Cloud Resources Research Articles

A multi-hierarchy particle swarm optimization-based algorithm for cloud workflow scheduling

In this paper, we consider the market-driven workflow scheduling problem on heterogeneous cloud resources with deadline constraints. The transmission delay between dependent tasks on different virtual machines (VMs) is considered. The objective is to minimize the total monetary cost, which is computed based on the on-demand price structure. Inspired by the heuristic and meta-heuristic algorithms, a multi-hierarchy particle swarm optimization (MHPSO) algorithm is proposed, which mainly consists of three components: (1) the workflow aggregation method to pretreat the workflow structure considering the transmission cost and the VM utilization; (2) the initial population generating method to create a set of initial particles with the given encoding method and the solution generation method; and (3) the hierarchical evolving process where particles are divided into multiple groups and evolved iteratively. We calibrate the parameters and components of the proposal statistically over five well-known workflows with different deadline settings. A comparison of the proposed algorithm to existing methods for the considered problem is carried out. Experimental results demonstrate the proposal is effective for the problem under study.

Dynamic offloading technique for real-time edge-to-cloud computing in heterogeneous MEC–MCC and IoT devices

In the realm of Mobile Cloud Computing (MCC), where mobile environments converge with cloud capabilities to address challenges related to performance, environmental considerations, and security, the complexity of task offloading intensifies with the growing number of cloud providers and application units. While Mobile Edge Computing (MEC) shows promise in enhancing MCC performance, the central research challenge revolves around efficiently determining when to offload computational tasks to the cloud or handle them locally at the edge. It involves finding the optimal balance between utilizing cloud resources and enhancing edge computing performance. This study aims to streamline the decision-making process, seeking a harmonious balance between optimal cloud resource utilization and improved edge computing performance. To this end, we introduce HECOM (Hybrid Edge-to-Cloud Offloading for Heterogeneous Computing), a strategic model that combines three decision-making algorithms and an efficient hybrid computation offloading method. HECOM swiftly identifies optimal offloading servers for individual application units, addressing the challenge of efficient offloading decision-making. Importantly, HECOM provides optimal solutions without waiting for all training processes to conclude. Evaluation results from experiments and simulations affirm that the proposed model exhibits impressive energy efficiency, reducing consumption by 25%, increasing offloading ratio by 30%, and minimizing latency and time delay by 15%, making it ideal for real-time applications. It significantly enhances network resource allocation and achieves a more balanced overall performance profile.

Using a data driven approach for comprehensive Life Cycle Assessment and effective eco design of the Internet of Things: taking LoRa-based IoT systems as examples

As we reach the limits of our current technologies and the number of connected devices grows, scientists put more efforts to estimate and reduce the ecological damage of the Internet of Things. Unfortunately, recent literature related to Life Cycle Assessment and eco design of IoT systems suffers from a major inconvenience so far: it does not put sensor data in the focus of attention. This paper aims to point out explicitly the essential role of this aspect for modeling reference flows, and demonstrate its relevance for appropriate environmental assessment and practical eco design. Also, it aims to illustrate that such modeling process must happen in a comprehensive way. For this, our work relies on a case study addressing smart metering, and we proceed as follows: based on available documentation and inspired by certain aspects of different technologies, we deduce the maximal capacities of key electronic components and construct an unfavorable data flow scenario, to get a rough idea of the reference flow and the long-term impact of our system during its use phase. Results from this procedure are later contrasted with results obtained from a packet traffic analysis, in which local and internet data flow are examined carefully. At the end, we verify the importance of sensor data theoretically and empirically, and we conclude that the reference flow and the impact contributors of a system could be affected not only by the local data transit but also by the complex interactions between edge devices and cloud resources. All our findings are discussed to nourish the state-of-the-art around the environmental impact of using full IoT systems and their sustainable design.

Enhancing Cloud Resource Management Based on Intelligent System

Cloud computing is a significant technology model with the strongest potential to change the way IT activities are carried out. The cloud offers low-cost and scalable IT resources. Cloud providers strive to improve business outcomes so cloud systems become more complex. The intelligent cloud has a number of issues, including optimizing the cloud service and designing and distributing resources adaptively and economically. There is a rising trend toward adopting intelligent technologies to better cloud management in particular. This paper introduces an intelligent method to manage cloud resources without delay or trouble. Starting with designing a system based on a genetic algorithm (GA) as a computational tool to achieve the goal and plan a predictive request then depending on the round robin (RR) algorithm to allocate the requests for processing. To evaluate the performance of this method, the proposed algorithm was verified in 145 requests. Where the implementation system achieved a reasonable result.

Efficient Resource Utilization in IoT and Cloud Computing

With the proliferation of IoT devices, there has been exponential growth in data generation, placing substantial demands on both cloud computing (CC) and internet infrastructure. CC, renowned for its scalability and virtual resource provisioning, is of paramount importance in e-commerce applications. However, the dynamic nature of IoT and cloud services introduces unique challenges, notably in the establishment of service-level agreements (SLAs) and the continuous monitoring of compliance. This paper presents a versatile framework for the adaptation of e-commerce applications to IoT and CC environments. It introduces a comprehensive set of metrics designed to support SLAs by enabling periodic resource assessments, ensuring alignment with service-level objectives (SLOs). This policy-driven approach seeks to automate resource management in the era of CC, thereby reducing the dependency on extensive human intervention in e-commerce applications. This paper culminates with a case study that demonstrates the practical utilization of metrics and policies in the management of cloud resources. Furthermore, it provides valuable insights into the resource requisites for deploying e-commerce applications within the realms of the IoT and CC. This holistic approach holds the potential to streamline the monitoring and administration of CC services, ultimately enhancing their efficiency and reliability.

Detecting and Preventing of DDoS Attack in Cloud Computing Environment Based on Hybrid Technique (Cloudflare and WAF)

The adoption of cloud computing has revolutionized data storage and processing globally. Nevertheless, the need for a close watch on security is paramount. Hence, the aim of this paper, to develop a cloud security model that detects and prevents the risks of Distributed Denial of Service (DDoS) attacks in cloud computing systems which are gravely potent and, on the increase, today. This was done using two approaches: analyzing Transmission Control Protocol/Internet Protocol (TCP/IP) header features of incoming packets in cloud computing environment in order to detect and classify spoofed IP address during DDoS attack via a custom-made Web Application Firewall (WAF); and the integration of the cloud resources with Cloudflare. The result shows that a total of 1,625,192 packets were transmitted in a short period which were captured and analyzed via Wireshark. Several TCP errors were observed over a very short time interval which indicated successful DDoS attack effectively crashing the system. The result varied when the custom-made WAF was put in place, and the attacking lab machine launched a TCP syn flood attack against the web server on port http port 80. A total of 2,353,585 packets were transmitted in a short period which were captured and analyzed using Wireshark and contained less TCP errors indicating successful mitigation of DDoS attacks. When the resources were hosted online and integrated with Cloudflare, integrity checks were successful before the resources were loaded, indicating complete mitigation of attacks. Keywords: Bandwidth, Botnet, Cloudflare, Wireshark and Zombie

Optimizing Workload Scheduling in Cloud Paradigm using Robust Neutrosophic C-Means Clustering Boosted with Fish School Search

In the present internet world, accessing cloud resources for a low cost, according to their needs, is available to all users. Sharing resources is becoming increasingly necessary as people complete their activities in the cloud. It becomes essential for distributed workloads to be optimized to perform efficient workload scheduling and progressing resource utilization in a cloud environment. Scheduling cloud resources considerably benefits from the invention of machine learning and metaheuristic models to address this scenario. Though many existing algorithms are developed in cloud-based task scheduling using unsupervised clustering methods, the problem of unknown task requirements or resource availability in adverse conditions is still challenging. In this study, an uncertainty-based unsupervised technique is constructed to group incoming tasks according to the required resources, and it is scheduled to the most suitable resources more prominently. This paper introduced a Robust Neutrosophic C-Means Clustering boosted with the fish school search algorithm (RNCM-FSSA) for clustering the incoming tasks and the resources based on their requirement and availability. With the degree of indeterminacy, neutrosophic C-means discriminating the deterministic and indeterministic schemes and scheduling them to the optimal resources more effectively. Using the fitness value computed by FFSA, the potential cluster centroids are utilized for clustering, thus avoiding the early convergence in the grouping process. The simulation results explore that the robustness of the proposed RCNM-SSA achieves better resource utilization, the degree of imbalance is minimal, and computation complexity is also considerably decreased compared with other unsupervised models.

RAaaS: Resource Allocation as a Service in multiple cloud providers

Cloud users require a set of specific computing needs for their applications, while cloud providers offer a variety of computing products and services on the Internet. These two cloud players make deals through the use of service level agreements (SLAs) where, for instance, prices and levels of quality of service (QoS) are defined. From the cloud user’s point of view, building a robust set of SLAs becomes a challenging problem when multiple cloud providers are present in the market. The allocation of resources in the cloud to run complex applications with guaranteed reliable, secure and acceptable response times is not an easy task, and this paper aims to tackle this problem. This work describes a resource allocation service that aims to optimize the user’s request of cloud resources (virtual machines — VMs) onto multiple Infrastructure-as-a-Service (IaaS) cloud providers. The Resource-Allocation-as-a-Service (RAaaS) proposed in this paper works as a standalone service between cloud users and cloud providers, and it relies on three different requirements: reliability, processing, and mutual trust. The proposed resource allocation service is carried out using the three very common types of VM billing models: on-demand, reserved and spot, where the spot cost model is employed to furnish low-cost resources for the application allocation to improve its reliability. The contributions of this paper are threefold: (i) a three-dimension SLA encompassing reliability, processing, and trust; (ii) an integer linear program (ILP) to schedule cloud-based VMs to applications considering the three-dimension SLA model, and (iii) a heuristic algorithm to mitigate possible QoS violations. Experimental results show that the proposed RAaaS procedure is capable of optimizing resource allocation considering multiple criteria in the SLA while mitigating the extra costs introduced by mutual trust between customers using redundant spot instances allocation.

A Novel Approach to Cloud Resource Management: Hybrid Machine Learning and Task Scheduling

A Novel Approach to Cloud Resource Management: Hybrid Machine Learning and Task Scheduling

Cloud Computing With Resource Allocation Based on Ant Colony Optimization

In this study, we explore the intricacies of cloud computing technologies, with an emphasis on the challenges and concerns pertinent to resource allocation. Three opti- mization techniques—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) — have been meticulously analyzed concerning their applications, objectives, and operational methodologies. The study underscores these algorithms' pivotal role in enhancing cloud resource optimization, while also elucidat- ing their respective merits and limitations. As the complexity of cloud computing escalates, devising efficacious strategies for resource management and alloca- tion becomes imperative. Such strategies are paramount in aiding organizations in cost containment and performance amplification. The ensuing comparative analysis has been crafted to offer a holistic insight into the three algorithms, thus empowering cloud providers to judiciously select an optimization technique that aligns with the unique de- mands and challenges of their cloud computing infrastructure.

DDoS attack prediction using a honey badger optimization algorithm based feature selection and Bi-LSTM in cloud environment

Users are provided access to on-demand services through the Internet with the assist of cloud computing. Services can be accessed at any time and from any place. Although delivering useful services, this model remains vulnerable to security problems. The accessibility of cloud resources is affected by Distributed Denial of Service (DDoS) attacks, which also present security risks for cloud computing. Unable to access data from cloud services, various advanced risks such as malware injection, packagingas well as virtual machine escapes and DDoS are developed by the attackers. Recently, numerous models were designed for detecting attacks in the cloud, but still they lack certain reasons. To alleviate these concerns, this proposed method presented a DDoS attack prediction using a honey badger optimization algorithm based on feature selection and Bi-LSTM in a cloud environment. Input features are gathered from the DDoS attack dataset as the first step in the process. Following this, input features are transmitted into preprocessing steps, including Bayesian and Z-Score normalization. Preprocessed data is sent into the feature selection phasethat employs Honey Badger Optimization (HBO). In this case, the features are chosen by decreasing their MSE to obtain the best feature. Then, optimal features are fed into the Bi-directional Long Short term Memory (Bi-LSTM) classifier for predicting DDoS attacks. The proposed model is also examined using certain existing approaches, including LSTM, DNN, DBN and ANN. When the performance was examined using the existing method, the Bi-LSTM model achieved 97% accuracy, 95% sensitivity, 90% specificity, 3% error, 94% precision and so on. The proposed model is effective at finding DDoS in a cloud environment.

A Novel Approach of Reducing Energy Consumption by utilizing Big Data analysis in Mobile Cloud Computing

With the rapid proliferation of smart mobile devices and increasing adoption of cloud computing services, energy efficiency has become an important issue in mobile cloud environments. High energy consumption not only results in higher operational costs but also creates sustainability concerns related to cloud infrastructure and services. This paper proposes leveraging big data techniques such as machine learning and predictive analytics to optimize resource allocation and reduce energy consumption in mobile cloud computing. The massive amount of data on factors like user behavior, mobility patterns, network availability, and resource utilization can provide key insights to improve energy efficiency. We present an intelligent predictive framework to forecast mobile cloud resource demands and enable dynamic scaling of cloud configurations aligned to current needs. By proactively adapting cloud resources based on learned models and detected usage patterns, over-provisioning and under-utilization can be minimized. Specifically, we demonstrate how clustering, classification, regression, and times series models derived from contextual usage data can significantly improve energy efficiency when integrated with mobile cloud management systems. The proposed approaches are validated experimentally using simulated workloads and real-world trajectory data sets. Results indicate average energy savings of 42% and up to 62% for certain user groups compared to conventional cloud resource allocation techniques. This work provides an important contribution toward building more sustainable and energy efficient mobile cloud computing systems to meet the mobility and computing demands of the future through the transformative power of big data analytics.

Using IGMP Protocol to Improve the Latency of Cloud Computing

To solve the problem of network latency of cloud computing, organizations usually use the edge computing, which means shorter physical distance from the client, or the parallel computing method, which means separate the task to multi cloud servers. However, these two major solutions do not effectively solve the problem of network latency caused by multiple clients accessing the same resources. In this paper, a new strategy is proposed based on the operation mode of Internet Group Management Protocol (IGMP) to solve the networks latency and waste of network resources caused by multiple clients’ access. This paper would perform the comparison tasks by using Amazon Web Services (AWS). To show the differences, there would be a simulated test of 1000 clients who are trying to access cloud resources from one cloud server. By comparing the total time of 1000 clients receiving their resources, the original group takes 5309 seconds for the cloud server to process the tasks. The test group takes 5034 seconds for the cloud server to process the tasks, which is about 5.68% improvement. Through the research, the conclusion is that if cloud resources are partition properly, the grouping strategy could effectively alleviate the networks latency problem of multiple clients.

Virtual Pedagogy: Scenarios for Future Learning with VR and AR Technologies

Modern education and science are at the stage of active practical implementation of digital technologies aimed at improving the efficiency of the educational process. The fourth wave of industrial development offers digital and technological solutions that enhance the practical learning experience through the use of immersive technologies such as augmented and virtual reality. The purpose of this study is to assess the possibilities of using currently available Virtual Reality / Augmented Reality (VR/AR) technologies and to develop specific recommendations for improving the quality of the educational process, as well as to explore promising areas for the development of immersive education. The study used methods of criterion-based selection of the optimal tool for creating educational virtual content. This selection was made among the research nomenclature of currently available relevant digital platforms based on the calculation of specialised metrics for assessing the focal technical and technological capabilities of the studied cloud resources. Based on the results of this study, the optimal and currently available means of creating educational content has been identified, which provides users with the opportunity to use VR/AR technologies without acquiring special skills and abilities. Pedagogical scenarios that have already been immersed and can be adapted to different sectors of education are proposed. Probable vectors for the further development of educational VR/AR technologies are considered, and a hypothesis is formed about the need to use real experience in implementing immersive and virtualised educational processes to adjust and moderate the future development of global education in the context of the and Education 4.0 paradigms. The results of this study indicate the dynamic development of the field of immersive and virtualised educational space, formulate practical recommendations for using currently available tools for creating virtual educational content, and also point to the problem of low scientific support for the development of the studied field, which requires a systematic approach and a generally accepted strategy in the transformational processes of civilisational evolution. According to the results of the study, it is recommended to actively introduce available cloud-based platforms for creating virtual educational content into educational processes, as well as to use a nomenclature of pedagogical scenarios that have already been adapted and transformed to the use of VR/AR technologies in educational processes. It is necessary to constantly monitor and moderate the processes of involving immersive and virtualisation tools in the educational space.

Cloud-load forecasting via decomposition-aided attention recurrent neural network tuned by modified particle swarm optimization

Recent improvements in networking technologies have led to a significant shift towards distributed cloud-based services. However, adequate management of computation resources by providers is vital to maintain the costs of operations and quality of services. A robust system is needed to forecast demand and prevent excessive resource allocations. Extensive literature review suggests that the potential of recurrent neural networks with attention mechanisms is not sufficiently explored and applied to cloud computing. To address this gap, this work proposes a methodology for forecasting load of cloud resources based on recurrent neural networks with and without attention layers. Utilized deep learning models are further optimized through hyperparameter tuning using a modified particle swarm optimization metaheuristic, which is also introduced in this work. To help models deal with complex non-stationary data sequences, the variational mode decomposition for decomposing complex series has also been utilized. The performance of this approach is compared to several state-of-the-art algorithms on a real-world cloud-load dataset. Captured performance metrics (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document}, mean square error, root mean square error, and index of agreement) strongly indicate that the proposed method has great potential for accurately forecasting cloud load. Further, models optimized by the introduced metaheuristic outperformed competing approaches, which was confirmed by conducted statistical validation. In addition, the best-performing forecasting model has been subjected to SHapley Additive exPlanations analysis to determine the impact each feature has on model forecasts, which could potentially be a very useful tool for cloud providers when making decisions.

Technical Note—Cloud Cost Optimization: Model, Bounds, and Asymptotics

A Near-Optimal Capacity and Scheduling Policy for Cloud Users In “Cloud Cost Optimization: Model, Bounds, and Asymptotics,” Qu, Dawande, and Janakiraman study a long-term, dynamic resource-optimization problem for firms managing various cloud resources to process incoming computing tasks over time. Cloud resources vary in attributes, such as computing speed, memory, accelerators, and storage, tailored to different computing tasks, such as data warehousing, scientific computing, machine learning, and data processing. Firms can choose reserved resources for long-term commitments at lower costs or on-demand resources for flexibility at a higher price. Firms face three key trade-offs: the cost disparity between reserved and on-demand resources, the variation in resource attributes affecting performance and cost, and the challenge of balancing delay and resource costs. We propose an asymptotically optimal policy, demonstrating its effectiveness through a detailed numerical study based on Amazon Web Services data.

Privacy-preserving deep learning on big data in cloud

In the analysis of big data, deep learning is a crucial technique. Big data analysis tasks are typically carried out on the cloud since it offers strong computer capabilities and storage areas. Nevertheless, there is a contradiction between the open nature of the cloud and the demand that data owners maintain their privacy. To use cloud resources for privacy-preserving data training, a viable method must be found. A privacy-preserving deep learning model (PPDLM) is suggested in this research to address this preserving issue. To preserve data privacy, we first encrypted the data using homomorphic encryption (HE) approach. Moreover, the deep learning algorithm's activation function—the sigmoid function—uses the least-squares method to process nonaddition and non-multiplication operations that are not allowed by homomorphic. Finally, experimental results show that PPDLM has a significant effect on the protection of data privacy information. Compared with Non-Privacy Preserving Deep Learning Model (NPPDLM), PPDLM has higher computational efficiency.

Fault Tolerance of Cloud Infrastructure with Machine Learning

Abstract Enhancing the fault tolerance of cloud systems and accurately forecasting cloud performance are pivotal concerns in cloud computing research. This research addresses critical concerns in cloud computing by enhancing fault tolerance and forecasting cloud performance using machine learning models. Leveraging the Google trace dataset with 10000 cloud environment records encompassing diverse metrics, we systematically have employed machine learning algorithms, including linear regression, decision trees, and gradient boosting, to construct predictive models. These models have outperformed baseline methods, with C5.0 and XGBoost showing exceptional accuracy, precision, and reliability in forecasting cloud behavior. Feature importance analysis has identified the ten most influential factors affecting cloud system performance. This work significantly advances cloud optimization and reliability, enabling proactive monitoring, early performance issue detection, and improved fault tolerance. Future research can further refine these predictive models, enhancing cloud resource management and ultimately improving service delivery in cloud computing.

RACE Resource Aware Cost-Efficient Scheduler for Cloud Fog Environment

Abstract: Fog computing is one of the new computing structures which takes the Cloud to the verge of the network. The structure is formulated for applications that need low latency. Fog Computing has been projected to improve the disadvantages of Cloud Computing. The system is confronted with the variability of dynamic resources that are heterogeneous and distributed. Hence, efficient scheduling and resource allocation are necessary to maximize the use of these resources and the satisfaction of users. In this paper, a resource-aware scheduler RACE (Resource Aware Cost-Efficient Scheduler) is proposed to distribute the incoming application modules to Fog devices that maximize resource utilization at the Fog layer, reduces the monetary cost of using Cloud resources with minimum execution time of applications and minimum bandwidth usage. This RACE comprises of two algorithms. The Module Scheduler in RACE categorizes the incoming application modules according to their computation and bandwidth requirements which are then placed by Compare Module. Comprehensive experimental results obtained from the simulation by using ifogsim simulator show that our approach performs better in most of the cases as compared to the Traditional Cloud placement and the baseline algorithm.

Strategies for efficient resource management in federated cloud environments supporting Infrastructure as a Service (IaaS)

The increased market activity and visibility have resulted in a growing demand for additional computer resources to meet the needs of cloud clients. Delivering promised quality of service (QoS) while fulfilling diverse resource demands dynamically might be difficult for a single Cloud Service Provider (CSP). Federated cloud computing, also known as distributed cloud computing, is an established paradigm in which CSPs collaborate to aggregate unused resources, resulting in financial and QoS benefits. Notably, this strategy improves availability and reliability while overcoming individual CSP difficulties in preserving QoS amid fluctuations in resource demand. The federated cloud successfully leverages computing resources even during low-demand periods, demanding a comprehensive resource management strategy for Infrastructure as a Service (IaaS) inside participating CSPs. Such a strategy is crucial for preserving QoS, ensuring availability and dependability, and optimizing underutilized computing resources. This research presents the novel IaaS cloud design, revealing a unique methodology that reimagines traditional cloud systems. The proposed IaaS cloud framework investigates virtual machine migration and resource consolidation, building a strong foundation founded on IaaS principles and emphasizing the crucial role of virtualization. The technique introduces ground-breaking concepts such as Cloud User (CU) and Reputation Management, which are strengthened by specific algorithms that improve cloud service security and trust. Furthermore, the combination of Trust Manager (TM) and Broker Manager (BM) components strengthens SLA control and trust evaluation, aligning smoothly with IaaS standards to improve service quality and reliability. User Profiling, which is classified into private, social, and corporate profiles, provides a separate lens for successful cloud user management inside the IaaS landscape, allowing for customised service delivery. SMI and cutting-edge ranking algorithms, such as the Deep Q-based Algorithm, optimize cloud service provider selection and ranking—an important aspect of IaaS. The use of the Banker's algorithm and a comprehensive Service Level Agreement (SLA) Management plan provides efficient resource allocation, mirroring recognized IaaS standards. This research study not only throws light on these trailblazing techniques, but it also establishes a new standard for IaaS cloud design and resource management.