instant social surplus while satisfying all users' preferences in the EC system. Declaring true preferences is a weakly dominant strategy for the users. The experimental results show that our mechanism outperforms user equilibrium and random selection strategies in terms of the experienced end-to-end latency

Mobile data and computation offloading in mobile cloud computing

Le trafic mobile augmente considérablement en raison de la popularité des appareils mobiles et des applications mobiles. Le déchargement de données mobiles est une solution permettant de réduire la congestion du réseau cellulaire. Le déchargement de calcul mobile peut déplacer les tâches de calcul d'appareils mobiles vers le cloud. Dans cette thèse, nous étudions d'abord le problème du déchargement de données mobiles dans l'architecture du cloud computing mobile. Afin de minimiser les coûts de transmission des données, nous formulons le processus de déchargement des données sous la forme d'un processus de décision de Markov à horizon fini. Nous proposons deux algorithmes de déchargement des données pour un coût minimal. Ensuite, nous considérons un marché sur lequel un opérateur de réseau mobile peut vendre de la bande passante à des utilisateurs mobiles. Nous formulons ce problème sous la forme d'une enchère comportant plusieurs éléments afin de maximiser les bénéfices de l'opérateur de réseau mobile. Nous proposons un algorithme d'optimisation robuste et deux algorithmes itératifs pour résoudre ce problème. Enfin, nous nous concentrons sur les problèmes d'équilibrage de charge afin de minimiser la latence du déchargement des calculs. Nous formulons ce problème comme un jeu de population. Nous proposons deux algorithmes d'équilibrage de la charge de travail basés sur la dynamique évolutive et des protocoles de révision. Les résultats de la simulation montrent l'efficacité et la robustesse des méthodes proposées.Global mobile traffic is increasing dramatically due to the popularity of smart mobile devices and data hungry mobile applications. Mobile data offloading is considered as a promising solution to alleviate congestion in cellular network. Mobile computation offloading can move computation intensive tasks and large data storage from mobile devices to cloud. In this thesis, we first study mobile data offloading problem under the architecture of mobile cloud computing. In order to minimize the overall cost for data delivery, we formulate the data offloading process, as a finite horizon Markov decision process, and we propose two data offloading algorithms to achieve minimal communication cost. Then, we consider a mobile data offloading market where mobile network operator can sell bandwidth to mobile users. We formulate this problem as a multi-item auction in order to maximize the profit of mobile network operator. We propose one robust optimization algorithm and two iterative algorithms to solve this problem. Finally, we investigate computation offloading problem in mobile edge computing. We focus on workload balancing problems to minimize the transmission latency and computation latency of computation offloading. We formulate this problem as a population game, in order to analyze the aggregate offloading decisions, and we propose two workload balancing algorithms based on evolutionary dynamics and revision protocols. Simulation results show the efficiency and robustness of our proposed methods