Redundant Servers Research Articles

A cloud‐edge service offloading method for the metaverse in smart manufacturing

SummaryWith the development of artificial intelligence, cloud‐edge computing and virtual reality, the industrial design that originally depends on human imagination and computing power can be transitioned to metaverse applications in smart manufacturing, which offloads the services of metaverse to cloud and edge platforms for enhancing quality of service (QoS), considering inadequate computing power of terminal devices like industrial sensors and access points (APs). However, large overhead and privacy exposure occur during data transmission to cloud, while edge computing devices (ECDs) are at risk of overloading with redundant service requests and difficult central control. To address these challenges, this paper proposes a minority game (MG) based cloud‐edge service offloading method named COM for metaverse manufacturing. Technically, MG possesses a distribution mechanism that can minimize reliance on centralized control, and gains its effectiveness in resource allocation. Besides, a dynamic control of cut‐off value is supplemented on the basis of MG for better adaptability to network variations. Then, agents in COM (i.e., APs) leverage reinforcement learning (RL) to work on MG history, offloading decision, QoS mapping to state, action and reward, for further optimizing distributed offloading decision‐making. Finally, COM is evaluated using a variety of real‐world datasets of manufacturing. The results indicate that COM has 5.38% higher QoS and 8.58% higher privacy level comparing to benchmark method.

Budget-Constrained Optimal Deployment of Redundant Services in Edge Computing Environment

With the development of multi-access edge computing (also called mobile edge computing, MEC), more and more service-based applications are deployed to edge servers in order to ensure desired quality of service (QoS), especially in the scenario of Internet of things (IoT). In edge environment, how to reasonably deploy application services emerges as a challenging problem due to the limited resources, heterogeneous servers and different geographical locations of users. Benefiting from its reusability, a single service can be used by multiple applications. Yet only a few studies about the deployment problem in edge environment consider such property of services. This work considers the redundant deployment of reused services by different applications, so as to achieve high QoS. Due to the importance of cost for providers, it aims to minimize transmission cost and network latency under the constraint of deployment budget. This work firstly builds a redundant service deployment model under heterogeneous edge environment, and defines it as a multi-objective optimization problem under a given budget constraint. Then, service priority is calculated to determine redundancy, and K-medoids clustering algorithm based on request frequency filtering is used to conduct edge server selection. It next proposes a genetic algorithm based on priority to obtain an optimized plan. Finally, this work conducts experiments on real-world datasets to prove the superiority of the proposed method over existing ones.

Traffic Organization Service for Maritime Autonomous Surface Ships (MASS) with Different Degrees of Autonomy

Maritime Autonomous Surface Ships (MASS) have been an important direction for the development of intelligent shipping. However, most current international research on MASS focuses on navigation assistance technologies such as perception and decision-making, ignoring MASS’s traffic organization and management. The traffic organization service (TOS) under the e-Navigation strategy also has not researched MASS. In this paper, we propose the notion of on-demand service to MASS with different degrees of autonomy (DoA) and develop a new maritime service (MS) applicable to the MASS with various (DoA) following the e-Navigation technical architecture. We first analyze MASS requirements with different degrees of autonomy in traffic organization to define the service information. Then, based on the traditional TOS, we developed the MASS traffic organization service (MTOS), consisting of an operational architecture, five subsystems, and four services. In particular, we proposed a phased service trigger mechanism to solve the problem of publishing untimely and redundant service information. Tianjin port and Huanghua port were selected as cases study for simulation experiments; the study finding revealed that MTOS could provide standardized, accurate, and efficient traffic organization service for MASS with different degrees of autonomy on demand. The contribution can be applied in the port operation to improve traffic safety.

Lease Management System

This project is based on Lease MANAGEMENT SYSTEM, which, in a strategic alliance with Salesforce.com, develops an application for managing properties. LMS is an application that incorporates all the functions necessary to manage different processes of the real estate sector with the best practices in mind. It is delivered as a service (SaaS) and accessed via Internet from any device. Salesforce.com (www.salesforce.com) provides the technological platform that supports the data storage, the security elements and protocols and the information backup in redundant servers in the EEUU, Europe and Asia, and a series of transversal functions such as messaging, information intelligence system, user profiles, among many others. It manages the relationship with clients and owner, it maintains Management processes, Buildings and units’ information, areas information, and various types of information and update about it. This study aimed to design and develop a cloud-based Lease management system that provides detailed and summarized information on these critical areas to guide LMS administrators in planning and decision-making. It is accessible anywhere anytime as data is stored remotely and made available to users over the internet. This project is user-friendly, usable, performance, and security; and good in terms of robustness. This implies that other owners may adopt this system for more efficient information management and more effective management decisions and to maintain good relationships with Clients LMS allows the management of real estate businesses.

Blockchain Technology for e-Government

Abstract: Information and communication technologies are used by e-Government to deliver governmental services to people and companies instantly, effectively, and efficiently. The majority of current e-government systems are centrally located on redundant servers and databases that may have single points of failure, rendering the systems susceptible to cyber-attacks. Blockchain technology makes it possible to build a decentralised, incredibly secure system without the need for a central authority to monitor transactions. Blockchain technology, which encrypts data and distributes it over the network, improves information security and privacy and gives governments new chances to increase transparency, stop fraud, and foster citizen confidence. In this paper, we primarily cover the overview of blockchain technology, architecture of blockchain, consensus models and the blockchain use cases in e-Government

Architecture Design and Reliability Evaluation of a Novel Software-Defined Train Control System

Communication-based train control (CBTC) has been the prevailing technology of the urban transit signaling system. However, CBTC also faces a few issues to extend and maintain because of its complicated structure. This paper presents a novel urban transit signaling system architecture, software-defined train control (SDTC), which is based on cloud and high-speed wireless communication technology. The core functions of the proposed SDTC, including the onboard controller, are implemented in the cloud platform, with only sensors and input–output (IO) units remaining on the trackside and the train. Because of the scalable framework, the system function can be expanded according to the user’s demand, making signaling as a service possible. With warm standby server redundancy, SDTC has better reliability. Compared with the traditional CBTC architecture, the mean time between failures is improved by 39% by calculating typical project parameters by the Markov model based on some assumptions.

Exponentially‐spider monkey optimization based allocation of resource in cloud

The increasing desire for distributed computing systems has attracted huge interest in memory and computing resources. The cloud provides on-demand access to provide a flexible allocation of resources for reliable services. Therefore, there should be a provision in which resources are accessible to request users to satisfy user needs. In classical techniques, the allocation of resources by satisfying power and Quality-of-Services is a challenging aspect. This paper devises a novel technique for optimal resource allocation, namely, Exponentially Spider Monkey Optimization (E-SMO). Here, the proposed E-SMO is devised by combining Exponential Weighted Moving Average and Spider Monkey Optimization (SMO). Besides, the fitness function is newly devised considering resource utilization and resource cost. After that, the cloud resources employ a switching strategy to reduce power consumption to prevent the switching of redundant servers. For optimal switching, the proposed E-SMO is utilized with other fitness factors that compute the number of applications assigned in the physical machine using the switching state is in OFF condition. Thus, the server switching model is incorporated to activate or deactivate the server when not in use for effective resources utilization. The proposed E-SMO algorithm outperformed other methods with the maximal resource utilization of 0.874, the minimal resource cost of 0.302, and the minimal power usage of 0.275.

Performance and availability evaluation of an smart hospital architecture

Low latency and high availability of resources are essential characteristics to guarantee the quality of services in health systems. Hospital systems must be efficient to prevent loss of human life. Smart hospitals promise a health revolution by capturing and transmitting patient data to doctors in real-time via a wireless sensor network. However, there is a significant difficulty in assessing the performance and availability of such systems in real contexts due to failures not being tolerated and high implementation costs. This paper adopts analytical models to assess the performance and availability of intelligent hospital systems without having to invest in real equipment beforehand. Two Stochastic Petri Net models were proposed to represent intelligent hospital architectures. One model is used to assess performance, and another to assess availability. The models are pretty parametric, making it possible to calibrate the resources, service times, times between failures, and times between repairs. The availability model, for example, allows you to define 48 parameters, allowing you to evaluate a large number of scenarios. The analysis showed that the arrival rate in the performance model is an impacting parameter. It was possible to observe the close relationship between MRT, resource utilization, and discard rate in different scenarios, especially for high arrival rates. Three scenarios were explored considering the second model. The highest availability results were observed in scenario A, composed of server redundancy (local and remote). Such scenario—with redundancy—presented an availability of 99.9199%, that is, 7.01 h/year of inactivity. In addition, this work presents a sensitivity analysis that identifies the most critical components of the architecture. Therefore, this work can help hospital system administrators plan more optimized architectures according to their needs.

Design of Reliable Disaster Recovery System through Integrated Server Redundancy

Even before the September 11 terrorist attacks in the United States in 2001, information systems prepared against disasters in Korea were extremely weak. However, as various domestic and foreign accident cases have occurred, it is recognized that preparations for this are necessary. Accordingly, at present, each institution has prepared and implemented various backup policies to protect the institution's information and data in case of disaster. Therefore, in this paper, we conducted a study to design a more stable and efficient disaster recovery system by building redundancy for server operating in integrated data center. To do this, we analyzed the redundancy design for the integrated disaster recovery server and designed the overall system configuration. Also, the design results were analyzed by testing web server redundancy and switch redundancy. In this paper, the proposed design method for stabilization and efficiency of disaster recovery system is the redundant construction of integrated server and switch. In other words, the disaster recovery system was composed of active storage and standby storage, and data stabilization was promoted through real-time replication of each other. In the existing disaster recovery system, there is a problem in stabilizing replication because there is no monitoring system for internal replication between storage arrays. To solve this problem, we designed a system that replicates all data in active storage to standby storage in real time and monitors the replication status. Therefore, introducing service conversion automation from the main system, which is the method designed in this paper, to the disaster recovery system, improves the stability and reliability of the service of the local governments, so that it is possible to operate a more efficient and advanced disaster recovery system.

Reliability Analysis of a Smart Parking Information System: The Case of Hong Kong

Smart cities are aimed at improving citizens’ life quality. However, they also bring unintended pitfalls of information insecurity, and these have not been studied sufficiently in an empirical way. System reliability as one of the quality attributes of information security, was investigated within the context of smart parking information system due to its close relevance with the ongoing smart city development globally. Through fuzzy fault tree analysis with the aid of structured interviews, it was found that a failure in a central system server may be caused by malicious attacks, human errors, and hardware and software failures. Survey results showed that improving firewalls and renewing recovery plans are necessary to defend against rampant malicious attacks; human errors as a non-technological factor may be reduced by well-planned training programs besides good operational governance and a timely recovery plan. Although system downtime is unavoidable, it can be reduced by proactive solutions including data backup and the provision of redundant servers and power supplies. All in all, an integrated approach is needed to mitigate against system unreliability.

Reliability analysis of (<i>n</i>) clients system under star topology and copula linguistic approach

This paper contrasts with the study of a computer laboratory system with n clients under star topology and k-out-of-n: G scheme. The system has two servers, one is operational and another a redundant server. The main server connects the clients, and failure of the main server acknowledge as a partial failure. The system also has another needed device such as switch and router, and failure in switch and router brings the system in the entire failed situation. The main server bears a partial failure, and the redundant server before the repair of the main server brings the system in a completely failed state. The failure rates of transmuting one state to another state are constant and presume to follow an exponential time distribution. The repairs follow two types of distributions the system analysed by employing the supplementary variable and the Laplace transform. The various traditional measures of system performance through reliability context have computed for different types of failures and repair. Some computations reliability parameters corresponding to assumed values of parameters has illustrated as examples.

Reliability analysis of (<i>n</i>) clients system under star topology and copula linguistic approach

This paper contrasts with the study of a computer laboratory system with n clients under star topology and k-out-of-n: G scheme. The system has two servers, one is operational and another a redundant server. The main server connects the clients, and failure of the main server acknowledge as a partial failure. The system also has another needed device such as switch and router, and failure in switch and router brings the system in the entire failed situation. The main server bears a partial failure, and the redundant server before the repair of the main server brings the system in a completely failed state. The failure rates of transmuting one state to another state are constant and presume to follow an exponential time distribution. The repairs follow two types of distributions the system analysed by employing the supplementary variable and the Laplace transform. The various traditional measures of system performance through reliability context have computed for different types of failures and repair. Some computations reliability parameters corresponding to assumed values of parameters has illustrated as examples.

Optimal Remanufacturing Service Resource Allocation for Generalized Growth of Retired Mechanical Products: Maximizing Matching Efficiency

Maximizing the residual value of retired products and reducing process consumption and resource waste are vital for Generalized Growth-oriented Remanufacturing Services (GGRMS). Under the GGRMS, the traditional product-oriented remanufacturing methods to be changed: the products in GGRMS should be divided into multiple parts for maximizing residual value of different parts. However, this increases the difficulty of resource matching for service activities. To improve the efficiency of resource matching, we first used rough-fuzzy number and structural entropy weighting method to perform a coupling analysis on all service activities in the generalized growth scheme set, and to merge redundant service activities. We then considered the interests of both the service providers and integrators and added flexible impact factors to establish a service resource optimization configuration model, and solved it with the Non-Dominated Sorting Genetic Algorithm (NSGA-II). Finally, we, using a retired manual gearbox an experiment, optimized the service resource allocation for its generalized growth scheme set. The experimental results shown that the overall matching efficiency was increased by 74.56% after merging redundant service activities, showing that the proposed method is suitable for the resource allocation of the generalized growth for complex single mechanical products, and can offer guidelines to the development of RMS.

Directed FCFS infinite bipartite matching

We consider an infinite sequence consisting of agents of several types and goods of several types, with a bipartite compatibility graph between agent and good types. Goods are matched with agents that appear earlier in the sequence using FCFS matching, if such are available, and are lost otherwise. This model may be used for two-sided queueing applications such as ride sharing, Web purchases, organ transplants, and for parallel redundant service queues. For this model, we calculate matching rates and delays. These can be used to obtain waiting times and help with design questions for related service systems. We explore some relations of this model to other FCFS stochastic matching models.

A methodology to assess the availability of next-generation data centers

Cloud data center providers benefit from software-defined infrastructure once it promotes flexibility, automation, and scalability. The new paradigm of software-defined infrastructure helps facing current management challenges of a large-scale infrastructure, and guarantying service level agreements with established availability levels. Assessing the availability of a data center remains a complex task as it requires gathering information of a complex infrastructure and generating accurate models to estimate its availability. This paper covers this gap by proposing a methodology to automatically acquire data center hardware configuration to assess, through models, its availability. The proposed methodology leverages the emerging standardized Redfish API and relevant modeling frameworks. Through such approach, we analyzed the availability benefits of migrating from a conventional data center infrastructure (named Performance Optimization Data center (POD) with redundant servers) to a next-generation virtual Performance Optimized Data center (named virtual POD (vPOD) composed of a pool of disaggregated hardware resources). Results show that vPOD improves availability compared to conventional data center configurations.

An adaptive redundant reservation admission in virtual cloud environment

In cloud platforms, resource reservation service is an effective approach to providing desirable quality of service (QoS) for user applications. However, conventional reservation service might result in lower resource utilisation and higher rejection rate if it is excessively applied. In this paper, we proposed an adaptive redundant reservation strategy, which uses overlapping technique to implement reservation admission and enable resource providers dynamically determine the best redundant degree at runtime. By overlapping a new reservation with an existing one, a request whose reservation requirements can not be satisfied in a traditional way might be accepted. Also, by dynamically determining the best redundant degree, the proposed strategy can obtain optimal trade-offs between performance and reliability for cloud platforms. Experimental results show that the proposed reservation service can bring about remarkably higher resource utilisation and lower rejection rate when using redundant reservation service at the price of a slightly increasing of reservation violations.

Dynamic Cost-Aware Routing of Web Requests.

Work within next generation networks considers additional network convergence possibilities and the integration of new services to the web. This trend responds to the ongoing growth of end-user demand for services that can be delivered anytime, anywhere, on any web-capable device, and of traffic generated by new applications, e.g., the Internet of Things. To support the massive traffic generated by the enormous user base and number of devices with reliability and high quality, web services run from redundant servers. As new servers need to be regularly deployed at different geographical locations, energy costs have become a source of major concern for operators. We propose a cost aware method for routing web requests across replicated and distributed servers that can exploit the spatial and temporal variations of both electricity prices and the server network. The method relies on a learning automaton that makes per-request decisions, which can be computed much faster than regular global optimization methods. Using simulation and testbed measurements, we show the cost reductions that are achievable with minimal impact on performance compared to standard web routing algorithms.

Duo: Software Defined Intrusion Tolerant System Using Dual Cluster

An intrusion tolerant system (ITS) is a network security system that is composed of redundant virtual servers that are online only in a short time window, called exposure time. The servers are periodically recovered to their clean state, and any infected servers are refreshed again, so attackers have insufficient time to succeed in breaking into the servers. However, there is a conflicting interest in determining exposure time, short for security and long for performance. In other words, the short exposure time can increase security but requires more servers to run in order to process requests in a timely manner. In this paper, we propose Duo, an ITS incorporated in SDN, which can reduce exposure time without consuming computing resources. In Duo, there are two types of servers: some servers with long exposure time (White server) and others with short exposure time (Gray server). Then, Duo classifies traffic into benign and suspicious with the help of SDN/NFV technology that also allows dynamically forwarding the classified traffic to White and Gray servers, respectively, based on the classification result. By reducing exposure time of a set of servers, Duo can decrease exposure time on average. We have implemented the prototype of Duo and evaluated its performance in a realistic environment.

Research on Robust Model for Web Service Selection

Abstract Most existing service quality models use defined Quality of Service parameters for service selection.Since a large number of uncertainties exist, the QoS performance indicators in the runtime of the composite services obtained through the model and method based on the determined QoS will be worse even beyond the range acceptable to the users. Web services can solve this problem and become the most reasonable solution in the current application environment. Web services can solve this problem and become the most reasonable solution in the current application environment. In the process of actual service delivery, the key to the failure of the service composition to meet the requirements is caused by uncertain factors. In order to solve the above problems, this paper first proposes a local optimal selection model based on uncertain QoS. The Web service selection method based on uncertain QoS has been progressing in recent years. Second, in order to reduce the time spent on selection, a redundant service selection result is given. The experimental results on the data set show that the proposed model and method can effectively select more robust services for users.

Redundant service in group of single-channel systems with different priorities assigned to request copies

Redundant service in group of single-channel systems with different priorities assigned to request copies