Cloud Management System Research Articles

Similarity-Based Secure Deduplication for IIoT Cloud Management System

Similarity-Based Secure Deduplication for IIoT Cloud Management System

The BMS Tool Monitoring Vertiv UPS and Vision Lithium-Ion Battery System

In my previous work published in an acknowledged publication, titled "Factory test of a TP-100 Lithium Ion Vision Battery System for possible Implementation in Soweto, Johannesburg, South Africa," According to the findings from the study report, the TP-100 Vision Lithium-Ion battery system was found to be suitable for implementing a wind turbine system in Soweto. Furthermore, the study's findings indicated that Lithium-Ion batteries were the optimal choice for storing energy in wind turbines. These batteries provided significant total cost of ownership (TCO) reductions over a 10-year period, without the inconvenience and expenses associated with replacing lead-acid batteries. This research paper aims to validate the precision of parameters obtained from the integration of a 160 kVA Vetiv Three Phase UPS to a TP-100 Vision Lithium Ion Battery system in contrast to those obtained from the Battery Monitoring System (BMS) while using Tool BMS Version 1.3 Software. The results validation was evident as the parameters acquired from the UPS and Battery system were found to be accurate when compared to those observed through the BMS tool. Finally, utilizing the Smart Cloud Management System (SCMS), validity was shown by the ability to remotely monitor the operation of both the UPS and the Li-ion Battery system.

FaaS for IoT: Evolving Serverless towards Deviceless in I/Oclouds

The burgeoning paradigms of Fog and Edge computing propose delegating Cloud-related tasks to the network’s periphery, thus placing computational resources closer to data producers. This shift promises to boost the performance of IoT-based services, providing swift response times while conserving bandwidth. Despite their potential, the current Edge/Fog computing platforms must provide the required flexibility for dynamic service orchestration within a data-oriented context. Addressing this gap, the Function-as-a-Service (FaaS) model emerges as an exceptional strategy for Edge/Fog deployments. Its ability to manage an ever-expanding ecosystem of devices with remarkable flexibility and efficiency holds considerable promise. This paper articulates a novel approach to enhancing the adaptability of IoT Edge/Fog deployments. We propose an innovative extension to OpenStack, an open-source Cloud management system, which pushes its functionality towards the network Edge. Our approach empowers OpenStack to facilitate FaaS services within a distributed IoT infrastructure, thus infusing unprecedented adaptability and efficiency into the Edge/Fog computing paradigms.

Online cloud management and quality monitoring of the whole process of undergraduate thesis

Abstract The undergraduate thesis is an important practical teaching part of the undergraduate teaching program, and it is important to manage its quality. In this paper, an online cloud management system for the thesis is designed according to the requirement analysis, and the ARIMA model in time series is used to predict the number of thesis supervisors by the supervisor and give a reminder for the supervisor’s guidance decision. For the risk management of dissertation progress, a Bayesian network-based assessment method is proposed. From the perspective of dissertation similarity, a process of dissertation quality analysis based on the SimHash algorithm is constructed. The average error rate of ARIMA is only 0.190, which is optimized to a certain extent compared with other algorithms. 20 samples of the inference operation results have no sample with the probability of the dissertation progress delay level at High below 40%.

IoT-Based Big Data Secure Transmission and Management over Cloud System: A Healthcare Digital Twin Scenario

The Internet of Things (IoT) was introduced as a recently developed technology in the telecommunications field. It is a network made up of real-world objects, things, and gadgets that are enabled by sensors and software that can communicate data with one another. Systems for monitoring gather, exchange, and process video and image data captured by sensors and cameras across a network. Furthermore, the novel concept of Digital Twin offers new opportunities so that new proposed systems can work virtually, but without differing in operation from a “real” system. This paper is a meticulous survey of the IoT and monitoring systems to illustrate how their combination will improve certain types of the Monitoring systems of Healthcare–IoT in the Cloud. To achieve this goal, we discuss the characteristics of the IoT that improve the use of the types of monitoring systems over a Multimedia Transmission System in the Cloud. The paper also discusses some technical challenges of Multimedia in IoT, based on Healthcare data. Finally, it shows how the Mobile Cloud Computing (MCC) technology, settled as base technology, enhances the functionality of the IoT and has an impact on various types of monitoring technology, and also it proposes an algorithm approach to transmitting and processing video/image data through a Cloud-based Monitoring system. To gather pertinent data about the validity of our proposal in a more safe and useful way, we have implemented our proposal in a Digital Twin scenario of a Smart Healthcare system. The operation of the suggested scenario as a Digital Twin scenario offers a more sustainable and energy-efficient system and experimental findings ultimately demonstrate that the proposed system is more reliable and secure. Experimental results show the impact of our proposed model depicts the efficiency of the usage of a Cloud Management System operated over a Digital Twin scenario, using real-time large-scale data produced from the connected IoT system. Through these scenarios, we can observe that our proposal remains the best choice regardless of the time difference or energy load.

Modeling and optimizing micro-service based cloud elastic management system

Cloud elasticity is a comprehensive solution that takes into account all running applications and resources, including the cloud management system. Cloud resources and applications are constantly changing in terms of capacity and behavior, implying a dynamic change in the architecture and characteristics of the cloud management system. Following the micro-service architecture pattern, the new trend in application modeling is to decompose it into components and make them standalone cooperated modules. By customizing the application operation modules to match new tasks, this design provides the application with rapid adaptation agility to changes in requirements. This paper proposes a full-stack micro-service-based elastic cloud management system that elastically scales and manages cloud resources. The proposed model focuses on the elastic scaling performance of micro-service management modules by analyzing cloud management in three areas: interactions, end-to-end delay, and communication. In addition, optimizing the decoupling and orchestration scheduling of micro-service cloud management system components to achieve elastic management scaling. To the best of our knowledge, no work has investigated cloud management as a workload required for elastic resource provisioning. In comparison to a monolithic architecture, the proposed micro-service-based model achieves 45% to 65% enhancement in elastic scaling performance.

IoT intelligent agent based cloud management system by integrating machine learning algorithm for HVAC systems

Collection of extensive sensor data from HVAC chillers has facilitated the development of smart cloud management systems to increase the energy efficiency of buildings. However, limited by computing resource and algorithm performance, smart energy management technique and its fault diagnosis method in the literature suffer from low response speed and generalization capacity. To this end, a novel IoT intelligent agent based cloud management system for HVAC systems to improve operational efficiency and safety. The smart cloud management system integrates fundamental framework with machine learning algorithm for fault detection and diagnosis. After preprocessing the data collected by IoT agents, an algorithm is constructed to predict virtual sensor values based on fault-free conditions. The calculated residuals of the actual values and virtual values on both normal and faulty conditions are used as inputs to an extreme gradient boosting algorithm that predicts the fault level. The diagnosis results are compared with other methods such as support vector machine, multi-layer perceptron and random forest. The k-fold cross validation indicated that the proposed methodology can achieve superior overall generalization performance with 67.8%, 70.5% and 71.6% while that of the conventional method were 59.4%, 63.9% and 68.3%. This study will contribute to the practical applications of smart cloud management system in building energy systems.

Research on integrated test technology of relay protection device based on cloud-edge collaboration

The relay protection debugging of smart substation is limited to single unit test. For the intelligent equipment across bays, it can only be tested in batches and one by one, and there is no means to test the integrated system of bus and main transformer with multiple bays. This paper proposes an integrated test technology of intelligent substation based on cloud-edge collaboration. Build an integrated test system based on cloud edge collaboration, which is cross bays, multi intelligent devices and remote vertical connection. Open up the test island of single equipment. Establish equipment signal acquisition and output, multi-protocol access of edge equipment, unified cloud management and process monitoring system. It can realize the value integration of high-quality resources such as substation managers, testers and test expert database. This method improves the testing comprehensiveness, pertinence and timeliness.

Tourism cloud management system: the impact of smart tourism

This study investigates the possibility of supporting tourists in a foreign land intelligently by using the Tourism Cloud Management System (TCMS) to enhance and better their tourism experience. Some technologies allow tourists to highlight popular tourist routes and circuits through the visualisation of data and sensor clustering approaches. With this, a tourist can access the shared data on a specific location to know the sites of famous local attractions, how other tourists feel about them, and how to participate in local festivities through a smart tourism model. This study surveyed the potential of smart tourism among tourists and how such technologies have developed over time while proposing a TCMS. Its goals were to make physical/paper tickets redundant via the introduction of a mobile app with eTickets that can be validated using camera and QR code technologies and to enhance the transport network using Bluetooth and GPS for real-time identification of tourists’ presence. The results show that a significant number of participants engage in tourist travels, hence the need for smart tourism and tourist management. It was concluded that smart tourism is very appealing to tourists and can improve the appeal of the destination if smart solutions are implemented. This study gives a first-hand review of the preference of tourists and the potential of smart tourism.

KNOWLEDGE BASE CLOUD FRAMEWORK FOR EFFECTIVE KNOWLEDGE RETENTION IN SOFTWARE SUPPLY CHAIN

— Most researches focus and suggest Database and Data Warehouses to manage and manipulate organizational knowledge, although both are more suited for running data and information. Hence, they lack the basic constructs required to manipulate knowledge. This research introduces a framework for a new type of Knowledge Base (KB), which refers to as a Software Service Supply Chain Knowledge Base CLOUD (SSSC KB CLOUD) framework. SSSC KB CLOUD framework provides and explores the infrastructure for manipulating knowledge during the developmental process of Software Service Supply Chain (SSSC). Also, it explores the main technical components of the SSSC KB CLOUD and shows how these components integrate and work together to achieve the main aims of SSSC KB CLOUD. The proposed framework shows the life cycle of knowledge in SSSC. It highlights the source of knowledge in SSSC and how it passes the SSSC KM process for sorting and refining. Then, it suggests a SSSC KB CLOUD framework to store and manipulate this knowledge. Finally, it provides the SSSC experts with an interface to access and retrieve knowledge from the SSSC KB CLOUD. The SSSC KB CLOUD consists of four components: Knowledge Cloud Management System (KCMS), Prior Projects Knowledge Marts (PPKM), Experts’ Base (EB), and SSSC Group Formation CLOUD. KCMS provides a management system of SSSC KB CLOUD, while, PPKM is considered a knowledge repository of the knowledge captured during the development of projects, it also stores and holds all the retained SSSC knowledge. Moreover, EB stores and holds the SSSC experts’ details, including the area of expertise, experience, education, etc. Thus, SSSC KB CLOUD holds the experts’ details and records in its EB. Given the previously mentioned components, the SSSC KB CLOUD framework suggests SSSC group formation CLOUD, a new functionality of forming a new SSSC experts’ group to work and complete the new client project.

Swarm-Like Distributed Algorithm for Scheduling a Microservice-Based Application to the Cloud Servers

Introduction of virtualization containers and container orchestrators fundamentally changed the landscape of cloud application development. Containers provide an ideal way for practical implementation of microservice-based architecture, which allows for repeatable, generic patterns that make the development of reliable, distributed applications more approachable and efficient. Orchestrators allow for shifting the accidental complexity from inside of an application into the automated cloud infrastructure. Existing container orchestrators are centralized systems that schedule containers to the cloud servers only at their startup. In this paper, we propose a swarm-like distributed cloud management system that uses live migration of containers to dynamically reassign application components to the different servers. It is based on the idea of “pheromone” robots. An additional mobile agent process is placed inside each application container to control the migration process. The number of parallel container migrations needed to reach an optimal state of the cloud is obtained using models, experiments, and simulations. We show that in the most common scenarios the proposed swarm-like algorithm performs better than existing systems, and due to its architecture it is also more scalable and resilient to container death. It also adapts to the influx of containers and addition of new servers to the cloud automatically.

AN INTEGRATED FRAMEWORK FOR SLA-AWARE MULTI-OBJECTIVE TASK SCHEDULING IN CLOUD COMPUTING

Cloud services are offered to consumers based on Service Level Agreements (SLAs) signed between Cloud Service Provider (CSP) and consumer. Due to on-demand provisioning of resources there is exponential growth of cloud consumers. Job scheduling is one of the areas that has attracted researchers to improve performance of cloud management system. Along with the on premise infrastructure, Small and Medium Enterprises (SMEs) also depend on public cloud infrastructure (leading to hybrid cloud) for seamless continuity of their businesses. In this context, ensuring SLAs and effective management of hybrid cloud resources are major challenging issues to be considered. Hence, there is a need for an effective scheduling algorithm which considers multiple objective functions like SLA (deadline), cost and energy while making scheduling decisions. Most of the state of the art schedulers in hybrid cloud environment considered single objective function. However, in real world, it is inadequate for scheduling effectiveness. To overcome this problem, we proposed an integrated framework which ensures SLAs (deadline), cost effectiveness and energy efficiency with an underlying scheduling algorithm known as SCE-TS. This algorithm is evaluated with different workloads and SLAs using a cloud platform. The empirical study revealed that the proposed framework improves scheduling efficiency in terms of meeting SLAs, cost and energy efficiency. It is evaluated and compared with the state of the art and found to be effective in making scheduling decisions in cloud environment.

Cost-enabled QoS aware task scheduling in the cloud management system

Maintaining the quality of service (QoS) related parameters is an important issue in cloud management systems. The lack of such QoS parameters discourages cloud users from using the services of cloud service providers. The proposed task scheduling algorithms consider QoS parameters such as the latency, make-span, and load balancing to satisfy the user requirements. These parameters cannot sufficiently guarantee the desired user experience or that a task will be completed within a predetermined time. Therefore, this study considered the cost-enabled QoS-aware task (job) scheduling algorithm to enhance user satisfaction and maximize the profit of commercial cloud providers. The proposed scheduling algorithm estimates the cost-enabled QoS metrics of the virtual resources available from the unified resource layer in real-time. Moreover, the virtual machine (VM) manager frequently updates the current state-of-the art information about resources in the proposed scheduler to make appropriate decisions. Hence, the proposed approach guarantees profit for cloud providers in addition to providing QoS parameters such as make-span, cloud utilization, and cloud utility, as demonstrated through a comparison with existing time-and cost-based task scheduling algorithms.

Development of unmanned aerial vehicle cloud management system with the application of geographic information technology

Development of unmanned aerial vehicle cloud management system with the application of geographic information technology

Business modeling as a tool for developing cloud services in the «university-employer» system

The aim and objectives of the article is to describe the process of developing the business architecture of cloud service models, technological requirements for cloud service business models for the main business processes that determine the quality of student training and are necessary for managing the system of joint implementation of the educational process of the University, organizational requirements for the structure of the cloud management system for interaction between the University and employers, in order to obtain educational results, meet the requirements of Federal State Educational Standards and meet the needs of employers.

Internet‐of‐Things‐Infrastructure‐as‐a‐Service: The democratization of access to public Internet‐of‐Things Infrastructure

SummaryWe propose a new cloud service model called Internet‐of‐Things (IoT)‐Infrastructure‐as‐a‐Service (IoT‐IaaS). Under an IoT‐IaaS, an IaaS cloud service provider can go beyond offering IoT data streams and offer access to IoT infrastructure to users, the same way data center infrastructure is shared in the IaaS model. We developed a reference design for the proposed IoT‐IaaS cloud service model, built from open source components. We demonstrate the proposed reference design in the form of a virtualized‐IoT (vIoT) testbed. The vIoT testbed uses the OpenStack cloud management system and Raspberry Pi embedded computers (standing in for IoT devices) configured as Nova‐compute nodes and LXD containers. We also developed a simple mechanism for shared access to high data rate sensors (a camera) and low data rate sensors (temperature‐humidity) by multiple tenants. We tested the performance of container launch times, memory access (with and without ZRAM optimization kernel module enabled), CPU, and file I/O for applications running inside Linux containers and compared it with the same performance when running on the Raspberry Pi's host OS. We conclude that there is no significant performance penalty for executing the edge component of an IoT application inside a Linux container, rather than directly on the device's host OS.

Blockchain and AI amalgamation for energy cloud management: Challenges, solutions, and future directions

In the recent years, the Smart Grid (SG) system faces various challenges like the ever-increasing energy demand, the enormous growth of renewable energy sources (RES) with distributed energy generation (EG), the extensive Internet of Things (IoT) devices adaptation, the emerging security threats, and the foremost goal of sustaining the SG stability, efficiency and reliability. To cope up these issues there exists, the energy cloud management (ECM) system, which combines the infrastructure for energy, with intelligent energy usage and value-added services as per consumers demand. To achieve these, efficient demand-side forecasting and secure data transmission are the key factors. The energy management issues pose extreme gravity in finding sustainable solutions by using the blockchain (BC) and Artificial Intelligence (AI). AI-based techniques support various services such as energy load prediction, classification of the consumer, load management, and analysis where the BC provides data immutability and trust mechanism for secure energy management. Therefore, this paper reviews several existing AI-based approaches along with the advantages and challenges of integrating the BC technology and AI in the ECM system. We presented a decentralized AI-based ECM framework for energy management using BC and validate it using a case study. It is shown that how BC and AI can be used to mitigate ECM with security and privacy issues. Finally, we highlighted the open research issues and challenges of the BC-AI-based ECM system.

Elastic Cloud Logs Traces, Storing and Replaying for Deep Machine Learning

Abstract Storing logs and retrieving it in cloud computing environment is a critical task for Deep Machine Learning models. Time to load large sizes of logs and pre-processing them (prepare logs to be used in learning phase) is a significant issue to machine learning modules that influence training and decision phases. Storing sampled logs in an optimal way that consumes minimum desk storage with accurate and fast replay will give any cloud management system an advantage to start processing logs immediately. Using a practical dimensional reduction method to reduce logs sizes and recover them will improve cloud management system in taking right scaling action and will reduce deep machine learning time complexity. There are many methods in literature for feature extraction and dimensional reduction methods such as Principle Components Analysis (PCA) and wavelet transform. These two methods have a good reputation in processing data set for different goals in machine learning methods like image recognition, signal processing and prediction. In this work, an analysis and comparison is made between PCA and wavelet as methods of cloud logs (considered as data set) dimensional reduction and compression to reduce logs size and logs recover time in order to be replayed in a faster way. This will reduce logs processing complexity, logs foot print and increase decision accuracy by focusing on logs main feature in cloud data center management system.

大数据图片云端管理系统设计与实现

大数据图片云端管理系统设计与实现

Dynamic K-Means Clustering of Workload and Cloud Resource Configuration for Cloud Elastic Model

Cloud elasticity involves timely provisioning and de-provisioning of computing resources and adjusting resources size to meet the dynamic workload demand. This requires fast, and accurate resource scaling methods at minimum cost (e.g. pay as you go) that match with workload demands. Two dynamic changing parameters must be defined in an elastic model, the workload resource demand classes, and the data center resource reconfiguration classes. These parameters are not labeled for cloud management system while data center logs are being captured. Building an advance elastic model is a critical task, which defines multiple classes under these two categories i.e. for workload and for provisioning. A dynamic method is therefore required to define (during configuration time window) the workload classes and resource provisioning classes. Unsupervised learning model such as K-Means has many challenges such as time complexity, selection of optimum number of clusters (representing the classes), and determining centroid values of the clusters. All clustering methods depend on minimizing mean square error between center of population in same class member. These methods are often enhanced using guidelines to find out the centroids, but they suffer from K-Means limitations. For the application of clustering cloud log traces, most of the reported work use K-Means clustering to label workload types. However, there is no work reported that label data center scaling classes. In this work, a novel method is proposed to analyze the characteristics of both workloads and datacenter configurations using clustering method, and is based on random variable model transformation (kernel density estimator) guide. This method enhances K-Means clustering by automatically determining optimum number of classes and finding the mean centroids for the clusters. In addition, it improves the accuracy and the time complexity of standard K-Means clustering model, by best correlating between clustering attributes using statistical correlation methods.