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Showing papers on "Shared resource published in 2021"


Journal ArticleDOI
Jiaheng Wang1, Xintong Ling1, Yuwei Le1, Yongming Huang1, Xiaohu You1 
TL;DR: A unified framework of the blockchain radio access network (B-RAN) is proposed as a trustworthy and secure paradigm for 6G networking by utilizing blockchain technologies with enhanced efficiency and security.
Abstract: With the deployment of fifth-generation (5G) wireless networks worldwide, research on sixth-generation (6G) wireless communications has commenced. It is expected that 6G networks can accommodate numerous heterogeneous devices and infrastructures with enhanced efficiency and security over diverse, e.g. spectrum, computing and storage, resources. However, this goal is impeded by a number of trust-related issues that are often neglected in network designs. Blockchain, as an innovative and revolutionary technology that has arisen in the recent decade, provides a promising solution. Building on its nature of decentralization, transparency, anonymity, immutability, traceability and resiliency, blockchain can establish cooperative trust among separate network entities and facilitate, e.g. efficient resource sharing, trusted data interaction, secure access control, privacy protection, and tracing, certification and supervision functionalities for wireless networks, thus presenting a new paradigm towards 6G. This paper is dedicated to blockchain-enabled wireless communication technologies. We first provide a brief introduction to the fundamentals of blockchain, and then we conduct a comprehensive investigation of the most recent efforts in incorporating blockchain into wireless communications from several aspects. Importantly, we further propose a unified framework of the blockchain radio access network (B-RAN) as a trustworthy and secure paradigm for 6G networking by utilizing blockchain technologies with enhanced efficiency and security. The critical elements of B-RAN, such as consensus mechanisms, smart contract, trustworthy access, mathematical modeling, cross-network sharing, data tracking and auditing and intelligent networking, are elaborated. We also provide the prototype design of B-RAN along with the latest experimental results.

60 citations


Journal ArticleDOI
TL;DR: A state–space–time network-based bi-objective mixed integer programming model is constructed to optimize the vehicle routes in order to meet customer demands for essential materials with the lowest cost and highest emergency response speed under limited transportation resources.
Abstract: The occurrence of natural disasters or accidents causes the obstruction or interruption of road traffic connectivity and affects the transportation of essential materials, especially for cross-regional delivery under emergency situations. Affected by COVID-19, government administrators establish cross-regional quarantine roadblocks to reduce the risk of virus transmission caused by cross-regional transportation. In this study, we propose an emergency logistics network design problem with resource sharing under collaborative alliances. We construct a state–space–time network-based bi-objective mixed integer programming model to optimize the vehicle routes in order to meet customer demands for essential materials with the lowest cost and highest emergency response speed under limited transportation resources. A two-stage hybrid heuristic algorithm is then proposed to find good-quality solutions for the problem. Clustering results are obtained using a 3D k-means clustering algorithm with the consideration of time and space indices. The optimization of the initial population generated by the improved Clarke and Wright saving method and improved nondominated sorting genetic algorithm-II with elite retention strategy provides stable and excellent performance for the searching of Pareto frontier. The cost difference of the entire emergency logistics network before and after collaboration, i.e., the profit, is fairly allocated to the participants (i.e., logistics service providers) through the Shapley value method. A real-world case in Chongqing City, China is used to validate the effectiveness of the proposed model and algorithm. This study contributes to smart transportation and logistics system in emergency planning and has particular implications for the optimal response of existing logistics system to the current COVID-19 pandemic.

48 citations


Journal ArticleDOI
TL;DR: Results suggest that the proposed collaborative mechanism with multi-depot and multi-period resource sharing can improve the degree of synchronization within a collaborative logistics network, and thus contribute to sustainable development of urban logistics distribution networks.
Abstract: Collaboration among logistics operators offers an effective way to improve customer service and freight transportation efficiency. One form of collaboration is the sharing of logistics resources (e.g., delivery vehicles). Existing studies on collaboration and resource sharing have not sufficiently accounted for the time frame within which collaboration happens, and they mostly assume that collaboration among logistics operators occurs in a single time period. This study addresses the issue of collaboration across multiple time periods, in which logistics resources can be shared between different service time periods, by formulating and solving a two-echelon collaborative multi-depot multi-period vehicle routing problem (2E-CMDPVRP). The 2E-CMDPVRP is formulated as a multi-objective integer programming model that minimizes logistics operational costs, service waiting times, and number of vehicles in multiple service periods. A hybrid heuristic algorithm with three-dimensional k-means clustering and improved reference point-based non-dominated sorting genetic algorithm-III (IR-NSGA-III) is proposed to solve the multi-objective optimization model. Comparative analysis results show that the proposed IR-NSGA-III outperforms existing algorithms in terms of the minimization of logistics operational costs, service waiting times, and number of vehicles. The minimum costs remaining saving method and strictly monotonic path selection principle are combined to determine the best profit allocation schemes and the optimal coalition sequences. An empirical case study of a multi-depot multi-period logistics network in Chongqing, China, is used to validate the proposed model and solution algorithm. Results suggest that the proposed collaborative mechanism with multi-depot and multi-period resource sharing can improve the degree of synchronization within a collaborative logistics network, and thus contribute to sustainable development of urban logistics distribution networks.

44 citations


Journal ArticleDOI
TL;DR: This work designs a contract-based incentive mechanism to motivate vehicles to share their computation resources with service requesters and designs multi-step smart contracts to achieve secure resource sharing and defend against the malicious behaviours of service requester and vehicles with selfish purposes.
Abstract: Vehicular edge computing (VEC) extends edge computing to vehicular networks by exploiting computation resources of vehicles to offload tasks from other vehicles and pedestrians However, VEC faces several critical challenges such as the potential security issues caused by untrusted and opaque environment and the lack of incentive mechanism under asymmetric information scenario To solve the above challenges, we propose a consortium blockchain for secure resource sharing in VEC We first design multi-step smart contracts to achieve secure resource sharing and defend against the malicious behaviours of service requesters and vehicles with selfish purposes Then, a byzantine fault tolerance-based proof-of-stake (BFT-based PoS) consensus protocol is applied in consortium blockchain to reach consensus efficiently Furthermore, we design a contract-based incentive mechanism to motivate vehicles to share their computation resources with service requesters The optimal contracts are derived to maximize the service requesters’ expected utility as well as social welfare Finally, simulation results demonstrate that the proposed incentive mechanism is more effective and efficient than the traditional schemes

38 citations


Journal ArticleDOI
TL;DR: In this paper, a block-chain empowered federated learning framework is proposed to enhance the security and privacy by integrating blockchain into a federated Learning scheme for maintaining the trained parameters.
Abstract: In 5G and beyond networks, the increasing inclusion of heterogeneous smart devices and the rising privacy and security concerns, are two crucial challenges in terms of computation complexity and privacy preservation for Artificial Intelligence (AI)-based solutions. In this regard, federated learning emerges as a new technique, which enlarges the scale of training data, and protects the privacy of user data. The development of edge computing makes it possible to apply federated learning to beyond 5G. However, the security of local parameters, the learning quality, and the varying computing and communication resources, are crucial issues that remain unexplored in federated learning schemes. In this article, we propose a block-chain empowered federated learning framework, and present its potential application scenarios in beyond 5G. We enhance the security and privacy by integrating blockchain into a federated learning scheme for maintaining the trained parameters. In particular, we formulate the resource sharing task as a combinational optimization problem while taking resource consumption and learning quality into account. We design a deep reinforcement learning based algorithm to find an optimal solution to the problem. Numerical results show that the proposed scheme achieves high accuracy and good convergence.

36 citations


Journal ArticleDOI
TL;DR: A hybrid heuristic algorithm that combines the extended k-means clustering and tabu search non-dominated sorting genetic algorithm-II (TS-NSGA-II) to search a large solution space is developed and ensures that the optimal solution is found efficiently through initial solution filtering and the combination of local and global searches.
Abstract: Collaboration such as resource sharing among logistics participants (LPs) can effectively increase the efficiency and sustainability of logistics operations, especially in the transportation and distribution of fresh and perishable products that require special infrastructure (e.g., refrigerated trucks/vehicles). This study tackles a collaborative multi-center vehicle routing problem with resource sharing and temperature control constraints (CMCVRP-RSTC). Solving the CMCVRP-RSTC by minimizing the total cost and the number of refrigerated vehicles returns a fresh logistics operational strategy that pinpoints how a multi-center fresh logistics distribution network can be reorganized to highlight potential collaboration opportunities. To find the solution to the CMCVRP-RSTC, we develop a hybrid heuristic algorithm that combines the extended k-means clustering and tabu search non-dominated sorting genetic algorithm-II (TS-NSGA-II) to search a large solution space. This hybrid heuristic algorithm ensures that the optimal solution is found efficiently through initial solution filtering and the combination of local and global searches. Furthermore, we explore how to motivate individual LPs to collaborate by analyzing the benefits of collaboration to each LP. Using the minimum costs remaining savings method and the strictly monotonic path rule, a cost saving calculation model is proposed to find the best profit allocation scheme where each collaborating LP keeps benefiting from long-term collaboration. An empirical case study of Chongqing City, China indicates the efficiency of our proposed collaborative mechanism and optimization algorithms. Our study will help improve the efficiency of logistics operation significantly and contribute to the development of more intelligent logistics systems and smart cities.

34 citations


Journal ArticleDOI
TL;DR: In this article, a 3D customer clustering algorithm with split load strategies is developed to reassign each customer to its favorable service provider considering multiple customer service characteristics, and a hybrid genetic algorithm with tabu search is designed to optimize the pickup and delivery routes and maximize the logistics resource utilization.
Abstract: Optimization of collaborative multi-depot pickup and delivery logistics networks (CMDPDLN) with split loads and time windows involves a customer demand splitting strategy and a multi-depot pickup and delivery vehicle routing problem under time window constraints. In the collaborative network, the customer demand splitting scheme based on customer clustering aims to achieve the balance of demands’ spatial distribution and improve the efficiency of logistics transportation. The multi-depot pickup and delivery vehicle routing problem focuses on establishing a collaborative network optimization model to coordinate the pickup and delivery services among multiple depots and determine the optimal routes with reduced operating cost through logistics resource sharing. A 3D customer clustering algorithm with split load strategies is developed to reassign each customer to its favorable service provider considering multiple customer service characteristics. A hybrid genetic algorithm with tabu search is designed to optimize the pickup and delivery routes and maximize the logistics resource utilization. A realistic logistics network in Chongqing, China is used to test the performance of the proposed solution methods for the CMDPDLN optimization. Computational results show the effectiveness of customer clustering and demand splitting in simplifying and improving the large-scale collaborative network, and the adaptability of the hybrid algorithm in finding the minimal-cost vehicle routes. Therefore, the collaboration and demand split strategy adopted in network optimization can provide a reference for logistics operational management and facilitate sustainable pickup and delivery networks.

30 citations


Journal ArticleDOI
TL;DR: This article addresses computational, storage, connectivity and intelligence concerns using a collaborative approach to engage multiple Internet of Things nodes such as connected- vehicles, drones and mobile devices for the provisioning of QoS-optimal complex service compositions in autonomous mobile networks.
Abstract: The Fifth Generation (5G) communication technology has paved the way for intelligent and diversified Internet of Connected Vehicles (IoCV) services that meet stringent Quality of Service (QoS) requirements Both Artificial Intelligence (AI) and Blockchain are playing and will continue to play an imperative role in providing secure and decentralized resource sharing to solve complex and time-sensitive problems at the edge The integration of both those techniques will enhance the performance of smart vehicular services, especially in beyond 5G (B5G) networks Ensuring secure transactions in complex autonomous network architectures is an immense challenge This article addresses computational, storage, connectivity and intelligence concerns using a collaborative approach to engage multiple Internet of Things (IoT) nodes such as connected- vehicles, drones and mobile devices for the provisioning of QoS-optimal complex service compositions in autonomous mobile networks Continuous and fast compositions emerge using decentralized decisions and interactions with diversified neighboring nodes with the aid of reinforcement learning Blockchain is used to ensure that nodes interact with each other verifiably and record transactions without the need for trusted intermediaries We assess whether having an AI-enabled blockchain collaborative composition solution improves service availability and delivery of smart city vehicular services

28 citations


Journal ArticleDOI
TL;DR: The social needs, framework composition, main functions and expected objectives of the system are introduced, the existing main problems and development bottlenecks are analyzed, in combination with the teaching practice, and the experience of distance teaching through the actual applications are shared.

28 citations


Journal ArticleDOI
TL;DR: In this paper, a multi-workflow scheduling algorithm based on the Multi-Criteria Decision Making (MCDM) approach, TOPSIS (Technique of Order Preference by Similarity to Ideal Solution) is presented.

26 citations


Journal ArticleDOI
19 Oct 2021-Sensors
TL;DR: In this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset, where a multi-agent system is proposed to provide the complete management of the network from the edge to the cloud.
Abstract: In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%.

Journal ArticleDOI
TL;DR: A data-driven RAN slicing mechanism based on a resource sharing algorithm running at the Slice Orchestrator (SO) level is proposed, which allows to meet the stringent requirements of two main classes of network slices in 5G, i.e., enhanced Mobile Broadband and Ultra-Reliable and Low-Latency Communication.
Abstract: One of the main challenges when it comes to deploying Network Slices is slicing the Radio Access Network (RAN). Indeed, managing RAN resources and sharing them among network slices is an increasingly difficult task, which needs to be properly designed. The goal is to improve network performance and introduce flexibility and greater utilization of network resources by accurately and dynamically provisioning the activated network slices with the appropriate amounts of resources to meet their diverse requirements. In this paper, we propose a data-driven RAN slicing mechanism based on a resource sharing algorithm running at the Slice Orchestrator (SO) level. This algorithm computes the necessary radio resources to be used by each deployed network slice. These resources are adjusted periodically based on current estimates of achievable throughput performance derived from channel quality information, and in particular from the Channel Quality Indicator (CQI) values of the users of each network slice retrieved from the RAN. CQI information is reported to base stations by the User Equipment (UE) following standard procedures, but extracting and frequently reporting it from base stations to the SO may result in significant communication overhead. To mitigate this overhead while maintaining at the SO level an accurate view of UE channel qualities, we propose a machine learning approach to infer the stability of UE channel conditions, as well as predictive schemes to reduce the CQI reporting intensity based on the inferred channel status. Through extensive simulations, we demonstrate the efficiency of our data-driven RAN slicing framework, which allows to meet the stringent requirements of two main classes of network slices in 5G, i.e., enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low-Latency Communication (URLLC).

Journal ArticleDOI
TL;DR: In this article, a dynamic radio access network slicing resource sharing method aimed to guarantee optimal service level agreements through the monitoring of each slice tenant's key performance indicators is presented, and the solution is validated using a testbed based on the main 5G functionalities.
Abstract: Emerging 5G systems will need to seamlessly guarantee novel types of services in a multi-do-main ecosystem. New methodologies of network and infrastructure sharing facilitate the cooperation among the operators, exploiting the core and access sections of the system architecture. Network slicing (NS) is the operators' best technique for building and managing a network. Without NS, the 5G requirements in terms of flexibility, optimal resource utilization, and investment returns cannot materialize. Before slicing is commercially available, different sections of the 5G architecture should be modified to include NS. In this work, we present a novel dynamic radio access network slicing resource sharing method aimed to guarantee optimal service level agreements through the monitoring of each slice tenant's key performance indicators. The experiments are conducted following the 3GPP specifications, and the solution is validated using a testbed based on the main 5G functionalities.

Journal ArticleDOI
TL;DR: EdgeGO, a mobile resource-sharing framework that employs mobile edge servers to provide a cost-effective deployment of 6G edge computing, which enables edge resource sharing for massive IoT devices is proposed.
Abstract: With the remarkable development of the 5G technologies, more and more real-time and complex computational tasks from the Internet-of-Things (IoT) systems can be fulfilled by 5G edge servers. While the ultra-dense deployment is required for 5G edge services, in the upcoming era of 6G with an even more limited communication range, it is almost impossible to achieve 6G service coverage with dense deployments. To address this fundamental limit, we propose EdgeGO, a mobile resource-sharing framework that employs mobile edge servers to provide a cost-effective deployment of 6G edge computing, which enables edge resource sharing for massive IoT devices. Unlike traditional mobile cloudlets, EdgeGO exploits the asynchronization between requests receiving and results returning to decouple the stringent delay and resource requirements for edge computing. As a result, the server moving and task processing could be paralleled. Besides, EdgeGO incorporates a two-layer iterative updating algorithm, which jointly optimizes path planning and task scheduling to improve the overall task efficiency. Extensive simulation results show that, by careful managing mobility and task execution of the edge servers, EdgeGO is able to drastically increase the resource utilization by 166.67% and decrease the deployment cost of 6G edge computing by 25.58%.

Journal ArticleDOI
TL;DR: Con-Pi as discussed by the authors exploits the concept of containerization and harnesses Docker containers to run IoT applications as micro-services, and operates in a distributed manner across multiple RPis and enables them to share resources.
Abstract: Edge and Fog computing paradigms overcome the limitations of cloud-centric execution for different latency-sensitive Internet of Things (IoT) applications by offering computing resources closer to the data sources. Small single-board computers (SBCs) like Raspberry Pis (RPis) are widely used as computing nodes in both paradigms. These devices are usually equipped with moderate speed processors and provide support for peripheral interfacing and networking, making them well-suited to deal with IoT-driven operations such as data sensing, analysis, and actuation. However, these small Edge devices are constrained in facilitating multi-tenancy and resource sharing. The management of computing and peripheral resources through centralized entities further degrades their performance and service quality significantly. To address these issues, a fully distributed framework, named Con-Pi, is proposed in this work to manage resources at the Edge or Fog environments. Con-Pi exploits the concept of containerization and harnesses Docker containers to run IoT applications as micro-services. %Moreover, Con-Pi operates in a distributed manner across multiple RPis and enables them to share resources. The software system of the proposed framework also provides a scope to integrate different IoT applications, resource and energy management policies for Edge and Fog computing. Its performance is compared with the state-of-the-art frameworks through real-world experiments. The experimental results show that Con-Pi outperforms others in enhancing response time and managing energy usage and computing resources through its distributed offloading model. Further, we have developed an automated pest bird deterrent system using Con-Pi to demonstrate its suitability in developing practical solutions for various IoT-enabled use cases, including smart agriculture.

Journal ArticleDOI
TL;DR: In this paper, a critical perspective is developed towards understanding and analyzing both the potential positive and negative impacts of the shared consumption promoted by digital platforms, based on two major parts: identifying the type of the resource that is shared; and investigating how sharing that resource can affect the sustainability of its consumption and other consumption patterns that it may promote.
Abstract: Digital platforms have enabled huge efficiency in coordinating sharing practices among a large number of users. The proliferation of sharing platforms has formed a phenomenon often referred to – albeit not unanimously – as ‘the sharing economy’ or, more precisely, ‘the Digital Sharing Economy (DSE)’1. What makes the DSE special is its ability to enhance access to a wide variety of material and immaterial resources within large and spatially distributed communities of consumers; a feature that could not exist in traditional, small-scale sharing. This characteristic has been known as the enabling role of digital Information and Communication Technology (ICT) in transforming sharing practices and scaling up sharing networks. Such extensive changes brought by digital advancements can raise a number of important questions concerning sustainability (Salomon and Mokhtarian, 2008), as changes often come along with both opportunities and risks. From a sustainability perspective, an evident impact of sharing resources is improved efficiency in consumption. Through sharing, the utilization of a resource increases to serve more demand, which translates to an optimization effect. Nevertheless, it is possible that increased efficiency is followed by unwanted impacts such as rebound effects. Therefore, to develop a critical perspective, efforts should be directed towards understanding and analyzing both the potential positive and negative impacts of the shared consumption promoted by digital platforms. Such analysis can be based on two major parts: First, identifying the type of the resource that is shared; second, investigating how sharing that resource can affect the sustainability of its consumption and other consumption patterns that it may promote.

Journal ArticleDOI
TL;DR: A contract theory-based incentive mechanism that maximizes the social welfare of the vehicular networks by motivating neighboring vehicles to participate in sharing their resources and derives an optimal contract scheme for computational task offloading.
Abstract: The proliferation of compute-intensive services in next-generation vehicular networks will impose an unprecedented computation demand to meet stringent latency and resource requirements. Vehicular edge or fog computing has been a widely adopted solution to enhance the computational capacity of vehicular networks; however, the computation requirements of these compute hungry applications will surpass the capabilities of such a solution. To address this challenge, the on-board resources of neighboring mobile vehicles can be utilized. However, such resource utilization requires an incentive mechanism to motivate privately owned neighboring vehicles to participate in sharing their resources. In this paper, we propose a contract theory-based incentive mechanism that maximizes the social welfare of the vehicular networks by motivating neighboring vehicles to participate in sharing their resources. The proposed approach enables the Road Side Units (RSUs) to provide appropriate rewards by offering a tailored contract to each resource sharing vehicle based on their contribution and unique characteristics. Moreover, we derive an optimal contract scheme for computational task offloading, taking into account the individual rationality and incentive-compatible constraints. Finally, we perform numerical evaluations to demonstrate the effectiveness of our proposed scheme. The proposed scheme achieves up to 28% higher computing resource utilization, 17.2% lower energy consumption per computing resource utilization, and 17.1% lesser energy consumption per task completed when compared to the linear pricing incentive baseline.

Journal ArticleDOI
TL;DR: The recently developed blockchain technology characterized by successfully enabling consensus in an untrustworthy environment is introduced and a new architecture for resource sharing and transactions in fog computing networks is proposed, named Blockchain-Enabled Resource Sharing and Transactions in Fog Computing (B-ReST).
Abstract: Driven by the extensively emerging applications requiring big data processing, a series of heterogeneous network architectures have been proposed to meet user experience requirements Among them, the concept of fog computing facilitates the effective integration and utilization of ubiquitous computing resources In fog computing scenarios, willingness and service billing issues become significant to computing resource sharing and transactions In this article, the recently developed blockchain technology characterized by successfully enabling consensus in an untrustworthy environment is introduced Based on the blockchain technology, we propose a new architecture for resource sharing and transactions in fog computing networks, named Blockchain-Enabled Resource Sharing and Transactions in Fog Computing (B-ReST) The physical architecture, functional architecture, and workflow in B-ReST are defined We also discuss the key technologies in B-ReST such as the smart contracts, the consensus mechanism and the requester and provider matching (RPM) The wireless characteristics of fog computing and blockchain technology are closely combined to make full and efficient use of ubiquitous computing resources To prove the feasibility of the proposed architecture, the RPM problem is solved by a deep reinforcement learning (DRL) based method Simulation results show the advantages of B-ReST to realize resource sharing and transactions, and the performance of B-ReST based on the DRL method has been enhanced Challenges and future research directions are summarized as well

Journal ArticleDOI
TL;DR: VECMAN is proposed, a framework for energy-aware resource management in VEC systems composed of two algorithms: a resource selector algorithm that determines the participating vehicles and the duration of resource sharing period and an energy manager algorithm that manages computing resources of the participating Vehicles with the aim of minimizing the computational energy consumption.
Abstract: In Vehicular Edge Computing (VEC) systems, the computing resources of connected Electric Vehicles~(EV) are used to fulfill the low-latency computation requirements of vehicles. However, local execution of heavy workloads may drain a considerable amount of energy in EVs. One promising way to improve the energy efficiency is to share and coordinate computing resources among connected EVs. However, the uncertainties in the future location of vehicles make it hard to decide which vehicles participate in resource sharing and how long they share their resources so that all participants benefit from resource sharing. In this paper, we propose VECMAN, a framework for energy-aware resource management in VEC systems composed of two algorithms: (i) a resource selector algorithm that determines the participating vehicles and the duration of resource sharing period; and (ii) an energy manager algorithm that manages computing resources of the participating vehicles with the aim of minimizing the computational energy consumption. We evaluate the proposed algorithms and show that they considerably reduce the vehicles computational energy consumption compared to the state-of-the-art baselines. Specifically, our algorithms achieve between 7\% and 18\% energy savings compared to a baseline that executes workload locally and an average of 13\% energy savings compared to a baseline that offloads vehicles workloads to RSUs.

Posted Content
26 Sep 2021
TL;DR: In this article, a Stackelberg differential game based resource sharing mechanism is proposed to facilitate the resource trading between the cloud computing service provider (CCP) and different edge computing service providers (ECPs).
Abstract: Recently, the boosting growth of computation-heavy applications raises great challenges for the Fifth Generation (5G) and future wireless networks. As responding, the hybrid edge and cloud computing (ECC) system has been expected as a promising solution to handle the increasing computational applications with low-latency and on-demand services of computation offloading, which requires new computing resource sharing and access control technology paradigms. This work establishes a software-defined networking (SDN) based architecture for edge/cloud computing services in 5G heterogeneous networks (HetNets), which can support efficient and on-demand computing resource management to optimize resource utilization and satisfy the time-varying computational tasks uploaded by user devices. In addition, resulting from the information incompleteness, we design an evolutionary game based service selection for users, which can model the replicator dynamics of service subscription. Based on this dynamic access model, a Stackelberg differential game based cloud computing resource sharing mechanism is proposed to facilitate the resource trading between the cloud computing service provider (CCP) and different edge computing service providers (ECPs). Then we derive the optimal pricing and allocation strategies of cloud computing resource based on the replicator dynamics of users' service selection. These strategies can promise the maximum integral utilities to all computing service providers (CPs), meanwhile the user distribution can reach the evolutionary stable state at this Stackelberg equilibrium. Furthermore, simulation results validate the performance of the designed resource sharing mechanism, and reveal the convergence and equilibrium states of user selection, and computing resource pricing and allocation.

Journal ArticleDOI
TL;DR: A distributed feedback-based RTO (DFRTO) framework for optimal resource sharing in an industrial symbiotic setting where a master coordinator updates the shadow price for the shared resource, and the different subsystems locally optimize their operation using feedback control for the given shadow price.

Journal ArticleDOI
TL;DR: This article presents an NFV-enabled 5G paradigm for the industry with the guarantee of URLLC through service chain acceleration and dynamic blockchain-based spectrum resource sharing among a variety of industry applications running in NVF-based equipment.
Abstract: 5G networks are expected to provide cost-efficient, reliable, and flexible services for industrial productions and applications potentially, by introducing emerging network technologies like blockchain and network functions virtualization (NFV), which virtualizes network functions and runs them on standard infrastructure rather than customized hardware. However, how to deal with the emerging security challenges and fulfil the requirement of ultra-reliable and low-latency communications (URLLC) has not been fully resolved. In this article, we present an NFV-enabled 5G paradigm for the industry with the guarantee of URLLC through service chain acceleration and dynamic blockchain-based spectrum resource sharing among a variety of industry applications running in NVF-based equipment. First, we elaborate the benefits and shortcomings of NFV for industry, by executing an industry application experiment in virtualized and nonvirtualized data center networks. Then, we illustrate an NFV-enabled 5G paradigm for URLLC in detail, with a special focus on the service chain acceleration and spectrum sharing built on NFV, blockchain, software-defined networking, and mobile edge computing. Finally, we establish a mathematical model to study the worst-cast transmission latency of NFV-enabled 5G with the input of the bursty traffic. The proposed model can be exploited to support the plan, management, and optimization of NFV-enabled 5G URLLC systems for industry.

Proceedings ArticleDOI
17 May 2021
TL;DR: In this paper, a learning-powered resource management system tailored to the microservice environment is proposed, which can improve the mean and p95 response time by up to 80% and 77.5% respectively compared with conventional schemes.
Abstract: The microservice architecture is a hot trend which proposes to transform the traditional monolith application into massive dynamic and irregular small services. To boost the overall throughput and ensure the guaranteed latency, it is desirable to process massive service requests in parallel with efficient resource sharing in data centers. However, the disaggregation nature of microservice unavoidably upscales the design space of resource management and increases its complexity. In this paper, we propose AlphaR, a learning-powered resource management system tailored to the microservice environment. The basic idea of AlphaR is to generate microservice-specific resource management policies for improving efficiency. Specifically, we take the first step to use bipartite graph as a convenient abstraction for application built with microservices. Based on this, we devise a bipartite feature inference approach named Bi-GNN to extract the temporal characteristics of microservices. Furthermore, we implement a policy network to select appropriate resource allocation choices for maximizing the performance in resource-constrained data centers. AlphaR can improve the mean and p95 response time by up to 80% and 77.5% respectively compared with conventional schemes.

Journal ArticleDOI
TL;DR: A fully decentralized and low-complexity online algorithm, DPoS, for multi-resource slicing based on the primal-dual approach and posted price mechanism that can not only achieve close-to-offline-optimal performance, but also have low algorithmic overheads.
Abstract: Network slicing is the key to enable virtualized resource sharing among vertical industries in the era of 5G communication. Efficient resource allocation is of vital importance to realize network slicing in real-world business scenarios. To deal with the high algorithm complexity, privacy leakage, and unrealistic offline setting of current network slicing algorithms, in this paper we propose a fully decentralized and low-complexity online algorithm, DPoS, for multi-resource slicing. We first formulate the problem as a global social welfare maximization problem. Next, we design the online algorithm DPoS based on the primal-dual approach and posted price mechanism. In DPoS, each tenant is incentivized to make its own decision based on its true preferences without disclosing any private information to the mobile virtual network operator and other tenants. We provide a rigorous theoretical analysis to show that DPoS has the optimal competitive ratio when the cost function of each resource is linear. Extensive simulation experiments are conducted to evaluate the performance of DPoS. The results show that DPoS can not only achieve close-to-offline-optimal performance, but also have low algorithmic overheads.

Journal ArticleDOI
TL;DR: In this paper, a fully distributed framework, named Con-Pi, is proposed to manage resources at the edge or fog environments, which exploits the concept of containerization and harnesses Docker containers to run IoT applications as micro-services.
Abstract: Edge and Fog computing paradigms overcome the limitations of cloud-centric execution for different latency-sensitive Internet of Things (IoT) applications by offering computing resources closer to the data sources. Small single-board computers (SBCs) like Raspberry Pis (RPis) are widely used as computing nodes in both paradigms. These devices are usually equipped with moderate speed processors and provide support for peripheral interfacing and networking, making them well-suited to deal with IoT-driven operations such as data sensing, analysis, and actuation. However, these small Edge devices are constrained in facilitating multi-tenancy and resource sharing. The management of computing and peripheral resources through centralized entities further degrades their performance and service quality significantly. To address these issues, a fully distributed framework, named Con-Pi, is proposed in this work to manage resources at the Edge or Fog environments. Con-Pi exploits the concept of containerization and harnesses Docker containers to run IoT applications as micro-services. The software system of the proposed framework also provides a scope to integrate different IoT applications, resource and energy management policies for Edge and Fog computing. Its performance is compared with the state-of-the-art frameworks through real-world experiments. The experimental results show that Con-Pi outperforms others in enhancing response time and managing energy usage and computing resources through its distributed offloading model. Further, we have developed an automated pest bird deterrent system using Con-Pi to demonstrate its suitability in developing practical solutions for various IoT-enabled use cases, including smart agriculture.

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a simulation framework for fog devices that can use end devices to handle the peak computation load to provide better Quality of Services (QoS), where regional fog nodes are deployed at network edge locations which are used as an intelligent agent to handle computation requests by either scheduling them on local servers, cloud data centers, or at the under-utilized end-user devices.
Abstract: Fog computing has proved its importance over legacy cloud architectures for computation, storage, and communication where edge devices are used to facilitate the delay-sensitive applications. The inception of fog nodes has brought computing intelligence close to the end-devices. Many fog computing frameworks have been proposed where edge devices are used for computation. In this paper, we proposed a simulation framework for fog devices that can use end devices to handle the peak computation load to provide better Quality of Services (QoS). The regional fog nodes are deployed at network edge locations which are used as an intelligent agent to handle the computation requests by either scheduling them on local servers, cloud data centers, or at the under-utilized end-user devices. The proposed device-to-device resource sharing model relies on Ant Colony Optimization (ACO) and Earliest Deadline First(EDF) Algorithm to provide a better quality of service using device available at multi-layer design. The concept of using IoT devices as fog nodes has improved the performance of legacy fog based systems. The proposed work is benchmarked in terms of system cost, efficiency, energy, and quality of service. Further, the proposed framework is with xFogSim in terms of task efficiency.

Journal ArticleDOI
TL;DR: SplitPred as mentioned in this paper proposes a split-learning-based framework that enables FL clients to maximize available resources within their local network without compromising the benefits of a FL approach (i.e., privacy and shared learning).
Abstract: The proliferation of Industry 4.0 has made modern industrial assets a rich source of data that can be leveraged to optimise operations, ensure efficiency, and minimise maintenance costs. The availability of data is advantageous for asset management, however, attempts to maximise the value of this data often fall short due to additional constraints, such as privacy concerns and data stored in distributed silos that is difficult to access and share. Federated Learning (FL) has been explored to address these challenges and has been demonstrated to provide a mechanism that allows highly distributed data to be mined in a privacy-preserving manner and offering new opportunities for a collaborative approach to asset management. Despite the benefits, FL has some challenges that need to be overcome to make it fully compatible for asset management or more specifically predictive maintenance applications. FL requires a set of clients that participate in the model training process, however, orchestration, device heterogeneity and scalability can hinder the speed and accuracy in the context of collaborative predictive maintenance. To address this challenge, this work proposes a split-learning-based framework (SplitPred) that enables FL clients to maximise available resources within their local network without compromising the benefits of a FL approach (i.e., privacy and shared learning). Experiments performed on the benchmark C-MAPSS data-set demonstrate the advantage of applying SplitPred in the FL process in terms of efficient use of resources, i.e., model convergence time, accuracy, and network load.

Posted Content
TL;DR: This work presents BEAT, an automated, transparent, and accountable end-to-end architecture for network sharing based on blockchain and smart contracts, due to its permissioned nature allowing for industry-compliant SLAs with stringent governance.
Abstract: Infrastructure sharing is a widely discussed and implemented approach and is successfully adopted in telecommunications networks today. In practice, it is implemented through prior negotiated Service Level Agreements (SLAs) between the parties involved. However, it is recognised that these agreements are difficult to negotiate, monitor and enforce. For future 6G networks, resource and infrastructure sharing is expected to play an even greater role. It will be a crucial technique for reducing overall infrastructure costs and increasing operational efficiencies for operators. More efficient SLA mechanisms are thus crucial to the success of future networks. In this work, we present "BEAT", an automated, transparent and accountable end-to-end architecture for network sharing based on blockchain and smart contracts. This work focuses on a particular type of blockchain, Permissioned Distributed Ledger (PDL), due to its permissioned nature allowing for industry-compliant SLAs with stringent governance. Our architecture can be implemented with minimal hardware changes and with minimal overheads.

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TL;DR: This paper presents a survey of various existing token-based distributed mutual exclusion algorithms (TBDMEA) in the focus of their performance measures and fault-tolerant capabilities which comprises the associated open challenges and directions to future research.
Abstract: The problem of mutual exclusion is a highly focused area in the distributed architecture. To avoid inconsistency in data, mutual exclusion ensures that no two processes running on different processors are allowed to enter into the same shared resource simultaneously in the system. In recent years, the consistent development of ongoing internet and mobile communication technologies, the devices, infrastructure and resources in networking systems like Ad Hoc Networks are becoming more complex and heterogeneous. Various algorithms have been introduced as a solution to mutual exclusion problem in the domain of distributed architecture over the past years. The performance and adaptability of these solutions depend on the different strategies used by them in the system. Various classifications of these strategies have been proposed such as token-based and non-token-based (also, permission-based). This paper presents a survey of various existing token-based distributed mutual exclusion algorithms (TBDMEA) in the focus of their performance measures and fault-tolerant capabilities which comprises the associated open challenges and directions to future research. In conjunction with traditional to latest proposed TBDMEA, token-based distributed group mutual exclusion algorithms (TBDGMEA) and token-based self-stabilizing distributed mutual exclusion algorithms (TBStDMEA) have also been surveyed in this paper as new variants of the token-based scheme.

Journal ArticleDOI
TL;DR: This article proposes a novel downlink resource sharing strategy, where multiple D2D users (D2Ds) share the resource of a single cellular user (CU), and each D1D pair reuses multiple channels from different cellular users (CUs).
Abstract: The Device-to-device (D2D) networks bear a close resemblance to future Internet of Things (IoT) networks. Resource management is an important aspect for realization of D2D communication in upcoming IoT networks. In this article, we select underlay in-band D2D communication as it is more beneficial in terms of spectral efficiency even though it comes at the cost of interference with cellular communication. To deal with this difficulty, we propose a novel downlink resource sharing strategy, where multiple D2D users (D2Ds) share the resource of a single cellular user (CU), and each D2D pair reuses multiple channels from different cellular users (CUs). The proposed scheme adopts a channel selection technique, wherein multiple channels of CU can be shared by each D2D user. Furthermore, optimal power for each D2D user is determined through the Lagrangian dual optimization method. The formulated power control maximization scheme nicely balances the total transmission power of D2D and D2D sum-rate. The proposed channel and power allocation problem aims at maximizing the D2D sum-rate by increasing the number of active D2D links while preserving the quality of service (QoS) of CU. Finally, a relationship between energy efficiency (EE) and transmit power of D2D is investigated through an EE maximization problem. The overall system performance is evaluated in terms of the D2D shared ratio, throughput gain of the network, and computational complexity of the proposed optimal strategy. Further, the merits of using the proposed resource sharing scheme over the existing schemes are also verified through numerical results.