Search

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]

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]

Cloud computing offers internet-based services to customers. Infrastructure as a service offers consumers virtual computer resources including networking, hardware, and storage. Cloud-hosting startup delays hardware resource allocation by several minutes. Predicting computer demand will address this problem. The performance comparison showed that combining these algorithms was the best way to create a dynamic cloud data centre that efficiently used its resources. One of these challenges is the need of practicing effective SLA management in order to prevent the possibility of SLA breaches and the repercussions of such violations. Exponential Smoothing and Artificial Neural Network (ANN) models in terms of managing SLAs from the point of view of cloud customers as well as cloud providers. We proposed an Exponential Smoothing and Artificial Neural Network model (ESANN) for SLA violation and predict the CPU utilization from time series data. This model includes SLA monitoring, energy consumption, CPU utilization, and accuracy prediction. Experiments show that the suggested approach helps cloud providers reduce service breaches and penalties. ESANN outperforms Exponential Smoothing, LSTM, RACC-MDT, and ARIMA by attaining 6.28%, 16.2%, 27.33%, and 31.2% on the combined performance indicator of Energy SLA Violation, which measures both energy consumption and SLA compliance. [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]

The rapid growth of cloud services for hosting applications in the scientific, commercial, web, and social networks has led to enormous growth in the number of large-scale data centers. By shifting the costs of data center maintenance, hardware, and software from customers to service providers using a pay-as-you-go policy, service providers and customers are benefited. On the other hand, the massive growth of data centers has been accompanied by challenges that have limited the boundaries of this technology. Thus, researchers in this field tend to focus on eliminating these limitations. Since virtualization is at the core of cloud computing, allocating Virtual Machines (VMs) to physical hosts in the Infrastructure as a Service layer (IaaS) is one of the most significant challenges. Nonetheless, the VM allocation problem is a combinatorial optimization problem that is known to be NP-Hard. In this paper, we presented a comprehensive analysis of virtual machine placement problem and outlined different approaches to solving it. This paper aims to provide insight into the challenges and issues for recent virtual machine placement strategies. The current study aims to comprehensively classify the physical resource allocation for VMs by overviewing available trends. [ABSTRACT FROM AUTHOR]

The Proliferation of on-demand usage-based IT services, as well as the diverse range of cloud users, have led to the establishment of energy-hungry hefty cloud data centers. Therefore, cloud service providers are striving to reduce energy consumption for cost-saving and environmental sustainability issues of data centers. In this direction, Virtual Machine (VM) consolidated is a widely used approach to optimize hardware resources at cost of performance degradation due to unnecessary migrations. Hence, the motivation of the proposed approach is to minimize energy consumption while maintaining the performance of cloud data centers. This leads to a reduction in the overall cost and an increase in the reliability of cloud service providers. To achieve this goal Predictive Virtual Machine Consolidation (PVMC) algorithm is proposed using the exponential smoothing moving average (ESMA) method. In the proposed algorithm, the ratio of deviation to utilization is calculated for VM selection and placement. migrating the high CPU using VMs or we can restrict steady resource-consuming VMs from migration. The outcomes of the proposed algorithm are validated on computer-based simulation under a dynamic workload and a variable number of VMs (1–290). The experimental results show an improvement in the mean threshing index (40%, 45%) and instruction energy ratio (15%, 17%) over the existing policies. Hence, the proposed algorithm could be used in real-world data centers for reducing energy consumption while maintaining low service level agreement violations. [ABSTRACT FROM AUTHOR]

Cloud computing in the current scenario comes with a large pool of resources, pay-per-use model and reliable infrastructure. Cloud optimization relies on resource optimization to improve the performance and reliability of the cloud. Fault in the cloud places an important role in defining the reliability of the cloud. The identification of fault is a challenging issue in a modular cloud environment. The researchers have developed various methods for the fault-aware scheduling of cloud resources. The fault-aware resource allocation includes static, dynamic, meta-heuristic, and learning-based approaches. In this article, we primarily focused on existing fault-aware resource allocation techniques and then we proposed a model that will primarily focus on fault forecast in tasks allocation. The projected model is based nature-inspired heuristic approach and intelligent artificial neural network. The fault-tolerant aware ANN-based proposed model focuses on performance improvement and reliability testing proactively. The proposed model surpasses the existing state of art methods for proactive and reactive fault-aware scheduling techniques in a large scale datacenter. The results and discussions section support the reliability assertion of the fault-tolerant aware human brain and nature-inspired model. [ABSTRACT FROM AUTHOR]

The data integrity verification process in cloud has become more promising research area in several Internet of Things (IoT) applications. The traditional data verification approaches use encryption in order to preserve data. Moreover, fog computing is considered as extensively employed virtualized platform and it affords various services including storage as well as services interconnected to computing and networking between user and data center based on standard cloud computing. Moreover, fog computing is an extensive description of cloud computing. Thus, fog servers effectively decrease the latency by integrating fog servers. In this paper, novel model for data integrity authentication and protection is designed in IoT cloud-fog model. This method mainly comprises fog nodes, cloud server, IoT nodes, and key distribution center. Here, dynamic and secure key is produced based on the request to key distribution center based on hashing, Exclusive OR (XOR), homomorphic encryption and polynomial. The fog nodes are employed to encrypt the data gathered from IoT nodes as well as allocate the nearby nodes based on Artificial Bee Colony-based Fuzzy-C-Means (ABC FCM) – based partitioning approach. The proposed data integrity authentication approach in IoT fog cloud system outperformed than other existing methods with respect to detection rate, computational time and memory usage of 0.8541, 34.25 s, and 54.8 MB, respectively. [ABSTRACT FROM AUTHOR]

Cloud computing is gaining a huge popularity for on-demand services on a pay-per-use basis. However, single data centre is restricted in offering the services, as it does not have unlimited resource capacity mostly in the peak demand time. Generally, the count of Virtual Machines (VM) is more in public cloud; still, the security is not ensured. In contrast, the VMs are limited in private cloud with high security. So, the consideration of security levels in task scheduling is remains to be more critical for secured processing. This works intends to afford the optimization strategies for optimal task scheduling with multi-objective constraints in cloud environment. Accordingly, the proposed optimal task allocation framework considers the objectives such as execution time, risk probability, and task priority. For this, a new hybrid optimization algorithm known as Clan Updated Seagull Optimization (CUSO) algorithm is introduced in this work, which is the conceptual blending of Elephant Herding Optimization (EHO) and Seagull Optimization Algorithm (SOA). Finally, the performance of proposed work is evaluated over other conventional models with respect to certain performance measures. [ABSTRACT FROM AUTHOR]

Cloud computing is growing massively in the world of information technology. As a result of which the number of servers and virtual machines is increasing day by day. It leads to huge energy consumption and carbon emission. In this regard, task scheduling has drawn the attention of cloud service providers to reduce energy consumption and increase cloud utilization. This is a crucial issue in a heterogeneous cloud environment. In this paper, energy consumption issues of cloud datacenters are addressed and an Energy-Aware Cloud Task Scheduling (EACTS) algorithm is proposed. It is taking the concept from traditional min-min, max-min, and sufferage heuristics and merge with the energy model. These heuristics are implemented on the heterogeneous cloud environment. EACTS energy model focuses on the estimation of energy consumption in cloud datacenters. The proposed algorithms estimated the makespan, cloud utilization, and energy consumption for a benchmark dataset. The experimental results obtained from EACTS algorithm provides a valid arbitration between energy efficiency and makespan. It has shown the comparison between above mentioned algorithms for various scheduling parameters. [ABSTRACT FROM AUTHOR]

Recently, the virtual machine deployment algorithm uses physical machine less or consumes higher energy in data centers, resulting in declined service quality of cloud data centers or rising operational costs, which leads to a decrease in cloud service provider's earnings finally. According to this situation, a resource clustering algorithm for cloud data centers is proposed. This algorithm systematically analyzes the cloud data center model and physical machine's use ratio, establishes the dynamic resource clustering rules through k-means clustering algorithm, and deploys the virtual machines based on clustering results, so as to promote the use ratio of physical machine and bring down energy consumption in cloud data centers. The experimental results indicate that, regarding the compute-intensive virtual machines in cloud data centers, compared to contrast algorithm, the physical machine's use ratio of this algorithm is improved by 12% on average, and its energy consumption in cloud data center is lowered by 15% on average. Regarding the general-purpose virtual machines in cloud data center, compared to contrast algorithm, the physical machine's use ratio is improved by 14% on average, and its energy consumption in cloud data centers is lowered by 12% on average. Above results demonstrate that this method shows a good effect in the resource management of cloud data centers, which may provide reference to some extent. [ABSTRACT FROM AUTHOR]

Management of IT services is rapidly adapting to the cloud computing environment due to optimized service delivery models. Geo distributed cloud data centers act as a backbone for providing fundamental infrastructure for cloud services delivery. Conversely, their high growing energy consumption rate is the major problem to be addressed. Cloud providers are in a hunger to identify different solutions to tackle energy management and carbon emission. In this work, a multi-cloud environment is modeled as geographically distributed data centers with varying solar power generation corresponding to its location, electricity price, carbon emission, and carbon tax. The energy management of the workload allocation algorithm is strongly dependent on the nature of the application considered. The task deadline and brownout information is used to bring in variation in task types. The renewable energy-aware workload allocation algorithm adaptive to task nature is proposed with migration policy to explore its impact on carbon emission, total energy cost, brown and renewable power consumption. [ABSTRACT FROM AUTHOR]

With ever-growing technical advances, performance of complex scientific and engineering applications has arrived at petaflops and exaflops range. However, massive power drawn from the large scale computing infrastructure has caused commensurate rise in electricity consumption, escalating data center ownership costs besides leaving carbon footprints. Judicious scheduling of complex applications with an objective to reduce overall makespan and reduced energy consumption has become one of the biggest confront in the realm of computing architectures. This paper presents a survey on energy efficient scheduling algorithms based on dynamic voltage and frequency scaling (DVFS) and dynamic power management (DPM) techniques. The parameters considered are mainly the makespan, processor energy (dynamic and static) consumption, and network energy (communication) consumption, wherever appropriate during task scheduling. [ABSTRACT FROM AUTHOR]

In this paper, we address the issue of resource allocation in a Cloud Computing environment. Since the need for cloud resources has led to the rapid growth of data centers and the waste of idle resources, high-power consumption has emerged. Therefore, we develop an approach that reduces energy consumption. Decision-making for adequate tasks and virtual machines (VMs) with their consolidation minimizes this latter. The aim of the proposed approach is energy efficiency. It consists of two processes; the first one allows the mapping of user tasks to VMs. Whereas, the second process consists of mapping virtual machines to the best location (physical machines). This paper focuses on this latter to develop a model by using a deep neural network and the ELECTRE methods supported by the K-nearest neighbor classifier. The experiments show that our model can produce promising results compared to other works of literature. This model also presents good scalability to improve the learning, allowing, thus, to achieve our objectives. [ABSTRACT FROM AUTHOR]

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]

By comparing several cloud computing of big data network center during COVID-19, this paper proposes a new topology model, which realizes two functions of cloud computing big data center caching and big data real-time distribution. In addition, cloud computing network requires higher performance than traditional application big data center, which makes the consideration of network platform construction performance different from the traditional understanding. During COVID-19, we deeply understood the underlying attributes of cloud, combined with the topology model, we can realize the decoupling of cloud computing big data system, change the situation of direct connection between upstream and downstream, and have more reliable and efficient transmission of message and command big data. [ABSTRACT FROM AUTHOR]

Network virtualization technology releases human resources to some extent through network and cloud computing technology, reducing the workload of staff. The application of network virtualization technology in cloud computing data centers is based on this condition to improve work quality and efficiency. The purpose of this paper is to use the fuzzy algorithm to realize network virtualization of cloud computing data center. In this paper, we study the adaptive fuzzy control in depth, and conduct the practical application based on the basic knowledge of adaptive fuzzy control we learned, achieved "learn to use". Apply the design of adaptive fuzzy control to the load balancing algorithm of the network virtual cloud computing data center, realized the load balancing algorithm of the network virtual cloud computing data center based on adaptive fuzzy control. According to the load balancing algorithm based on adaptive fuzzy control to achieve this algorithm by using Internet knowledge, and designed the load balancing system of the whole network virtual cloud computing data center. Test the whole load balancing system which has been achieved, and obtained the performance variance curve of the system under different algorithms. Then obtained advantages and disadvantages of the algorithm by analyzing the experimental data. The experimental results show that the proposed method can effectively improve the execution performance of communication-intensive applications and ensure the stable execution of the application. At the same time, the algorithm inherits the advantages of the general fuzzy control load balancing algorithm. The stability is strong and the variance curve does not appear pulsed fluctuation. There is also no divergence phenomenon with time increased. [ABSTRACT FROM AUTHOR]

Cloud computing is a new framework, which is facing a numerous type of challenges including resource management and energy consumption of data centers. One of the most important duties of cloud service providers is to manage resources and schedule tasks for reducing energy consumption in data centers. In this paper, fuzzy logic is used for finding most adequate DC, improved DVFS algorithm is used for selecting ideal host and developed version of EDF-VD algorithm is utilized for Task scheduling and load balance in cloud computing. Our approach improvement to the current methods including EEVS, DVFS, Homogeneous, MBFD and EEVS-N. [ABSTRACT FROM AUTHOR]

Cloud computing technology in the epoch of large data, which subvert the traditional data computing storage model completely, it is a new type of computing service model. And an increasing number of individuals become the subscribers of the cloud computing, the size of the data center is also growing, the problem of the low utilization of resource and high energy consumption are serious. Then how can we use resources efficiently, reduce energy consumption and improve service quality for the large data center. It is an important method to improve the performance of the entire cloud computing server cluster by the improvement of the cloud computing platform. This paper has studied in-depth research on mainstream open source cloud computing platforms such as OpenStackand improved the resource scheduling mechanism which existed. The works are composed of two parts: the virtual machine initial placement mechanism based on resource performance perceptionand the dynamic migration mechanism of virtual machine based on load perception. [ABSTRACT FROM AUTHOR]

Though cloud data center is highly adapted for flexible, scalable and highly available computing and storage resources, it is vulnerable to failures. Predicting the occurrence of server failure and taking appropriate preventive measures are essential in the cloud data center. In order to develop fault-tolerant cloud data center, a Fault Tree Analysis (FTA) based failure aware virtual machine (VM) migration technique is proposed. A fault tree is constructed for server failure event. The server failure is estimated by analyzing the fault tree and the virtual machines are migrated proactively to an alternate server before the failure. Simulations have been carried out and performance of the proposed technique is analyzed in terms of throughput. The results show that the proposed technique outperforms the other state of art techniques. [ABSTRACT FROM AUTHOR]

With the advent of cloud computing, a cost-effective and reliable choice to employ IT infrastructure, the cyber-physical systems (CPS) are transforming into loosely coupled cloud and fog CPS. The sensor information from physical processes at CPS is continuously processed by fog computing nodes and is forwarded for advanced data analytics offered as a service from the cloud. The computation offloaded by fog devices are initiated as Virtual Machines (VMs) in the cloud data center. The effective placement of these VMs into minimum Physical Machines (PMs) involves economic and environmental issues. Recent research works signify the use of First-Fit Decreasing (FFD) based heuristic techniques to address this NP-Hard problem as a vector bin-packing problem. In this research work, we present a set of hybrid heuristics and an ensemble heuristic to improve the solution quality. The simulation results show that the proposed heuristics are highly scalable and economical in comparison with the individual heuristic-based approaches. [ABSTRACT FROM AUTHOR]

Cloud computing is an integrated service, which provides a new versatile of programming paradigm with a good number of features to dynamically migrate virtualized computing services between physical servers of various cloud data centers. This technology made the users and cloud service providers (CSPs) to manage the loads as per the demand. The effective management of loads in the cloud environment gives a rise to introduce new management methods, which are responsible for equally distributing the incoming workloads among all the available VMs in an effective way for a given period of time. To achieve this requirement various schemes have been proposed. However, many of them are not suitable to find the optimal information about required resources to fulfil the user demands. To accommodate this, in this paper, an approach named heuristic and fair-queuing based VM load balancing (HFQ-LB) has been introduced. With the help of fair-queuing, an efficient strategy has been designed. This configures the number of incoming loads for finding an appropriate VM for the assignment to satisfy the goal, i.e., it maximizes the utilization of resources according to the user demand. With the help of CloudSim, the proposed algorithm is validated in terms of makespan, waiting time, CPU utilization, and VM utilization by creating a multiple number of data centers. The proposed algorithm is also compared with other existing eight algorithms. Experimental results show that the proposed algorithm outperforms these existing algorithms in terms of makespan, waiting time, CPU utilization, and VM utilization. [ABSTRACT FROM AUTHOR]

Cloud computing has become ubiquitous, offers an economical solution for convenient on-demand access to computing resources, which enable the resource-constrained clients to execute extensive computation. However, outsourcing of data and computation to the cloud server is a great cause of concern, such as confidentiality of input/output and verifiability of the result. This paper addresses the problem of designing outsourcing algorithm for linear regression analysis (LR), which is an important data analysis technique and widely applied across multiple domains. The outsourcing framework illustrated by the following scenario: a client is having a large dataset and needs to perform regression analysis, but unable to process due to lack of computing resources. Therefore, the client outsources the computation to the cloud server. In the proposed LR outsourcing algorithm, the client outsources LR problem to the cloud server without revealing to them either the input dataset and the output. The algorithm is a non-interactive solution to the client, it sends only input and receives output along with the proof of verification from the cloud server. The client in the proposed algorithm able to verify the correctness of result with an optimal probability. The analytical analysis shows that the algorithm is successfully meeting the challenges of correctness, security, verifiability, and efficiency. The experimental evaluation validates the proposed algorithm. The result analysis shows that the algorithm is highly efficient and endorses the practical usability of the algorithm. [ABSTRACT FROM AUTHOR]

In Cloud computing, data centers gain popularity as an effective platform for scheduling of resources and hosting cloud applications. However, tremendous amount of energy is consumed by these data centers which leads to high operational costs and contributes towards carbon footprints to the environment. Therefore, there is need of energy aware cloud based framework which schedules computing resources automatically by considering energy consumption as a QoS parameter itself. In this paper, we present fuzzy logic based energy-aware autonomic resource scheduling framework for cloud for energy efficient scheduling of cloud computing resources in data centers. We have evaluated the proposed framework in CloudSim based simulation environment and real cloud environment. The experimental results show that the proposed framework performs better in terms of resource utilization and energy consumption along with other QoS parameters. [ABSTRACT FROM AUTHOR]