Showing papers on "Shared resource published in 2020"

Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics

Abstract: Driven by technologies such as mobile edge computing and 5G, recent years have witnessed the rapid development of urban informatics, where a large amount of data is generated. To cope with the growing data, artificial intelligence algorithms have been widely exploited. Federated learning is a promising paradigm for distributed edge computing, which enables edge nodes to train models locally without transmitting their data to a server. However, the security and privacy concerns of federated learning hinder its wide deployment in urban applications such as vehicular networks. In this article, we propose a differentially private asynchronous federated learning scheme for resource sharing in vehicular networks. To build a secure and robust federated learning scheme, we incorporate local differential privacy into federated learning for protecting the privacy of updated local models. We further propose a random distributed update scheme to get rid of the security threats led by a centralized curator. Moreover, we perform the convergence boosting in our proposed scheme by updates verification and weighted aggregation. We evaluate our scheme on three real-world datasets. Numerical results show the high accuracy and efficiency of our proposed scheme, whereas preserve the data privacy.

Efficient Computing Resource Sharing for Mobile Edge-Cloud Computing Networks

Abstract: Both the edge and the cloud can provide computing services for mobile devices to enhance their performance. The edge can reduce the conveying delay by providing local computing services while the cloud can support enormous computing requirements. Their cooperation can improve the utilization of computing resources and ensure the QoS, and thus is critical to edge-cloud computing business models. This paper proposes an efficient framework for mobile edge-cloud computing networks, which enables the edge and the cloud to share their computing resources in the form of wholesale and buyback. To optimize the computing resource sharing process, we formulate the computing resource management problems for the edge servers to manage their wholesale and buyback scheme and the cloud to determine the wholesale price and its local computing resources. Then, we solve these problems from two perspectives: i) social welfare maximization and ii) profit maximization for the edge and the cloud. For i), we have proved the concavity of the social welfare and proposed an optimal cloud computing resource management to maximize the social welfare. For ii), since it is difficult to directly prove the convexity of the primal problem, we first proved the concavity of the wholesaled computing resources with respect to the wholesale price and designed an optimal pricing and cloud computing resource management to maximize their profits. Numerical evaluations show that the total profit can be maximized by social welfare maximization while the respective profits can be maximized by the optimal pricing and cloud computing resource management.

Green logistics location-routing problem with eco-packages

Abstract: Optimization of the green logistics location-routing problem with eco-packages involves solving a two-echelon location-routing problem and the pickup and delivery problem with time windows. The first echelon consists of large eco-package transport, which is modeled by a time-discretized transport-concentrated network flow programming in the resource sharing state–space–time (SST) network. The second echelon focuses on small eco-package pickups and deliveries, established by the cost-minimized synchronization-oriented location routing model that minimizes the total generalized cost, which includes internal transportation cost, value of eco-packages, short-term benefits and environmental externalities. In addition, the Gaussian mixture clustering algorithm is utilized to assign customers to their respective service providers in the pickup and delivery process, and a Clarke–Wright saving method-based non-dominated sorting genetic algorithm II is designed to optimize pickup and delivery routes, and improve their cost-effectiveness and degree of synchronization. Different strategy testing results are used in the service phase as input data to calculate the cost of the transport phase, which is solved through a Lagrangian relaxation approach. The 3D SST network representation innovatively captures the eco-package route sequence and state transition constraints over the shortest path in the pickup and delivery at any given moment of the transport phase. A large-scale logistics network in Chengdu, China, is used to demonstrate the proposed model and algorithm, and undertake sensitivity analysis considering the life cycle of green eco-packages.

Efficient and Secure Multi-User Multi-Task Computation Offloading for Mobile-Edge Computing in Mobile IoT Networks

Abstract: Mobile edge computing (MEC) is a new paradigm to alleviate resource limitations of mobile IoT networks through computation offloading with low latency. This article presents an efficient and secure multi-user multi-task computation offloading model with guaranteed performance in latency, energy, and security for mobile-edge computing. It does not only investigate offloading strategy but also considers resource allocation, compression and security issues. Firstly, to guarantee efficient utilization of the shared resource in multi-user scenarios, radio and computation resources are jointly addressed. In addition, JPEG and MPEG4 compression algorithms are used to reduce the transfer overhead. To fulfill security requirements, a security layer is introduced to protect the transmitted data from cyber-attacks. Furthermore, an integrated model of resource allocation, compression, and security is formulated as an integer nonlinear problem with the objective of minimizing the weighted sum of energy under a latency constraint. As this problem is considered as NP-hard, linearization and relaxation approaches are applied to transform the problem into a convex one. Finally, an efficient offloading algorithm is designed with detailed processes to make the computation offloading decision for computation tasks of mobile users. Simulation results show that our model not only saves about 46% of system overhead consumption in comparison with local execution but also scale well for large-scale IoT networks.

Amazon, Google and Microsoft Solutions for IoT: Architectures and a Performance Comparison

Abstract: Internet of Things (IoT) aims to connect the real world made up of devices, sensors and actuators to the virtual world of Internet in order to interconnect devices with each other generating information from the gathered data. Devices, in general, have limited computational power and limited storage capacity. Cloud Computing (CC) has virtually unlimited capacity in terms of storage and computing power, and is based on sharing resources. Therefore, the integration between IoT and CC seems to be one of the most promising solutions. In fact, many of the biggest companies that offer Cloud Services are focusing on the IoT world to offer services also in this direction to their users. In this paper we compare the three main Cloud Platforms (Amazon Web Services, Google Cloud Platform and Microsoft Azure) regarding to the services made available for the IoT. After describing the typical architecture of an IoT application, we map the Cloud-IoT Platforms services with this architecture analyzing the key points for each platform. At the same time, in order to conduct a comparative analysis of performance, we focus on a service made available by all platforms (MQTT middleware) building the reference scenarios and the metrics to be taken into account. Finally, we provide an overview of platform costs based on different loads. The aim is not to declare a winner, but to provide a useful tool to developers to make an informed choice of a platform depending on the use case.

Efficient Resource Allocation and Power Control for LTE-A D2D Communication With Pure D2D Model

Abstract: To support the rising number of user equipments (UEs), LTE-A allows some UEs directly talking with each other to facilitate spectrum reuse, which is known as device-to-device (D2D) communication . Since D2D UEs (DUEs) consume resources and bring out interference, how to allocate resources and power is important. Existing studies seek to make more DUEs reuse resources of cellular UEs (CUEs, which are the UEs talking with the eNB) to increase throughput. However, it is inefficient for some CUEs (e.g., those near cell edge) to share resources with others due to high interference. Thus, a new sharing paradigm, called the pure D2D model , is proposed to allow DUEs sharing resources without involving CUEs for flexibility. This new model is helpful to IoT (Internet of Things) applications, where the overwhelming majority of devices are usually DUEs. The paper defines an optimization problem to maximize links supported in the network, and proposes a D2D resource allocation and power control (DRAPC) framework. By vertex coloring, DRAPC gives a preliminary grouping of UEs for resource allocation. Then, each group is carefully reformed by exchanging members and adding new ones, so as to increase signal quality and degree of resource sharing. Simulation results show that DRAPC not only improves network performance but also guarantees fairness among links.

Collaborate or Separate? Distributed Service Caching in Mobile Edge Clouds

Abstract: With the development of 5G technology, mobile edge computing is emerging as an enabling technique to promote Quality of Service (QoS) of network services. In particular, the response latency of network services can be significantly reduced by deploying cloudlets at 5G base stations in mobile edge clouds. Network service providers that usually deploy their services in remote clouds now shift their services from the remote clouds to the network edge in the proximity of users. However, the permanent placement of their services into edge clouds may not be economic, since computing and bandwidth resources in edge clouds are limited and relatively expensive. A smart way is to cache the services that are frequently requested by mobile users in edge clouds. In this paper, we study the problem of service caching in mobile edge network under a mobile service market with multiple network service providers completing for both computation and bandwidth resources of the edge cloud. We propose an Integer Linear Program (ILP) and a randomized rounding algorithm, for the problem without resource sharing among the network service providers. We also devise a distributed and stable game-theoretical mechanism for the problem with resource sharing among the network service providers, with the objective to minimize the social cost of all network service providers, by introducing a novel cost sharing model and a coalition formation game. We analyze the performance of the mechanism by showing a good guaranteed gap between the solution obtained and the optimal one, i.e., Strong Price of Anarchy (SPoA). We finally evaluate the performance of our algorithms by extensive simulations, and the obtained results show that the social cost of all players can be reduced significantly via allowing cooperation among network service providers in service caching.

Virtualizing FPGAs in the Cloud

Abstract: Field-Programmable Gate Arrays (FPGAs) have been integrated into the cloud infrastructure to enhance its computing performance by supporting on-demand acceleration. However, system support for FPGAs in the context of the cloud environment is still in its infancy with two major limitations, i.e., the inefficient runtime management due to the tight coupling between compilation and resource allocation, and the high programming complexity when exploiting scale-out acceleration. The root cause is that FPGA resources are not virtualized. In this paper, we propose a full-stack solution, namely ViTAL, to address the aforementioned limitations by virtualizing FPGA resources. Specifically, ViTAL provides a homogeneous abstraction to decouple the compilation and resource allocation. Applications are offline compiled onto the abstraction, while the resource allocation is dynamically determined at runtime. Enabled by a latency-insensitive communication interface, applications can be mapped flexibly onto either one FPGA or multiple FPGAs to maximize the resource utilization and the aggregated system throughput. Meanwhile, ViTAL creates an illusion of a single and large FPGA to users, thereby reducing the programming complexity and supporting scale-out acceleration. Moreover, ViTAL also provides virtualization support for peripheral components (e.g., on-board DRAM and Ethernet), as well as protection and isolation support to ensure a secure execution in the multi-user cloud environment. We evaluate ViTAL on a real system - an FPGA cluster composed of the latest Xilinx UltraScale+ FPGAs (XCVU37P). The results show that, compared with the existing management method, ViTAL enables fine-grained resource sharing and reduces the response time by 82% on average (improving Quality-of-Service) with a marginal virtualization overhead. Moreover, ViTAL also reduces the response time by 25% compared to AmorphOS (operating in high-throughput mode), a recently proposed FPGA virtualization method.

Trusted Cloud-Edge Network Resource Management: DRL-Driven Service Function Chain Orchestration for IoT

Abstract: Private and public networks sharing resources for Internet of Things (IoT) network through network function virtualization (NFV) and software-defined networking (SDN) forms a heterogeneous cloud-edge environment. However, the heterogeneous cloud-edge network faces trust and adaptation issues in resource allocation. To address these two problems, we introduce consortium blockchain and deep reinforcement learning (DRL) to construct the trusted and auto-adjust service function chain (SFC) orchestration architecture. In the architecture, this article integrates the consortium blockchain into the distributed SFC orchestration model to realize trusted resource sharing. In addition, for realizing auto-adjusted service provision, this article designs a dynamic hierarchical SFC orchestration algorithm (DHSOA) based on DRL to minimize the orchestration cost and improve the quality of service. Moreover, considering the dynamics of network entities, this article proposes a time-slotted model to support dynamic service migration which adapts to the high-mobility IoT network. The simulation results show that DHSOA has better performance than the link-state routing algorithm and deep $Q$ -network placement algorithm not only in cost saving of 15.8% and 10.1% but also in time saving of 22.0% and 10.0%.

Efficient Algorithms for Delay-Aware NFV-Enabled Multicasting in Mobile Edge Clouds With Resource Sharing

Abstract: Stringent delay requirements of many mobile applications have led to the development of mobile edge clouds, to offer low latency network services at the network edges. Most conventional network services are implemented via hardware-based network functions, including firewalls and load balancers, to guarantee service security and performance. However, implementing hardware-based network functions usually incurs both a high capital expenditure (CAPEX) and operating expenditure (OPEX). Network Function Virtualization (NFV) exhibits a potential to reduce CAPEX and OPEX significantly, by deploying software-based network functions in virtual machines (VMs) on edge-clouds. We consider a fundamental problem of NFV-enabled multicasting in a mobile edge cloud, where each multicast request has both service function chain and end-to-end delay requirements. Specifically, each multicast request requires chaining of a sequence of network functions (referred to as a service function chain) from a source to a set of destinations within specified end-to-end delay requirements. We devise an approximation algorithm with a provable approximation ratio for a single multicast request admission if its delay requirement is negligible; otherwise, we propose an efficient heuristic. Furthermore, we also consider admissions of a given set of the delay-aware NFV-enabled multicast requests, for which we devise an efficient heuristic such that the system throughput is maximized, while the implementation cost of admitted requests is minimized. We finally evaluate the performance of the proposed algorithms in a real test-bed, and experimental results show that our algorithms outperform other similar approaches reported in literature.

Blockchain-based shared manufacturing in support of cyber physical systems: concept, framework, and operation

Abstract: Shared Manufacturing (SharedMfg), a Peer-to-Peer (P2P)-based resource sharing paradigm boosted by the wide-spread of sharing economy, servitization and Internet of things, tends to massively extend the scope of resource sharing in both vertical and horizontal directions, and as a consequence, it amplifies a credibility gap in the manufacturing area. To respond to this problem, and meanwhile, promoting the SharedMfg, blockchain is attempted to integrate into the SharedMfg. Hence, this paper proposes the Blockchain-based SharedMfg (BSM) framework in support of the application of Cyber Physical Systems (CPS). At the same time, Resource Operation Blockchain (ROB) is constructed for the core operation of BSM framework, which carries out on the basis of a consensus mechanism (i.e., Proof-of-Participation) and a Smart Contract Network (SCN), to facilitate the P2P-based resource sharing paradigm. A prototype system is implemented by the Ethereum framework together with discussions to validate the feasibility of ROB. In brief, BSM framework complementarily combines the blockchain and SharedMfg, beneficial to promote both modes.

Resource Sharing and Task Offloading in IoT Fog Computing: A Contract-Learning Approach

Abstract: With the rapid development of smart devices and compute-intensive applications, fog computing has emerged as a promising solution to accommodate the ever-increasing computational demands. Particularly, in the peak time, the computational tasks can be offloaded from the overloaded base stations to fog servers by leveraging the under-utilized computational resources at the demand side. However, there are two major obstacles hindering the wide deployment of fog computing in Internet of things, which are the lack of an effective incentive mechanism and a task offloading algorithm. In this paper, we develop a two-stage resource sharing and task offloading approach by integrating contract theory with computational intelligence. In the first stage, we propose an efficient incentive mechanism to encourage servers to share their residual computational resources by employing the contract theory. In the second stage, a distributed task offloading algorithm is proposed by leveraging the online learning capability of multi-armed bandit. Specifically, we propose a distance-aware, occurrence-aware, and task-property-aware volatile upper confidence bound algorithm to minimize the long-term delay of task offloading. Finally, extensive simulations are carried out to validate the performance of the proposed algorithm.

User-Centric Edge Sharing Mechanism in Software-Defined Ultra-Dense Networks

Abstract: The emerging mobile edge computing (MEC) evolutionarily extends the cloud services to the network edge. In order to efficiently coordinate distributed edge resources, software defined networking (SDN) at the network edge has been explored to realize the integrated management of communication, computation, and cache (3C) resources. However, many research efforts, in software-defined edge networks, are mainly devoted to 1C or 2C resource sharing. Motivated by high service performance and user demands, we propose a user-centric edge resource sharing model for software-defined ultra-dense network (SD-UDN) where multiple MEC servers around small base stations (SBSs) can share their 3C resources through OpenFlow-enabled switches. In particular, the service models of MEC servers and users are formulated to optimize the service process by minimizing the service delay, which is NP-hard. To address this NP-hard issue, a service association model is constructed based on design structure matrix (DSM), and a simulated annealing algorithm is employed to further optimize the service association model for reducing time complexity and offering a nearoptimal solution. Compared with traditional 1C or 2C resource sharing, the proposed edge resource sharing model can guarantee lower service delay for users.

Models and an exact method for the Unrelated Parallel Machine scheduling problem with setups and resources

Abstract: This paper deals with the Unrelated Parallel Machine scheduling problem with Setups and Resources (UPMSR) with the objective of minimizing makespan. Processing times and setups depend on machine and job. The necessary resources could be: specific resources for processing, needed for processing a job on a machine; specific resources for setups, needed to do the previous setup before a job is processed on a machine; shared resources, understanding these as unspecific resources that could also be needed in both processing or setup. The number of scarce resources depends on machine and job. As an industrial example, in a plastic processing plant molds are the specific resource for processing machines, cleaning equipment is the specific resource for setups and workers are the unspecific shared resource to operate processing machines and setup cleaning equipment. A mixed integer linear program is presented to model this problem. Also a three phase algorithm based on mathematical exact method is introduced. Model and algorithm are tested in a comprehensive and extensive computational campaign. Tests show good results for different combinations of useE of resources and in most cases come to less than 2.7% of gap against lower bound for instances of 400 jobs.

Sharing Distributed and Heterogeneous Resources toward End-to-End 5G Networks: A Comprehensive Survey and a Taxonomy

Abstract: Regardless of the context to which it is applied, sharing resources is well-recognized for its considerable benefits. Since 5G networks will be service-oriented, on-demand, and highly heterogeneous, it is utmost important to approach the design and optimization of the network from an end-to-end perspective. In addition, in order to ensure end-to-end performance, this approach has to entail both wireless and optical domains, altogether with the IoT, edge, and cloud paradigms which are an indispensable part of the 5G network architecture. Shifting from the exclusive ownership of network resources toward sharing enables all participants to cope with stringent service requirements in 5G networks, gaining significant performance improvements and cost savings at the same time. The main objective of this paper is to survey the literature on resource sharing, providing an in-depth and comprehensive perspective of sharing by recognizing the main trends, the techniques which enable sharing, and the challenges that need to be addressed. By providing a taxonomy which brings the relevant features of a comprehensive sharing model into focus, we aim to enable the creation of sharing models for more efficient future communication networks. We also summarize and discuss the relevant issues arising from network sharing, that should be properly tackled in the future.

Incentive-Aware Micro Computing Cluster Formation for Cooperative Fog Computing

Abstract: Fog computing is envisioned as a promising approach for supporting emerging computation-intensive applications on capacity and battery constrained mobile Internet of Things (IoT) devices. Technically speaking, a massive crowd of devices in close proximity can be harvested and collaborate for computation and communication resource sharing. Hence fog computing enables significant potentials in low-latency and energy-efficient mobile task execution. However, without an efficient incentive mechanism to stimulate resource sharing among devices, the benefits of fog computing cannot be fully realized. Leveraging coalitional game theory, this work presents an efficient incentive mechanism to incentivize mutually-beneficial resource cooperation among the devices for collaborative task execution. In particular, to efficiently achieve mutually beneficial task execution, the proposed mechanism groups the devices into multiple micro computing clusters (MCCs). Within each MCC, devices can exchange mutually beneficial actions by helping to compute or transmit tasks, making all of their performances no worse than local execution or execution in the fog server. The solution to the MCC formation is devised by both centralized and decentralized schemes and further proven to admit nice properties such as top coalition, core solution, individual rationality and computational efficiency. Extensive numerical studies demonstrate the superior performance of our MCC formation mechanisms.

Social-Aware Incentive Mechanisms for D2D Resource Sharing in IIoT

Abstract: The industrial Internet of Things (IIoT), as one of the indispensable paradigms of the future network, challenges existing computing network architectures by supporting computational-intensive applications. In the IIoT, resource-rich industrial devices may share idle computing resource to lightweight nodes through device-to-device (D2D) technology, whereas such resource sharing is under social and locality constraints. When industrial devices are carried by human or installed on manned machines, resource sharing more likely occurs among social-trustworthy and locality-adjacent devices. In this article, we propose two social-aware incentive mechanisms for D2D resource sharing in the IIoT, namely one-hop-based social-aware incentive mechanism (OSIM) and relay-based social-aware incentive mechanism (RSIM). In the OSIM, resource-constrained devices bid for offloading tasks using a Vickrey–Clarke–Groves auction, while the RSIM relaxes the locality constraint to two hops to achieve a higher resource utilization ratio. Extensive simulation results show that the performance of the proposed mechanisms can significantly improve the system efficiency while maintaining truthfulness.

Cloud computing using load balancing and service broker policy for IT service: a taxonomy and survey

Abstract: Cloud computing is the big boom technology in IT industry infrastructure. Many people are moving to cloud computing because of dynamic allocation of resources and reduction in cost. Cloud computing delivers infrastructure, software, and platforms as a service to all consumers. But still, it has numerous issues related to performance unpredictability, resource sharing, security, storage capacity, availability of resources on each requirement, data confidentiality and many more. Load balancing and service brokering are the two main key areas, which ensures reliability, scalability, minimize response time, maximize throughput and cost in the cloud environment. These are the main things we have to focus to improve the performance of the computation. This survey paper presents a comparative and comprehensive study of various load balancing algorithm used in the load balancer and brokering policy used for each service and their scheduling types. The objectives of this survey is to (1) Determine, illustrate, compare and analyze newer methods developed for load balancing and service brokering (the most notable problem) by systematically reviewing papers from the year 2015 to 2018; (2) Classify and analyze techniques based on the key parameters in cloud computing techniques; (3) Ultimately set an updated, thorough and rigorous discussion on load balancing and service broker techniques so as to motivate and direct with valuable references for future research development and direction.

Learn to Compress CSI and Allocate Resources in Vehicular Networks

Abstract: Resource allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. In this paper, we develop a hybrid architecture consisting of centralized decision making and distributed resource sharing (the C-Decision scheme) to maximize the long-term sum rate of all vehicles. To reduce the network signaling overhead, each vehicle uses a deep neural network to compress its observed information that is thereafter fed back to the centralized decision making unit. The centralized decision unit employs a deep Q-network to allocate resources and then sends the decision results to all vehicles. We further adopt a quantization layer for each vehicle that learns to quantize the continuous feedback. In addition, we devise a mechanism to balance the transmission of vehicle-to-vehicle (V2V) links and vehicle-to-infrastructure (V2I) links. To further facilitate distributed spectrum sharing, we also propose a distributed decision making and spectrum sharing architecture (the D-Decision scheme) for each V2V link. Through extensive simulation results, we demonstrate that the proposed C-Decision and D-Decision schemes can both achieve near-optimal performance and are robust to feedback interval variations, input noise, and feedback noise.

Communication, Computation, and Caching Resource Sharing for the Internet of Things

Abstract: The Internet of Things connects a large number of smart mobile devices with the Internet, where these devices are embedded with often limited communication, computation, and caching resources. To address the heterogeneity of these devices and achieve efficient overall system resource utilization, researchers have proposed various device-to-device resource sharing models, enabling mobile devices to form device-todevice connections and to share their resources for cooperative task execution. Most of these existing works, however, considered scenarios where mobile devices can share one or two types of resources, and hence inadequately explore the potential of resource sharing among mobile devices. In this article, we introduce a general framework where mobile devices can share any combination of the three types of resources, and it can generalize many existing deviceto- device resource sharing models. In addition, it can achieve more efficient resource allocation by offering mobile devices more flexibility in terms of resource sharing. Based on the proposed framework, we focus on discussing two issues: the optimization issue, regarding how to schedule resources among devices; and the economic issue, regarding how to motivate the device owners to share their resources. We introduce the challenges and potential solutions to these two issues. We further outline several open issues and future directions for the proposed general resource sharing framework.

Regional Intelligent Resource Allocation in Mobile Edge Computing Based Vehicular Network

Abstract: The advancement of 5G technology has brought the prosperous development of Internet of Vehicles (IoV). IoV services are not only computational intensive but also extremely sensitive to the delay. As a promising computing paradigm, mobile edge computing (MEC) can be applied to IoV scenarios. However, due to the limited resources of a single MEC server, it is difficult to cope with the suddenly increased computation loads caused by emergencies, or the intensive resource requests from busy regions. Therefore, we propose a novel regional intelligent management vehicular system with dual MEC planes, in which MEC servers in the same region cooperate with each other to achieve resource sharing. We classify computing tasks into different types according to their delay tolerances and focus on the optimization problem of resource allocation for different type tasks. And then, we design a resource allocation algorithm based on deep reinforcement learning, which can adapt to the changeable MEC environment to process high-dimensional data. Simulation results confirm that our proposed scheme is feasible and effective.

Blockchain-Based Reputation Management for Task Offloading in Micro-Level Vehicular Fog Network

Abstract: With the widespread adoption of the internet of things (IoT) technologies towards building a smart city, connected devices often offload computation tasks to nearby edge locations (base stations) to reduce overall computation and network delay. However, serving an ever-increasing number of end devices at these traditional edge locations is becoming impossible, subsequently making them fail to deliver the agreed quality of service to all requesting devices. However, the backend cloud data center is available to serve these requests but incurred additional communication delay, thus, unsuitable for delay-sensitive applications. Furthermore, the fact that the underlying network is inherently ad hoc which makes it prone to malicious nodes affecting its overall performance. In this work, we propose a secure fog computing paradigm where roadside units (RSUs) are used to offload tasks to nearby fog vehicles based on repute scores maintained at a distributed blockchain ledger. The experimental results demonstrate a significant performance gain in terms of queuing time, end-to-end delay, and task completion rate when compared to the baseline queuing-based task offloading scheme.

A Resource Sharing Scheme for Intercell D2D Communication in Cellular Networks: A Repeated Game Theoretic Approach

Abstract: Device-to-device (D2D) communication is an emerging IoT technology for improved performance of cellular networks in terms of aspects like spectral efficiency, battery lifetime, coverage range. The 5G cellular network is supposed to provide a perception of 99.999% network availability. One of the approaches to obtain it is by intercell D2D communication that eliminates the issue of unavailability of bandwidth between two users connected with different base stations (BSs). The interference mitigation is a key challenge while using licensed cellular spectrum for D2D links. It is seen that the resource allocation to the D2D links is a complex problem when D2D pairs are connected with different base stations. This paper presents a dynamic Resource-Block (RB) sharing scheme between the D2D users and cellular users in a multi-cell cellular network. Here a D2D user pair plays a Repeated Game with the nearby base stations in such a way that the players share a subset of their initial allotted resource to maximize their utility. The game is played by every intercell D2D player with the base stations simultaneously. We show that the punishment period of a deviating player may be reduced by invoking an additional penalty factor for its fast adherence. In addition, we also compute the optimal number of initial orthogonal RBs that is allocated to the D2D and the cellular users. Evaluation of our proposed scheme in comparison to few well known existing techniques shows a marked improvement in throughput of the overall system.

Green Cloud Multimedia Networking: NFV/SDN Based Energy-Efficient Resource Allocation

Abstract: The rapid growth of communications and multimedia network services such as Voice over Internet Protocol (VoIP) have caused these networks to face a crisis in resources from two perspectives: 1. Lack of resources and, as a result, overload; 2. Redundancy of resources and, as a result, energy loss. Cloud computing allows the scale of resources to be reduced or increased on demand. Many of the gains obtained from the cloud computing come from resource sharing and virtualization technology. On the other hand, the emerging concept of Software-Defined Networking (SDN) can provide a global view of the entire network for integrated resource management. Network Function Virtualization (NFV) can also be used to virtually implement a variety of network devices and functions. In this paper, we present an energy-efficient framework called GreenVoIP to manage the resources of virtualized cloud VoIP centers. By managing the number of VoIP servers and network equipment, such as switches, this framework not only prevents overload but also supports green computing by saving energy. Finally, GreenVoIP is implemented and evaluated on real platforms, including Floodlight, Open vSwitch, and Kamailio. The results suggest that the proposed framework can minimize the number of active devices, prevent overloading, and provide service quality requirements.

Network education recommendation and teaching resource sharing based on improved neural network

Abstract: Intelligent network teaching system provides learners with abundant teaching resources and convenient, excellent and efficient learning environment. However, network teaching resources are widely distributed and difficult to centralize. Resource sharing has become a key problem to be solved in the network environment. The current research on online education resource recommendation mainly focuses on offline education, and there are few studies on online education resources. Based on this, this study studies the link prediction methods in online education and establishes appropriate models for online education. In the research, through improved analysis of traditional algorithms, an improved neural network path sorting algorithm based on path sorting method is proposed. At the same time, we use the path sorting algorithm based on random walk model and neural network-path sorting algorithm to realize the link prediction problem in the online learning knowledge base. In addition, the performance analysis of the algorithm is carried out by contrast method, and the performance comparison analysis is carried out by combining various common traditional recommendation algorithms with the research algorithm of this study.