Cloud-based Data Centers Research Articles

Towards Dynamic Request Updating With Elastic Scheduling for Multi-Tenant Cloud-Based Data Center Network

Towards Dynamic Request Updating With Elastic Scheduling for Multi-Tenant Cloud-Based Data Center Network

Cloud Based Data Centre for Managing Logistics in the Disaster

Considering the many man-made and natural disasters that have occurred in recent years, such as the September 2001 earthquake and the tsunami in Japan, the importance of problem solving processes cannot be ignored. The total damage caused by the disaster in Japan alone is estimated at more than $300 billion. Transportation and communications play an important role in disaster response and management to minimize loss of life, economic costs and impacts. Our research includes the development of emergency response systems for disasters of all sizes, focused on transportation systems designed to use and communicate information and technology. In this article, we discuss the use of technology for disaster management using smart transportation, including mobile, mobile and cloud. The system is intelligent because it can collect information from many places and locations, including the situation and make decisions regarding the use of the communication process, making the vehicle more efficient. Hybrid vehicle communications based on vehicle-to-vehicle and vehicle-to-infrastructure protocols are being exploited opportunistically. The effectiveness of our system has been demonstrated by simulating the impact of disasters on the real urban environment and comparing the situation with our existing disaster management systems. We declare that the adoption of our plan has led to the improvement and stabilization of traffic and evacuation.

Effective integration of the internet of things and ensemble learning approaches for enhancing sudden topple recognition assisted by cloud computing technology

Falls provide a significant public health hazard globally for the senior population. Untreated Sudden Topple in the elderly leads to functional loss and a notable decline in mobility, autonomy, and quality of life. Early identification of Sudden Topple is essential for a person's well-being or to provide needed care. Telehealth data centers need scalable processing and storing resources to accommodate the increasing number of individuals. Specialized methods that enable the transfer of just pertinent data are necessary. This study presents a Hybrid System composing Cloud Computing and the Internet of Things (IoT) (HS-CC-IoT) to monitor many elderly individuals, identify Sudden Topple, and alert caretakers. The experiments were conducted to reveal the necessary criteria for facilitating the operation of large-scale systems. The research assessed many machine learning algorithms for their appropriateness in detection. The experimental tests to identify sudden topples are in cloud-based data centers and on an Edge IoT gadget with an Ensemble Learning Algorithm. Experiments on the user-to-cloud data transfer showed that a substantial decrease in the quantity of saved and transferred data is possible when conducting Sudden Topple identification on the Edge.

Building Foods Data Automation Platform Using Cloud Computing Type PaaS

Sources of information on the availability and prices of basic foods are spread on URLs, HTTP(s) on various websites in the Medan City government. Data synchronization and data integration between websites can produce quality information sourced from various public IP Address protocols. In a cloud-based Data Center, the Front End and Back End computer network devices are interconnected via the Internet. Data is stored as JSON and synced in realtime to every connected client. Platform metrics and Activity logs are collected automatically, routed to other locations using diagnostic settings. Cloud service solutions include IaaS (Infrastructure as a Service), SaaS (Software as a Service), and PaaS (Platform as a Service). PaaS integrates various operating systems and user applications. Secondary data collection takes public data that can be accessed using Web Crawling and Web Scraping techniques. Web API software components automatically update data between the client and server. Firebase Realtime Database can store and synchronize data in real time. The configuration of data requests that can be served by the food data cloud platform is 85% with a configuration runtime of 80%-95%.

Protect medical records by using blockchain technology

<span>Recently, medical records have been stored and shared locally, which creates a risk of data loss or corruption; these records may be stored and shared through cloud-based central data centers; nonetheless, this strategy has disadvantages, such as the need for significant storage space and the privacy concerns associated with network-wide data sharing. Medical records require increased security and confidentiality, furthermore, these records must be protected when transmitted to and shared with doctors of the same specialization, to solve this problem, blockchain technology was used as a decentralized technology that offers a secure and immutable way to store and protect information. In this paper, a website is designed that uses blockchain technology to save medical records and employs smart contract technology in the ethereum blockchain to govern the creation and display of files and sharing. An interplanetary file system was used to offer a mechanism for the decentralized storage of medical images and reports while maintaining their accessibility on a global scale, it is available only for doctors who have the authority to access them, and the proposed system proved efficient in saving and sharing medical records with high security and less cost.</span>

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.

Transformasi Digital Dalam Rangka Mendukung Penerapan Sistem Jaminan Halal Berdasarkan Pernyataan Pelaku Usaha (Studi Kasus Di IKM Es Krim XYZ)

In order to increase the competitiveness of small and medium industries, it is necessary to increase the capacity of IKM in all business value chains. The government has imposed mandatory halal certificates for products in the region as a form of guarantee for halal products. For this reason, in order to encourage the competitiveness of IKM by penetrating the market through halal product guarantee certification, it is necessary to identify the conformity of the production of IKM XYZ with the Halal Product Assurance System for Micro, Small, and Medium Entreprises (MSMEs) based on the Circular Letter of the Head of the Halal Product Assurance Organizing Agency Number B-5952/BD.II./P.II.I.2/HM.00/09/2021. The following case study research was carried out using the method of identifying the initial conditions of IKM XYZ through interviews, then conducting an assessment of the Decree of the Head of the Halal Product Guarantee Agency Number 33 of 2022. Based on the identification results, it was determined that improvement was needed on the halal traceability aspect which had an impact on the inspection of the arrival of materials; transportation and distribution of materials and products; and Product launch/sales. In line with the Making Indonesia 4.0 policy that digital transformation is carried out in order to meet sustainability standards, the findings of improving the halal process are carried out by adopting digitalization. For this reason, digitization is carried out in digital bookkeeping activities to meet the criteria for halal guarantees in the form of a cloud-based data center. Based on the discussion conducted, it was concluded that the application of database technology based on number processing and cloud storage can provide answers to the findings of identification of halal product guarantees.

ACO-Inspired Load Balancing Strategy for Cloud-Based Data Centre with燩redictive Machine Learning Approach

Virtual Machines are the core of cloud computing and are utilized to get the benefits of cloud computing. Other essential features include portability, recovery after failure, and, most importantly, creating the core mechanism for load balancing. Several study results have been reported in enhancing load-balancing systems employing stochastic or biogenetic optimization methods. It examines the underlying issues with load balancing and the limitations of present load balance genetic optimization approaches. They are criticized for using higher-order probability distributions, more complicated solution search spaces, and adding factors to improve decision-making skills. Thus, this paper explores the possibility of summarizing load characteristics. Second, this study offers an improved prediction technique for pheromone level prediction over other typical genetic optimization methods during load balancing. It also uses web-based third-party cloud service providers to test and validate the principles provided in this study. It also reduces VM migrations, time complexity, and service level agreements compared to other parallel standard approaches.

Management and Organization of Storage in Hospitals, Health Storehouses, and Health Care Facilities

Store management includes managing warehouses, inventory data, holding and storing medications, and safeguarding and protecting products. In the supply chain, a store is where we keep stock. Keeping goods on hand ensures that upcoming client demand will be met. As a result, every company keeps a certain amount of inventory on hand to meet client demand. Inventory requires many points of stocking and typically has more items than other hospital inventories. The trade-off between inventory costs and the degree of necessary service that each surgeon expects to get makes inventory management in an OR (operating room) more challenging than it would be in a manufacturing environment. The store's management should facilitate the most efficient and dependable supply chain possible without significantly compromising quality, wasting resources, or committing theft. It has been noted that the volume of data is growing exponentially over time. Scientifically, vast databases are also becoming increasingly relevant to shared resources. These sorts of enormous and vast databases are often kept in cloud-based data centers. Since the majority of scientific data is in huge volumes and organizations and industries may not always have access to the storage space, processing resources, computing capacity, or upkeep of such data. As a result, the majority of businesses employ cloud services.

Detection and Prevention of Cyber Attacks on Cloud-Based Data Centers using Machine Learning

Detection and Prevention of Cyber Attacks on Cloud-Based Data Centers using Machine Learning

Digital twin intelligent system for industrial internet of things-based big data management and analysis in cloud environments

This work surveys and illustrates multiple open challenges in the field of industrial Internet of Things (IoT)-based big data management and analysis in cloud environments. Challenges arising from the fields of machine learning in cloud infrastructures, artificial intelligence techniques for big data analytics in cloud environments, and federated learning cloud systems are elucidated. Additionally, reinforcement learning, which is a novel technique that allows large cloud-based data centers, to allocate more energy-efficient resources is examined. Moreover, we propose an architecture that attempts to combine the features offered by several cloud providers to achieve an energy-efficient industrial IoT-based big data management framework (EEIBDM) established outside of every user in the cloud. IoT data can be integrated with techniques such as reinforcement and federated learning to achieve a digital twin scenario for the virtual representation of industrial IoT-based big data of machines and room temperatures. Furthermore, we propose an algorithm for determining the energy consumption of the infrastructure by evaluating the EEIBDM framework. Finally, future directions for the expansion of this research are discussed.

A Computational Offloading Method for Edge Server Computing and Resource Allocation Management

The emerging technology of mobile cloud is introduced to overcome the constraints of mobile devices. We can achieve that by offloading resource intensive applications to remote cloud-based data centers. For the remote computing solution, mobile devices (MDs) experience higher response time and delay of the network, which negatively affects the real-time mobile user applications. In this study, we proposed a model to evaluate the efficiency of the close-end network computation offloading in MEC. This model helps in choosing the adjacent edge server from the surrounding edge servers. This helps to minimize the latency and increase the response time. To do so, we use a decision rule based Heuristic Virtual Value (HVV). The HVV is a mapping function based on the features of the edge server like the workload and performance. Furthermore, we propose availability of a virtual machine resource algorithm (AVM) based on the availability of VM in edge cloud servers for efficient resource allocation and task scheduling. The results of experiment simulation show that the proposed model can meet the response time requirements of different real-time services, improve the performance, and minimize the consumption of MD energy and the resource utilization.

Pareto based ant lion optimizer for energy efficient scheduling in cloud environment

The tremendous growth of cloud-based data centres leads to significant amount of energy. Thus, the prime concern for the cloud service providers is to generate environment friendly solution by minimizing the energy consumption. Further, due to increased demand of scientific workflow applications, cloud providers face a challenging issue of efficient workflow scheduling by minimizing the makespan. To address the above-mentioned contradictory issues, we proposed a Pareto based multi-objective discrete ant lion optimization algorithm (PBMO-DALO) to solve workflow-scheduling problem in cloud data centres with minimizing the conflicting objectives of makespan and energy consumption simultaneously. Distinguished from the original ant lion optimization algorithm, the proposed algorithm involves new encoding scheme for ants and antlions, their random walk and selection of fitter antlion for trap building to address the discrete nature of the workflow scheduling problem. Subsequently, the PBMO-DALO uses the Pareto dominance and crowding distance approach to tackle optimization of multiple objectives and to achieve the optimal solutions. The simulation results indicate that the proposed PBMO-DALO algorithm overwhelms other competing algorithms and generates good trade-off solutions with better convergence and uniform diversity.

Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers

Cloud computing is a computing paradigm which meets the computational and storage demands of end users. Cloud-based data centers need to continually improve their performance due to exponential increase in service demands. Efficient task scheduling is essential part of cloud computing to achieve maximum throughput, minimum response time, reduced energy consumption and optimal utilization of resources. Bio-inspired algorithms can solve task scheduling difficulties effectively, but they need a lot of computational power and time due to high workload and complexity of the cloud environment. In this research work, Hybrid ant genetic algorithm for task scheduling is proposed. The proposed algorithm adopts features of genetic algorithm and ant colony algorithm and divides tasks and virtual machines into smaller groups. After allocation of tasks, pheromone is added to virtual machines. The proposed algorithm effectively reduces solution space by dividing tasks into groups and by detecting loaded virtual machines. Due to the minimum solution space of proposed algorithm, convergence and response time is significantly decreased. It finds a feasible scheduling solution to minimize the running time of workflows and tasks. The proposed algorithm achieved 64% decrease in execution time and 11% decrease in overall data center costs.

Detection and Classification of Malicious Flows in Software-Defined Networks Using Data Mining Techniques.

The increasing availability of mobile devices and applications, the progress in virtualisation technologies, and advances in the development of cloud-based distributed data centres have significantly stimulated the growing interest in the use of software-defined networks (SDNs) for both wired and wireless applications. Standards-based software abstraction between the network control plane and the underlying data forwarding plane, including both physical and virtual devices, provides an opportunity to significantly increase network security. In this paper, to secure SDNs against intruders’ actions, we propose a comprehensive system that exploits the advantages of SDNs’ native features and implements data mining to detect and classify malicious flows in the SDN data plane. The architecture of the system and its mechanisms are described, with an emphasis on flow rule generation and flow classification. The concept was verified in the SDN testbed environment that reflects typical SDN flows. The experiments confirmed that the system can be successfully implemented in SDNs to mitigate threats caused by different malicious activities of intruders. The results show that our combination of data mining techniques provides better detection and classification of malicious flows than other solutions.

An Approximation Algorithm to Maximize User Capacity for an Auto-Scaling VoD System

In a video-on-demand (VoD) service, blockbuster videos have stable and predictable popularity, but the traffic can vary significantly within short timescale. To efficiently serve the user pool in a geographic region, we consider a regional auto-scaling cloud-based data center consisting of multiple servers. For efficient storage, we partition the videos into fixed-size blocks. To respond to dynamic user traffic in a timely and cost-effective manner, we may activate or deactivate each server according to the traffic while keeping at least one replica for each block in the active servers. We maximize the user capacity of the active servers (and hence minimizing the number of active servers at any time) by jointly optimizing block allocation in the servers, server selection at each traffic level, and request dispatching to a server. We believe that this is the first work to study such problem for an auto-scaling cloud-based VoD data center. We first formulate the problem and show its NP-hardness. We then propose AVARDO ( A uto-scaling V ideo A llocation and R equest D istribution O ptimization), a simple but efficient approximation algorithm with proven optimality. AVARDO operates the servers like a stack, with a server being pushed into or popped from the existing active server set according to some optimized traffic thresholds. We prove that AVARDO approaches the theoretical optimum as the block size reduces. Trace-driven experimental results based on large-scale real-world video data further validate that AVARDO is closely optimal. It achieves significantly higher user capacity as compared with other state-of-the-art and traditional schemes, and reduces the optimality gap by multiple times.

Blockchain-Based Distributed Patient-Centric Image Management System

In recent years, many researchers have focused on developing a feasible solution for storing and exchanging medical images in the field of health care. Current practices are deployed on cloud-based centralized data centers, which increase maintenance costs, require massive storage space, and raise privacy concerns about sharing information over a network. Therefore, it is important to design a framework to enable sharing and storing of big medical data efficiently within a trustless environment. In the present paper, we propose a novel proof-of-concept design for a distributed patient-centric image management (PCIM) system that is aimed to ensure safety and control of patient private data without using a centralized infrastructure. In this system, we employed an emerging Ethereum blockchain and a distributed file system technology called Inter-Planetary File System (IPFS). Then, we implemented an Ethereum smart contract called the patient-centric access control protocol to enable a distributed and trustworthy access control policy. IPFS provides the means for decentralized storage of medical images with global accessibility. We describe how the PCIM system architecture facilitates the distributed and secured patient-centric data access across multiple entities such as hospitals, patients, and image requestors. Finally, we deployed a smart contract prototype on an Ethereum testnet blockchain and evaluated the proposed framework within the Windows environment. The evaluation results demonstrated that the proposed scheme is efficient and feasible.

Green Cloud Software Engineering for Big Data Processing

Internet of Things (IoT) coupled with big data analytics is emerging as the core of smart and sustainable systems which bolsters economic, environmental and social sustainability. Cloud-based data centers provide high performance computing power to analyze voluminous IoT data to provide invaluable insights to support decision making. However, multifarious servers in data centers appear to be the black hole of superfluous energy consumption that contributes to 23% of the global carbon dioxide (CO2) emissions in ICT (Information and Communication Technology) industry. IoT-related energy research focuses on low-power sensors and enhanced machine-to-machine communication performance. To date, cloud-based data centers still face energy–related challenges which are detrimental to the environment. Virtual machine (VM) consolidation is a well-known approach to affect energy-efficient cloud infrastructures. Although several research works demonstrate positive results for VM consolidation in simulated environments, there is a gap for investigations on real, physical cloud infrastructure for big data workloads. This research work addresses the gap of conducting real physical cloud infrastructure-based experiments. The primary goal of setting up a real physical cloud infrastructure is for the evaluation of dynamic VM consolidation approaches which include integrated algorithms from existing relevant research. An open source VM consolidation framework, Openstack NEAT is adopted and experiments are conducted on a Multi-node Openstack Cloud with Apache Spark as the big data platform. Open sourced Openstack has been deployed because it enables rapid innovation, and boosts scalability as well as resource utilization. Additionally, this research work investigates the performance based on service level agreement (SLA) metrics and energy usage of compute hosts. Relevant results concerning the best performing combination of algorithms are presented and discussed.

Server and Storage Performance Testing for Data Center Construction

The increase in data triggered by the smartphone revolution has led to a growing interest in cloud-based data centers. However, while much research has been conducted on virtualization technologies for cloud data centers, there has not been enough research on the performance of physical systems. As manufacturers only provide performance figures measured in their own system’s favor, objective and consistent performance testing metrics and methods are needed for performance measurement. This paper suggests some performance testing metrics and methods for cloud-based physical systems using TPC, SPECjbb and SPC.

Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge

Remote monitoring of older adults and detecting dangers in the state of human health have become essential elements of modern telemedicine. Falls are a frequent reason for deaths or post-traumatic complications in the elderly. Therefore, the early detection of falls can be crucial for the survival of a person or for providing necessary support. However, telemedicine data centers require scalable computing and storage resources for the growing number of monitored people. Dedicated approaches that allow for minimal data transmission of strictly interesting cases are also required.In this paper, we show a scalable architecture of a system that can monitor thousands of older adults, detect falls, and notify caregivers. Scalability tests that disclose requirements to enable large scale system operations were also performed. Moreover, we validated several Machine Learning models to evaluate their suitability in the detection process. Among the tested models, Boosted Decisions Trees resulted in the best classification performance. We also experimentally tested the detection of falls inside a Cloud-based data center and on an Edge IoT device. Results of tests on the device-to-cloud data transmission confirmed that significant reduction in the size of stored and transmitted data can be achieved while performing fall detection on the Edge.