Computing Services Research Articles

MEC Network Resource Allocation Strategy Based on Improved PSO in 5G Communication Network

Relying on features such as high-speed, low latency, support for cutting-edge technology, internet of things, and multimodality, 5G networks will greatly contribute to the transformation of Web 3.0. In order to realize low-latency and high-speed information exchange in 5G communication networks, a method based on the allocation of network computing resource in view of edge computing model is proposed. The method first considers three computing modes: local device computing, local mobile edge computing (MEC) server computing, and adjacent MEC server computing. Then, a multi-scenario edge computing model is further constructed for optimizing energy consumption and delay. At the same time, the encoding-decoding mode is used to optimize PSO algorithm and combined with the improvement of fitness function, which can effectively support the communication network to achieve reasonable allocation of resources, ensuring efficiency of information exchange in the network. In the end, the results show that when the number of users is 500, the method can complete the task assignment within 44s.

A security optimization scheme for data security transmission in UAV-assisted edge networks based on federal learning

To solve the problem that mobile users cannot handle all data tasks by themselves due to the huge amount of data at present, this paper proposes a four-layer model of efficient and secure multiuser multitask computing offload. Since the user needs to offload the data to the server for processing through the wireless communication link, and users have mobility, it is difficult to achieve dynamic updates of user status and data information based on traditional cloud computing data processing methods, and it is difficult to ensure security during data unloading. In this paper, we studied how to realize the efficient and safe transmission of mobile users' data tasks when deploying Mobile Edge Computing (MEC) servers with artificial intelligence to assist in data processing on Unmanned Aerial vehicles (UAVs). First of all, to ensure the efficient use of computing resources, we use compression algorithms to reduce transmission overhead. Secondly, to enhance the security of the data transmission process, we have designed a security layer in the system model, which mainly uses secure multiparty technology to encrypt sensitive data, and a data transmission algorithm based on artificial intelligence is designed to assure the efficient and secure transmission of data in the network. Finally, through the analysis of the simulation results, we can know that the proposed strategy in this study has better performance than other existing strategies.

Value co-creation in a high-performance computing (HPC) service ecosystem: opportunities for European SMEs

PurposeThis paper aims to develop a conceptual framework as a valuable investigative tool for future empirical studies examining stakeholders’ interactions within a high-performance computing (HPC) service ecosystem and identifying the enablers for better value co-creation under various service provisioning contexts.Design/methodology/approachThis conceptual paper follows a theory adaptation research design that systematically examines the literature on a substantive topic encompassing HPC technology adoption and usage by European small and medium-sized enterprises (SMEs). Furthermore, it introduces theoretical perspectives from service innovation research as its methodological approach (method theory) to develop a theoretical framework for value co-creation within an HPC service ecosystem.FindingsThe conceptual framework presented in this paper identifies the critical resource elements of SMEs, which can potentially be integrated with other stakeholders’ resources and complement the limited internal resources of SMEs for enhanced value co-creation within an HPC service ecosystem.Research limitations/implicationsThe conceptualisation of an HPC service ecosystem and the potential of value co-creation through integrating stakeholders’ resources, presented in this paper, underscore further empirical research in this direction.Practical implicationsFindings from this paper can inform European policymakers for regional development to frame regional strategies and policies that enable SMEs to better uptake HPC services. Service providers can revisit their business models to reconfigure their organisational resources for enhanced value proposition and co-creation.Social implicationsThis study advances the agenda of transformative service research that aims uplifting changes and societal well-being through a more inclusive and sustainable value co-creation among a wider set of stakeholders within the service ecosystem.Originality/valueThe paper addresses an under-investigated research gap by examining SMEs’ adoption of HPC services and opportunities for higher value co-creation using a service ecosystem approach based on multi-actor interaction, collaboration and resource integration.

B 3 : Backdoor Attacks against Black-box Machine Learning Models

Backdoor attacks aim to inject backdoors to victim machine learning models during training time, such that the backdoored model maintains the prediction power of the original model towards clean inputs and misbehaves towards backdoored inputs with the trigger. The reason for backdoor attacks is that resource-limited users usually download sophisticated models from model zoos or query the models from MLaaS rather than training a model from scratch, thus a malicious third party has a chance to provide a backdoored model. In general, the more precious the model provided (i.e., models trained on rare datasets), the more popular it is with users. In this article, from a malicious model provider perspective, we propose a black-box backdoor attack, named B 3 , where neither the rare victim model (including the model architecture, parameters, and hyperparameters) nor the training data is available to the adversary. To facilitate backdoor attacks in the black-box scenario, we design a cost-effective model extraction method that leverages a carefully constructed query dataset to steal the functionality of the victim model with a limited budget. As the trigger is key to successful backdoor attacks, we develop a novel trigger generation algorithm that intensifies the bond between the trigger and the targeted misclassification label through the neuron with the highest impact on the targeted label. Extensive experiments have been conducted on various simulated deep learning models and the commercial API of Alibaba Cloud Compute Service. We demonstrate that B 3 has a high attack success rate and maintains high prediction accuracy for benign inputs. It is also shown that B 3 is robust against state-of-the-art defense strategies against backdoor attacks, such as model pruning and NC.

A Network Fault Prediction-Based Service Migration Approach for Unstable Mobile Edge Environment

How to perform efficient service migration in a mobile edge environment has become one of the research hotspots in the field of service computing. Most service migration approaches assume that the mobile edge network on which the migration depends is stable. However, in practice, these networks often fluctuate greatly due to the fault of edge devices, resulting in unexpected service interruptions during the migration process. Besides, most of the existing solutions do not consider the migration cost and path selection in the event of edge network fault. Aiming at the above problems, we propose a service migration approach based on network fault prediction (SMNFP) for mobile edge environment. The SMNFP method first introduces the software-defined network as a global controller, which is used to monitor and collect the changing of the edge network and schedule the migration tasks. Second, a network fault prediction model based on Wide&Deep model is proposed to predict the upcoming faults in the network according to the alarm information of network equipment. Finally, the service migration problem is constructed as a Markov decision process, and a fault penalty function is introduced to avoid faulty nodes, together with the deep Q-learning method to solve the migration strategy. Simulation experiments are conducted on the public metro network fault dataset, and results show the proposed method can effectively predict network faults and complete service migration.

Joint computation offloading and resource allocation in space-air-terrestrial integrated networks for IoT Applications

Internet of Things (IoT) devices can reduce their energy consumption by computation offloading. However, IoT devices located in areas without deployed ground communication facilities face significant challenges in computation offloading. For this reason, we propose the space-air-terrestrial integrated networks (SATINs) and design a three-tier computing framework for providing computing services to IoT devices. In the computing framework, device's task can be computed locally, on mobile edge computing (MEC) servers in the air layer, or on cloud servers in the ground. In this article, we jointly optimize the computation offloading decisions of tasks and computing resource allocation of MEC servers. We aim at minimizing the total cost of executing tasks while satisfying both the time constraints of tasks and capacity constraints of MEC servers. And the total cost includes the energy cost of IoT devices and the usage cost of servers. Since the computation offloading decisions are binary variables, the joint optimization problem is a mixed integer nonlinear programming (MINLP) problem and is NP-hard. To tackle this problem, we use relaxation technique and Majorize-minimize (MM) method to transform the optimization problem into a series of convex problems for solving. Moreover, we propose a distributed algorithm based on Lagrange dual decomposition method with low time complexity. Experimental results demonstrate that our proposed distributed algorithm can effectively reduce the total cost of the system compared with other benchmark algorithms.

Real-time Classification of Fetal Status Based on Deep Learning and Cardiotocography Data.

This study uses convolutional neural networks (CNNs) and cardiotocography data for the real-time classification of fetal status in the mobile application of a pregnant woman and the computer server of a data expert at the same time (The sensor is connected with the smartphone, which is linked with the web server for the woman and the computer server for the expert).Data came from 5249 (or 4833) cardiotocography traces in Anam Hospital for the mobile application (or the computer server). 150 data cases of 5-minute duration were extracted from each trace with 141,001 final cases for the mobile application and for the computer server alike. The dependent variable was fetal status with two categories (Normal, Abnormal) for the mobile application and three categories (Normal, Middle, Abnormal) for the computer server. The fetal heart rate served as a predictor for the mobile application and the computer server, while uterus contraction for the computer server only. The 1-dimension (or 2-dimension) Resnet CNN was trained for the mobile application (or the computer server) during 800 epochs.The sensitivity, specificity and their harmonic mean of the 1-dimension CNN for the mobile application were 94.9%, 91.2% and 93.0%, respectively. The corresponding statistics of the 2-dimension CNN for the computer server were 98.0%, 99.5% and 98.7%. The average inference time per 1000 images was 6.51 micro-seconds.Deep learning provides an efficient model for the real-time classification of fetal status in the mobile application and the computer server at the same time.

Adoption of Cloud Computing in the organizations: a bibliometric analysis of this technology within a digital transformation context

Purpose: The purpose of this study is identify and analyze articles on the adoption of Cloud Computing services in the business field. The study intends to demonstrate the increasing relevance and impact of Cloud Computing technology on business processes.Design/methodology/approach: The study adopted a Bibliometric Review methodology to collect and analyze data. A total of 1,330 articles were collected from the Scopus (Elsevier) database, and various aspects such as authors, journals, and countries were considered. The analysis includes the use of maps to visualize the co-occurrence of terms, co-citation of references, and bibliographic coupling.Findings: The investigation reveals that the adoption of Cloud Computing services in the business environment is a rapidly growing area of research. The study provides an overview of the theme and highlights the significance of Cloud Computing technology in enhancing business processes’ efficiency.Research limitations/implications: The study’s limitations include relying solely on articles available in the Scopus (Elsevier) database and focusing on the period between 2008 and 2020. Future research can expand the analysis by including a broader range of databases and considering a more recent timeframe.Practical implications: The findings of this study have practical implications for businesses, as they highlight the benefits of adopting Cloud Computing services. The technology offers low cost and flexible use, contributing to increased efficiency in business processes.Social implications: The adoption of Cloud Computing services can have significant social impacts by enabling businesses to provide enhanced value to their clients. The technology’s efficiency and flexibility contribute to improved service delivery and customer satisfaction.Originality/value: This study contributes to the advancement of knowledge in the field of Cloud Computing adoption in the business field. The bibliometric analysis provides a comprehensive overview of the research landscape and highlights the key contributions and trends in this area.

Deep Learning in the Ubiquitous Human-Computer Interactive 6G Era: Applications, Principles and Prospects.

With the rapid development of enabling technologies like VR and AR, we human beings are on the threshold of the ubiquitous human-centric intelligence era. 6G is believed to be an indispensable cornerstone for efficient interaction between humans and computers in this promising vision. 6G is supposed to boost many human-centric applications due to its unprecedented performance improvements compared to 5G and before. However, challenges are still to be addressed, including but not limited to the following six aspects: Terahertz and millimeter-wave communication, low latency and high reliability, energy efficiency, security, efficient edge computing and heterogeneity of services. It is a daunting job to fit traditional analytical methods into these problems due to the complex architecture and highly dynamic features of ubiquitous interactive 6G systems. Fortunately, deep learning can circumvent the interpretability issue and train tremendous neural network parameters, which build mapping relationships from neural network input (status and specific requirements of a 6G application) to neural network output (settings to satisfy the requirements). Deep learning methods can be an efficient alternative to traditional analytical methods or even conquer unresolvable predicaments of analytical methods. We review representative deep learning solutions to the aforementioned six aspects separately and focus on the principles of fitting a deep learning method into specific 6G issues. Based on this review, our main contributions are highlighted as follows. (i) We investigate the representative works in a systematic view and find out some important issues like the vital role of deep reinforcement learning in the 6G context. (ii) We point out solutions to the lack of training data in 6G communication context. (iii) We reveal the relationship between traditional analytical methods and deep learning, in terms of 6G applications. (iv) We identify some frequently used efficient techniques in deep-learning-based 6G solutions. Finally, we point out open problems and future directions.

Reinforcement Learning for Reducing the Interruptions and Increasing Fault Tolerance in the Cloud Environment

Cloud computing delivers robust computational services by processing tasks on its virtual machines (VMs) using resource-scheduling algorithms. The cloud’s existing algorithms provide limited results due to inappropriate resource scheduling. Additionally, these algorithms cannot process tasks generating faults while being computed. The primary reason for this is that these existing algorithms need an intelligence mechanism to enhance their abilities. To provide an intelligence mechanism to improve the resource-scheduling process and provision the fault-tolerance mechanism, an algorithm named reinforcement learning-shortest job first (RL-SJF) has been implemented by integrating the RL technique with the existing SJF algorithm. An experiment was conducted in a simulation platform to compare the working of RL-SJF with SJF, and challenging tasks were computed in multiple scenarios. The experimental results convey that the RL-SJF algorithm enhances the resource-scheduling process by improving the aggregate cost by 14.88% compared to the SJF algorithm. Additionally, the RL-SJF algorithm provided a fault-tolerance mechanism by computing 55.52% of the total tasks compared to 11.11% of the SJF algorithm. Thus, the RL-SJF algorithm improves the overall cloud performance and provides the ideal quality of service (QoS).

Synchronism system for generating ultrasonic images of complex geometry pieces using industrial robots

This paper presents a synchronism system that has been designed to facilitate the generation of ultrasonic images of pieces with complex geometry through the use of industrial robots. Modern robotic manipulators and, more specifically, industrial robotic arms integrated with server computers, sensors and actuators have revolutionized the way automated non-destructive testing is performed. Currently there are commercial industrial robots that have the precision, speed and repetitiveness in their movements that make them suitable for use in numerous non-destructive testing inspections whose designs are carried out by small and medium sized specialized companies. Automatic ultrasonic inspection of complex parts remains one of the most difficult challenges according to the specific and increasingly exigent demands of the markets. The closed configuration of these complex robotic arms makes it difficult to maintain adequate synchronism between the movement of the robot and the acquisition of the data, making it difficult to generate ultrasonic images consistent with the geometry of the part. This is a serious problem in the inspection of aerospace components where high quality is necessary to assess the condition of the inspected component. In this paper, we present an autonomous independent external system that provides control signals to synchronize the ultrasound system with the robot trajectories without needing to access its position in real time. A methodology to obtain the timing pattern for a given part inspected with a given robotic system will also be presented here.

Blockchain-Enabled Internet of Things (IoT) Applications in Healthcare: A Systematic Review of Current Trends and Future Opportunities

The use of advanced computer technology in the healthcare industry has the potential to improve patient care and therapeutic results. The goal of this project is to improve data security, privacy, and decentralisation in healthcare by integrating blockchain and Internet of Things (IoT) technologies. The adoption of IoT devices makes it possible to gather and analyse patient sensory data in real–time; however centralised processing and storage present problems such as data manipulation and privacy issues. The study investigates the creation of a decentralised IoT-based e-healthcare system that takes these issues into account by utilising blockchain technology. In addition, the paper also emphasises how blockchain use has advanced smart contract technologies. Smart contracts provide safe user authentication for IoT device access, assuring responsibility, traceability, and data integrity. The study investigates the potentially game-changing applications of blockchain technology in healthcare, such as enhanced data interoperability, patient-cantered care, reduced administrative procedures, and increased transaction transparency. The report also highlights the significance of blockchain in managing pharmaceutical supply chains, considering the essential influence on patient welfare and safety. Effective management is essential in the healthcare business because supply chain interruptions or breaches can have serious implications. The present level of research in blockchain-enabled IoT applications for healthcare is examined comprehensively using the PRISMA framework and records from the Scopus database. The three most important research topics are cloud computing, fog computing, and medical services. The results highlight the important role that blockchain-enabled IoT applications have played in enhancing data security and privacy in the healthcare industry. Real-time data gathering, precise diagnoses, individualised treatments, and simplified administrative procedures are all made possible by the integration of blockchain and IoT. Additionally, scalable solutions and insightful data for healthcare decision-making are provided via fog computing, cloud computing, machine learning, and smart contracts.

MagicScaler: Uncertainty-Aware, Predictive Autoscaling

Predictive autoscaling is a key enabler for optimizing cloud resource allocation in Alibaba Cloud's computing platforms, which dynamically adjust the Elastic Compute Service (ECS) instances based on predicted user demands to ensure Quality of Service (QoS). However, user demands in the cloud are often highly complex, with high uncertainty and scale-sensitive temporal dependencies, thus posing great challenges for accurate prediction of future demands. These in turn make autoscaling challenging---autoscaling needs to properly account for demand uncertainty while maintaining a reasonable trade-off between two contradictory factors, i.e., low instance running costs vs. low QoS violation risks. To address the above challenges, we propose a novel predictive autoscaling framework MagicScaler , consisting of a Multi-scale attentive Gaussian process based predictor and an uncertainty-aware scaler. First, the predictor carefully bridges the best of two successful prediction methodologies---multi-scale attention mechanisms, which are good at capturing complex, multi-scale features, and stochastic process regression, which can quantify prediction uncertainty, thus achieving accurate demand prediction with quantified uncertainty. Second, the scaler takes the quantified future demand uncertainty into a judiciously designed loss function with stochastic constraints, enabling flexible trade-off between running costs and QoS violation risks. Extensive experiments on three clusters of Alibaba Cloud in different Chinese cities demonstrate the effectiveness and efficiency of MagicScaler , which outperforms other commonly adopted scalers, thus justifying our design choices.

Development and experimental verification of an IoT sensing system for drive-by bridge health monitoring

Vehicles equipped with various types of sensors have the great potentials to effectively evaluate the health conditions of a population of bridges at a low cost. However, existing drive-by structural health monitoring (SHM) methods acquire vehicle vibration responses offline and export them to a computer for postprocessing. Furthermore, the vehicle trajectory information on the bridge is important for scaling up the drive-by SHM for in situ applications, which is not synchronously measured by existing systems. Therefore, a single-board computer-based IoT sensing system for continuous and real-time drive-by bridge health monitoring is developed in this study. The developed IoT sensing system integrates a triaxial microelectromechanical system (MEMS) accelerometer, temperature sensor, GPS and 4G module on Raspberry Pi 4 Model B. The sensor node can be mounted on a moving vehicle to collect the triaxial acceleration responses, temperature and GPS information. A graphical user interface (GUI) is developed based on the Python Tkinter package to remotely control the sensor node and visualise the collected data in real time. The fast Fourier transform of the measured acceleration responses is performed on the sensor node inboard processor. The raw data are sent to both the cloud server and remote terminal computer through a 4G module. The goal is to provide a low-cost, accurate and scalable sensing system for easy implementation of drive-by bridge health monitoring. The system architecture and workflow of the developed IoT sensing system are presented in detail. A series of experimental tests are conducted to validate the accuracy of the measured acceleration responses and feasibility of using the developed IoT sensing system for drive-by SHM applications.

RANCANGAN JARINGAN LAN MENGGUNAKAN ROUTING PROTOKOL EIGRP DI SMK AL-KHAIRIYAH 1

Local Area Network (LAN) is a group of computers connected in an area that is not so broad in one office or building. The routing protocol makes the network more efficient in exchanging data, the EIGRP routing protocol is a distance vector routing protocol that uses metric calculations like EIGRP. Activities in SMK AL-KHAIRIYAH 1 have used a computer as a tool, and do not have a LAN network. The problem is that data sharing is limited, no computer server connects to make it easier to store data and backup in an organized manner. The data collection techniques used direct observation at SMK Al-Kahiriyah 1 and interviewing the principal. The result of the research is that the LAN network system uses the EIGRP routing protocol that allow all necessary data to stored on the server and facilitate data exchange and processing.

An Overview of Algorithms for Solving Vehicle Routing Problems in the Quantum-Classical Cloud

Introduction. The hope of solving the problem of the avalanche-like growth of requirements for computing power, essential for solving complex routing problems and other problems of combinatorial optimization, relies on the latest quantum computers, in the development of which governments and corporations invest multi-billion investments. The article examines modern routing algorithms and performs their analysis and verification, if the authors of the algorithm provided appropriate test programs. The purpose of the article is to review the current state of development in the field of development of routing algorithms for hybrid quantum-classical clouds, analyze them and propose a classification of algorithms. Results. Modern quantum computers (QCs) make it possible to find approximate solutions to some of mathematical problems faster than classical computers. The inaccuracy of the solutions obtained by the QC is a consequence of physical and technological limitations: calculation errors are caused by thermal noise, a small number of computational elements - qubits and connections between them, which requires the decomposition of the problem and the use of heuristic algorithms. The analysis of approaches to the solution of optimization problems on QC allows us to single out: quantum response and variational search of eigenvalues based on quantum logic gates as the general directions of development of the vast majority of algorithms for solving routing problems. The considered algorithms reduce the vehicle routing problem to a quadratic unconstrained binary optimization problem, which is isomorphic to the Hamilton-Ising model. In this form, the problem is suitable for embedding in QC, which finds an approximate solution that has the best statistical reliability or corresponds to the quantum state with the lowest energy. As a separate class, vehicle routing algorithms for classical computers that use quantum computing to accelerate problem solving can be distinguished. For example, neural networks that calculate weighting factors using QC or an ant algorithm that calculates a pheromone trail in a hybrid cloud. It should be mentioned the quantum-inspired algorithms, which are based on software tools for the simulation of QC and the corresponding libraries and allow creating an effective class of algorithms for solving problems of vehicle routing. Conclusions. Combining hardware quantum annealing with a number of software tools for calculating optimization problems for classical computers in a hybrid quantum-classical cloud service allows to obtain advantages in speed and accuracy of some types of complex optimization problems of a commercial scale, in particular, routing vehicles, which is already bringing substantial profits to a number of corporations. Keywords: vehicle routing problem, quantum computer, annealing, combinatorial optimization, traveling salesman problem, clustering, qubit.

Evaluating Service Computing Framework with Scrum through a Case Study

The Service Computing Framework with Scrum (SCFS) is a framework that combines Services Computing Systems Engineering (SCSE) with the Scrum Framework. This framework was created to produce a system with a good service concept by following the SCSE workflow in an agile manner through the application of Scrum principles. This paper will provide an overview of how to evaluate the SCFS framework using SOA and Agile principles through a case study. The research methodology used in this paper is a Design Science Research Methodology (DSRM). This research results in project evaluations using SCFS with good Service Oriented Architecture (SOA) parameter results. The SCFS framework can also be used in Agile development environments with fairly good parameters of velocity, story points, sprint burn down, release burn up, value delivered, and job satisfaction. In case studies conducted, this framework can help accelerate the release of service systems. The first release on the test only took 34 working days, 18 working days to get the second release, and 21 working days to get the third release. If done as a whole in one cycle, the service system will take approximately 73 working days to be used.

Multi-Key Homomorphic Encryption Scheme with Multi-Output Programmable Bootstrapping

Multi-key Homomorphic Encryption (MKHE) scheme can homomorphically evaluate ciphertexts encrypted by different keys, which can effectively protect the privacy information of data holders in the joint computing of cloud services. Since the first full Homomorphic encryption scheme was proposed, bootstrapping is the only way to realize the arbitrary depth homomorphic computation of MKHE schemes. But bootstrap operation is quite expensive. In order to implement fast bootstrapping in MKHE schemes, previous works proposed multi-key TFHE schemes to implement low-latency bootstrapping and output a univariate function of messages after bootstrapping, called Programmable Bootstrapping (PBS). However, these schemes can only encrypt single-bit messages. PBS only outputs a function. And after a homomorphic operation, a bootstrap is required, which undoubtedly results in an increase in the cost of the whole multi-key homomorphic encryption operation. In this paper, we propose a MKHE scheme for multi-output PBS. For this purpose, we study the encryption method and homomorphic operation steps of MKHE, and add BFV homomorphic encryption multiplication and multi-key ciphertext relinearization. We separate the homomorphic operation from bootstrapping. We homomorphically evaluate test polynomials for multiple functions. In contrast to previous MKHE schemes, we support the output of multiple message-related functions with a single bootstrapping operation on the ciphertext. It is no longer limited to encrypting single-bit plaintext, and an effective ciphertext packaging technology is added. According to the analysis given in this paper, it is known that in the scenario of multi-party joint computation, the proposed scheme can be implemented with less bootstrapping when the same number of functions are homomorphically operated. This will effectively reduce the computational overhead.

A blockchain-based privacy-preserving auditable authentication scheme with hierarchical access control for mobile cloud computing

Blockchain-based authentication mode, a fundamental solution to prevent unauthorized access behavior, gradually becomes a focus in future distributed mobile cloud computing (MCC) services. However, due to the transparent and immutable characteristics of blockchain, users’ access behaviors are facing huge security and privacy threats. Storing the encrypted data on chain is an effective way to address these issues, but access permission confirmation and update in the form of ciphertext is the main bottleneck. To this end, this paper proposes a blockchain-based unified authentication and hierarchical access control scheme for the MCC environment, which provides both privacy protection and auditability. In the proposed scheme, users can access multiple MCC services with different access permissions using a single credential. To protect the privacy of both users and service providers, while still supporting auditability, the data on the public ledger is blinded using Pedersen commitments. Besides, the proposed scheme provides flexible dynamic updating in encrypted form. Theoretical analysis indicates that the proposed scheme can meet various security and privacy requirements in the MCC environment. Compared with related schemes, it has better communication efficiency. Therefore, the proposed scheme is more suitable for the actual MCC environment.

Sistem Keamanan Server Linux CentOS Dengan Metode Port Knock dan RST Cookies

The increase in online-based services makes a security system for server computers increasingly needed. A server computer is a device that must always be available to be accessed anytime and anywhere. Some of the security systems needed for server computers include security for ssh port access for remote server access needs and a security system to protect servers from Denial of Service (DoS) attacks which can make the server down and completely inaccessible. In this study, a security system is proposed for a server computer with the Linux CentOS operating system on a port 22 secure shell(ssh) access system using the port knock method and a security system to prevent Denial of Service (DoS) attacks on server computers using the RST Cookies method. The simulation results from the port 22 Secure Shell (SSH) access experiment for the server computer can work well where port 22 Secure Shell(SSH) will remain closed and cannot be accessed carelessly except by accessing several ports first according to predefined port knocking rules. Likewise, the implementation of a security system with the RST Cookies method works very well to prevent Denial of Service (DoS) attacks and can still keep the server accessible with a good response time of under 1 ms