Abstract In order to comprehensively evaluate the achievements of the 'Belt and Road' in integrated transportation, researchers need to optimize the method of generating evaluation indices and construct the framework structure of the 'Belt and Road' transportation index system. This paper used GDELT database as data source and obtained full text data of English news in 25 countries along ‘the Belt and Road’. The paper also introduced the topic model, combined with the unsupervised method (latent Dirichlet allocation, LDA) and the supervision method (labeled LDA) to mine the topics contained in the news data. It constructed the transportation development model and analyzed the development trend of transportation in various countries. The study found that the development trend of transportation in the countries along the line is unbalanced, which can be divided into four types: rapid development type, stable development type, slow development type and lagging development type. The method of this paper can effectively extract temporal and spatial variation of news events, discover potential risks in various countries, support real-time and dynamic monitoring of the social development situation of the countries along the border and provide auxiliary decision support for implementation of the ‘the Belt and Road’ initiative, which has important application value.

Special Section on AI-Empowered Internet of Things for Smart CitiesOne of the key enablers for smart cities is the Internet of Things (IoT), which can exploit stateof-the-art communication technologies to support advanced services.However, IoT devices and management systems are typically manufactured by multiple vendors with multiple processes and standards.Furthermore, these devices will generate large amounts of data from different sources and types of sensors, which cannot be effectively processed by traditional methods.In addition, the unstructured data in IoT plays an important role for building smart cities, whereas transmitting and processing these unstructured data consume substantial energy.Therefore, the data transmission and processing in IoT for smart cities should be performed in a more intelligent manner.Recently, artificial intelligence (AI) has emerged as a powerful weapon that supports very efficient data analysis and makes accurate decisions on service provisions of various kinds.Combining IoT with advanced AI technology can make the city smarter.AI-empowered solutions, such as deep learning and reinforcement learning, can better process the vast amounts of real-time data that stream from IoT devices to support intelligent services for smart cities.In light of this potential, this special section provides a venue to comprehensively cover algorithms, frameworks, technologies, and applications of AI-empowered IoT for smart cities.After a strict peer review, 10 papers were selected for publication in this special issue.Details of these selected papers are as follows."Power Side-Channel Analysis of RNS GLV ECC Using Machine and Deep Learning Algorithms" by Mehrabi et al. proposes an RNS (Residue Number system) GLV (Gallant Lambert Vanstone) elliptic curve cryptography core that is immune to machine-learning-and deep-learning-based sidechannel attacks.The experimental analysis confirms that the proposed crypto core does not leak any information about the private key, and therefore it is suitable for hardware implementations."ISDNet: AI-Enabled Instance Segmentation of Aerial Scenes for Smart Cities" by Garg et al. proposes ISDNet (Instance Segmentation and Detection Network), a novel network to perform instance segmentation and object detection on visual data captured by UAVs.This work enables aerial image analytics for various needs in a smart city.ISDNet makes use of effective anchors to accommodate varying object scales and sizes.The proposed method obtains state-of-the-art results in the aerial context.In "Robust Facial Image Super-Resolution by Kernel Locality-Constrained Coupled-Layer Regression," Gao et al. design a robust context-patch facial image super-resolution scheme via a KLC2LR (Kernel Locality-constrained Coupled-layer Regression) scheme to obtain the desired high-resolution version from the acquired low-resolution image.The compared experiments in the noisy and noiseless cases have verified that the suggested methodology performs better than many existing predominant facial image super-resolution methods.

Server consolidation and application consolidation through virtualization are key performance optimizations in cloud-based service delivery industry. In this paper, we argue that it is important for both cloud consumers and cloud providers to understand the various factors that may have significant impact on the performance of applications running in a virtualized cloud. This paper presents an extensive performance study of network I/O workloads in a virtualized cloud environment. We first show that current implementation of virtual machine monitor (VMM) does not provide sufficient performance isolation to guarantee the effectiveness of resource sharing across multiple virtual machine instances (VMs) running on a single physical host machine, especially when applications running on neighboring VMs are competing for computing and communication resources. Then we study a set of representative workloads in cloud-based data centers, which compete for either CPU or network I/O resources, and present the detailed analysis on different factors that can impact the throughput performance and resource sharing effectiveness. For example, we analyze the cost and the benefit of running idle VM instances on a physical host where some applications are hosted concurrently. We also present an in-depth discussion on the performance impact of colocating applications that compete for either CPU or network I/O resources. Finally, we analyze the impact of different CPU resource scheduling strategies and different workload rates on the performance of applications running on different VMs hosted by the same physical machine.

User-perceived performance continues to be the most important QoS indicator in cloud-based data centers today. Effective allocation of virtual machines (VMs) to handle both CPU intensive and I/O intensive workloads is a crucial performance management capability in virtualized clouds. Although a fair amount of researches have dedicated to measuring and scheduling jobs among VMs, there still lacks of in-depth understanding of performance factors that impact the efficiency and effectiveness of resource multiplexing and scheduling among VMs. In this paper, we present the experimental research on performance interference in parallel processing of CPU-intensive and network-intensive workloads on Xen virtual machine monitor (VMM). Based on our study, we conclude with five key findings which are critical for effective performance management and tuning in virtualized clouds. First, colocating network-intensive workloads in isolated VMs incurs high overheads of switches and events in Dom0 and VMM. Second, colocating CPU-intensive workloads in isolated VMs incurs high CPU contention due to fast I/O processing in I/O channel. Third, running CPU-intensive and network-intensive workloads in conjunction incurs the least resource contention, delivering higher aggregate performance. Fourth, performance of network-intensive workload is insensitive to CPU assignment among VMs, whereas adaptive CPU assignment among VMs is critical to CPU-intensive workload. The more CPUs pinned on Dom0 the worse performance is achieved by CPU-intensive workload. Last, due to fast I/O processing in I/O channel, limitation on grant table is a potential bottleneck in Xen. We argue that identifying the factors that impact the total demand of exchanged memory pages is important to the in-depth understanding of interference costs in Dom0 and VMM.

Virtualization can provide significant benefits in virtualized data centers by enabling efficient and effective live migration to ensure service level agreement(SLA). Most of existing studies make decision on which bad virtual machines (VMs) should be migrated to which appropriate physical machines (PMs) in terms of resource utilizations. However, migration actions may degrade migrated application performance due to extra CPU and bandwidth consumptions. Furthermore, negative performance interferences amongst applications scheduled to the same PM may arise given the poor performance isolations of VMs on a PM. We design and implement a VM migration selection system with less migration costs and application performance interferences, called LVMCI (Live Virtual machine Migration with less Costs and application Interference). We propose a migration cost evaluation model to analyze quantitatively the aspects (i.e. throughput and response latency) of application performance degradation. Dirty rate and frequent dirty rate are two key factors that affect iteration time and downtime. We implement a tool that measures these parameters before VMs are migrated. We distinguish the performance degradation of migrated applications caused by memory iteration phase and stop-and-copy phase, which helps to select VM migrated. Besides that, we propose a performance interference model which helps to select the destination PM. The experimental results show that our system can estimate memory iteration time and downtime with high accuracy, and ensures a high level of SLAs by minimizing performance degradation during migration process and performance interference among co-located VMs at the destination PM.

Live migration of virtual machines(VMs) is widely used for system management in virtualized servers. When the loads increase and SLAs of some applications are violated, dynamic migration of virtual machines across physical machines (PMs) has the potential to ensure a high level of meeting the SLAs. Because of consuming extra CPU and bandwidth, application performance may be degraded during the migration process. However, different applications have different performance degradation. We design and implement a VM migration selection method that decides which VMs should be migrated. It can not only eliminate resouce competition on the PM, but also have less performance degradation during the migration process. We propose a performance degration-aware model to analyze applications' performance degradation which is directly sensitive to users. We analyze migration source code and find that memory size, dirty rate and frequent dirty rate are key factors that affect iteration time and downtime. We implement a tool that measures dirty rate and frequent dirty rate before VMs are migrated. we make a distinction between memory iteration phrase and stop-and-copy phrase owing to different performance degradation. The experimental results show that our method is effective.

Virtualization is a key technology for cloud based data centers to implement the vision of infrastructure as a service (IaaS) and to promote effective server consolidation and application consolidation. However, current implementation of virtual machine monitor does not provide sufficient performance isolation to guarantee the effectiveness of resource sharing, especially when the applications running on multiple virtual machines of the same physical machine are competing for computing and communication sources. In this paper, we present our performance measurement study of network I/O applications in virtualized cloud. We focus our measurement based analysis on performance impact of co-locating applications in a virtualized cloud in terms of throughput and resource sharing effectiveness, including the impact of idle instances on applications that are running concurrently on the same physical host. Our results show that by strategically co-locating network I/O applications, performance improvement for cloud consumers can be as high as 34%, and the cloud providers can achieve over 40% performance gain.

Server virtualization offers the ability to slice large, underutilized physical servers into smaller, parallel virtual machines (VMs), enabling diverse applications to run in isolated environments on a shared hardware platform. Effective management of virtualized cloud environments introduces new and unique challenges, such as efficient CPU scheduling for virtual machines, effective allocation of virtual machines to handle both CPU intensive and I/O intensive workloads. Although a fair number of research projects have dedicated to measuring, scheduling, and resource management of virtual machines, there still lacks of in-depth understanding of the performance factors that can impact the efficiency and effectiveness of resource multiplexing and resource scheduling among virtual machines. In this paper, we present our experimental study on the performance interference in parallel processing of CPU and network intensive workloads in the Xen Virtual Machine Monitors (VMMs). We conduct extensive experiments to measure the performance interference among VMs running network I/O workloads that are either CPU bound or network bound. Based on our experiments and observations, we conclude with four key findings that are critical to effective management of virtualized cloud environments for both cloud service providers and cloud consumers. First, running network-intensive workloads in isolated environments on a shared hardware platform can lead to high overheads due to extensive context switches and events in driver domain and VMM. Second, co-locating CPU-intensive workloads in isolated environments on a shared hardware platform can incur high CPU contention due to the demand for fast memory pages exchanges in I/O channel. Third, running CPU-intensive workloads and network-intensive workloads in conjunction incurs the least resource contention, delivering higher aggregate performance. Last but not the least, identifying factors that impact the total demand of the exchanged memory pages is critical to the in-depth understanding of the interference overheads in I/O channel in the driver domain and VMM.

With Cloud Computing gaining tremendous importance in the recent past, understanding low-level implications of the cloud infrastructure becomes necessary. One of the key technologies deployed in large Cloud infrastructures namely the Amazon EC2 for providing isolation and separate protection domains for multiple clients is virtualization. Therefore, identifying the performance bottlenecks in a virtualized setup and understanding the implications of workload combinations and resource configurations on the overall I/O performance helps both the cloud providers in managing their infrastructure efficiently and also their customers by means of better performance. In this paper we present the measurement results of detailed experiments conducted on a virtualized setup focusing on the storage I/O performance. We categorize our experimental evaluation into four components, each of which presenting some significant factors that affect storage I/O performance. Our experimental results can be useful for cloud application developers to tune their applications for better I/O performance and for the cloud service providers to make more effective decisions on resource provisioning and workload scheduling.

Trust has been recognized as an important factor for information security in open and dynamic environments, such as Internet applications, p2p systems etc. On the basis of analyzing existing trust management systems, this paper proposes a Distributed Trust Management based on Authorizing Negotiation (DTMAN). DTMAN presents a number of innovative features. First, it can authorize strangers by using authorizing negotiation, so it is very suitable for open and dynamic environments. Second, a high efficient algorithm for compliance checking is developed to support DTMAN, whose time complexity and space complexity are both O(n) (where n is the cardinality of the set of authorization credentials). The experimental result shows that the algorithm of DTMAN is more efficient than others.

Trust has been recognized as an important factor for grid security. This paper proposes a decentralized automated trust negotiation framework, FORT, to establish trust relationship between service providers and service requesters in grids. FORT presents many innovative features. First, FORT is decentralized, so it scales well and is well-suited for large-scale grids. Second, FORT refines its policy language with attribute constraint, so it can provide the support for effective protection of sensitive information of the two negotiation parties and flexible limitation of delegation range. Last, we employ multithreaded technology to speed up negotiation. This paper depicts the implementation of FORT and designs experiments to evaluate its performance. Experimental results show that FORT can effectively protect sensitive services at the cost of little performance of systems and is scalable.

Trust and reputation are important decision-making factors in cooperative computing systems. It is a fundamental but challenging task for reputation systems to estimate entity's reputation accurately and efficiently in distributed environment. We propose EntityTrust, a global reputation mechanism in cooperative computing systems. EntityTrust introduces a new direct feedback metrics to reflect the dynamic feature of trust. Besides that, an entity's feedback credibility can affect its reputation in a direct way. Experimental results show that EntityTrust can improve the accuracy and efficiency of evaluation for global reputation. Moreover, it can effectively combat malicious behaviors presented in the cooperative communities.

It has been a fundamental but challenging problem to gain assurance of the trustworthiness of service providers or requesters and ensure their interests. We present the formal definition of trust management, and then propose a trust management, CASTTE, to secure sensitive services and requesters in grids. CASTTE verifies access trust by using trust negotiation so as to protect sensitive services, and protects sensitive information of the two negotiators effectively by using a negotiation strategy based on protection tree. Furthermore, we utilize trust force to specify provision trust and apply trust force to service selection. This paper implements CASTTE and designs experiments to evaluate its performance. The experimental results show that it can not only protect sensitive services at the cost of little performance of systems, but also identify good services from bad ones effectively.

The monitoring service is a crucial component in the service oriented grid infrastructure. Objectives and requirements for the grid monitoring system have been summarized in this paper. To cope with the dynamic and large-scale nature of the grid, a scalable distributed monitoring system is proposed, which can support easy and rapid deployment of monitoring services in the grid environments. The BitTorrent protocol is adopted to facilitate the distribution and automated deployment of the monitoring system. The deployment of monitoring service for the newly joined node and the update of the deployed components can be initiated by the nodes within the system to reduce the administrator's participation. The maintenance cost and human errors might happen in the deployment process can be reduced. With the help of the peer-to-peer networks, our proposed system can automatically adapt to failures in network connections or nodes. Service capacity of our proposed system is given out. Simulation results show that our proposed system supports efficient and rapid deployment to a large scale grid and provides a robust platform to efficiently monitor the grid resources.

With the increase of the computing demand, high performance server clusters are becoming one of the most important computing infrastructures. The current clusters are designed to meet peak load with all the computing resources keeping running. However, this static reservation with full computing resources can not adapt to the time-varying computing requirement, and may incur low resource utilization and needless power consumption when the cluster system is underloaded. In this paper, we present an extensible architecture of cluster management system. This architecture promises a good extensibility by integrating job scheduler and resource manager in loose couple. Concentrating on the power saving of large-scale clusters, we describe the power model of servers, and based on the presented management system architecture, we propose a novel resource management way, adaptive pool based resource management (APRM) method, for adaptive provision of computing resources in accordance with the time- varying workload demand. APRM enables a cost- effective operating by providing dynamic computing capacity with automatic resource control. We validated APRM on the energy efficiency and quality of service (QoS) by simulation measurement, and the results showed that APRM yields significant power saving with little impact on QoS.