Secure and Efficient Computing Resource Management in Blockchain-Based Vehicular Fog Computing

In vehicular fog computing(VFC),the resource transactions in the Internet of Vehicles(IoV)have become a novel resource management scheme that can improve system resource utilization and the quality of vehicle services.In this paper,in order to improve the security and fairness of resource transactions,we design a blockchain-based resource management scheme for VFC.First,we propose the concept of resource coin(RC)and develop a blockchain-based secure computing reource trading mechanism in terms of RC.As a node of the blockchain network,the roadside unit(RSU)participates in verifying the legitimacy of transactions and the creation of new blocks.Next,we propose a resource management scheme based on contract theory,encouraging parked vehicles to contribute computing resource so that RSU could complete proof of work(PoW)quickly,improve the success probability of block creation and get RC rewards.We use the gradient descent method to solve the computing resource utilization that can maximize the RC revenue of RSUs and vehicles during the block creation.Finally,the performance of this model is validated in simulation result and analysis.

A Task-Resource Joint Management Model with Intelligent Control for Mission-Aware Dispersed Computing

Dispersed computing can link all devices with computing capabilities on a global scale to form a fully decentralized network,which can make full use of idle computing resources.Realizing the overall resource allocation of the dispersed computing system is a significant challenge.In detail,by jointly managing the task requests of external users and the resource allocation of the internal system to achieve dynamic balance,the efficient and stable operation of the system can be guaranteed.In this paper,we first propose a task-resource joint management model,which quantifies the dynamic transformation relationship between the resources consumed by task requests and the resources occupied by the system in dispersed computing.Secondly,to avoid downtime caused by an overload of resources,we introduce intelligent control into the task-resource joint management model.The existence and stability of the positive periodic solution of the model can be obtained by theoretical analysis,which means that the stable operation of dispersed computing can be guaranteed through the intelligent feedback control strategy.Additionally,to improve the system utilization,the task-resource joint management model with bi-directional intelligent control is further explored.Setting control thresholds for the two resources not only reverse restrains the system resource overload,but also carries out positive incentive control when a large number of idle resources appear.The existence and stability of the positive periodic solution of the model are proved theoretically,that is,the model effectively avoids the two extreme cases and ensure the efficient and stable operation of the system.Finally,numerical simulation verifies the correctness and validity of the theoretical results.

A Resource Management Algorithm for Virtual Machine Migration in Vehicular Cloud Computing

In recent years,vehicular cloud computing(VCC)has gained vast attention for providing a variety of services by creating virtual machines(VMs).These VMs use the resources that are present in modern smart vehicles.Many studies reported that some of these VMs hosted on the vehicles are overloaded,whereas others are underloaded.As a circumstance,the energy consumption of overloaded vehicles is drastically increased.On the other hand,underloaded vehicles are also drawing considerable energy in the underutilized situation.Therefore,minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in the VCC environment.The proper and efcient utilization of the vehicle’s resources can reduce energy consumption signicantly.One of the solutions is to improve the resource utilization of underloaded vehicles by migrating the over-utilized VMs of overloaded vehicles.On the other hand,a large number of VM migrations can lead to wastage of energy and time,which ultimately degrades the performance of the VMs.This paper addresses the issues mentioned above by introducing a resource management algorithm,called resource utilization-aware VM migration(RU-VMM)algorithm,to distribute the loads among the overloaded and underloaded vehicles,such that energy consumption is minimized.RU-VMM monitors the trend of resource utilization to select the source and destination vehicles within a predetermined threshold for the process of VM migration.It ensures that any vehicles’resource utilization should not exceed the threshold before or after the migration.RU-VMM also tries to avoid unnecessary VM migrations between the vehicles.RU-VMM is extensively simulated and tested using nine datasets.The results are carried out using three performance metrics,namely number of nal source vehicles(nfsv),percentage of successful VM migrations(psvmm)and percentage of dropped VM migrations(pdvmm),and compared with threshold-based algorithm(i.e.,threshold)and cumulative sum(CUSUM)algorithm.The comparisons show that the RU-VMM algorithm performs better than the existing algorithms.RU-VMM algorithm improves 16.91%than the CUSUM algorithm and 71.59%than the threshold algorithm in terms of nfsv,and 20.62%and 275.34%than the CUSUM and threshold algorithms in terms of psvmm.

A Review of Dynamic Resource Management in Cloud Computing Environments

In a cloud environment, Virtual Machines (VMs) consolidation andresource provisioning are used to address the issues of workload fluctuations.VM consolidation aims to move the VMs from one host to another in order toreduce the number of active hosts and save power. Whereas resource provisioningattempts to provide additional resource capacity to the VMs as needed in order tomeet Quality of Service (QoS) requirements. However, these techniques have aset of limitations in terms of the additional costs related to migration and scalingtime, and energy overhead that need further consideration. Therefore, this paperpresents a comprehensive literature review on the subject of dynamic resourcemanagement (i.e., VMs consolidation and resource provisioning) in cloud computing environments, along with an overall discussion of the closely relatedworks. The outcomes of this research can be used to enhance the developmentof predictive resource management techniques, by considering the awareness ofperformance variation, energy consumption and cost to efficiently manage thecloud resources.

Optimal and Effective Resource Management in Edge Computing

Edge computing is a cloud computing extension where physical compu-ters are installed closer to the device to minimize latency.The task of edge data cen-ters is to include a growing abundance of applications with a small capability in comparison to conventional data centers.Under this framework,Federated Learning was suggested to offer distributed data training strategies by the coordination of many mobile devices for the training of a popular Artificial Intelligence(AI)model without actually revealing the underlying data,which is significantly enhanced in terms of privacy.Federated learning(FL)is a recently developed decentralized profound learning methodology,where customers train their localized neural network models independently using private data,and then combine a global model on the core server together.The models on the edge server use very little time since the edge server is highly calculated.But the amount of time it takes to download data from smartphone users on the edge server has a significant impact on the time it takes to complete a single cycle of FL operations.A machine learning strategic planning system that uses FL in conjunction to minimise model training time and total time utilisation,while recognising mobile appliance energy restrictions,is the focus of this study.To further speed up integration and reduce the amount of data,it implements an optimization agent for the establishment of optimal aggregation policy and asylum architecture with several employees’shared learners.The main solutions and lessons learnt along with the prospects are discussed.Experiments show that our method is superior in terms of the effective and elastic use of resources.

For Mega-Constellations: Edge Computing and Safety Management Based on Blockchain Technology

In mega-constellation Communication Systems, efficient routing algorithms and data transmission technologies are employed to ensure fast and reliable data transfer. However, the limited computational resources of satellites necessitate the use of edge computing to enhance secure communication.While edge computing reduces the burden on cloud computing, it introduces security and reliability challenges in open satellite communication channels. To address these challenges, we propose a blockchain architecture specifically designed for edge computing in mega-constellation communication systems. This architecture narrows down the consensus scope of the blockchain to meet the requirements of edge computing while ensuring comprehensive log storage across the network. Additionally, we introduce a reputation management mechanism for nodes within the blockchain, evaluating their trustworthiness, workload, and efficiency. Nodes with higher reputation scores are selected to participate in tasks and are appropriately incentivized. Simulation results demonstrate that our approach achieves a task result reliability of 95% while improving computational speed.

A Self-Attention Based Dynamic Resource Management for Satellite-Terrestrial Networks

The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power support,which is an important development direction of future communications.In this paper,we take into account a multi-scenario network model under the coverage of low earth orbit(LEO)satellite,which can provide computing resources to users in faraway areas to improve task processing efficiency.However,LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex,which makes the extraction of state information a daunting task.Therefore,we explore the dynamic resource management issue pertaining to joint computing,communication resource allocation and power control for multi-access edge computing(MEC).In order to tackle this formidable issue,we undertake the task of transforming the issue into a Markov decision process(MDP)problem and propose the self-attention based dynamic resource management(SABDRM)algorithm,which effectively extracts state information features to enhance the training process.Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.

Resource management at the network edge for federated learning

Federated learning has been explored as a promising solution for training machine learning models at the network edge,without sharing private user data.With limited resources at the edge,new solutions must be developed to leverage the software and hardware resources as the existing solutions did not focus on resource management for network edge,specially for federated learning.In this paper,we describe the recent work on resource manage-ment at the edge and explore the challenges and future directions to allow the execution of federated learning at the edge.Problems such as the discovery of resources,deployment,load balancing,migration,and energy effi-ciency are discussed in the paper.

Multiuser computational imaging encryption and authentication with OFDM-assisted key management

Single-pixel imaging(SPI)enables an invisible target to be imaged onto a photosensitive surface without a lens,emerging as a promising way for indirect optical encryption.However,due to its linear and broadcast imaging principles,SPI encryption has been confined to a single-user framework for the long term.We propose a multi-image SPI encryption method and combine it with orthogonal frequency division multiplexing-assisted key management,to achieve a multiuser SPI encryption and authentication framework.Multiple images are first encrypted as a composite intensity sequence containing the plaintexts and authentication information,simultaneously generating different sets of keys for users.Then,the SPI keys for encryption and authentication are asymmetrically isolated into independent frequency carriers and encapsulated into a Malus metasurface,so as to establish an individually private and content-independent channel for each user.Users can receive different plaintexts privately and verify the authenticity,eliminating the broadcast transparency of SPI encryption.The improved linear security is also verified by simulating attacks.By the combination of direct key management and indirect image encryption,our work achieves the encryption and authentication functionality under a multiuser computational imaging framework,facilitating its application in optical communication,imaging,and security.

A Method for Trust Management in Cloud Computing: Data Coloring by Cloud Watermarking

With the development of Internet technology and human computing, the computing environment has changed dramatically over the last three decades. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtuMized resources are provided as services. With virtualization technology, cloud computing offers diverse services （such as virtual computing, virtual storage, virtual bandwidth, etc.） for the public by means of multi-tenancy mode. Although users are enjoying the capabilities of super-computing and mass storage supplied by cloud computing, cloud security still remains as a hot spot problem, which is in essence the trust management between data owners and storage service providers. In this paper, we propose a data coloring method based on cloud watermarking to recognize and ensure mutual reputations. The experimental results show that the robustness of reverse cloud generator can guarantee users＇ embedded social reputation identifications. Hence, our work provides a reference solution to the critical problem of cloud security.

A New Mobility Management and Routing Scheme for Mobile Computing Environments

Mobility management is an essential component in enabling mobile hosts to move seamlessly from one location to another while maintaining the packet routing efficiency between the corresponding hosts. One of the concerns raised with the internet engineering task force(IETF)mobile IP standard is the excessive signaling generated for highly mobile computers. This paper introduces a scheme to address that issue by manipulating the inherent client-server interaction which exists in most applications to provide the correspondent host with the current mobile host binding. To evaluate the performance of the scheme, typical internet application sessions involving a mobile host is simulated and the signaling and routing costs are examined. Results show a substantial reduction in the mobility management overhead as well as the total cost of delivering packets to the mobile host.

Adaptation Management of Mountain Tourism Service: The Case of the Source Regions of the Yangtze and Yellow River

Mountain areas are often rich in ecological diversity and recreational opportunities. Mountain tourism is thought to be an effective and important means for maintaining and expanding rural economies and, thus, improving the living conditions of rural societies. As mountain tourism service research is a professional field with several disciplines involved, a multi-disciplinary management pIatform is needed and it facilitates participation in sustainable mountain development by diverse stakeholders. With the source regions of the Yangtze and the Yellow River as a case study, this paper presents a conceptual framework for an adaptation management of mountain tourism services according to technical, policy, social and economic dimensions. The framework is based on a vulnerability assessment of mountain ecosystems, and can serve as a reference for the development of tourism service in other mountain areas.

Study on Management and Control of Nonpoint Source Pollution from Urban Surface Runoff

Urbanization is the dominant form of land-use change in terms of impacts on water quality, hydrology, physical proper- ties of watersheds and their nonpoint source (NPS) pollution po- tential at present. Urbanization has changed the source, process and sink of urban NPS pollution, especially raised the pollution load of urban runoff NPS in receiving water. Urban runoff pollu- tion is a hot spot of research on NPS. This paper analyzed type, source and harm of the NPS pollutants of urban runoff and its influence on the receiving water. Through estimating NPS pollu- tion load of urban runoff and summarizing the law and character- istics of urban runoff NPS systemically, study on management and control of urban runoff NPS pollution was focused on the applica- tion of BMPs (best management practices). It is a fresh method- ology that management and control on NPS pollution from urban surface runoff was analyzed by methods of landscape ecology, environmental economics and environmental management. The paper provided a scientific reference for mitigating urban water environment pressure and an effective method for management and control of NPS pollution from urban surface runoff..

Optimal Resource Allocation in Fog Computing for Healthcare Applications

In recent years,the significant growth in the Internet of Things(IoT)technology has brought a lot of attention to information and communication industry.Various IoT paradigms like the Internet of Vehicle Things(IoVT)and the Internet of Health Things(IoHT)create massive volumes of data every day which consume a lot of bandwidth and storage.However,to process such large volumes of data,the existing cloud computing platforms offer limited resources due to their distance from IoT devices.Consequently,cloudcomputing systems produce intolerable latency problems for latency-sensitive real-time applications.Therefore,a newparadigm called fog computingmakes use of computing nodes in the form of mobile devices,which utilize and process the real-time IoT devices data in orders of milliseconds.This paper proposes workload-aware efficient resource allocation and load balancing in the fog-computing environment for the IoHT.The proposed algorithmic framework consists of the following components:task sequencing,dynamic resource allocation,and load balancing.We consider electrocardiography(ECG)sensors for patient’s critical tasks to achieve maximum load balancing among fog nodes and to measure the performance of end-to-end delay,energy,network consumption and average throughput.The proposed algorithm has been evaluated using the iFogSim tool,and results with the existing approach have been conducted.The experimental results exhibit that the proposed technique achieves a 45%decrease in delay,37%reduction in energy consumption,and 25%decrease in network bandwidth consumption compared to the existing studies.

Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data center.Smart city benefitted from offloading to edge point.Consider a mobile edge computing(MEC)network in multiple regions.They comprise N MDs and many access points,in which everyMDhasM independent real-time tasks.This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)algorithm.The proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system cost.In addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted resources.The TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading decision-making.Finally,the SGO algorithm is used for the parameter tuning of the DBN model.The simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

Resource management and job scheduling of China earthquake grid experiment system: Construction of resource management and job dynamic scheduling model ProRMJS

Grid technique is taken as the third generation internet technology and resource management is the core of it. Aiming at the problems of resource management of CEDAGrid （China Earthquake Disaster Alleviation and Simulation Grid） in its preliminary construction, this paper presents a resource management and job scheduling model： ProRMJS to solve these problems. For platform supposed agreeably each computing node can provide computation service, ProRMJS uses ＂computation pool＂ to support scheduler, and then the scheduler allocates jobs dynamically according to computing capability and status of each node to ensure the stability of the platform. At the same time, ProRMJS monitors the status of job on each node and sets a time threshold to manage the job scheduling. By estimating the computing capability of each node, ProRMJS allocates jobs on demand to solve the problem of supposing each node can finish the job acquiescently. When calculating the computing capability of each node, ProRMJS allows for the various factors that affect the computing capability and then the efficiency of the platform is improved. Finally the validity of the model is verified by an example.

Adaptive Trust Management Framework in P2P Grid Computing

In P2P Grid computing systems, the authorization decision is often tackled by two different trust management methods： policy-based approach, where authorization are built on logical rules and verifiable properties encoded in signed credentials, and reputation-based approach, based on collecting, aggregating and disseminating reputation among the peers. However, the overhead caused by proof of compliance on authorization and the absence of certifying authorities may negate the strong and objective security advantages of policy-based approach, whilst vagueness, complexity and inaccurate characterization caused by reputation evolution may eliminate the quantitative and flexible advantages of reputation-based approach. We propose an adaptive trust management framework, which combines the merit of policy proof and reputation evolution such that authorization is aware of not only the strong and objective security traits, but also the calculability and the availability security traits. Finally, the framework of system is proposed.

Planning Management of Multiagent-based Distributed Open Computing Environment Model

his paper examines planning management problems in a Multiagentbased Distributed Open Computing Environment Model (MDOCEM). First the meaning of planning management in MDOCEM is introduced, and then a formal method to describe the associated task partition problems is presented, and a heuristic algorithm which gives an approximate optimum solution is given. Finally the task coordination and integration of execution results are discussed.

A User Identity Management Protocol for Cloud Computing Paradigm

Cloud computing paradigm is a service oriented system that delivers services to the customer at low cost. Cloud computing needs to address three main security issues: confidentiality, integrity and availability. In this paper, we propose user identity management protocol for cloud computing customers and cloud service providers. This protocol will authenticate and authorize customers/providers in other to achieve global security networks. The protocol will be developed to achieve the set global security objectives in cloud computing environments. Confidentiality, integrity and availability are the key challenges of web services’ or utility providers. A layered protocol design is proposed for cloud computing systems, the physical, networks and application layer. However, each layer will integrate existing security features such as firewalls, NIDS, NIPS, Anti-DDOS and others to prevent security threats and attacks. System vulnerability is critical to the cloud computing facilities;the proposed protocol will address this as part of measures to secure data at all levels. The protocol will protect customers/cloud service providers’ infrastructure by preventing unauthorized users to gain access to the service/facility.

Development of Management System for Regional Pollution Source Based on SuperMap Objects

Based on the integration of C#.net and SuperMap Objects(tool software of component GIS),the management system of regional pollution source is developed.It mainly includes the demand analysis of system,function design,database construction,program design and concrete realization in the management aspect of pollution source.