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The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of cloud computing, efficient resource allocation and workload prediction are paramount for optimizing infrastructure utilization, cost management, and overall service quality. As a result, an efficient resource management approach involves pooling available resources among many customers in a manner that considers their energy usage, SLAs, and forecasting accuracy all at once. Based on the performance analysis results, it is determined that integrating these techniques would result in the most efficient and adaptable cloud data centre. We proposed a Model based on Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) for handling SLAs in the cloud from both the perspective of consumers and service providers. Subsequently, we delve into the methodology of applying deep CNN and LSTM models to the problem of workload prediction in cloud environments. This methodology encompasses data preprocessing, model architecture, training parameters, and the choice of performance metrics. To anticipate CPU consumption from time series data and detect SLA violations, we suggested a DCNN-LSTM model. The accuracy prediction, energy usage, CPU use, and Service Level Agreement monitoring are all a part of this model. The proposed method is effective in helping cloud providers cut down on service violations and associated fines. Regarding the composite metric of Energy SLA Violation, which assesses the combined aspects of energy use and adherence to Service Level Agreements (SLAs), DCNN-LSTM surpasses ARIMA-LSTM, CNN, LSTM, and ARIMA 6.8%, 10.88%, 16.6%, and 22.4%, respectively. [ABSTRACT FROM AUTHOR]

Summary: The expeditious deployment of Cloud applications and services on wide‐ranging Cloud Data Centres (CDC) gives rise to the utilization of many resources. Moreover, by the increase in resource utilization, virtualization also greatly impacts achieving desired performance. The major challenges in virtualization are detecting over‐utilized or under‐utilized hosts at the right time and the proper scaling of Virtual Machines (VM) on the accurate host. Auto‐scaling in Cloud Computing allows the service providers to scale up or down the resources automatically and provides on‐demand computing power and storage capacities. Effective utilization and autonomous scaling of resources eventually reduce the load, energy consumption, and operating costs. In this paper, an efficient auto‐scaling approach for predicting host load through VM migration has been proposed. The ensemble method using different time‐series forecasting models has been proposed to forecast the approaching workload on the host. Based on this predicted load, different algorithms have been devised to detect over‐utilized and under‐utilized hosts and VMs can be migrated. The designed approach has been validated by experimentation on a real‐time Google cluster dataset. The proposed technique significantly improves average CPU utilization and reduces over‐utilization and under‐utilization. It also minimizes response time, service level agreement violations, and the slighter number of migrations and scaling overhead. [ABSTRACT FROM AUTHOR]

Nowadays, Cloud services are gaining importance among users due to their cost-effectiveness and highly scalable resources. To meet the user's demands, several data centres are built across the globe, which has severe environmental as well as economical concerns. Energy consumption is one of the most significant issue faced by cloud service providers. Prediction of accurate resource usage of the physical machine helps in effective utilization of resources in a data centre, resulting in minimizing an active number of physical machines, which helps to minimize the energy consumption of a data centre. Although several models till date focus on virtual machine consolidation with a notion of reducing energy consumption, the reduction of operational physical machines has not gathered enough attention. In this paper, we propose a prediction model to predict resource utilization of physical machines, which enables to effectively utilize the entire data centre's resources to reduce energy consumption. First, the raw time series workload is processed to enhance the value of its features for better training and prediction of mean resource utilization in the cloud data centre using the proposed Sum Average (SA) algorithm. Afterward, Gaussian Mixture Model (GMM) is employed to cluster heterogeneous machines of data centre based on its resource usage which helps to analyze the prediction for each kind of configured machine available in a data centre. In addition, the Long Short Term Memory model (LSTM) is employed to predict the mean resource usage of physical machines for every clustered machine. Furthermore, the effectiveness of our proposed model is evaluated using the Google cluster trace usage dataset. Lastly, the proposed model is compared with Linear Regression, Moving Average, and Auto Regression Integrated Moving Average model. Root Mean Square Error (RMSE) analysis states that our proposed model outperforms the other compared techniques [ABSTRACT FROM AUTHOR]

Reducing the energy consumption while guaranteeing the quality of service (QoS) in the cloud data centers is challenge task for cloud providers. Dynamic virtual machine (VM) consolidation technology is regarded as a promising approach to satisfy goals. Considering dynamic workload of physical machine (PM) results in VM migration and high resources utilization of PM results in resources contention among VMs that affects working performance of VMs. Hence, it is vital to provide an efficient approach for dynamic VM placement during the consolidation to achieve the objectives while alleviating resources contention among VMs in the data centers. In this paper, the proposed strategy called LBVMP aims to build a novel conception consisting of a balancing flat surface of a PM in terms of CPU, RAM, bandwidth (BW) and another proportion flat surface that the remaining resources capacity of the targeted PM was divided by the request resources (CPU, RAM and BW) of a VM. Then LBVMP calculates the distance between two plats to evaluate VM allocation solutions. Extensive experimental results based on the CloudSim simulator demonstrate that compared with the state-of-the-art algorithm BCAVMP, the proposed strategy enables to reduce the cloud data centers of energy consumption, the number of migrations, SLAV, ESV by an average of 3.50%, 9.40%, 78.40%, 79.91%, respectively. [ABSTRACT FROM AUTHOR]

Wireless networks have become increasingly important in the human resources industry, with cloud data centres playing a critical role in commercial information infrastructure and human resource procedures. Effective human resource management is essential for modern enterprises, so proper human resource planning is crucial for smooth and rapid growth. To achieve this, enterprises must predict the supply and demand of future human resources based on development needs and external environmental factors and formulate a human resource plan that meets their development needs. However, the success or failure of human resource planning depends on accurately forecasting human resource demand. The wireless human resource cloud data centres provide numerous advantages over traditional data centres, including flexibility, efficiency, scalability, security, and data analysis capabilities. Forecasting human resource demand through these centres remains difficult because traditional forecasting techniques can be less effective, leading to inaccurate forecasts. To address this issue, this study proposes a model that combines fuzzy categorization theory with sophisticated forecasting methods to forecast human resource demand. To enhance forecasting reliability and accuracy by efficiently utilizing the data resources of wireless human resource cloud data centres and taking into account a wide range of influencing factors. Industries that use wireless HR cloud data centres are more likely to stay ahead of the competition. It uses fuzzy classification theory and applies fuzzy classification methods to later-stage forecasting to overcome the limitations of traditional forecasting methods, which sample changes may disrupt. The study also uses the Markov chain method, which is suitable for predicting system objects with large random fluctuations. The effectiveness and feasibility of the model are demonstrated by analyzing and verifying the research object's data. This study provides a reliable and effective forecasting method for human resource demand using wireless cloud data centres, which can assist organizations in making data-driven decisions and enhancing their competitive advantage in the market. [ABSTRACT FROM AUTHOR]

Summary: Virtual machine (VM) consolidation is to assign a set of VMs requested by operators to the physical machines (PMs) in the data centers so that certain cost, profit or performance objective is optimized, subject to the PMs resource capacity constraints. It is an important mean to decrease the total power consumption by reducing the number of active physical machines (PM) in a cloud. In this context, most of the existing solutions rely on highly frequent live migration to reduce the number of active physical machines. However, live migration is a high resource consumption and time‐consuming operation, thus, frequent use of live migration not only increases energy consumption but also affects the stability of the physical machine, which in turn affects the services on the virtual machine. Reducing the number of active physical machines while reducing the number of live migrations is a major challenge in the face of massive fluctuating virtual machine loads. In order to solve this problem, in this paper, we present a VM consolidation algorithm for predictable loads (VCPL) to reduce the live migration operations. First, we present a cyclic usage prediction (CUP) method to predict the load in a whole cycle (a day) of a VM. Then, we separate the VMs with stable and cyclic load out from others and consolidate them to PMs by using VCPL to make sure each PM has a stable load. Thus, energy can be reduced by avoiding most of live migration operations, and the stability of the data center can be observably improved. We evaluate our solution through simulations on real‐world workloads, the results show that, 66% of long‐term VMs have stable and cyclic loads and are predictable, by using VCPL, the live migration operations occurring on the PMs which accommodate those VMs can be reduced significantly than other solutions. [ABSTRACT FROM AUTHOR]

Hard disk is the main storage device for cloud service, and there always contain massive disks deployed in a data center. Disk failure occur frequently in data center, which may lead to data loss and other disasters, so there have urgent needs for a failure prediction method of hard disk so as to ensure service reliability. This paper proposes a temporal prediction model based on LSTM. Firstly, the SMART data of the disk is analyzed, and the Pearson correlation coefficient is used to analyze the correlation between the monitoring time series data of the faulty disk and the normal disk, and the monitoring index with the lowest correlation is selected as the fault feature; secondly, for the problem of serious imbalance of positive and negative samples in the SMART dataset, the SMOTEENN algorithm is introduced for oversampling to obtain a balanced dataset of positive and negative samples. The proposed method improves accuracy by 8.268% and F1-score by 8.657% compared to the conventional method. [ABSTRACT FROM AUTHOR]

A cloud data center provides various facilities such as storage, data accessibility, and running many specific applications on cloud resources. The unpredictable demand for service requests in cloud workloads affects the availability of resources during scheduling. It raises the issues of inaccurate workload prediction, lack of fulfillment in resource demands, load unbalancing, high power consumption due to heavy loads, and problems of under and overutilization of resources. Therefore, an efficient scheduling technique and an accurate forecasting model are needed to overcome these issues. Also, to deal with these challenges and provide optimal solutions, researchers must have a robust knowledge of cloud workloads, their types, issues, existing technologies, their advantages and disadvantages. However, previous research indicates limited systematic review studies exist for cloud workload applications with prediction-based scheduling techniques. Therefore, a survey is required that provides information related to cloud workload. To fulfill this requirement, the current study collects the related articles published in the past years. This paper is a systematic review study of prediction-based scheduling techniques that extract and evaluate data based on five criteria. It includes the datasets of different workload applications, resources, current prediction and scheduling techniques, and their related parameters. The survey is quite useful for academicians who want to select the problem and develop new techniques for issues related to cloud workload applications. It also gives an idea of existing approaches that are already implemented and employed. [ABSTRACT FROM AUTHOR]

In order to manage the load on dispersed data centers and cut down on energy established on time usage, agent-based resource allocation is given attention. Using a targeted load balancer (TLB), we suggest an energy-aware agent-based resource allocation in this research to enhance quality of service in a cloud setting. This agent is first set up to keep track of the resource load resulting from the request that has been assigned a job. Cloud watch also keeps an eye on energy levels to determine the typical payload size of resource execution. The TLB establishes new instance state to assign the resource based on the payload weight. To shorten the execution time, the dynamic hyper switching model develops a balancing mechanism. The suggested system achieves high performance in resource management by creating load balancer that is efficiently targeted to cut down on computation time and cost depending on energy levels. In comparison to existing techniques, the suggested parallelized homogeneous job in the cloud environment produces greater performance up to 95.5% while maintaining the time execution utilizing switching state of execution. This maintains the reduced CPU consumption, which dependent on the lowering of temporal complexity. [ABSTRACT FROM AUTHOR]

Nowadays, academic records are converted into digital form, but there are two major challenges that remain unresolved. First is achieving secure and reliable academic record storage; second is recognising academic record sharing and confirming the security of sharing process. In this research work, Proof of Continuous Work (PoCW) consensus algorithm fostered blockchain (BC) system is proposed for securing students educational records (PoCW-BC-SSED). It consists of three process, institutions joining process, blockchain-enabled secure data storage and blockchain-enabled secure data sharing. For institution joining procedure, new institution tries to link the BC network to create its public along with private keys. BC-Enabled Secure Data Storage is based on PoCW blockchain algorithm. The academic records are maintained in independent data centres of the institutions as well as shared with other universities as part of the BC-Enabled Secure Information Sharing process. The shared data is kept on file in BC. The proposed method protects the records on the storage server with maximal security and reliability using consistency check protocol. The proposed method is executed using Ethereum. The efficiency of proposed method is estimated with the help of several performances metrics.The proposed method attains 19.67%, 22.38%, 27.45% higher accuracy; 28.92%, 25.83%, 19.46% higher precision while compared with existing methods such as BC-based approach with academic information protected storage and transmission scheme(EduRSS-BC-SSED), blockchain-based models for securing student information systems that develops the highly trusted ledger for storing the vital information(MOOC-BC-SSED) and BC base proof of work approach for developing the trust in ubiquitous higher education(BC-PoW-SSED) respectively. [ABSTRACT FROM AUTHOR]

Tiara Surabaya Pharmacy is one of the pharmacies that already has several branches spread across Surabaya and has used a computerized system in carrying out its transaction activities. However, the system is still offline, so if there is a change in data, the central branch will send the latest data to other branches using a flash drive. So it took quite a long time for other pharmacy branches to update the data. Given these constraints, the authors designed an integrated system by utilizing a storage cloud system with the aim that pharmacies can easily send their latest data via an online system. So there is effectiveness in sending data from the center to other branches. [ABSTRACT FROM AUTHOR]

Cloud computing (CC) is a technology that delivers its service by means of the internet. In the modern scenario, cloud storage services have gained attention. The cloud environment confronts data breaches expansively in cloud storage, which might bring about the disclosure of personal in addition to corporate data. Thus, the requirement arises for the creation of a more foremost authentication system. Customary authentication schemes depend on techniques, like Password Authentications Protocol (PAP), Challenge Handshakes Authentication Protocols (CHAP), as well as One-Time Pads (OTP), which are often susceptible to malevolent attacks as well as security threats. To shun such issues, this paper proposed a Modified ECC centred secure data transfer and a '3'-factor authentication scheme in the untrusted cloud environment. The proposed work comprises '3' steps: authentication, data compression, and safe data transfer. In the authentication phase, the SHA-512 algorithm along with CCP is utilized. After that, the user-uploaded data is compressed utilizing CHA on the server-side. Next, MECC encrypts the compressed data, and then, safely uploaded it to the cloud server (CS). In the investigational appraisal, the proposed work is contrasted with the prevailing methods. The outcomes proved that the proposed work renders better security than the prevailing methods. [ABSTRACT FROM AUTHOR]

At present, most causal consistency models based on cloud storage can no longer meet the needs of delay-sensitive applications. Moreover, the overhead of data synchronization between replicas is too high. This paper proposes a causal consistency model of edge–cloud collaborative based on grouping protocol. The model is based on the edge–cloud collaboration architecture, partitions cloud data centers and groups edge nodes by distributed hash tables, and stores a subset of the complete data set in nodes located at the edge of the network, thereby realizing partial geo-replication in edge–cloud collaboration environment. At the same time, we design a group synchronization algorithm called Imp_Paxos, so that the update only needs to be synchronized to the main group, which reduces the visibility delay of the update and decreases the data synchronization overhead. Besides, a sort timestamp is proposed in this paper, which generates different timestamps according to the type of update to track causality, keeping the amount of metadata managed in a relatively stable and low state. Therefore, the proposed model reduces the overhead of metadata for system management and improves throughput quantity of system. Experiments show that our model performs well in terms of throughput, operation latency, and update visibility latency compared with existing causal consistency models. [ABSTRACT FROM AUTHOR]

Cloud block storage systems support diverse types of applications in modern cloud services. Characterizing their input/output (I/O) activities is critical for guiding better system designs and optimizations. In this article, we present an in-depth comparative analysis of production cloud block storage workloads through the block-level I/O traces of billions of I/O requests collected from two production systems, Alibaba Cloud and Tencent Cloud Block Storage. We study their characteristics of load intensities, spatial patterns, and temporal patterns. We also compare the cloud block storage workloads with the notable public block-level I/O workloads from the enterprise data centers at Microsoft Research Cambridge, and we identify the commonalities and differences of the three sources of traces. To this end, we provide 6 findings through the high-level analysis and 16 findings through the detailed analysis on load intensity, spatial patterns, and temporal patterns. We discuss the implications of our findings on load balancing, cache efficiency, and storage cluster management in cloud block storage systems. [ABSTRACT FROM AUTHOR]

The drastic growth in cloud utilization makes the investigator faces a crucial issue with the virtual machine (VM) placement. Generally, VMs are allocated to physical servers for provisioning on-demand requests. This process is relatively straightforward but leads to performance degradation based on resource availability. Also, the optimal VM placement leads to unnecessary migration and network traffic. Thus, cloud storage system efficiency relies on the placement of the VM and the service provided by it. Various investigators attempt to provide an efficient placement strategy which improves QoS and reduces the computation cost. This research work concentrates on handling the VM placement problem and reducing data centres' power consumption and resource utilization. Here, a multi-objective constraint is formulated and resolved using bio-inspired optimization approach. An improved Grey-wolf optimization (IGWO) approach is proposed to leverage the multi-level optimal VM placement to provide an optimal solution concerning exploration and exploitation. The theoretical model depicts the proposed (IGWO) efficiency, which substantially enhances the system performance. The proposed IGWO model gives better results than the prevailing approaches. The extensive simulation process is carried out in a MATLAB environment. [ABSTRACT FROM AUTHOR]

Real-time tracking and surveillance of patients' health has become ubiquitous in the healthcare sector as a result of the development of fog, cloud computing, and Internet of Things (IoT) technologies. Medical IoT (MIoT) equipment often transfers health data to a pharmaceutical data center, where it is saved, evaluated, and made available to relevant stakeholders or users. Fog layers have been utilized to increase the scalability and flexibility of IoT-based healthcare services, by providing quick response times and low latency. Our proposed solution focuses on an electronic healthcare system that manages both critical and non-critical patients simultaneously. Fog layer is distributed into two halves: critical fog cluster and non-critical fog cluster. Critical patients are handled at critical fog clusters for quick response, while non-critical patients are handled using blockchain technology at non-critical fog cluster, which protects the privacy of patient health records. The suggested solution requires little modification to the current IoT ecosystem while decrease the response time for critical messages and offloading the cloud infrastructure. Reduced storage requirements for cloud data centers benefit users in addition to saving money on construction and operating expenses. In addition, we examined the proposed work for recall, accuracy, precision, and F-score. The results show that the suggested approach is successful in protecting privacy while retaining standard network settings. Moreover, suggested system and benchmark are evaluated in terms of system response time, drop rate, throughput, fog, and cloud utilization. Evaluated results clearly indicate the performance of proposed system is better than benchmark. [ABSTRACT FROM AUTHOR]

Cloud technology is a worldwide storage architecture in which consumers use resources that are continuously provided, such as computing, storage, and networking. Computing has led to internet servers being content in electronic platforms that are distributed by numerous consumers. The main issue with the data center is dealing with the millions of ideas that come in regularly. As a result, the Scheduling Model (SM) utilizing the cloud datacenters (SM-CDC) technique is developed in this research to handle cloud storage centers' tasks that are stored in electronic devices. The task is spread across all present virtual machines, and the online data is secured from overloaded. The data distribution is established by considering the value and state of the smart device, which aids in the fair allocation of tasks and effective use of resources. Task load (TL) involves managing these demands, and the workload input should be distributed evenly and consistently among the numerous processors. According to the findings of the trial, SM-CDC has the best performance in the terms of least finishing time of 16.76 ms, a high computation offloading rate of 93%, a minimum reaction time of 8 ms, a minimum CPU time of 5.25 ms. [ABSTRACT FROM AUTHOR]

Farming has a plethora of difficult responsibilities, and plant monitoring is one of them. There is also an urgent need to increase the number of alternative techniques for detecting plant diseases, which is now lacking. The agriculture and agricultural support sectors in India provide employment for the great majority of the country's people. In India, the agricultural production of the country is directly connected to the country's economic growth rate. In order to sustain healthy plant development, a variety of processes must be followed, including consideration of environmental factors and water supply management for the optimal production of crops. It is inefficient and uncertain in its outcomes to use the traditional method of watering a lawn. The devastation of more than 18% of the world's agricultural produce is caused by disease attacks on an annual basis. Because it is difficult to execute these activities manually, identifying plant diseases is essential to decreasing losses in the agricultural product business. In addition to diagnosing a wide range of plant ailments, our method also includes the identification of infections as a prophylactic step. Below is a detailed description of a farm-based module that includes numerous cloud data centers and data conversion devices for accurately monitoring and managing farm information and environmental elements. This procedure involves imaging the plant's visually obvious signs in order to identify disease. It is recommended that the therapy be used in conjunction with an application to minimize any harm. Increased productivity as a result of the suggested approach would help both the agricultural and irrigation sectors. The plant area module is fitted with a mobile camera that captures images of all of the plants in the area, and all of the plants' information is saved in a database, which is accessible from any computer with Internet access. It is planned to record information on the plant's name, the type of illness that has been afflicted, and an image of the plant. In a wide range of applications, bots are used to collect images of various plants as well as to prevent disease transmission. To ensure that all information given is retained on the Internet, data is collected and stored in cloud storage as it becomes essential to regulate the condition. According to our findings from our research on wide images of healthy and ill fruit and plant leaves, real-time diagnosis of plant leaf diseases may be done with 98.78% accuracy in a laboratory environment. We utilized 40,000 photographs and then analyzed 10,000 photos to construct a DCDM deep learning model, which was then used to train additional models on the data set. Using a cloud-based image diagnostic and classification service, consumers may receive information about their condition in less than a second on average, with the process requiring only 0.349 s on average. [ABSTRACT FROM AUTHOR]

Cloud services have become a demand in big cloud data centers for allocating energy-efficient resources and resource utilization. Most current solutions consider a small number of resource categories, which causes an imbalanced load or the needless activation of real servers. Thus, the virtual machine placement-based bin padding algorithm was proposed and analyzed with the equilibrium state of multi-resource utilization. Additionally, the impact of imbalanced resources causes weak virtual machine task allocation that induces poor VMs' scheduling activities. Therefore, this work incorporates efficient conjectured scheduling to prioritize the task based on computational time with trust parameters calculation. Moreover, to improve the performance, the new abatement strategy was used with efficient resource management, which generates a physical host to minimize energy burning and increase the quality of service to the data center by reducing overload. Implementing the proposed algorithm efficiently utilizes the memory of up to 290 MB for processing 90 MB files with a low computational time of 23 s Further algorithms tested on ten benchmark functions show that they outperform similar algorithms. Thus, the proposed model applies to individual servers in the cloud. [ABSTRACT FROM AUTHOR]

With the widespread popularity of cloud storage, a growing quantity of tenants prefer to upload their massive data to remote cloud data center for saving local cost. Due to the great market prospect, a large quantity of enterprises provide cloud storage services, which are equipped with different prices, reliability, security, and so on. Hence, outsourced data transfer has become a fundamental requirement for tenants to flexibly change cloud service providers (CSPs) to enjoy more suitable services. Nevertheless, how to guarantee the data integrity when the data are transferred from a cloud data center to another is a concern of tenants. To solve this concern, we design a new validation data structure, namely, counting Bloom filter tree (CBFT), which can be viewed as a specific binary tree based on CBF. Then, we present an efficient outsourced data transfer scheme supporting provable data deletion, in which tenants can flexibly change CSPs and transfer their outsourced data blocks from a cloud data center to another without retrieving them. At the same time, after the data are successfully transferred, tenants can validate the transferred data integrity and usability on the new cloud data center and permanently delete the transferred data from the old cloud data center. Moreover, the formal security analysis proves that our new solution can achieve all of the anticipant security goals without interaction with a third party. At last, we develop a prototype system and implement our new solution, thus providing accurate performance evaluation, which intuitively presents the high efficiency and practicality of our new solution. [ABSTRACT FROM AUTHOR]

Because the distributed storage system is based on network technology, it can store data in multiple independent low-cost physical storage devices, and it is also suitable for large-capacity storage, so it has become more and more popular. Today, common applications of distributed storage systems include cloud storage services, data center storage services, and P2P storage services. Typical ones are GFS, HDFS, OceanStore, and Dynamo. Due to regional and economic differences, the development level of global e-commerce (b2c) is very inconsistent. b2c contains the following key tags: buying and selling, which is the core of the website platform. E-commerce provides business users with transparent information and high-quality cheap products. Logistics is the basic guarantee for customers to execute transactions, and it is also a strict indicator of the website platform. There will be many visits during the operation of the e-commerce system, and the number of users in the early stage will increase exponentially. A safe and efficient e-commerce system can provide users with one-stop transaction support and convenient transaction processes. The personalized recommendation system has formulated some rules for certain fields, based on these rules, and defined certain types of knowledge for certain items to meet the needs of certain users and use the defined reasoning rules to generate recommendation results. [ABSTRACT FROM AUTHOR]

Computational science simulations produce huge volumes of data for scientific research organizations. Often, this data is shared by data centers distributed geographically for storage and analysis. Data corruption in the end-to-end route of data transmission is one of the major challenges in distributing the data geographically. End-to-end integrity verification is therefore critical for transmitting such data across data centers effectively. Although several data integrity techniques currently exist, most have a significant negative influence on the data transmission rate as well as the storage overhead. Therefore, existing data integrity techniques are not viable solutions in high performance computing environments where it is very common to transfer huge volumes of data across data centers. In this study, we propose a two-phase Bloom-filter-based end-to-end data integrity verification framework for object-based big data transfer systems. The proposed solution effectively handles data integrity errors by reducing the memory and storage overhead and minimizing the impact on the overall data transmission rate. We investigated the memory, storage, and data transfer rate overheads of the proposed data integrity verification framework on the overall data transfer performance. The experimental findings showed that the suggested framework had 5% and 10% overhead on the total data transmission rate and on the total memory usage, respectively. However, we observed significant savings in terms of storage requirements, when compared with state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

To monitor the status and mission progress of automated guided vehicles (AGVs), most platforms typically obtained real-time data through a data acquisition system that is deployed on the end vehicles. The data acquired from an end vehicle are generally sparse but are required frequently, and an examination process using cloud storage cannot commence until the device's raw data are received. To reduce communication costs, the proposed edge-based monitoring system (EMS) applies edge computation to move the data examination from the cloud to an end site. The data buffered in the end device could be pre-processed by some detectors. For example, checking the energy is adequate for returning to the base. Thus, buffering data on the end device helps to minimize the time required by the decision maker for abnormal events, e.g., shutdowns caused by exhausted energy. In addition to adopting the common methods of storing, processing, and analyzing data at the data center, the EMS moves some time-sensitive services to the end vehicle. Moreover, after obtaining real-time motion data, the edge computing architecture immediately targets abnormal actions and sends reaction commands to shorten the decision making delay caused by the communication cost between the end vehicles and cloud storage sites, thereby avoiding collisions or accidents. The EMS has been implemented to monitor AGV and unmanned aerial vehicles. The EMS primarily monitored the power and motion of the vehicles. It also combined task-oriented motion commands for monitoring unexpected vehicle motions during tasks. If an abnormal event occurred, immediate warnings were provided through a notification interface and were immediately processed by the EMS to ensure safety during task execution. After checking data consistency between the EMS and the real device, the EMS reveals the corrected status of the device with very little delay. Therefore, the EMS could help with minimizing the time taken to make decisions. Moreover, the EMS has been modified to be deployed on drones to confirm its cross-platform applicability. In the simulations of drones, the EMS also got similar results to the simulations of AGVs. Therefore, the EMS could reduce the time in examining abnormal events and has cross-platform functionality. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) and cloud computing are combined to form a cloud computing data center, and cloud computing provides virtualization, storage, computing, and other support services for IoT applications. Data is the foundation and core of cloud IoT platform applications, and massive multisource heterogeneous IoT data aggregation and storage have basic requirements such as real-time, security, and scalability. This paper focuses on the aggregation and storage methods of massive heterogeneous cloud IoT data, solving the multisource data aggregation problem caused by inconsistent protocols and the heterogeneous data storage problem caused by inconsistent data types. A heterogeneous network protocol adaptation and data aggregation method is proposed for the multisource data aggregation problem caused by protocol inconsistency. A protocol adaptation layer is set up in the IoT virtual gateway to achieve compatibility with multiple types of data aggregation protocols, ensuring adaptive access to different types of IoT nodes, and on this basis, data is transmitted to the cloud IoT platform through a unified interface, shielding the variability of IoT sensing devices. Given the problems of device forgery and malicious tampering in the data aggregation process, we propose a fast authentication and data storage method for IoT devices based on "API key" and implant the device authentication API key into the protocol adaptation layer of the virtual gateway to realize the source authentication of IoT nodes and ensure the authenticity of data. [ABSTRACT FROM AUTHOR]

For this special section on "geographies of the digital," we explore how DNA‐based data storage is touted as an alternative to traditional storage modalities and pitched by the data storage industry as a more efficient, stable, and long‐term archival solution than that offered by current technologies. In analyzing how DNA is soaked in the language of sustainability, life, and longevity by those trumpeting the new technology, we situate emergent discourses proposing DNA as a remedy to energy‐, water‐, and land‐intensive data centres and cloud storage. While DNA is not an "online" data storage technology, we show that the prospects of biological computation have altered the imagined futurity of cloud infrastructure. We then explain how DNA data storage works, and we complete the paper with three case studies—Microvenus, The National Film and Sound Archive of Australia, and The Arch Mission Foundation's Lunar Library—offering a critique of "the archive" as it is framed through these nascent scientific and technological discourses. In sum, we argue that DNA‐based data storage is imbricated by an apocalyptic thinking, and that the temporality and timing of this technology speaks to growing, unevenly distributed, planetary anxieties. Key Messages: Vocabularies and metaphors used to promote DNA storage serve an important function to bridge discourses of sustainability and expansionist Big Tech practices.There is a tension between current DNA data storage initiatives/case studies and environmentally sustainable practices.The "timeliness" of DNA data storage is based on the collapsing of multiple temporalities in a form of apocalyptic alarmism. [ABSTRACT FROM AUTHOR]

The growing demand for cloud computing and storage necessitates the expansion of Data Centers, thereby increasing energy consumption and environmental footprint. The data-driven dynamic thermal control presents a promising solution to mitigate this issue, as it can offer improved thermal control strategies and address the intricate and stochastic nature of the thermal environment. Nonetheless, existing solutions typically exhibit deficiencies in terms of reliability and practicability. To this end, this paper proposes a feasible uncertainty-aware online learning method with reliable dynamics models and controllers. Specifically, Deep Learning models with the capacity for uncertainty quantification are employed to account for the imperfections in pre-trained models, hence providing reliable predictions. The Cross-Entropy Method and Monte Carlo trajectory sampling solve optimization problems while considering uncertainties. It is mathematically analyzed that the uncertainty quantified by the system dynamics model can be captured with sufficient trajectory samples, rendering a reliable controller. A practical framework is proposed and tested on a widely used Data Center model to mitigate the insufficient capacity of Deep Learning models to quantify uncertainties. The results demonstrate its superior performance during the initial training stage, achieving a 66% reduction in violations and 3.76% power savings compared to the default controller. Continuous active online learning enhances its adaptability, resulting in an 80% reduction in violations and a 6% energy saving. The thermal control strategies are thus improved by the proposed new solutions and insights. [ABSTRACT FROM AUTHOR]

Cloud computing has revolutionized the modes of computing. With huge success and diverse benefits, the paradigm faces several challenges as well. Power consumption, dynamic resource scaling, and over- and under-provisioning issues are challenges for the cloud computing paradigm. The research has been carried out in cloud computing for resource utilization prediction to overcome over- and under-provisioning issues. Over-provisioning of resources consumes more energy and leads to high costs. However, under-provisioning induces Service Level Agreement (SLA) violation and Quality of Service (QoS) degradation. Most of the existing mechanisms focus on single resource utilization prediction, such as memory, CPU, storage, network, or servers allocated to cloud applications but overlook the correlation among resources. This research focuses on multi-resource utilization prediction using Functional Link Neural Network (FLNN) with hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The proposed technique is evaluated on Google cluster traces data. Experimental results show that the proposed model yields better accuracy as compared to traditional techniques. [ABSTRACT FROM AUTHOR]

The fast access to data and high parallel processing in high-performance computing instigates an urgent demand on the improvement of the NVMe storage within modern data centers. However, the former NVMe virtualization’s unsatisfactory performance demonstrates that NVMe devices are often underutilized within cloud platforms. An NVMe virtualization mechanism with high performance and device sharing has captured researchers and developers’ attention. This article introduces MDev-NVMe, a new virtualization solution for NVMe storage device with (1) full NVMe storage virtualization for VMs running native NVMe driver, (2) a mediated pass-through mechanism for NVMe management, and (3) adaptive configuration of active polling optimization to simultaneously achieve high throughput, low latency performance, and substantial device scalability. We practically implement the MDev-NVMe as a Linux kernel module. This article subsequently evaluates MDev-NVMe with Intel OPTANE and P3600 SSD by comparing several mainstream NVMe virtualization mechanisms using application-level I/O benchmarks. MDev-NVMe with active polling can demonstrate a 142 percent improvement over native (interrupt-driven) throughput and over 2.5 × the Virtio throughput with only 70 percent native average latency and 31 percent Virtio average latency. Finally, the advantages of MDev-NVMe and the importance of adaptive polling are discussed, offering evidence that MDev-NVMe is a superior virtualization choice for cloud storage. [ABSTRACT FROM AUTHOR]

The data center market grows rapidly with the increase of data and its corresponding applications (e.g., machine learning, cloud storage, Internet of Things, and so on). The growth is boosted recently due to the shift of activities online during the COVID-19 pandemic. Reducing the energy consumption of data centers faces various challenges that are further aggravated by the tropical conditions with high temperature and humidity in the tropics like Singapore. The prevailing siloed approach of operating the information technology (IT) and the facility systems separately has resulted in wasteful over-provisioning. The recently proposed approaches for energy usage minimization under various constraints including thermal safety scale poorly with the data center size and often result in non-optimal solutions. To advance the state of the art, we apply deep reinforcement learning (DRL) to address the scalability problem and achieve optimality over a long time horizon in reducing data center energy usage. In particular, we deploy the data-driven deep model and physical rule based model in lieu of the physical data center during the training and validation phases to manage the thermal safety risks caused by DRL's strategy of learning from errors. [ABSTRACT FROM AUTHOR]

In educational environments, technological adoption in the last 10 years has enabled a data-driven and decisionmaking paradigm in organizations. The integration of cloud services in schools and universities is a positive shift in the field of learning, but it also presents threats to all academic roles that need to be discussed in terms of protection, privacy, and confidentiality. Cloud storage brings the ubiquity of data to this technical transition and a delusive opportunity for cost savings. In many cases, this suggests that certain actors, beyond the control of schools and colleges, collect, handle and treat educational data on private servers and data centers. This privatization enables the manipulation of stored records, leaks, and unauthorized access. In this article, we expose the possibilities that open from the viewpoint of local technology adoption. We seek to reduce or even totally solve the detrimental effects of using cloud-based instructional and analytical technology, mixing or only using local technology. Technological methods that conform to this alternate viewpoint and new lines of study are also being suggested and created. [ABSTRACT FROM AUTHOR]

The advent of cloud infrastructure in which third-party cloud services may retain sensitive consumer and company data in storage environments underlines the need to advocate for encryption and multi-tenant shared processing as a primary security mechanism. Digital information movement, storage, and processing are widely defined in terms of "Data in Motion," "Data at Rest," and "Data in Usage," respectively. The implementation of security methods for each of these states can be viewed similarly. Transit data applies to data when being moved from one source of data to another. Transit data contains data sent across the network from back-end clients, programs, and repositories. There could be two data centers inside the same organizational network in the cloud, as a member of completely separate networks. This paper presents a novel architecture data-in-transit defender (DiTD), to protect data in transit; DiTD provides a novel security framework based on high-performance cloud computing. This protocol enables more efficient use of the key strength and time of symmetric block encrypted data, public-key cryptography (PUKC), cryptographic hash, and brief key exchange function. [ABSTRACT FROM AUTHOR]

Nowadays, cloud computing is known as an internet-based modern area among emerging technologies that brings up an environment, in which computing resources such as hardware, software, storage, etc. can be rented by cloud users based on a pay per use model. Since the size of cloud computing is widely expanding and the number of cloud users is also increasing day by day, high energy consumption becomes a serious concern in the operation of complex cloud data centers. In this regards, Virtual Machine (VM) consolidation plays a vital role in utilizing cloud resources in an efficient manner. It migrates the running VMs from overloaded Physical Machines (PMs) to other PMs considering multiple factors, such as migration overhead, energy consumption, resource utilization, and migration time. Since the VM consolidation issue is known as an NP-hard problem, various nature‐inspired meta-heuristic algorithms aiming to solve this problem have been utilized in recent years. However, a lack of systematic and detailed survey study in this field is obvious. Therefore, this gap motivated us to provide the current paper aiming to highlight the role of nature-inspired meta-heuristic algorithms in the VM consolidation problem, review the existing approaches, offer a detailed comparison of approaches based on important factors, and finally, outline the future directions. [ABSTRACT FROM AUTHOR]

Traditional data centers are shifted toward the cloud computing paradigm. These data centers support the increasing demand for computational and data storage that consumes a massive amount of energy at a huge cost to the cloud service provider and the environment. Considerable energy is wasted to constantly operate idle virtual machines (VMs) on hosts during periods of low load. Dynamic consolidation of VMs from overloaded or underloaded hosts is an effective strategy for improving energy consumption and resource utilization in cloud data centers. The dynamic consolidation of VM from an overloaded host directly influences the service level agreements (SLAs), utilization of resources, and quality of service (QoS) delivered by the system. We proposed an algorithm, namely, GradCent, based on the Stochastic Gradient Descent technique. This algorithm is used to develop an upper CPU utilization threshold for detecting overloaded hosts by using a real CPU workload. Moreover, we proposed a dynamic VM selection algorithm called Minimum Size Utilization (MSU) for selecting the VMs from an overloaded host for VM consolidation. GradCent and MSU maintain the trade-off between energy consumption minimization and QoS maximization under specified SLA goal. We used the CloudSim simulations with real-world workload traces from more than a thousand PlanetLab VMs. The proposed algorithms minimized energy consumption and SLA violation by 23% and 27.5% on average, compared with baseline schemes, respectively. [ABSTRACT FROM AUTHOR]

Storing extensive data in cloud environments affects service quality, transmission speed, and access to information in systems, which is becoming a growing challenge. In storage improvement, reducing various costs and reducing the shortest path in the storage of distributed cloud data centers are among the important issues in the field of cloud computing. In this paper, particle swarm optimization (PSO) algorithm and learning automaton (LA) are used to minimize the cost of a data center, which includes communication, data transfer, and storage and optimization of communication between data centers. To improve storage in distributed data centers, a new model called LAPSO is proposed by combining LA and PSO, in which LA improves particle control by searching for particle speed and position. In this method, LA moves each particle in the direction where it has the best individual and group experiences. In multipeak problems, it does not fall into local optimums. Results of the experiments are shown on the dataset of spatial information and cadastre of country lands, which includes 13 data centers. The proposed method evaluates and improves the optimal position parameters, minimum route cost, distance, data transfer cost, storage cost, data communication cost, load balance, and access performance better than other methods. [ABSTRACT FROM AUTHOR]

The Internet and modern computing have produced explosive growth in data volumes, but also new ways to store data. When storing petabytes of data for analysis and to gain new insight, related decisions are made about the storage and processing of big data. Organizations physically store their data on servers in their own data centers. Storing data on servers in the own data center is called on-premises or onprem storage. In the big data world, there is a growing need in providing useful insights from different kinds of data and using data to infer vital information towards better decision making. The main challenge is to store such data with velocity into a commercial data warehouse. The effective solution for storing such data is to represent the unstructured data into structured data and maintain it using distributed clusters and cloud storage based on the user requirements. The structured data maintained in the cloud are cost efficient and infinitely scalable. To query these huge datasets from cloud storage, there is a need for distributed query engines like Hive, Impala, Presto, and Drill. These opensource Structured Query Language (SQL) engines are capable of querying enormous datasets almost instantaneously. The present work has an emphasis on distributed SQL engines like Hive and Impala that can query extremely large datasets. The focus is more on Hive and Impala which are the most widely deployed of these query engines. The outcome of the research helps the readers to understand the challenges being faced in providing and maintaining structured data in the cloud. [ABSTRACT FROM AUTHOR]

Cloud Storage Providers (CSPs) provide geography features of an area data stores offering numerous classes for storage accompanied by various costs. One significant problem faced by cloud consumers is how to utilize these storage classes to deliver a device with the time-varying workload on its objects at a minimal price. This price includes the price of resident (for example, storage, put and get prices) and the possible migration price (for example, network price). This paper proposes the Replication and Migration Cost Minimization (RMCM) algorithm in Green Cloud Computing to tackle this issue. This algorithm is using VM's direct migration method, which could decrease data centre prices and power usage by combining virtual resources. To decrease the price of data-placement for devices accompanied by varying workloads over time, developers must make optimal use of the price variation between storage and network services across multiple CSPs. This paper proposes an optimal cost-effective technique (OCET) for copying and migrating data into cloud data centres accompanied by numerous storage classes to attain this aim. This work goal to achieve price reduction in the load assign procedures in multiple data centre environments where virtual machines allocated to a provided data center taking into account energy price differences and the availability of local renewable power generation. The simulation outcomes demonstrate that the RMCM and OCET algorithm could reduce a replication and migration cost and decrease the power consumption of data centre in green cloud computing efficiently. [ABSTRACT FROM AUTHOR]

Cloud computing is well-known for delivering hosted services over the Internet. These services are mainly divided into three, such as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). Cloud computing uses the computer system resources, especially storage, and computing power, and is available on-demand without direct active management by the user. The term is generally used to describe data centers available to many users over the Internet. Large clouds are predominant today, and data de-duplication is one of the strategies used to solve the repetition of information. This paper proposes preventing the replication of documents and media records like pictures using the TinEye algorithm. The deduplication strategies are commonly used in the cloud server to decrease the server's gap. To save the unauthorized use of statistics and gaining access to and creating reproduction information on the cloud, the encryption approach is to encrypt the facts before being stored on a cloud server. In this proposed system CloudMe is used for cloud storage. All files are encrypted using the AES algorithm and saved in the real cloud. A secure system architecture layout provides secure deduplication. The design permits the cloud with the critical de duplication functionality to completely cast off the bigger storage and bandwidth price. Thus, the proposed system involves image files in opposition to deduplication strategies to eliminate more storage and bandwidth value. [ABSTRACT FROM AUTHOR]

The introduction of cloud computing systems brought with itself a solution for the dynamic scaling of computing resources leveraging various approaches for providing computing power, networking, and storage. On the other hand, it helped decrease the human resource cost by delegating the maintenance cost of infrastructures and platforms to the cloud providers. Nevertheless, the security risks of utilizing shared resources are recognized as one of the major concerns in using cloud computing environments. To be more specific, an intruder can attack a virtual machine and consequently extend his/her attack to other virtual machines that are co-located on the same physical machine. The worst situation is when the hypervisor is compromised in which all the virtual machines assigned to the physical node will be under security risk. To address these issues, we have proposed a security-aware virtual machine placement scheme to reduce the risk of co-location for vulnerable virtual machines. Four attributes are introduced to reduce the aforementioned risk including the vulnerability level of a virtual machine, the importance level of a virtual machine in the given context, the cumulative vulnerability level of a physical machine, and the capacity of a physical machine for the allocation of new virtual machines. Nevertheless, the evaluation of security risks, due to the various vulnerabilities' nature as well as the different properties of deployment environments is not quite accurate. To manage the precision of security evaluations, it is vital to consider hesitancy factors regarding security evaluations. To consider hesitancy in the proposed method, hesitant fuzzy sets are used. In the proposed method, the priorities of the cloud provider for the allocation of virtual machines are also considered. This will allow the model to assign more weights to attributes that have higher importance for the cloud provider. Eventually, the simulation results for the devised scenarios demonstrate that the proposed method can reduce the overall security risk of the given cloud data center. The results show that the proposed approach can reduce the risk of attacks caused by the co-location of virtual machines up to 41% compared to the existing approaches. [ABSTRACT FROM AUTHOR]

Presently, the cloud computing environment attracts many application developers to deploy their web applications on cloud data centers. Kubernetes, a well-known container orchestration for deploying web applications on cloud systems, offers an automatic scaling feature to meet clients' ever-changing demands with the reactive approach. This paper proposes a system architecture based on Kubernetes with a proactive custom autoscaler using a deep neural network model to handle the workload during run time dynamically. The proposed system architecture is designed based on the Monitor–Analyze–Plan–Execute (MAPE) loop. The main contribution of this paper is the proactive custom autoscaler, which focuses on the analysis and planning phases. In analysis phase, Bidirectional Long Short-term Memory (Bi-LSTM) is applied to predict the number of HTTP workloads in the future. In the planning phase, a cooling-down time period is implemented to mitigate the oscillation problem. In addition, a resource removal strategy is proposed to remove a part of the resources when the workload decreases, so that the autoscaler can handle it faster when the burst of workload happens. Through experiments with two different realistic workloads, the Bi-LSTM model achieves better accuracy not only than the Long Short-Term Memory model but also than the state-of-the-art statistical auto-regression integrated moving average model in terms of short- and long-term forecasting. Moreover, it offers 530 to 600 times faster prediction speed than ARIMA models with different workloads. Furthermore, as compared to the LSTM model, the Bi-LSTM model performs better in terms of resource provision accuracy and elastic speedup. Finally, it is shown that the proposed proactive custom autoscaler outperforms the default horizontal pod autoscaler (HPA) of the Kubernetes in terms of accuracy and speed when provisioning and de-provisioning resources. [ABSTRACT FROM AUTHOR]

This article, titled "Retracted: A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering," has been retracted by the publisher Hindawi due to evidence of systematic manipulation of the publication process. The investigation uncovered discrepancies in scope, description of the research, availability of data, inappropriate citations, incoherent content, and manipulated peer review. The retraction notice aims to alert readers that the article's content is unreliable. Wiley and Hindawi have implemented additional measures to ensure research integrity. The corresponding author has been given the opportunity to respond to the retraction. [Extracted from the article]

In order to overcome the problems of long time and low accuracy of traditional methods, a cloud computing data center information classification and storage method based on group collaborative intelligent clustering was proposed. The cloud computing data center information is collected in real time through the information acquisition terminal, and the collected information is transmitted. The optimization function of information classification storage location was constructed by using the group collaborative intelligent clustering algorithm, and the optimal solutions of all storage locations were evolved to obtain the elite set. According to the information attribute characteristics, different information was allocated to different elite sets to realize the classified storage of information in the cloud computing data center. The experimental results show that the longest time of information classification storage is only 0.6 s, the highest information loss rate is 10.0%, and the highest accuracy rate is more than 80%. [ABSTRACT FROM AUTHOR]