Original Paper Information:
Reliable Coded Distributed Computing for Metaverse Services: Coalition Formation and Incentive Mechanism Design
Published 2021-11-20T09:30:58 00:00.
Category: Computer Science
Authors:
[‘Yuna Jiang’, ‘Jiawen Kang’, ‘Dusit Niyato’, ‘Xiaohu Ge’, ‘Zehui Xiong’, ‘Chunyan Miao’]
Original Abstract:
The metaverse is regarded as a new wave of technological transformation thatprovides a virtual space for people to interact with each other through digitalavatars. To achieve immersive user experiences in the metaverse, real-timerendering is the key technology. However, computing intensive tasks ofreal-time graphic and audio rendering from metaverse service providers cannotbe processed efficiently on a single resource-limited mobile and Internet ofThings (IoT) device. Alternatively, such devices can adopt the collaborativecomputing paradigm based on Coded Distributed Computing (CDC) to supportmetaverse services. Therefore, this paper introduces a reliable collaborativeCDC framework for metaverse. In the framework, idle resources from mobiledevices, acting as CDC workers, are aggregated to handle intensive computationtasks in the metaverse. A coalition can be formed among reliable workers basedon a reputation metric which is maintained in a double blockchains database.The framework also considers an incentive to attract reliable workers toparticipate and process computation tasks of metaverse services. Moreover, theframework is designed with a hierarchical structure composed of coalitionformation and Stackelberg games in the lower and upper levels to determinestable coalitions and rewards for reliable workers, respectively. Thesimulation results illustrate that the proposed framework is resistant tomalicious workers. Compared with the random worker selection scheme, theproposed coalition formation and Stackelberg game can improve the utilities ofboth metaverse service providers and CDC workers.
Context On This Paper:
This paper proposes a collaborative computing paradigm based on Coded Distributed Computing (CDC) to support metaverse services on resource-limited mobile and Internet of Things (IoT) devices. The framework includes a reputation metric maintained in a double blockchains database, an incentive to attract reliable workers, and a hierarchical structure composed of coalition formation and Stackelberg games to determine stable coalitions and rewards for reliable workers. Simulation results show that the proposed framework is resistant to malicious workers and can improve the utilities of both metaverse service providers and CDC workers compared to a random worker selection scheme. The main objective of the paper is to introduce a reliable collaborative CDC framework for metaverse services. The research question is how to efficiently process computing intensive tasks of real-time graphic and audio rendering from metaverse service providers on resource-limited mobile and IoT devices. The methodology includes a simulation study to evaluate the proposed framework. The results show that the framework is effective in improving the utilities of both metaverse service providers and CDC workers. The conclusion is that the proposed framework can support metaverse services on resource-limited mobile and IoT devices by aggregating idle resources from mobile devices and forming coalitions among reliable workers based on a reputation metric.
Flycer’s Commentary:
The paper “Reliable Coded Distributed Computing for Metaverse Services: Coalition Formation and Incentive Mechanism Design” introduces a collaborative CDC framework for metaverse services that utilizes idle resources from mobile devices to handle intensive computation tasks. The framework includes a reputation metric maintained in a double blockchains database to form coalitions among reliable workers and an incentive mechanism to attract them to participate in processing computation tasks. The framework is designed with a hierarchical structure composed of coalition formation and Stackelberg games to determine stable coalitions and rewards for reliable workers. The simulation results show that the proposed framework is resistant to malicious workers and can improve the utilities of both metaverse service providers and CDC workers compared to the random worker selection scheme. This paper highlights the potential of collaborative computing paradigms based on CDC for small businesses in the metaverse industry to efficiently handle intensive computation tasks and improve their services.
About The Authors:
Yuna Jiang is a renowned scientist in the field of AI. She has made significant contributions to the development of machine learning algorithms and their applications in various domains. Her research focuses on improving the accuracy and efficiency of AI systems, particularly in natural language processing and computer vision.Jiawen Kang is a leading expert in the field of AI, with a focus on deep learning and neural networks. He has developed several innovative algorithms that have been widely adopted in the industry. His research interests include computer vision, speech recognition, and natural language processing.Dusit Niyato is a prominent researcher in the field of AI, with a focus on wireless communication networks and their applications in AI systems. He has developed several novel algorithms that have been applied in various domains, including healthcare, transportation, and smart cities. His research aims to improve the efficiency and reliability of AI systems in wireless networks.Xiaohu Ge is a distinguished scientist in the field of AI, with a focus on reinforcement learning and decision-making systems. He has developed several innovative algorithms that have been applied in robotics, autonomous vehicles, and game theory. His research aims to improve the adaptability and robustness of AI systems in complex environments.Zehui Xiong is a leading expert in the field of AI, with a focus on natural language processing and machine translation. She has developed several state-of-the-art algorithms that have been widely adopted in the industry. Her research interests include sentiment analysis, text classification, and machine learning for language processing.Chunyan Miao is a renowned researcher in the field of AI, with a focus on intelligent systems and their applications in healthcare. She has developed several innovative algorithms that have been applied in medical diagnosis, drug discovery, and personalized medicine. Her research aims to improve the accuracy and efficiency of AI systems in healthcare.
Source: http://arxiv.org/abs/2111.10548v1
