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To face with the surge of cloud computing services, energy problem and carbon mission constraint, more and more geo-distributed hybrid powered data centers are build over the world. Geo-distributed hybrid powered data centers are located in different locations equipped with different types of green energy source. In this work, we propose a periodic requests dispatcher, including a periodic model and a spatial balance algorithm, to minimize the brown energy use. The periodic model divides the long-term period into several time slots, and the spatial balance algorithm achieves energy optimization by balancing user requests in each time slot. Besides, a research case is simulated by Cloudsim platform. We conduct several comparative analysis in aspect of energy utilization, electricity cost and carbon emission. The experiment results show that the dispatcher can effectively reduce the electricity cost, and reasonably achieve the request distribution balance in geo-distributed hybrid powered data centers. [ABSTRACT FROM AUTHOR]

Nowadays, with the rapid expansion of cloud computing technology in processing Internet of Things (IoT) workloads, the demand for data centers has significantly increased, leading to a surge in CO2 emissions, power consumption, and global warming. As a result, extensive research and initiatives have been undertaken to tackle this problem. Two specific approaches focus on enhancing workload scheduling, a complex problem known as NP-Hard, and integrating scheduling into scientific workflows. In this investigation, we present a multi-objective Crow Search Algorithm (CSA) for optimizing both makespan and costs in scientific cloud workflows (CSAMOMC). We conduct a comparative analysis between our approach and the well-known HEFT and TC3pop algorithms, which are commonly used for reducing makespan and optimizing costs. Our findings demonstrate that CSAMOMC is capable of achieving an average makespan reduction of 4.42% and a cost reduction of 4.77% when compared to the aforementioned algorithms. [ABSTRACT FROM AUTHOR]

Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation. [ABSTRACT FROM AUTHOR]

Cloud computing provides consumers and organizations with shared pools of resources for data storage and processing and its optimization is essential as 98% of the allocated resources have been utilized only 86% of 98%. Hence, we carry out optimization to automatically allocate resources. In a cloud data center, Virtual machine placement is essential, and choosing the optimal physical machine to host the virtual machine is a critical step. The efficacy of the Virtual machine placement strategy has a considerable impact on cloud computing efficiency. Today, cloud computing optimization is needed for business goals and competition in the digital landscape for cost reduction (20–28%) and Energy consumption (16–22%), improving performance (30–42%) and scaling (12–14%) to meet changing business needs. Virtual machine placement optimization problems are a class of problems that arise in cloud computing when allocating resources to virtual machines across a set of physical machines or hosts. The goal is to optimize resource utilization (12–16%) while satisfying various constraints, such as performance requirements, availability, and energy efficiency than non-metaheuristic optimization techniques. Several virtual machine placement optimization problems include placement, consolidation, migration, and scheduling. Virtualization facilitated by virtual machine placement and migration meets the ever-increasing demands of a dynamic workload by transferring virtual machines inside cloud data center. Many resource management goals, including power efficiency, load balancing, fault tolerance, and system maintenance, are aided by virtual machine placement and migration. To propose a multi-objective Mayfly virtual machine placement algorithm with a massive cloud data center with different and multi-dimensional resources to handle these issues. A multi-objective, dynamic virtual machine placement strategy simultaneously reduces resource wastage, overcommitment ratio, migration time, service level agreement violation, and energy consumption. This paper presents a dynamic, multi-objective virtual machine placement strategy in cloud data centers based on overcommitment resource allocation to influence Virtual machine Physical machine mapping and achieved an increase in the range of 12.5–14.89% in allocation than the existing works. We validated our method by conducting a performance evaluation study using the CloudSim tool. The experimental results demonstrate that this article improves resource usage while reducing energy consumption, makespan, over-commitment, and physical machine overload. [ABSTRACT FROM AUTHOR]

Job scheduling plays a prominent part in cloud computing, and the production schedule of jobs can increase the cloud system's effectiveness. When serving millions of users at once, cloud computing must provide all user requests with excellent performance and ensure Quality of Service (QoS). A suitable task scheduling algorithm is needed to appropriately and effectively fulfil these requests. Several methods were proposed for job scheduling in cloud computing, but the existing techniques do not provide better efficiency. The Dual Interactive Wasserstein Generative Adversarial Network Optimized with Arithmetic Optimization Algorithm Based Job Scheduling in Cloud-Based Internet of Things is proposed to overcome this issue. Primarily, the data from the Alibaba dataset is preprocessed using the Kernel Co-Relation (KC) method. The preprocessed data is given to the Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) for task forecasting in the dynamic cloud environment, and it generates scheduled tasks as output. Then the Arithmetic Optimization Algorithm (AOA) is utilized to optimize the weight parameters of the Dual Interactive Wasserstein Generative Adversarial Network. The proposed method precisely predicts the future workload and diminishes extravagant power consumption at cloud data centres. The proposed method is implemented in MATLAB. The proposed method attains lower MSE (Mean Square Error), RMSE (Root Mean Square Error), MAPE (Mean Squared Prediction Error), MAE (Mean Absolute Error), and higher results without optimization algorithm before and after normalization compared to the current approaches. [ABSTRACT FROM AUTHOR]

Traditional scheduling techniques are designed to reduce processing times while disregarding energy costs. One way of lowering energy usage is to implement scheduling strategies that distribute tasks to specified resources, which influence the processing time and power usage. Among the primary objectives to be achieved in cloud computing, power and energy consumption of the cloud environment have become issues due to ecological and economic reasons. Despite the existence of research efforts from the past pertaining to the same topic, an ideal solution to this problem has not yet been found. One of the main drawbacks of utilizing cloud computing is that the cloud data centers hosting cloud computing applications use higher volumes of energy, adding to the increased functioning cost and carbon footprint in the environment, which in turn increases the need for energy-efficient systems. In this research work, a hybrid optimization algorithm is presented, which is intended to minimize energy in the cloud computing environment. Thus, the crow search algorithm (CSA) and the sparrow search algorithm (SSA) are combined to obtain the proposed hybrid model. The hybrid approach achieves the optimal position in the shortest period of time and with the least amount of energy, load, and makespan, hence improving system performance. The experiments were conducted using three setups with different task sizes. The analysis while using the task size = 300 shows that the proposed method improved QoS, Resource Utilization (RU) and decreased makespan, energy usage, and load at a rate of 0.5073, 4.4035, 0.0331, and 0.0014, respectively. [ABSTRACT FROM AUTHOR]

Cloud data centers provide incredible services to their customers ubiquitously based on demand and pay-per-use strategy. In virtualized data centers (DC), CPU, RAM, and bandwidth are assigned to a virtual machine (VM) from a group of pooled resources. One of the key issues for virtualized DC is VMs consolidation as it achieves better performance and also reduces the cost. In recent years, researchers have paid more attention to developing the global best solution in DCs. Meanwhile, the global best solution results in a number of redundant migrations and is not desirable for large-scale cloud computing environments. While designing modern software, care must be taken for scaling processes based on consumer demands to slash down the costs of the system. This research proposes an effective strategy based on optimization-enabled VM scaling-based load distribution and optimal switching strategy in the cloud data center. Here, horizontal scaling or vertical scaling is employed to address the overloading complications. The network structure of the cloud is defined depending upon the fat tree model and load distribution of cloud DCs is carried out using pelican Taylor manta ray foraging optimization (P-Taylor MRFO) algorithm by considering multiple objectives, such as power, load, latency, and bandwidth. Based on the load distribution, the switching of the cloud DC to the desired mode is carried out utilizing actor critic neural network. If the system is overloaded, either vertical scaling or horizontal scaling is done based on a predefined threshold elastic scaling using the proposed pelican Adam optimization algorithm (PAOA) based on the horizontal cost that is based on CPU, memory, and hard disk. The PAOA is devised by integrating Pelican optimization algorithm and Adam optimization. However, the proposed model has attained superior results with a minimum load of 0.329, power of 0.532, energy consumption of 0.357, and latency of 0.315. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, future workload prediction is an important requirement in cloud data centers to maintain flexibility and scalability of resources. However, due to unexpected peaks, drops in workload, noise, and redundancy in user requests, there is a considerable variance in resource demands, making it difficult to accurately predict workloads. Therefore, a self‐attention‐based progressive generative adversarial network (SAPGAN) optimized with Giza Pyramids Construction Algorithm (GPCA)‐based workload prediction is proposed for Sustainable Cloud Data Centers (CDC). At first, redundant data in historical data obtained through CDC are filtered utilizing Markov chain random field (MCRF) co‐simulation method. These pre‐processed historical data are supplied to the SAPGAN. The SAPGAN weight parameters are optimized by GPCA. The proposed method is analyzed using 2 benchmark datasets: HTTP traces from Saskatchewan and NASA. The simulation is implemented in JAVA. The performance metrics is examined to verify the efficacy of the proposed technique. The performance of the proposed approach provides 28.70%, 11.87%, and 14.79% higher accuracy; 30.15%, 11.72%, and 18.34% lesser energy consume for the dataset of NASA; and 5.32%, 2.45%, and 5.67% higher accuracy; 12.36%, 24.24%, and 34.16% lesser energy consume for the dataset of Saskatchewan HTTP traces compared with existing methods, such as auto‐adaptive learning in a dynamic cloud environment (AADEA‐WLP‐CDC), a neural network model depending on biphase adaptive learning for anticipating cloud data center workload (BALNN‐WLP‐CDC) and multiple scale ensemble of deep learning framework for multistep‐ahead cloud workload prediction (EMD‐LSTM‐GAN‐WLP‐CDC). [ABSTRACT FROM AUTHOR]

Load balancing is an element that must exist for a cloud server to function properly. Without it, there would be substantial lag and the server won't be able to operate as intended. In a Cloud computing (CC) establishing, load balancing is the process of dividing workloads and computer resources. The distribution of assets from a data centre involves many different factors, including load balancing of workloads in cloud environment. To make best use each virtual machine's (VM) capabilities, load balancing needs to be done in a way that ensures that all VMs have balanced loads. Both overloading and underloading are examples of load unbalance, and both of these types of load unbalance could be fixed by using techniques created especially for load balancing. The research that has been done on the subject have not attempted to take into account the factors that affect the problem of load unbalancing. Results indicate that the LSTM and DForest-based load balancing approach significantly improves cloud resource utilization, reduces response times, and minimizes the occurrence of overloading or underloading scenarios. To effectively design those programmes, it is essential to first understand the advantages and disadvantages of current methodologies before developing efficient AI-based load balancing programmes. Compared to existing method the proposed method is high accuracy 0.98, KNN accuracy is 0.97, SVM accuracy is 0.99, and DForest accuracy is 0.987. [ABSTRACT FROM AUTHOR]

The cloud-computing paradigm based on containers has progressively grown in recent years as a flexible strategy that has proven to be energy efficient. The increasing usage of the container as a service technology in data centers (DCs) among cloud providers highlights the necessity of the container installation design phase in cloud environments. Cloud providers attempt to enhance resource utilization and reduce energy consumption by employing various VM selection and placement policies. This procedure for placement acquires a new aspect, with containers now being deployed on virtual machines (VMs) and those guest VMs being installed on physical machines (PMs). The intricacy of this issue increases when the variety of the containers, VMs, and PMs is taken into account. In this paper, an optimal placement strategy for containers is proposed based on the bio-inspired algorithms. The firefly algorithm has been modified to use discretization strategy (Discrete Firefly Algorithm, DFF) and has also used local search mechanism (Discrete Firefly with Local Search Mechanism, DFFLSM). The proposed versions of firefly algorithm are compared with first fit, first fit decreasing, random algorithm and ant colony algorithm. The comparison is done based on average energy consumption, average active VM, average active PM and average overall service-level agreement violations in the DC. The results show that DFFLSM performs better than all pre-existing container placement algorithms in terms of energy efficiency. It reduces average energy consumption of DC by 9.32% and 40.85% and average active PM by 18.30% and 21.89% in homogenous and heterogeneous environment, respectively. [ABSTRACT FROM AUTHOR]

Large amounts of data are created from sensors in Internet of Things (IoT) services and applications. These data create a challenge in directing these data to the cloud, which needs extreme network bandwidth. Fog computing appears as a modern solution to overcome these challenges, where it can expand the cloud computing model to the boundary of the network, consequently adding a new class of services and applications with high-speed responses compared to the cloud. Cloud and fog computing propose huge amounts of resources for their clients and devices, especially in IoT environments. However, inactive resources and large number of applications and servers in cloud and fog computing data centers waste a huge amount of electricity. This paper will propose a Dynamic Power Provisioning (DPP) system in fog data centers, which consists of a multi-agent system that manages the power consumption for the fog resources in local data centers. The suggested DPP system will be tested by using the CloudSim and iFogsim tools. The outputs show that employing the DPP system in local fog data centers reduced the power consumption for fog resource providers. [ABSTRACT FROM AUTHOR]

The rapid development of cloud computing with virtualization technology has benefited both academia and industry. For any cloud data center at scale, one of the primary challenges is how to effectively orchestrate a large number of virtual machines (VMs) in a performance-aware and cost-effective manner. A key problem here is that the performance interference between VMs can significantly undermine the efficiency of cloud data centers, leading to performance degradation and additional operation cost. To address this issue, extensive studies have been conducted to investigate the problem from different aspects. In this survey, we make a comprehensive investigation into the causes of VM interference and provide an in-depth review of existing research and solutions in the literature. We first categorize existing studies on interference models according to their modeling objectives, metrics used, and modeling methods. Then we revisit interference-aware strategies for scheduling optimization as well as co-optimization-based approaches. Finally, the survey identifies open challenges with respect to VM interference in data centers and discusses possible research directions to provide insights for future research in the area. [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]

The process of remapping virtual machines (VMs) to host machines (HMs), which is defined as VM replacement, affects cloud data centers (DCs) performance. To enhance the performance, optimal replacement of VMs regarding conflicting objectives has been proposed in some research, most of which use the non-dominance method to compare generated solutions. Although the non-dominance approach reaches acceptable results, it is not desirable in the VM replacement problem since we need only one mapping of VMs to HMs as the optimal solution to this problem. Indeed, in this method, a solution is considered better if it outperforms another solution regarding all parameters otherwise, two solutions are considered equally good. Also, a solution can be better even though it demonstrates poor performance regarding some parameters. In this paper, we propose two enhanced multi-objective algorithms, Fuzzy-RLVMrB and Fuzzy-MOVMrB, to address the mentioned problems of the non-dominance algorithm. The proposed algorithms aim to balance the horizontal and vertical load between PMs in terms of processor, bandwidth, and memory. We simulated all algorithms using the CloudSim simulator and compared them in terms of horizontal and vertical load balance, energy consumption, and execution time. The simulation results depict that Fuzzy-RLVMrB and Fuzzy-MOVMrB algorithms outperform other algorithms in terms of vertical load balancing and horizontal load balancing. In addition, Fuzzy-RLVMrB surpasses other approaches in terms of energy consumption. [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]

Fog computing contains resource-constrained devices closer to user proximity, which are insufficient for compute-intensive and large workloads. Cloud computing can help to address this limitation by transparently integrating fog and cloud data centers. However, the existing state-of-the-art does not integrate cloud and fog resources for maintaining application performance by dynamically offloading the tasks to the cloud while minimizing the cost. This paper introduces a new workload/job placement method that performs two-way offloading between cloud and fog to efficiently utilize the fog resources while minimizing the cost of using cloud resources to serve the workload. Our proposed solution is evaluated on various setups, including small-scale and large-scale data centers having homogeneous and heterogeneous physical machines (PMs) with different job batches, and compared with the existing state-of-the-art baseline method. Overall, the proposed solution outperforms the baseline method by utilizing 40.87 % more fog resources while reducing × 1.68 times cloud cost. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) creates a large number of datasets, and these are handled in cloud data centers. IoT services are more delayed when data is sent over longer distances to the cloud. Node deployment is used to improve the performance of the multi-tier IoT-Fog environment by finding minimum distance with low Latency. Several methods have been discussed previously to improve the node deployment strategies but they do not provide good results. To overcome these issues, an Efficient and Multi-Tier Node Deployment Strategy using Variable Tangent Search Optimization Algorithm (VTSOA) is proposed in an IoT-Fog Environment. This Multi-Tier Node Deployment Strategy consists of several layers: IoT device layer, Fog layer, and cloud layer. The IoT device layer collects the data from external devices and is transmitted to the Fog layer. The fog layer contains several nodes. Hence, it increases the Latency of sending the data to the cloud. Therefore, VTSOA-based node deployment is done in the fog layer which finds the minimum distance nodes for effective communication. In this, the proposed approach is implemented in MATLAB. After that, the performance of this method is linked to various optimization algorithms. [ABSTRACT FROM AUTHOR]

On-demand provisioning of computing resources using the network over the Cloud consumes a tremendous amount of energy. The energy consumed does not scale out efficiently with the computational use of the resources. A large portion accounts for the power consumption to drive the hardware and cooling facilities for the hardware resources. Nowadays, energy and power consumption have become the most critical concerns in data centers. This work highlights a novel technique to achieve energy efficiency through server consolidation to address the abovementioned situations by effectively scheduling Virtual Machines (VMs) onto the servers and rescheduling active VMs upon deletion of existing VMs through cold migration. An advanced technology, i.e. Wake on LAN (WoL), is employed through power wake for dynamic and wake up of the servers on private Cloud. Compared to existing models, the proposed approach's maximum estimated energy and power saved are experimentally verified to be 54% and 50%, respectively. [ABSTRACT FROM AUTHOR]

Cloud computing has become a crucial paradigm for large-scale data-intensive applications, but it also brings challenges like energy consumption, execution time, heat, and operational costs. Improving workflow scheduling in cloud environments can address these issues and optimize resource utilization, leading to significant ecological and financial benefits. As data centres and networks continue to expand globally, efficient scheduling becomes even more critical for achieving better performance and sustainability in cloud computing. Schedulers mindful of energy and deadlines will assign resources to jobs in a way that consumes the least energy while upholding the task's quality standards. Because this scheduling involves a Non-deterministic Polynomial (NP)-hard problem, the schedulers are able to minimize complexity by utilizing metaheuristic techniques. This work has developed methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for optimizing the scheduler. Local search and exploration are respectably supported by heuristic algorithms. The algorithm's exploration and exploitation features must also be balanced. The primary objective is to optimize computation-intensive workflows in a way that minimizes both energy consumption and execution time while maximizing throughput. This optimization should be achieved without compromising the Quality of Service (QoS) guarantee provided to users. The focus is on striking a balance between energy efficiency and performance to enhance the overall efficiency and cost-effectiveness of cloud computing environments. According to the simulation findings, the suggested ABC has a higher guarantee ratio for 5000 jobs when compared to the GA, PSO, GA with the longest processing time, and GA with the lowest processing time, by 7.14 percent, 4.7 percent, 3.5 percent, and 2.3 percent, respectively. It is observed that the proposed ABC possesses qualities like high flexibility, great robustness, and quick convergence leading to good performance. [ABSTRACT FROM AUTHOR]

Over the last decade, the widespread adoption of cloud computing has spawned a new branch of the computing industry known as green cloud computing. Cloud computing is improving, and data centers are increasing at regular frequencies to meet the demands of users. On the other hand, cloud providers pose major environmental risks because massive data centers use a large amount of energy and leave a carbon footprint. One possible solution to this issue is the use of green cloud computing. However, clients face significant difficulties in adopting green cloud computing. This study aims to understand the problems faced by client organizations while considering green cloud computing. In addition, this study aims to empirically identify the solution to the challenges faced by green cloud computing practitioners. A questionnaire survey approach was used to get insight into green cloud computing practitioners concerning the challenges they faced and their solutions. Data were obtained from sixty-nine professionals in green cloud computing. The results revealed that "lack of quality of service", "lack of dynamic response", and "lack of services to satisfy client's requirements" are critical for green cloud computing. In addition, sixty-three practices for addressing the challenges in green cloud computing are also identified. The identified challenges and practices of green cloud computing will benefit the client organizations to update and revise their process to consider green cloud computing. In addition, it will also assist vendor organizations in developing, planning, and managing systems concerning client satisfaction. [ABSTRACT FROM AUTHOR]

Optical interconnects have emerged as promising solutions for assisting electrical interconnects in short‐reach scenarios, where high bandwidth density and energy efficiency are of particular importance. Mode‐division multiplexing (MDM), capable of enhancing the bandwidth density and energy efficiency of optical systems, has drawn tremendous interest. However, the chip‐to‐chip MDM optical interconnects are impeded by the multimode chip‐fiber interfaces, where the transverse modes on the MDM chip mismatch with the linear polarization modes in the circular core few‐mode fiber (CCF). Moreover, the high differential group delays (DGDs) of a conventional CCF make the digital signal processing (DSP) computation complex thus consuming high power. To overcome these bottlenecks, a new multimode coupling solution based on a rectangular core few‐mode fiber with ultra‐low DGDs and an integrated multimode coupler is proposed. Based on this coupling scheme, a chip‐to‐chip MDM transmission experiment is performed on the TE00, TM00, TE10, and TM10 modes, and the highest bandwidth density of 19.4 Tb s mm −1 is realized at the chip facet. The DSP power consumption is ≈6% of a conventional CCF‐based system. It is believed that this work will pave the way for ultra‐compact, high density, low cost, and low computation‐complexity optical interconnects, such as data centers, cloud computing, and telecommunications. [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]

Due to the extraordinary advances in tech companies and development in cloud computing, it has turned out to be ever more familiar for companies to integrate virtualization in data centers to entirely exploit their hardware sources. Accordingly, virtualization and security have gone under varied changes recently. Virtualization and its exceptional design have numerous advantages and characteristics over conventional non-virtualized technologies. Nevertheless, these novel characters form novel vulnerabilities and probable attacks in virtualized systems. Many recent studies have developed to detect those attacks, but they still have challenges like, need to invest more, if it must be done fast, data security could be at danger and scalability could be difficult. As a result, the primary goal of this study is to detect attack for getting secured virtualization in cloud. Therefore, the purpose of this research is to suggest a new, two-stage, protected virtualization paradigm for the cloud. At first, the "higher-order statistical features, flow-based features, mutual information (MI) based features and improved correlation features" are derived. Deep convolutional neural network (DCNN) 1 and 2 are then used to classify the derived features. To detect network assaults, LSTM 1 and 2 are applied to the outputs from DCNN 1 and 2. Here, the Self Customized Aquila Optimizer is used to adjust the DCNN weights to their optimum level (SC-AO). Additionally, analysis is performed using a range of metrics. Particularly, the proposed HC + SC-AO scheme attained improved accuracy of 0.938108 and 0.958249 for both the datasets respectively. [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]

To store, analyse and process the large volume of data generated by IoT traditional cloud computing, is used everywhere. However, the traditional cloud data centres have their limitations to handle high latency issues in time-critical applications of IoT and cloud. Their applications are computer gaming, e-healthcare, telemedicine and robot surgery. The high latency in IoTs and cloud includes high computational, communication latency (service) and network latencies. The vital requirement of IoT is to have minimum network, service and computation latencies for real-time applications. Network latency causes a delay in transmitting a message or communication from one location to another. Services that require data in real-time are almost impossible to access the data via the cloud. Traditional cloud computing approaches are unable to fulfil the quality-of-service (QoS) requirements in IoT devices. Researches related to latency reduction techniques are still in infancy. Some new approaches to minimize the latency for transmitting time-sensitive data in real-time are discussed in this paper for cloud and IoT devices. This research will help the researchers and industries to identify the techniques and technologies to minimize the latencies in IoT and cloud. The paper also discusses the research trends and the technical differences between the various technologies and techniques. With the increasing interest in the literature on latency minimization and its requirements for time-sensitive applications; it is important to systematically review and synthesize the approaches, tools, challenges and techniques to minimize latencies in IoT and cloud. This paper aims at systematically reviewing the state of the art of latency minimization to classify approaches, and techniques. The paper uses a PRISMA technique for a systematic review. The paper further identifies challenges and gaps in this regard for future research. We have identified 23 approaches and 32 technologies associated with latencies in the cloud and IoT. A total of 112 papers on latency reduction have been examined under this study. The existing research gaps and works for latency reduction in IoTs are discussed in detail. There are several challenges and gaps, which requires future research work for improving the latency minimization techniques and technologies. Finally, we present some open issues which will determine the future research direction. [ABSTRACT FROM AUTHOR]

Container-based Hadoop distributed file system (HDFS) storage has been widely used in cloud data center networks, while traditional HDFS has single point problem resulting in overall unavailability. In this paper, we mainly study the storage reliability of the Docker container-based HDFS cluster with single point of failure. Firstly, we investigate a data volume-based persistence solution of Hadoop with the single point failure and single backup strategy of HDFS cluster. Secondly, we propose an HDFS-based replica placement algorithm for data storage with considering the performance of the host and container nodes. Thirdly, we design the KADC-KNN data segmentation algorithm to effectively store the persistent data of the Docker container. Extensive experimental results show that this method can effectively ensure the stable storage and fast migration of cluster data. Compared with the most advanced algorithm, the proposed data volume persistence algorithm DVPS can improve the data reliability by 19.8%. The data partitioning algorithm KADC-KNN improves the partitioning accuracy by 20.2% and has lower time overhead. [ABSTRACT FROM AUTHOR]

In the last few years, fog computing has been recognized as a promising approach to support modern IoT applications based on microservices. The main characteristic of this application involve the presence of geographically distributed sensors or mobile end users acting as sources of data. Relying on a cloud computing approach may not represent the most suitable solution in these scenario due to the non-negligible latency between data sources and distant cloud data centers, which may represent an issue in cases involving real-time and latency-sensitive IoT applications. Placing certain tasks, such as preprocessing or data aggregation, in a layer of fog nodes close to sensors or end users may help to decrease the response time of IoT applications as well as the traffic towards the cloud data centers. However, the fog scenario is characterized by a much more complex and heterogeneous infrastructure compared to a cloud data center, where the computing nodes and the inter-node connecting are more homogeneous. As a consequence, the the problem of efficiently placing microservices over distributed fog nodes requires novel and efficient solutions. In this paper, we address this issue by proposing and comparing different heuristics for placing the application microservices over the nodes of a fog infrastructure. We test the performance of the proposed heuristics and their ability to minimize application response times and satisfy the Service Level Agreement across a wide set of operating conditions in order to understand which approach is performs the best depending on the IoT application scenario. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%. [ABSTRACT FROM AUTHOR]

Virtualisation is a major technology in cloud computing for optimising the cloud data centre's power usage. In the current scenario, most of the services are migrated to the cloud, putting more load on the cloud data centres. As a result, the data center's size expands resulting in increased energy usage. To address this problem, a resource allocation optimisation method that is both efficient and effective is necessary. The optimal utilisation of cloud infrastructure and optimisation algorithms plays a vital role. The cloud resources rely on the allocation policy of the virtual machine on cloud resources. A virtual machine placement technique, based on the Harris Hawk Optimisation (HHO) model for the cloud data centre is presented in this paper. The proposed HHO model aims to find the best place for virtual machines on suitable hosts with the least load and power consumption. PlanetLab's real-time workload traces are used for performance evaluation with existing PSO (Particle Swarm Optimisation) and PABFD (Best Fit Decreasing). The performance evaluation of the proposed method is done using power consumption, SLA, CPU utilisation, RAM utilisation, Execution time (ms) and the number of VM migrations. The performance evaluation is done using two simulation scenarios with scaling workload in scenario 1 and increasing resources for the virtual machine to study the performance in underloaded and overloaded conditions. Experimental results show that the proposed HHO algorithm improved execution time(ms) by 4%, had a 27% reduction in power consumption, a 16% reduction in SLA violation and an increase in resource utilisation by 17%. The HHO algorithm is also effective in handling dynamic and uncertain environments, making it suitable for real-world cloud infrastructures. [ABSTRACT FROM AUTHOR]

Big data frameworks such as Storm, Spark and Hadoop are widely deployed in commercial and research applications, the energy consumption of cloud data centers that support big data processing platforms is becoming more and more prominent. However, job scheduling is a complex problem in the presence of various service level agreement (SLA) goals, such as cost reduction and job performance improvement. The highly heterogeneous nature of clusters and the variability of resource requirements acrossworkloads make energy-efficient scheduling on big data platforms extremely complex under SLA constraints. Existing performance-based models and heuristic scheduling methods rely excessively on historical data and are difficult to optimize or modify for changes in load and clusters. In this paper, we construct an energy consumption model based on resource utilization and a reinforcement learning model for energy-efficient scheduling under SLA constraints for Spark clusters, and design two Deep Reinforcement Learning (DRL) algorithms. The cluster scheduler designed and implemented based on this model can automatically capture different load characteristics and inherent cluster characteristics, find the appropriate executor creation policy for resource allocation, and reduce cluster energy consumption under the constraint of job execution time. Experimental results show that the DRL scheduler proposed in this paper saves a maximum energy of about 33% under different load characteristics. [ABSTRACT FROM AUTHOR]

The above article from IET Communications, published online on 16 December 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as one of the most promising technologies for meeting rising computing needs. However, high-performance computing systems are more likely to fail due to the proliferation of components and servers. If a sub-system fails, the entire system may not be functional. In this regard, the occurrence of faults is tolerable using an efficient fault-tolerant method. Since cloud computing involves storing data on a remote network, system failures and congestion are the most common causes of faults. The paper presents a new approach to adopting a fault-tolerant mechanism that adaptively monitors health to detect faults, handles faults using a migration technique, and avoids network congestion. With the advantage of the Ant Colony Optimization (ACO) algorithm and active clustering, the load is distributed evenly in data centers. Simulation results indicate that our algorithm outperforms previous algorithms regarding total execution time and imbalance degree up to 10% and 17%, respectively. [ABSTRACT FROM AUTHOR]

VMP (Virtual Machine Placement) is a crucial technology for energy consumption optimization of the cloud data center. Existing works mainly focus on virtual machine consolidation to increase resource utilization and reduce computing energy consumption. However, existing studies usually ignore the thermal effect that an intensive workload on IT (Information Technology) equipment can raise energy consumption by cooling systems and generate hotspots. In addition, an excessive number of virtual machine migrations increases migration costs and risks violating the SLA (Service Level Agreement) signed with users. In this paper, we present a comprehensive system model and formulate the problem as a constrained multi-objective optimization. We propose a novel thermal-aware VMP strategy to solve the problem by jointly considering virtual machines' migration cost, energy consumption, and heat recirculation around server racks. Our strategy makes placement decisions using MOPFGA (Multi-objective algorithm based on Pathfinder Algorithm and Genetic Algorithm) that combines classic MOPFA and GA enhanced by OBL (Opposition Based Learning) for fast convergence and avoidance of local optimum. Extensive experiments based on CloudSim using real data center workload data from PlanetLab show that our algorithm overcomes the defects of the MOPFA (multi-objective pathfinder algorithm) and GA (genetic algorithm) and significantly improves the overall efficiency of a data center. Compared with several state-of-the-art algorithms, MOPFGA on average reduces virtual machine migrations by 77.52%, increases CRAC (Computer Room Air Conditioner) supply temperature by 1.24%, and reduces cooling energy consumption by 24.78% and computational energy consumption by 23.62%. [ABSTRACT FROM AUTHOR]

The evolution of cloud computing results in the development of numerous huge data-centers. These data-centers use large quantities of electrical energy, which in turn results in high operating cost and high emissions of CO 2 . The grounds for this excessive usage of energy is the inefficient usage of data-center resources (hosts). The resources in the data-center can be in either peak or idle or sleep state. It has been experimentally proven that hosts in the idle state consumes more energy than hosts in sleep state. Through an efficient and optimal VM placement strategy, an energy efficient resource utilization can be achieved, thereby reducing the number of idle hosts in the data-center. This paper introduces two energy efficient VM placement algorithms based on bin packing heuristics considering the physical machine's energy efficiency, Energy Efficient VM Placement (EEVMP) and Modified Energy Efficient VM Placement (MEEVMP), that can reduce the overall energy usage in the data-center. The EEVMP algorithm when compared with the default VM placement algorithm Power-Aware Best-Fit Decreasing algorithm (PABFD) of CloudSim, it reduces the energy consumption by 53%, average SLA violation by 3.5% and number of VM migrations by 64.47%. Further, we have performed MEEVMP algorithm where we achieve a reduction in energy consumption by 54.24%, average SLA violation by 4.39% and number of VM migrations by 67.713 % as compared to PABFD. [ABSTRACT FROM AUTHOR]

Data centers are very energy-intensive facilities that can generate various environmental impacts. Numerous energy, power, and environmental leverages exist and can help cloud providers and data center managers to reduce some of these impacts. But dealing with such heterogeneous leverages can be a challenging task that requires some support from a dedicated framework. This article presents a new approach for modeling, evaluating, and orchestrating a large set of technological and logistical leverages. Our framework is based on leverages modeling and Gantt chart leverages mapping. First experimental results based on selected scenarios show the pertinence of the proposed approach in terms of management facilities and potential impacts reduction. [ABSTRACT FROM AUTHOR]

Supercomputers have been driving innovations for performance and scaling benefiting several scientific applications for the past few decades. Yet their ecosystems remain virtually unchanged when it comes to integrating distributed data-driven workflows, primarily due to rather rigid access methods and restricted configuration management options. X-as-a-Service model of cloud has introduced, among other features, a developer-centric DevOps approach empowering developers of infrastructure, platform to software artefacts, which, unfortunately contemporary supercomputers still lack. We introduce vClusters (versatile software-defined clusters), which is based on Infrastructure-as-code (IaC) technology. vClusters approach is a unique fusion of HPC and cloud technologies resulting in a software-defined, multi-tenant cluster on a supercomputing ecosystem, that, together with software-defined storage, enable DevOps for complex, data-driven workflows like grid middleware, alongside a classic HPC platform. IaC has been a commonplace in cloud computing, however, it lacked adoption within multi-Petascale ecosystems due to concerns related to performance and interoperability with classic HPC data centres' ecosystems. We present an overview of the Swiss National Supercomputing Centre's flagship Alps ecosystem as an implementation target for vClusters for HPC and data-driven workflows. Alps is based on the Cray-HPE Shasta EX supercomputing platform that includes an IaC compliant, microservices architecture (MSA) management system, which we leverage for demonstrating vClusters usage for our diverse operational workflows. We provide implementation details of two operational vClusters platforms: a classic HPC platform that is used predominantly by hundreds of users running thousands of large-scale numerical simulations batch jobs; and a widely used, data-intensive, Grid computing middleware platform used for CERN Worldwide LHC Computing Grid (WLCG) operations. The resulting solution showcases reuse and reduction of common configuration recipes across vCluster implementations, minimising operational change management overheads while introducing flexibility for managing artefacts for DevOps required by diverse workflows. [ABSTRACT FROM AUTHOR]

Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for the submission of the jobs to the cloud centres. The preliminary aim is to reduce the overall power consumption which was ignored in the early days of cloud development. This was due to the performance expectations from cloud servers as they were supposed to provide all the services through their services layers IaaS, PaaS, and SaaS. As time passed and researchers came up with new terminologies and algorithmic architecture for the reduction of power consumption and sustainability, other algorithmic anarchies were also introduced, such as statistical oriented learning and bioinspired algorithms. In this paper, an indepth focus has been done on multiple approaches for migration among virtual machines and find out various issues among existing approaches. The proposed work utilizes elastic scheduling inspired by the smart elastic scheduling algorithm (SESA) to develop a more energy-efficient VM allocation and migration algorithm. The proposed work uses cosine similarity and bandwidth utilization as additional utilities to improve the current performance in terms of QoS. The proposed work is evaluated for overall power consumption and service level agreement violation (SLA-V) and is compared with related state of art techniques. A proposed algorithm is also presented in order to solve problems found during the survey. [ABSTRACT FROM AUTHOR]

Virtual machine placement (VMP) is carried out during virtual machine migration to choose the best physical computer to host the virtual machines. It is a crucial task in cloud computing. It directly affects data center performance, resource utilization, and power consumption, and it can help cloud providers save money on data center maintenance. To optimize various characteristics that affect data centers, VMs, and their runs, numerous VMP strategies have been developed in the cloud computing environment. This paper aims to compare the accuracy and efficiency of nine distinct strategies for treating the VMP as a knapsack problem. In the numerical analysis, we test out various conditions to determine how well the system works. We first illustrate the rate of convergence for algorithms, then the rate of execution time growth for a given number of virtual machines, and lastly the rate of development of CPU usage rate supplied by the nine methods throughout the three analyzed conditions. The obtained results reveal that the neural network algorithm performs better than the other eight approaches. The model performed well, as shown by its ability to provide near-optimal solutions to test cases. [ABSTRACT FROM AUTHOR]

The rapid development of internet of things (IoT) gadgets and the increase in the rate of sending requests from these devices to cloud data centers resulted in congestion and consequently service provisioning delays in the cloud data centers. Accordingly, fog computing emerged as a new computing model to address this challenge. In fogging, services are provisioned at the edge of the network using devices with computing and storage capabilities, which are located through the way to connect IoT devices to cloud data centers. Fog computing aims to alleviate the computing load in data centers and cut the delay of requests down, notably real-time and delay-sensitive requests. To achieve these goals, vitally important challenges such as scheduling requests, balancing the load, and reducing energy consumption, which affects performance and reliability in the edge-fog-cloud computing architecture, should be considered into account. In this paper, a reinforcement learning fog scheduling algorithm is proposed to address these challenges. The experimental results indicate that the proposed algorithm raises the load balance and diminishes the response time compared to the existing scheduling algorithms. Additionally, the proposed algorithm outperforms other approaches in terms of the number of used devices. [ABSTRACT FROM AUTHOR]