Cloud Network Research Articles

Making programmable packet scheduling time-sensitive with a FIFO queue

Time-Sensitive Networking (TSN) is an emerging technology for real-time and non-real-time hybrid networked systems. TSN is standardized by IEEE 802.1 TSN Task Group and is becoming widely used in various scenarios including the cloud network. However, existing programmable packet schedulers such as PIFO, PIEO, and AIFO in programmable switches either lack the ability to express most scheduling algorithms in TSN or introduce intolerable on-chip memory overhead (e.g., strict-priority queues). This makes programmable switches and NICs incapable of providing deterministic forwarding. In this paper, we present AIAO (Admission-In-Admission-Out), a new set of programmable scheduling primitives using just a single FIFO to support typical TSN scheduling algorithms, as well as other popular work-conserving algorithms. AIAO is inspired by AIFO but improves it with a group of high-speed packet ingress/egress admission control triggered by high-precise and globally synchronized time, thus being able to support time-sensitive scheduling. We implement AIAO and evaluate it with FPGA-based TSN switches. The preliminary results show that AIAO guarantees correctness for a typical TSN scheduling algorithm with minimal logic and memory overhead.

Integer Programming Models for Task Scheduling and Resource Allocation in Mobile Cloud Computing

In traditional mobile cloud computing, user tasks are uploaded and processed on a cloud server over the Internet. Due to the recent rapid increase in the number of mobile users connected to the network, due to overload of the Internet communication channels, there are significant delays in the delivery of data processed on cloud servers to the user. Furthermore, it complicates the optimal scheduling of the tasks of many users on cloud servers and the delivery of results. Scheduling is an approach used to reduce the tasks execution time by ensuring a balanced distribution of user tasks on cloud servers. The goal of scheduling is to ensure selection of appropriate resources to handle tasks quickly, taking into account user requirements. Whereas the goal of cloud service providers is to provide users with the required resources through performing effective scheduling so that both the user and the service provider can benefit. The article proposes a scheduling model to reduce processing time, network latency, and power consumption of mobile devices through optimal task placement in the cloudlet network in a mobile cloud computing environment.

A Novel Approach for Block Chain Technology based Cyber Security in Cloud Storage Using Hash Function

Block chain is generic name to describe the technology used by Bitcoin and other digital currency to record and secure transaction. This technology enables a highly accessible ledger with greatly reduced risk for tampering. The dynamic immutable, data ledger makes ideal for real time monitoring of the shipment of goods. Cloud is an important of distributed storage system of networking. Cloud system need for security, storage management, minimize the cloud cost and fast storage could be improved. The security using new most development security system of block chain technology is used to improve the cloud security. The data owner to be uploads the data on web page and access the folder. The user has been accessing the data on cloud storage using encryption and decryption using block chain based cyber security system. the problem for security in cloud storage because data transmission and data sharing, the alternate for security solution using Post Quantum-proof cryptography algorithm is used to improve the encryption and decryption process and more tight security for block chain technology for the cloud system. The SHA-3 512 Hash function algorithm automatically generate the key for data security enhancements of cloud networks. Post Quantum-proof cryptography algorithm has been improved the encryption Performance and reduced the power consumption, and increase the Latency performance, and boost up the security performances. Finally Post Quantum-proof cryptography algorithm for well support for security system of the cloud networks. Even some passwords, which are often cited as the weakest link in cyber security, may not be necessary.

Low Power but High Energy: The Looming Costs of Billions of Smart Devices

Individually, wall-powered Internet of Things devices are small: in form factor, in complexity, in function, and in power draw. However, at scale, and certainly at the scale optimistic forecasters project, these small devices add up to be a big energy problem. Just adding a single two watt sensor to each US building would add to more annual energy consumption than some small countries. Wall-powered IoT devices are also easier to create than their energy-constrained (i.e. battery-powered) counterparts, and marketed as more convenient (no hub required!), leading to their continued growth. Yet, unlike other energy consuming devices, there are no Energy Star (or equivalent) standards for smart devices. Despite having very infrequent active times, they draw power for functions like AC-DC conversion, wireless communication, and wakeup word detection continuously. Further, the discrete nature of devices and siloed nature of IoT ecosystems leads to significant redundancy in IoT devices. We posit that new techniques are needed to reverse this trend. This includes new techniques for auditing devices, systems that leverage existing devices rather than requiring new ones, and architectures that have less reliance on the cloud (and the energy overhead of network usage and cloud compute). The IoT is pitched to improve energy efficiency and reduce users' carbon footprints, but we need a new research agenda to ensure the devices themselves are not the next problem.

TCaS‐VN: A novel V2V communication scheme to improve traffic control and safety in vehicular cloud networks

SummaryRoad accidents and traffic congestion are the unavoidable consequences of the growing number of vehicles on the road. Conventional management systems are not effective in mitigating these issues. However, a range of technologies, including sensors, processors, vehicle‐based resources, and mobile cloud computing, can help solve traffic and safety problems. Vehicular cloud networks, a cutting‐edge technology for the development of intelligent transportation systems, face significant challenges, such as ensuring reliable service providers that maintain stable connections between vehicles. Roadside units (RSUs) play a crucial role in these networks, but they come with several drawbacks, including high costs, imbalanced load distribution, and unreliable connections. To address these issues, we propose the Traffic Control and Safety in Vehicular Networks (TCaS‐VN) algorithm, a cloud‐based sharing services framework for vehicles that eliminates the need for RSUs. This approach offers several advantages, including a twofold improvement in the lifetime of clusters, the elimination of noncluster head nodes, a 54% reduction in overhead, a 58% reduction in service search time, and a success rate of over 95% in service provisioning.

Cyber Crime Detection and Prevention Techniques on Cyber Cased Objects Using SVM and Smote

Conventional cybersecurity employs crime prevention mechanisms over distributed networks. This demands crime event management at the network level where Detection and Prevention of cybercrimes is a must. A new Framework IDSEM has been introduced in this paper to handle the contemporary heterogeneous objects in cloud environment. This may aid for deployment of analytical tools over the network. A supervised machine learning algorithm like SVM has been implemented to support IDSEM. A machine learning technique Like SMOTE has been implemented to handle imbalanced classification of the sample data. This approach addresses imbalanced datasets by oversampling the minority classes. This will help to solve Social Engineering Attacks (SEA) like Phishing and Vishing. Classification mechanisms like decision trees and probability functions are used in this context. The IDSEM framework could minimize traffic across the cloud network and detect cybercrimes maximally. When results were compared with existing approaches, the results were found to be good, leading to the development of a unique SMOTE algorithm.

Blockchain-Secure Mobility Data in Smart Campus

Acquiring knowledge of the patterns of human mobility within a university setting is a critical endeavor that can facilitate the development of effective strategies for future work programs. It is essential to ascertain the positions of campus inhabitants as they engage in daily activities that align with the institution’s work plan framework. Nonetheless, the paramount challenge associated with the presence of personal data on human movement is ensuring the utmost security of this sensitive information. Blockchain technology offers a solution by enabling the safeguarding of personal data through the decentralization of information, wherein individuals act as controllers in a distributed cloud network. In the present study, a straightforward system comprising GPS sensors and a Raspberry Pi is employed to detect personnel’s location data. The SHA256 algorithm is utilized to generate a hash that connects the constituent blocks, thereby significantly enhancing data security. The intricate hash computation is validated through the implementation of proof-of-work, which generates pertinent binary data at an expeditious block mining time of 16.64 milliseconds. This approach effectively thwarts cyberattacks and ensures the maximum protection of data.

An efficient load balancing technique using CAViaR-HHO enabled VM migration and replica management in cloud computing

Cloud computing is immense technology that offers distributed resources to a number of users who are present throughout the world. Cloud model is comprised of numerous virtual machines (VMs) and physical machines (PMs) to carry out user tasks effectively in a parallel manner but in some cases, the demand of the users may be high that resulting in the overloading of PMs and this condition deteriorates the performance of cloud network. For achieving effective virtualization in the cloud paradigm, energy and resource utilization are major properties that should be handled effectively and such properties are accomplished through effective management of workload by distributing load equivalently among VMs. By doing so, resource utilization of the network is enhanced and it only requires minimum energy to process the tasks. Numerous load-balancing algorithms have been introduced earlier to maintain load in a cloud environment, nevertheless, they are devoid of mitigating the number of task migrations. Hence, this research proposes an effective load balancing algorithm and replica management method using the proposed Conditional Autoregressive Value at risk by Regression Quantiles-Horse Herd Optimization (CAViaR-HHO) model. Here, the load is computed by considering some factors like Central Processing Unit (CPU), Million Instructions per Second (MIPS), bandwidth, memory, and frequency. VM migration and replica migration is effectively carried out using the proposed CAViaR-HHO model. Meanwhile, the developed method is devised by integration of Conditional Autoregressive Value at risk by Regression Quantiles (CAViaR) with Horse Herd Optimization Algorithm (HOA). However, the proposed CAViaR-HHO has achieved a load with a minimum value of 0.109, capacity with a maximum value of 0.591, resource utilization with a maximum value of 0.467, and minimum cost of 0.344. Using setup-1, when the number of tasks is 500, the capacity of the proposed method is 5.58%, 3.89%, 2.87%, 1.52%, and 0.67% higher when compared to the existing approaches namely, C-FDLA, K-means clustering + LB, Adaptive starvation threshold, EIMORM, and Dynamic replica creation method.

Secure-fault-tolerant efficient industrial internet of healthcare things framework based on digital twin federated fog-cloud networks

The Industrial Internet of Healthcare Things (IIoHT) is the emerging paradigm in digital healthcare. Context-aware healthcare sensors, local intelligent watches, healthcare devices, wireless communication technologies, fog, and cloud computing are all parts of the IIoHT used in healthcare. The ubiquitous healthcare services it provides to its users in practice. However, the current IIoHT healthcare frameworks have security and failure issues in mobile fog and cloud networks where they are spread out. This paper presents the secure, fault-tolerant IIoHT Framework based on digital twin (DT) federated learning-enabled fog-cloud models. The DT is an effective technology that makes virtual copies of servers at different locations. DT integrated with federated learning inside the fog and cloud environments, where the failure of tasks and execution improved for healthcare sensor data. The study aims to reduce processing time and the risk of task failure. The study presents the Secure and Fault-Tolerant Strategies (SFTS)-enabled IIoHT framework that optimizes wearable sensor data and executes it with the minimum offloading and processing delays. Simulation results show that the proposed work minimized the security risk by 40%, failure risk of tasks risk by 50%, and the training and testing time by 39% for sensor data during the execution of mobile fog cloud networks.

Secure blockchain assisted Internet of Medical Things architecture for data fusion enabled cancer workflow

In today’s digital healthcare landscape, numerous clinical institutions collaborate to enhance healthcare quality in a ubiquitous fog and cloud environment. Data fusion plays a vital role in healthcare collaboration, enabling the integration of diverse healthcare sources. The primary advantage is the improvement of healthcare treatments and the availability of services throughout the network. However, despite these benefits, there is room for improvement in addressing various security issues regarding collaboration among clinical healthcare institutions to meet data fusion requirements. The primary challenge lies in processing lung cancer workflow data fusion on heterogeneous nodes while ensuring security in fog cloud networks. As a result, security emerges as a critical issue in the digital healthcare system operating within this ubiquitous environment. We present the secure Blockchain Internet of Medical Things (BIoMT) architecture for lung cancer workflow data fusion processing in fog cloud networks. The BIoMT architecture introduces the Blockchain Data Fusion Secure (BDFS) algorithm framework, which consists of task scheduling and blockchain validation schemes. The study aims to minimize the makespan of the lung workflow tasks based on security and deadline constraints in fog and cloud networks. We consider security at an advanced level, where runtime ransomware attacks are also identified in fog and cloud networks. Simulation results demonstrate that BDFS outperforms all existing BIoMT architectures regarding workflow processing while adhering to the specified constraints. Overall, the BDFS algorithm presented in the BIoMT architecture provides an efficient and secure solution for lung cancer workflow data fusion in fog cloud networks, contributing to the advancement of digital healthcare systems in a ubiquitous environment.

Advanced set containment deep learned Rabin certificateless signcryption for secured transmission with big data in cloud

SummaryCloud computing is distributed type of technology to offers distant services by the internet to manage, access, and store data on their server. The cloud server manages and distributes large amounts of data. During large amounts of complex structured and unstructured data transactions, in order to protect the data and cloud resources from harmful activities by using the main task of the security system. Therefore, a novel intrusion detection system is required in cloud networks to detect malicious activity and improve secure data transmission. In this paper, a novel technique called advanced set containment deep learned Rabin certificateless signcryption (ADRES) technique is employed to secure data transmission for achieving better confidentiality, and integrity, in cloud computing. The ADRES technique includes three major phases namely Registration and key generation, signcryption, and unsigncryption. At the first phase, the user registers details to the cloud server using secure transmission in big data analytics. The cloud server generates the private and public keys for each registered user. In the second phase, the signcryption process is determined by cloud user that encrypts and generates the signature by using the Rabin cryptosystem. Then encrypted data is stored in a cloud server with minimum space complexity. Finally, the unsigncryption is performed by using signature verification and decryption. First, the signature verification is performed by applying an Advanced set containment Deep belief neural network for identifying the authorized or unauthorized user (i.e., attacker). The authorized user decrypts data using the Rabin cryptosystem to achieve the original data. Thus, secure data transmission is obtained through improved data confidentiality and integrity. Simulation of the proposed ADRES technique is performed with respect to data confidentiality level, data integrity rate, and computation time, space complexity with several numbers of data. Experimental results reported ADRES obtains the improved data transmission security with better confidentiality rate by 7%, integrity by 7%, and lesser time 16% and space complexity by 18% than the conventional methods.

Physics-informed graph neural networks accelerating microneedle simulations towards novelty of micro-nano scale materials discovery

To commercialize the micro-nano needle as a drug delivery or biosensing platform, it is essential to validate the effectiveness and reliability of the piercing process for each needle material composition. However, proper microneedle material selection and fabrication requires sophisticated and costly semiconductor technology. This work aims to accelerate the material selection process of microneedles, focusing on polymeric materials due to their biocompatibility and flexibility. In this study, simulations of microneedles with various materials are used to generate training and testing data for a physics-informed machine learning model to predict von Mises stress distribution on microneedles of new materials. The training dataset is comprised of results from fifteen different materials. Different machine learning models are used, such as traditional tree-based, neural network, point cloud network, and graph-based models. Random-index selection is utilized to reduce the required number of data points by an order of magnitude. The graph attention network model is the best-performing model for predicting the von Mises stress of microneedles, with a mean square error of 8.3×10−5MPa. The resulting model only requires 7 ms to evaluate a new microneedle material, significantly faster than practical fabrication in a laboratory. The models also successfully handle data with a decimal scale obtained from microstructure simulations and predict physical behavior such as stress curves.

IMPACT OF HIGH RADIO FREQUENCY SATELLITE OSCILLATIONS ON INITIATING EARTHQUAKES

Advanced satellite technology is worse than all the disasters on Earth due to the lack of dynamic security. The study of earthquakes is the most innovative research of all man-made disasters - no doubt, but there are new horizons of wonder for the thoughtful. Earthquakes are increasing unexpectedly in different countries of the world. People of most countries are worried about sudden earthquakes, but no one is able to reveal the main secret of these earthquakes. Many are blaming nature for this earthquake. Through this survey the exact origin of the matter has been presented to the whole world with proper evidence. Research shows that climate criminals are misusing advanced satellite technology to cause these earthquakes. According to research, misuse of advanced satellite technology causes artificial earthquakes at specific GPS locations on Earth, resulting in severe damage to humans, animals, plants and other objects. The intensity of these earthquakes is so intense that everyone is surprised. Studies have shown that the misuse of satellite technology has caused massive damage in man-made technological earthquake at a particular tectonic plate and other disasters. Due to these sudden earthquake, human suffering increases, standing institutions are demolished, road communication systems are ruptured, crop lands and residential areas are destroyed, individuals lose their lives. The study revealed that artificial earthquakes are caused by sudden radio-waves and strong oscillations of variable magnitude anywhere on earth. The study also showed the climate criminals use various fake messages and wireless tracking to influence policy-makers, the public and the media, as can be doubted in this study, All citizens and administrations should be aware and vigilant to prevent the rise of earthquake terror. The research shows that using advanced satellite technology, earthquakes can be created in time on any tectonic plate on the Earth. Moreover, the question remains that when technology was not invented, but earthquakes occurred - which were natural. Humans now generate artificial oscillations at specific GPS locations through earthquake simulation coding by multiplying the magnetosphere of previous natural earthquakes. The study shows that the EMMAST (Earthquake Mitigation and Management through Advanced Satellite Technology) model will make disaster mitigation systems safer and more efficient, saving millions of lives. The study on earthquakes is unique with world- class scientific research, which will open many research gateways for future generations.

PointDE: Protein Docking Evaluation Using 3D Point Cloud Neural Network.

Protein-protein interactions (PPIs) play essential roles in many vital movements and the determination of protein complex structure is helpful to discover the mechanism of PPI. Protein-protein docking is being developed to model the structure of the protein. However, there is still a challenge to selecting the near-native decoys generated by protein-protein docking. Here, we propose a docking evaluation method using 3D point cloud neural network named PointDE. PointDE transforms protein structure to the point cloud. Using the state-of-the-art point cloud network architecture and a novel grouping mechanism, PointDE can capture the geometries of the point cloud and learn the interaction information from the protein interface. On public datasets, PointDE surpasses the state-of-the-art method using deep learning. To further explore the ability of our method in different types of protein structures, we developed a new dataset generated by high-quality antibody-antigen complexes. The result in this antibody-antigen dataset shows the strong performance of PointDE, which will be helpful for the understanding of PPI mechanisms.

ABLA: Application-Based Load-Balanced Approach for Adaptive Mapping of Datacenter Networks

Cloud-based services are growing more rapidly than ever, and so does the management challenge on their providers’ side. Cloud-based datacenter networks are built with nodes of huge processing power, connected by high bandwidth capacities to carry their interior traffic requirements. However, such cloud networks still have limits that are imposed not necessarily by their physical components, but by the schemes of resource management being deployed. Traditionally, for an institute to provide services, it needs to have its own datacenter facility that interconnects its servers through a topology that matches its desired administrative policies and scaling objectives. With the theme of cloud-based IaaS, such datacenter topologies can be created virtually over the cloud. Nowadays, a significant part of those institutes who provide us with our daily services have their infrastructures hosted over cloud ones. Therefore, resources of such cloud networks need to be efficiently utilized, in order to keep their performance and hosting prices competitive. A typical datacenter network mainly consists of server nodes and network links. Besides the resources of the server nodes, the network bandwidth resources are considered a crucial key determinant for the whole datacenter performance. Indeed, a server without sufficient bandwidth capacities is almost useless. Proposals in the literature present schemes for resource utilization on either side of the problem at a time: the nodes or the links. Working in isolation can never deliver efficient mapping solutions. ABLA is an Application-Based, and Load balancing Approach for adaptive mapping proposal. ABLA’s methodology tackles both sides of the datacenter, the nodes and links. It starts by (1) breaking down the node’s resource requirement for the requested applications to be hosted over the virtual server machines besides (2) reading the topological connectivity and bandwidth requirements for each virtual node to all other nodes in the virtual datacenter topology. Compared to other models in the literature, the simulation results show that our proposed ABLA model provides for complete mapping services via load-balanced hosting networks. This allows for competitive hosting prices, with higher performance and service satisfaction rates.

A solution for resource allocation through complex systems in fog computing for the internet of things

To manage the IoT resources to process the massive amount of collected data in complex systems, Allocation of tasks to nodes to increase user satisfaction has been noticed by researchers. In allocating tasks, the necessity of a powerful auxiliary platform is felt. Cloud and fog networks are two suitable options for performing and completing tasks on the Internet of Things. In the cloud network, due to offloading tasks to remote computing centers, problems such as increasing complete time, increasing traffic in the network, and high energy consumption for the Internet of Things network occur. The fog network solves the problem of completing tasks due to reducing the geographical distance between Internet of Things devices and processor nodes. The devices in the fog network cooperate with the Internet of Things network’s diverse and variable nodes with time computing, storage, and processing resources. In this heterogeneous environment, the non-cooperation and dishonesty of malicious fog nodes through misrepresentation of shared resources and non-completion of tasks can disrupt the performance of the Internet of Things network. As a result, two fundamental challenges in the heterogeneous fog network include providing resources to allocate tasks and recognizing valid fog nodes. This paper focuses on creating a resource provider network to allocate tasks to authentic fog nodes with appropriate resources. When an Internet of Things node faces a shortage of resources and link prediction, it can form a resource supply network with neighboring nodes to solve the two stated challenges. For this purpose, in the first stage, efforts are made to select the most appropriate options among the neighboring nodes based on specific criteria. In the second step, reliable nodes are identified based on a trust mechanism, including resource trust and entity trust. In the last step, tasks are divided among reliable devices with sufficient resources. The evaluation of the proposed method in the OMNET++ simulator shows that the trust mechanism can lead to the identification of reliable sources and, as a result, reduce the time to complete tasks.

Digital Twin Power Grid Oriented Mobile Edge Network Resource Allocation Model

The integration of vast quantities of clean energy causes intense impact on the power grid reliability. Digital Twin can be used to handle the high‐growth data flow generated in the power grid, and thus accelerate its digitalization process to fulfill the clean energy consumption target. As the traditional central cloud network structure can no longer effectively support the operation of digital twin power grid, the mobile edge network can be used as a reasonable substitute. To support the information interaction process of the mobile edge network, considering total profit, time delay and energy consumption, this paper proposes the mobile edge network communication and computing resource joint allocation model. On the basis, the GA‐PSO algorithm is proposed to solve the resource allocation optimization function. The feasibility and effectiveness of the proposed model is verified by the related simulation experiment. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

3D facial attractiveness prediction based on deep feature fusion

AbstractFacial attractiveness prediction is an important research topic in the computer vision community. It not only contributes to the development of interdisciplinary research in psychology and sociology, but also provides fundamental technical support for applications like aesthetic medicine and social media. With the advances in 3D data acquisition and feature representation, this paper aims to investigate the facial attractiveness from deep learning and three‐dimensional perspectives. The 3D faces are first processed to unwrap the texture images and refine the raw meshes. The feature extraction networks for texture, point cloud, and mesh are then delicately designed, considering the characteristics of different types of data. A more discriminative face representation is derived by feature fusion for the final attractiveness prediction. During network training, the cyclical learning rate with an improved range test is introduced, so as to alleviate the difficulty in hyperparameter setting. Extensive experiments are conducted on a 3D FAP benchmark, where the results demonstrate the significance of deep feature fusion and enhanced learning rate in cooperatively facilitating the performance. Specifically, the fusion of texture image and point cloud achieves the best overall prediction, with PC, MAE, and RMSE of 0.7908, 0.4153, and 0.5231, respectively.

Federated auto-encoder and XGBoost schemes for multi-omics cancer detection in distributed fog computing paradigm

The digital healthcare paradigm has significantly improved based on distributed fog and cloud networks for cancer detection with multiple classes in recent years. The paradigm allows the collecting and training of cancer data on various computing nodes to make optimal cancer detection with their classes. In paradigm, multi-omics approaches (such as RNA, miRNA, and methylation) and machine learning techniques have achieved remarkable results in predicting cancer with different features. However, the digital healthcare paradigm for cancer detection concerning infrastructure still faces challenges related to security, execution delay, and improving the accuracy of cancer prediction in existing studies. These limitations affect the overall cancer detection results with more accuracy. In this paper, we are handling the research mentioned above limitations. We present a new paradigm for cancer detection with more accuracy, less processing delay, and more security based on fog cloud heterogeneous computing nodes. We present the Multi-Cancer Multi-Omics Clinical Dataset Laboratories (MCMOCL) Schemes to predict multi-cancers with multiple classes and consist of federated learning, auto-encoder, and XGBoost methods. The main objective of this study is to improve accuracy, reduce execution delay, and improve the security among heterogeneous cancer clinics in the architecture. Simulation results show that MCMOCL outperformed all existing machine models in terms of accuracy by 98% , processing delay by 61%, and security for the multi-classes types of cancers in heterogeneous fog cloud paradigm.

Management and Orchestration of Edge Computing for IoT: A Comprehensive Survey

With the development of telecommunication technologies and the proliferation of network applications in the past decades, the traditional cloud network architecture becomes unable to accommodate such demands due to the heavy burden on the backhaul links and long latency. Therefore, edge computing, which brings network functions close to end-users by providing caching, computing and communication resources at network edges, turns into a promising paradigm. Benefit from its nature, edge computing enables emerging scenarios and use cases like Augmented Reality (AR) and Internet of Things (IowT). However, it also creates complexities to efficiently orchestrate heterogeneous services and manage distributed resources in the edge network. In this survey, we make a comprehensive review of the research efforts on service orchestration and resource management for edge computing. We first give an overview of edge computing, including architectures, advantages, enabling technologies and standardization. Next, a comprehensive survey of state-of-the-art techniques in the management and orchestration of edge computing is presented. Subsequently, the state-of-the-art research on the infrastructure of edge computing is discussed in various aspects. Finally, open research challenges and future directions are presented as well.