Container-Based Internet of Vehicles Edge Application Migration Mechanism

Internet of Vehicles(IoV)applications integrating with edge com-puting will significantly drive the growth of IoV.However,the contradiction between the high-speed mobility of vehicles,the delay sensitivity of corre-sponding IoV applications and the limited coverage and resource capacity of distributed edge servers will pose challenges to the service continuity and stability of IoV applications.IoV application migration is a promising solution that can be supported by application containerization,a technology for seamless cross-edge-server application migration without user perception.Therefore,this paper proposes the container-based IoV edge application migration mechanism,consisting of three parts.The first is the migration trigger determination algorithm for cross-border migration and service degra-dation migration,respectively,based on trajectory prediction and traffic awareness to improve the determination accuracy.The second is the migration target decision calculation model for minimizing the average migration time and maximizing the average service time to reduce migration times and improve the stability and adaptability of migration decisions.The third is the migration decision algorithm based on the improved artificial bee colony algorithm to avoid local optimal migration decisions.Simulation results show that the proposed migration mechanism can reduce migration times,reduce average migration time,improve average service time and enhance the stability and adaptability of IoV application services.

An Incentive Mechanism Model for Crowdsensing with Distributed Storage in Smart Cities

Crowdsensing,as a data collection method that uses the mobile sensing ability of many users to help the public collect and extract useful information,has received extensive attention in data collection.Since crowdsensing relies on user equipment to consume resources to obtain information,and the quality and distribution of user equipment are uneven,crowdsensing has problems such as low participation enthusiasm of participants and low quality of collected data,which affects the widespread use of crowdsensing.This paper proposes to apply the blockchain to crowdsensing and solve the above challenges by utilizing the characteristics of the blockchain,such as immutability and openness.An architecture for constructing a crowdsensing incentive mechanism under distributed incentives is proposed.A multi-attribute auction algorithm and a k-nearest neighbor-based sensing data quality determination algorithm are proposed to support the architecture.Participating users upload data,determine data quality according to the algorithm,update user reputation,and realize the selection of perceived data.The process of screening data and updating reputation value is realized by smart contracts,which ensures that the information cannot be tampered with,thereby encouraging more users to participate.Results of the simulation show that using two algorithms can well reflect data quality and screen out malicious data.With the help of blockchain performance,the architecture and algorithm can achieve decentralized storage and tamper-proof information,which helps to motivate more users to participate in perception tasks and improve data quality.

Research on Suspension Gravity Compensation System of Lunar Rover with Magnetic Levitation Servo

In order to overcome the shortcomings of the traditional sling suspension method,such as complex structure of suspension truss,large running resistance,and low precision of position servo system,a gravity compensation method of lunar rover based on the combination of active suspension and active position following of magnetic levitation is proposed,and the overall design is carried out.The dynamic model of the suspension module of microgravity compensation system was established,and the decoupling control between the constant force component and the position servo component was analyzed and verified.The constant tension control was achieved by using hybrid force/position control.The position following control was realized by using fuzzy adaptive PID(proportional⁃integral⁃differential)control.The stable suspension control was realized based on the principle of force balance.The simulation results show that the compensation accuracy of constant tension could reach more than 95%,the position deviation was less than 5 mm,the position deviation angle was less than 0.025°,and the air gap recovered stability within 0.1 s.The gravity compensation system has excellent dynamic performance and can meet the requirements of microgravity simulation experiment of lunar rover.

A Fairness Resource Allocation Algorithm for Coverage and Capacity Optimization in Wireless Self-Organized Network

To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SON). In this paper, a novel CCO scheme is proposed to maximize utility function of the integrated coverage and capacity. It starts with the analysis on the throughput proportional fairness(PF) algorithm and then proposes the novel Coverage and Capacity Proportional Fairness(CCPF) allocation algorithm along with a proof of the algorithms convergence. This proposed algorithm is applied in a coverage capacity optimization scheme which can guarantee the reasonable network capacity by the coverage range accommodation. Next, we simulate the proposed CCO scheme based on telecom operators' real network data and compare with three typical resource allocation algorithms: round robin(RR), proportional fairness(PF) and max C/I. In comparison of the PF algorithm, the numerical results show that our algorithm increases the average throughput by 1.54 and 1.96 times with constructed theoretical data and derived real network data respectively.

Service Function Chain Orchestration across Multiple Clouds

Network function virtualization is a new network concept that moves network functions from dedicated hardware to software-defined applications running on standard high volume severs. In order to accomplish network services, traffic flows are usually processed by a list of network functions in sequence which is defined by service function chain. By incorporating network function virtualization in inter-data center(DC) network, we can use the network resources intelligently and deploy network services faster. However, orchestrating service function chains across multiple data centers will incur high deployment cost, including the inter-data center bandwidth cost, virtual network function cost and the intra-data center bandwidth cost. In this paper, we orchestrate SFCs across multiple data centers, with a goal to minimize the overall cost. An integer linear programming(ILP) model is formulated and we provide a meta-heuristic algorithm named GBAO which contains three modules to solve it. We implemented our algorithm in Python and performed side-by-side comparison with prior algorithms. Simulation results show that our proposed algorithm reduces the overall cost by at least 21.4% over the existing algorithms for accommodating the same service function chain requests.

Service Scheduling Based on Edge Computing for Power Distribution IoT

With the growing amounts of multi-micro grids,electric vehicles,smart home,smart cities connected to the Power Distribution Internet of Things(PD-IoT)system,greater computing resource and communication bandwidth are required for power distribution.It probably leads to extreme service delay and data congestion when a large number of data and business occur in emergence.This paper presents a service scheduling method based on edge computing to balance the business load of PD-IoT.The architecture,components and functional requirements of the PD-IoT with edge computing platform are proposed.Then,the structure of the service scheduling system is presented.Further,a novel load balancing strategy and ant colony algorithm are investigated in the service scheduling method.The validity of the method is evaluated by simulation tests.Results indicate that the mean load balancing ratio is reduced by 99.16%and the optimized offloading links can be acquired within 1.8 iterations.Computing load of the nodes in edge computing platform can be effectively balanced through the service scheduling.

An Image Classification Method Based on Deep Neural Network with Energy Model

The development of deep learning has revolutionized image recognition technology.How to design faster and more accurate image classification algorithms has become our research interests.In this paper,we propose a new algorithm called stochastic depth networks with deep energy model(SADIE),and the model improves stochastic depth neural network with deep energy model to provide attributes of images and analysis their characteristics.First,the Bernoulli distribution probability is used to select the current layer of the neural network to prevent gradient dispersion during training.Then in the backpropagation process,the energy function is designed to optimize the target loss function of the neural network.We also explored the possibility of using Adam and SGD combination optimization in deep neural networks.Finally,we use training data to train our network based on deep energy model and testing data to verify the performance of the model.The results we finally obtained in this research include the Classified labels of images.The impacts of our obtained results show that our model has high accuracy and performance.

A Blockchain Based Mobile Crowdsensing Market

Mobile crowdsensing(MCS)is an emerging pattern which means task initiators attract mobile users sensing with their own devices by some platforms.MCS could exploit idle resources in low cost,while it has lots of flaws,which impede its developments.First,isolations between different MCS systems leads to wastage of social resources.What’s more,current MCS always operate in a centralized way,which causes it vulnerable and unbelievable.Blockchain is a promising technology which could supply a credible and transparent environment.This paper construct a blockchain based MCS market and design smart contract for its operation.In our design,platform breaks isolation by blockchain,task initiators and mobile users manage their tasks by smart contract and bargain price with distributed algorithm.By this way,resource could be exploited better,and the market could be more fair.What’s more,the paper analyzes Walrasian Equilibrium(WE)in the market,and details how to deploy MCS in blockchain.Evalution results shows that Equilibrium could be found.

QoS-Aware and Resource-Efficient Dynamic Slicing Mechanism for Internet of Things

With the popularization of terminal devices and services in Internet of things(IoT),it will be a challenge to design a network resource allocation method meeting various QoS requirements and effectively using substrate resources.In this paper,a dynamic network slicing mechanism including virtual network(VN)mapping and VN reconfiguration is proposed to provide network slices for services.Firstly,a service priority model is defined to create queue for resource allocation.Then a slice including Virtual Network Function(VNF)placement and routing with optimal cost is generated by VN mapping.Next,considering temporal variations of service resource requirements,the size of network slice is adjusted dynamically to guarantee resource utilization in VN reconfiguration.Additionally,load balancing factors are designed to make traffic balanced.Simulation results show that dynamic slicing mechanism not only saves 22%and 31%cost than static slicing mechanism with extending shortest path(SS_ESP)and dynamic slicing mechanism with embedding single path(DS_ESP),but also maintains high service acceptance rate.

CMLP: Exploiting Caches at Multiple Levels of Proxies to Enhance Seamless Mobility Support in Information-Centric Networks

The recent evolution of the Internet towards "Information-centric" transfer modes has renewed the interest in exploiting proxies to enhance seamless mobility. In this work, we focus on the case of multiple levels of proxies in ICN architectures, in which content requests from mobile subscribers and the corresponding items are proactively cached to these proxies at different levels. Specifically, we present a multiple-level proactive caching model that selects the appropriate subset of proxies at different levels and supports distributed online decision procedures in terms of the tradeoff between delay and cache cost. We show via extensive simulations the reduction of up to 31.63% in the total cost relative to Full Caching, in which caching in all 1-level neighbor proxies is performed, and up to 84.21% relative to No Caching, in which no caching is used. Moreover, the proposed model outperforms other approaches with a flat cache structure in terms of the total cost.

A DRL-Based Container Placement Scheme with Auxiliary Tasks

Container is an emerging virtualization technology and widely adopted in the cloud to provide services because of its lightweight,flexible,isolated and highly portable properties.Cloud services are often instantiated as clusters of interconnected containers.Due to the stochastic service arrival and complicated cloud environment,it is challenging to achieve an optimal container placement(CP)scheme.We propose to leverage Deep Reinforcement Learning(DRL)for solving CP problem,which is able to learn from experience interacting with the environment and does not rely on mathematical model or prior knowledge.However,applying DRL method directly dose not lead to a satisfying result because of sophisticated environment states and huge action spaces.In this paper,we propose UNREAL-CP,a DRL-based method to place container instances on servers while considering end to end delay and resource utilization cost.The proposed method is an actor-critic-based approach,which has advantages in dealing with the huge action space.Moreover,the idea of auxiliary learning is also included in our architecture.We design two auxiliary learning tasks about load balancing to improve algorithm performance.Compared to other DRL methods,extensive simulation results show that UNREAL-CP performs better up to 28.6%in terms of reducing delay and deployment cost with high training efficiency and responding speed.

An Improved Frag-Shells Algorithm for Data Cube Construction Based on Irrelevance of Data Dispersion

On-Line Analytical Processing (OLAP) is based on pre-computation of data cubes, which greatly reduces the response time and improves the performance of OLAP. Frag-Shells algorithm is a common method of precomputation.However, it relies too much on the data dispersion that it performs poorly, when confronts large amount of highly disperse data. As the amount of data grows fast nowadays, the efficiency of data cube construction is increasingly becoming a significant bottleneck. In addition, with the popularity of cloud computing and big data, MapReduce framework proposed by Google is playing an increasingly prominent role in parallel processing. It is an intuitive idea that MapReduce framework can be used to enhance the efficiency of parallel data cube construction. In this paper, by improving the Frag-Shells algorithm based on the irrelevance of data dispersion, and taking advantages of the high parallelism of MapReduce framework, we propose an improved Frag-Shells algorithm based on MapReduce framework. The simulation results prove that the proposed algorithm greatly enhances the efficiency of cube construction.

历史的皱褶与展开:从《开明书店一九五〇—五二年编辑出版计划》说起

上海解放后,开明书店积极追求进步,率先申请公私合营,并制订了《开明书店一九五〇—五二年编辑出版计划》为将来发展做准备。计划涵盖了未来三年内的服务路线、出版量、造货成本等三部分内容,反映了开明书店上下对出版社未来的定位与期待。由于出版业的体制化改造,该计划未能实施,1953年开明书店与青年出版社合并组建为中国青年出版社,它最终成为行业变迁的见证。

A feedback control mechanism for adaptive SLO maintenance in dynamic service level management

With the increasing scale of information technology(IT)service system,traditional thresholdbased static service level management(SLM)solution appears to be inadequate to meet current increasingly management requirement of SLM.Due to the stochastic service request rate,the random inherent failure and load surge of IT devices during service operating stage of large scaled IT system,service level objective(SLO)maintenance issue has become a realistic and important issue in dynamic SLM.This paper proposes a closed-loop feedback control mechanism to adaptively maintain SLO that service provider(SP)guaranteed at service operation stage.The mechanism can automatically tune the capacity of IT infrastructure according to service performance dispersion and reduce SLO violations.Considering that the tuning operations also affect service performance,fuzzy control is applied to alleviate the negative effect caused by tuning operations.In the dynamic SLM system that is applied with this mechanism compared with the traditional threshold-based solution,it is proved that the amount of SLO violations obviously decreases,the reliability of the service system increases relatively,and the resource utilization of IT infrastructure is optimized.