Terminal devices (TDs) connect to networks through access points (APs) integrated into the edge server. This provides a prerequisite for TDs to upload tasks to cloud data centers or offload them to edge servers for execution. In this process, signal coverage, data transmission, and task execution consume energy, and the energy consumption of signal coverage increases sharply as the radius increases. Lower power leads to less energy consumption in a given time segment. Thus, power control for APs is essential for reducing energy consumption. Our objective is to determine the power assignment for each AP with same capacity constraints such that all TDs are covered, and the total power is minimized. We define this problem as a minimum power capacitated cover (MPCC) problem and present a minimum local ratio (MLR) power control approach for this problem to obtain accurate results in polynomial time. Power assignments are chosen in a sequence of rounds. In each round, we choose the power assignment that minimizes the ratio of its power to the number of currently uncovered TDs it contains. In the event of a tie, we pick an arbitrary power assignment that achieves the minimum ratio. We continue choosing power assignments until all TDs are covered. Finally, various experiments verify that this method can outperform another greedy-based way.

/pdf/a-local-ratio-based-power-control-approach-for-capacitated-1nf1nlez.pdf

A Local-Ratio-Based Power Control Approach for Capacitated Access Points in Mobile Edge Computing

The concept of the metaverse has gained increasing attention in recent years, and the development of various new technologies, including digital twin technology, has made it possible to see the metaverse coming to pass. Many academics have begun to investigate various problems after realizing the importance of digital twin technology in building the metaverse. However, when utilizing digital twin technology to construct a metaverse, there remains limited research on how to allocate multibase station resources. This research translates a multibase station wireless resource allocation problem into an integer linear programming constraint model when virtual service providers construct a metaverse. In addition, the optimal VCG reverse auction (OPT-VCGRA) mechanism is designed to maximize social welfare and solve the problem of IoT devices competing for base station wireless resources. Specifically, the problem of the optimal allocation of wireless channel resources and payment rule based on the Vickrey–Clarke–Groves mechanism is solved to achieve optimal allocation and calculation of payment prices. Since the optimal allocation problem is NP-hard, this paper also designs a metaverse digital twin resource allocation and pricing (MDTRAP) mechanism based on monotonic allocation and key value theory. The mechanism sends the resource allocation results of multiple base stations to IoT devices and calculates the price payment when building a metaverse in the real world. This paper shows that both auction mechanisms have incentive compatibility and individual rationality properties. Through experiments, this paper compares the two mechanisms in terms of social welfare, the number of winners, and the overall payment. The MDTRAP mechanism performs similarly to the OPT-VCGRA mechanism in terms of social welfare, the number of winners, and channel utilization but is far superior to the OPT-VCGRA mechanism in terms of execution time and total payment. The trustful experiment also verified the truthfulness of the MDTRAP mechanism. The experimental results show that the MDTRAP mechanism can be used to solve the resource allocation problem of multiple base stations to IoT devices when building a metaverse in the real world and can effectively maximize social welfare.

/pdf/a-truthful-mechanism-for-multibase-station-resource-2i3i65ks.pdf

A Truthful Mechanism for Multibase Station Resource Allocation in Metaverse Digital Twin Framework

A network slicing algorithm for cloud-edge collaboration hybrid computing in 5G and beyond networks

In this paper, we consider task offloading in vehicular edge computing systems. The limited battery limits the mileage of electric vehicles. For a longer driving distance, the task of the vehicle can not only be performed locally, but also offload task to save energy. In this way, the energy consumption of the system is reduced and the energy utilization rate of the whole system is improved. The task offloading involves optimization variables which are where to offload tasks and the frequency, and the optimization variables are coupled. So it is hard to decide how to allocate tasks. We propose that we can solve this problem by using genetic algorithm. Specifically, our experiment shows between 20% and 25% energy savings compared to the baseline which the tasks executes workload locally.

A Genetic Algorithm for Task Offloading problem in Vehicular Edge Computing

Cooperative perception is an important domain of autonomous driving that helps to improve road safety and traffic efficiency. Nevertheless, the large amount of sensed data and complicated algorithms make storage and computation for autonomous vehicles (AVs) challenging. Furthermore, not every AV needs to individually process all sensed data from other AVs because the environmental information is the same in a small region. Inspired by vehicular edge computing (VEC), where AVs are interconnected with the help of roadside units (RSUs) for better storage and computation capabilities, we propose a VEC-based architecture for cooperative perception and design a key task scheduling algorithm for the above challenges. Specifically, a time slot-based VEC architecture with the help of an RSU is designed, and the task scheduling problem in the proposed architecture is formulated as a multitask multitarget scheduling problem with assignment restrictions. A two-stage heuristic scheme (TSHS) is designed for the problem. Finally, extensive simulations indicate that the proposed architecture with the TSHS can enable cooperative perception, with a fast running speed and advanced performance, that is superior to that of the benchmarks, especially when most AVs face limitations in terms of storage and computation.

/pdf/a-vehicular-edge-computing-based-architecture-and-task-u7i9zhyh.pdf

A Vehicular Edge Computing-Based Architecture and Task Scheduling Scheme for Cooperative Perception in Autonomous Driving

Cloud computing is characterized by strong computing and storage capabilities, and edge computing has the advantages of low latency and low power consumption. Many service providers have begun to combine the advantages of cloud and edge computing to provide better quality of service, but the heterogeneity of cloud and edge computing represents a challenge for service deployment and resource allocation. This paper proposes a framework for cloud-edge collaboration based on live video webcast services and transforms the resource allocation problem into a constrained integer programming (IP) model. Additionally, we introduce an auction mechanism to solve the problem of resource competition among the anchor users in live services. By solving the IP resource allocation problem and Vickrey–Clarke–Groves mechanism, we obtain the optimal resource allocation mechanism. Based on the dominant resource proportion and matching model, we design a resource allocation mechanism for the online environment. These mechanisms can be used for reservation and live webcast scenarios. Furthermore, we prove that the two mechanisms have individual rationality and truthfulness. Our approach is characterized by high social welfare, high resource utilization and a short execution time.

Strategy-proof mechanism for online resource allocation in cloud and edge collaboration

The problem of online resource allocation in edge computing has become a research hotspot. Meanwhile, reinforcement learning (RL) is suitable for solving online problems. In this paper, we combine edge computing online resource allocation with RL. This combination enables edge computing resource providers to obtain more social welfare and improve resource utilization. Specifically, we define a dynamic resource allocation problem for edge computing: edge equipment users request resources from a nearby edge computing server (ECS), the amount of resources required varies among the users, and there are time limits for the completion of the requested tasks. Since this resource allocation problem is NP-hard, it cannot be solved in polynomial time. Therefore, we propose an algorithm based on the policy-gradient algorithm in RL to solve the problem. Our approach is experimentally compared with existing research in terms of social welfare and resource utilization, for which it achieves good results.

Laixin Chi

Papers

Strategy-proof mechanism for online resource allocation in cloud and edge collaboration

Reinforcement Learning-Based Resource Allocation in Edge Computing