Cloud Resources Research Articles

Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning

Due to its flexibility, cloud computing has become essential in modern operational schemes. However, the effective management of cloud resources to ensure cost-effectiveness and maintain high performance presents significant challenges. The pay-as-you-go pricing model, while convenient, can lead to escalated expenses and hinder long-term planning. Consequently, FinOps advocates proactive management strategies, with resource usage prediction emerging as a crucial optimization category. In this research, we introduce the multi-time series forecasting system (MSFS), a novel approach for data-driven resource optimization alongside the hybrid ensemble anomaly detection algorithm (HEADA). Our method prioritizes the concept-centric approach, focusing on factors such as prediction uncertainty, interpretability and domain-specific measures. Furthermore, we introduce the similarity-based time-series grouping (STG) method as a core component of MSFS for optimizing multi-time series forecasting, ensuring its scalability with the rapid growth of the cloud environment. The experiments performed demonstrate that our group-specific forecasting model (GSFM) approach enabled MSFS to achieve a significant cost reduction of up to 44%.

Cloud IaaS Optimization Using Machine Vision at the IoT Edge and the Grid Sensing Algorithm

Security grids consisting of High-Definition (HD) Internet of Things (IoT) cameras are gaining popularity for organizational perimeter surveillance and security monitoring. Transmitting HD video data to cloud infrastructure requires high bandwidth and more storage space than text, audio, and image data. It becomes more challenging for large-scale organizations with massive security grids to minimize cloud network bandwidth and storage costs. This paper presents an application of Machine Vision at the IoT Edge (Mez) technology in association with a novel Grid Sensing (GRS) algorithm to optimize cloud Infrastructure as a Service (IaaS) resource allocation, leading to cost minimization. Experimental results demonstrated a 31.29% reduction in bandwidth and a 22.43% reduction in storage requirements. The Mez technology offers a network latency feedback module with knobs for transforming video frames to adjust to the latency sensitivity. The association of the GRS algorithm introduces its compatibility in the IoT camera-driven security grid by automatically ranking the existing bandwidth requirements by different IoT nodes. As a result, the proposed system minimizes the entire grid’s throughput, contributing to significant cloud resource optimization.

Edge-Cloud Remote Sensing Data-Based Plant Disease Detection Using Deep Neural Networks with Transfer Learning.

The identification and control of plant diseases have become increasingly complex as agricultural lands expand globally. Traditional manual methods of monitoring and diagnosing diseases are not feasible for large-scale farms. The complexity is further heightened by the heterogeneity of data types collected through remote sensing methods, such as images, videos, and sensor readings, which need to be processed in real-time. This paper introduces a novel EdgeCloud Remote Sensing architecture that utilizes Deep Neural Networks (DNNs) with Transfer Learning to enhance the detection of plant diseases using remote sensing data. The proposed system employs a Fuzzy Deep Convolutional Neural Network (FCDCNN) to process multimodal data collected from both edge nodes and satellite point clouds. Transfer learning allows the sharing of model weights between cloud and edge nodes, thus optimizing performance without requiring frequent retraining. Simulations show that the proposed system achieves a 98% detection accuracy and reduces processing time by 25% compared to existing methods. By distributing tasks between edge and cloud resources, the system is able to process large datasets effectively and improve disease detection performance on vast farmlands.

Location-aware job scheduling for IoT systems using cloud and fog

The rapid growth of Internet of Things (IoT) applications generates vast volumes of high-speed data streams from numerous devices. Cloud computing solutions often handle and manage this data; however, for certain applications, the latency introduced by transmitting data from edge devices to the cloud may be unacceptable. This issue is exacerbated by the bandwidth constraints of public networks, which become significant barriers in large-scale IoT implementations. Consequently, effective resource management, service management, data storage, and power management require more robust infrastructure and complex protocols. An "intelligent gateway" based on fog computing can enhance the efficient utilization of cloud and network resources. Resource planning and management in a fog-cloud environment significantly impact system performance, particularly latency. This problem is known to be NP-hard. This paper addresses the challenge of lifetime optimization for scheduling data-intensive tasks in fog and cloud-based IoT systems. Initially, we propose an Integer Linear Programming (ILP) optimization model to frame the problem. Subsequently, we introduce a heuristic method named Data-Locality Aware Job Scheduling in Fog-Cloud (DLSFC), which is derived from the proposed formulation. The effectiveness of DLSFC is evaluated across various system characteristics, with results demonstrating that DLSFC achieves solutions within 85 % of the ideal outcome provided by the LP solver.

Designing an optimal task scheduling and VM placement in the cloud environment with multi-objective constraints using Hybrid Lemurs and Gannet Optimization Algorithm

ABSTRACT An efficient resource utilization method can greatly reduce expenses and unwanted resources. Typical cloud resource planning approaches lack support for the emerging paradigm regarding asset management speed and optimization. The use of cloud computing relies heavily on task planning and allocation of resources. The task scheduling issue is more crucial in arranging and allotting application jobs supplied by customers on Virtual Machines (VM) in a specific manner. The task planning issue needs to be specifically stated to increase scheduling efficiency. The task scheduling in the cloud environment model is developed using optimization techniques. This model intends to optimize both the task scheduling and VM placement over the cloud environment. In this model, a new hybrid-meta-heuristic optimization algorithm is developed named the Hybrid Lemurs-based Gannet Optimization Algorithm (HL-GOA). The multi-objective function is considered with constraints like cost, time, resource utilization, makespan, and throughput. The proposed model is further validated and compared against existing methodologies. The total time required for scheduling and VM placement is 30.23%, 6.25%, 11.76%, and 10.44% reduced than ESO, RSO, LO, and GOA with 2 VMs. The simulation outcomes revealed that the developed model effectively resolved the scheduling and VL placement issues.

Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique

Cloud computing has revolutionized the way businesses and individuals access and utilize computing resources. Efficient virtual machine placement is a critical aspect of optimizing resource utilization, reducing operational costs, energy consumption, service level agreement and minimum virtual machine migrations, execution time, and ensuring the overall performance of cloud services. This manuscript introduces a novel approach that combines the creative problem-solving capabilities of brainstorming with the computational power of Artificial Neural Networks (ANN) to address the virtual machine placement problem in cloud environments. In this study, we propose a hybrid technique that leverages the collective intelligence of human brainstorming to generate a diverse set of placement strategies. These strategies are then evaluated, refined, and optimized using an ANN model trained on historical cloud resource allocation workload logs. By integrating the human creative process with the data-driven predictive capabilities of ANN, our approach aims to overcome the limitations of traditional virtual machine placement algorithms, which often struggle to adapt to dynamic workloads and changing resource requirements. The manuscript provides a detailed description of the hybrid technique, including the process of brainstorming for generating placement strategies, data collection and preprocessing, ANN model development, and the integration of these components into an efficient placement system. We present experimental results demonstrating the effectiveness of our approach in optimizing resource allocation, improving service performance, and optimizing resource utilization, reducing energy consumption, service level agreement and minimum virtual machine migrations, reducing execution time compared to existing static, and meta-heuristic methods. The proposed Brain Storming with ANN based Hybrid Technique offers a promising solution for enhancing the efficiency of virtual machine placement in cloud computing environments. BSO-ANN outperforms the existing techniques using performance metrics (energy consumption(Kwh), execution time(ms), SLA violations, and number of migrations). It combines human ingenuity with data-driven insights to adapt to the ever-changing dynamics of cloud workloads. This manuscript contributes to the ongoing research in cloud resource management, offering a practical approach for cloud service providers and organizations to better utilize their resources and enhance the overall quality of cloud-based services. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd.

Automatically Reducing Privilege for Access Control Policies

Access control policies are programs used to secure cloud resources. These polices should only grant the necessary permissions that a given application needs. However, it is challenging to write and maintain policies as applications and their required permissions change over time. In this paper, we focus on the Amazon Web Services (AWS) IAM policy language and present an approach that, given a policy, synthesizes a modified policy that is more restrictive and better abides to the principle of least privilege. Our approach looks at the actual access history (e.g., access logs) used by an application and computes the least permissive local modification of the user-given policy that still provides the same permissions that were observed in the access history. We treat the problem of computing the least permissive policy as a generalization problem in a lattice of possible policies (i.e., the set of local modifications). We show that our synthesis algorithm comes with correctness guarantees and is amendable to an efficient implementation that is easy to parallelize. We implement our algorithm in a tool IAM-PolicyRefiner and evaluate it on policies attached to AWS roles with access logs. For each role, IAM-PolicyRefiner can compute easy-to-inspect refined policies in less than 1 minute, and the refined policies do not overfit to the requests in the log---i.e., the policies also allow requests in a left-out test set of requests.

Serverless Computing in the Edge-Cloud Continuum : Challenges, Opportunities, and a Novel Framework

This article explores the integration of serverless computing across the edge-cloud continuum, addressing the growing demand for low-latency, data-intensive applications in the era of the Internet of Things (IoT). We present EdgeServe, a novel framework that extends the serverless paradigm to encompass edge devices and cloud resources, overcoming the limitations of traditional cloud-centric approaches. Through comprehensive simulations and real-world case studies, including a smart city traffic management system, an industrial IoT predictive maintenance application, and a mobile augmented reality gaming platform, we evaluate the performance, scalability, and cost-effectiveness of our proposed framework. Our results demonstrate significant improvements in application response times, with latency reductions of up to 82% in time-critical functions, and an average cost reduction of 43% compared to cloud-only serverless deployments. We also observe a 28% decrease in overall energy consumption in certain scenarios. The article addresses key challenges in resource management, data consistency, adaptive function placement, and security in distributed environments. Our findings have important implications for application architects and cloud service providers, paving the way for a new generation of edge-native serverless platforms. This research contributes to the growing body of knowledge on distributed systems and offers insights into the future evolution of serverless computing in heterogeneous computing environments.

A multiple-input fluid queue model for performance evaluation of fog server in an intelligent vehicular network

Nowadays, there is a growing trend towards fog computing which is a relatively new concept that has been introduced as an extension of cloud computing. It is considered as a promising paradigm for intelligent vehicular networks due to its ability to reduce delays and enhance network efficiency. The utilization of fog computing plays a crucial role in tackling the distinct obstacles encountered in vehicular computing, including issues like real-time data processing, and limitations on bandwidth. By relocating computational resources closer to the network’s edge, it enhances the effectiveness, dependability, and safety of vehicular applications while simultaneously boosting privacy and security measures. In addition, fog offloading has played a pivotal role in fog computing and edge computing structures. Its objective is to efficaciously allocate the data and assign the processing of tasks among edge devices, fog nodes, and cloud resources. Efficient fog offloading strategies are adaptable, scalable, and are able to handle faults, making them indispensable for optimizing these architectures. Although efficiency and quality of service are crucial objectives in a fog computing based intelligent vehicular network environment, performance remains a significant concern that cannot be overlooked. In light of this, to evaluate the efficacy of the probabilistic offloading approach on a fog server, a thorough performance evaluation is necessary. This research proposes a fluid queue approach that accounts for the constant flow of data packets while evaluating a fog server’s performance. For a fog computing-based intelligent vehicular network (FCIVN) with numerous heterogeneous smart vehicles (SVs), we construct a multiple-input fluid queue to model the tasks handed over to the fog server in order to evaluate its performance. In an FCIVN, the arrivals are drawn from various sources at the fog server. Accordingly, in the proposed model the fluid queue is modulated by more than one independent and distinct finite-state birth–death processes (BDPs) which control the variable inflow, and another BDP which controls the variable outflow. We present the details concerning an intermediate fog server’s buffer occupancy distribution within an FCIVN. Further, we assess the performance measures in terms of the offered load, expected buffer-level, average throughput and average latency of the tasks in an FCIVN. Finally, quantitative illustrations are presented to demonstrate the appropriateness of the fluid queue model developed in this study. The results are found to be in consistence with the expected behaviour of these performance indicators.

Operational resilience implementation: Challenges and opportunities for UK building societies

Operational resilience is the preparedness and existence of resilient IT systems, processes and resources to provide services continuously during a disruption. This study examines the operational resilience implementation challenges and opportunities for UK building societies and wider financial institutions. The key challenges are resource constraints, understanding regulatory requirements, supplier concerns, domain knowledge, legacy systems and manual-driven processes. Some challenges are being addressed with awareness and training, improved governance and risk management, recruitment and industry support networks. The opportunities include realising the actual state of IT systems, adopting cloud solutions, resources and capabilities, as well as improved domain knowledge and a better understanding of services and markets. Regulators need to be more explicit about their expectations and requirements, while the industry needs to collaborate on setting up common frames of reference for operational resilience frameworks. Lastly, the study provides a holistic view of operational resilience, including its implementation and management challenges, control measures and emerging opportunities.

Enhancing Cloud Task Scheduling with Multi-Objective Optimization Using K-Means Clustering and Dynamic Resource Allocation

The scheduling and resource allocation procedure is an essential component of cloud resource management. Effective resource allocation is severely hampered by the task arrival rates' erratic and unclear behavior. To prevent under or overusing resources, an effective scheduling strategy is necessary. To improve scheduling and allocation performance, a multi-objective optimization technique is presented for the best resource allocation and task scheduling inside scientific workflow datasets in a heterogeneous environment. In the first stage, the system calculates four key metrics: Communication Cost, Computation Cost, Earliest Finished Time on a particular VM, and Total Task Length for a specific scientific workflow dataset. These metrics provide a comprehensive understanding of the resource requirements and help make informed scheduling decisions. In the second stage, tasks are clustered using the K-Means clustering algorithm. This clustering groups similar tasks together, making managing and scheduling them easier. In the third stage, the proposed resource allocation algorithm allocates the clustered tasks to the appropriate VMs. This step ensures that the tasks are assigned to the best-suited resources, optimizing the overall system performance and resource utilization. By following this multi-stage process, the system aims to achieve optimal resource allocation and task scheduling, thereby improving the efficiency and effectiveness of cloud resource management. The proposed method significantly outperforms PSO, CSO, and GWO by consistently achieving lower Makespan—under 400 units at 50 tasks—while maintaining high resource utilization rates above 0.75, demonstrating superior efficiency in task execution and resource management.

Work education and educational developments around sustainable livelihoods for sustainable career development and well-being

Covid-19, Artificial Intelligence (AI), and Climate Change, have disrupted work education, rendering sustainability of careers and livelihoods a concern. This paper outlines a collaborative response to that challenge, offering opportunities for sustainable livelihoods in a work education cloud collaboration, Project SLiC (Sustainable Livelihoods Collaboration). We have joined forces across nation states in the Global South/North to share cloud resources, focused on teaching a postgraduate course, Sustainable Livelihoods. Online modules are stored in a secure cloud site, from which local courses draw-down, autochthonously, whichever resources fit workforce development in context. We outline modules, and an evaluative process, in a proof-of-concept trial. Finally, we envisage how this initial collaboration may morph into a whole degree, including research supervision. We close with a call to career development professionals to share their unique expertise and experiences at the work education frontline, on how to develop this sustainable careers project, for the greater good.

Comprehensive data security and compliance framework for SMEs

Small and Medium-sized Enterprises (SMEs) are increasingly relying on cloud platforms to support critical business operations, making effective disaster recovery (DR) strategies essential for ensuring business continuity. This review proposes a robust disaster recovery framework tailored for SMEs, designed to minimize downtime and data loss in the event of a system failure, cyberattack, or natural disaster. The framework integrates advanced cloud technologies to create a cost-effective, scalable solution that aligns with the resource constraints of SMEs while providing enterprise-grade resilience. Key components of the disaster recovery framework include cloud-based data replication, automated backup solutions, and geo-redundant storage to ensure that data is continuously available and recoverable. This model employs real-time data synchronization and incremental backups to minimize Recovery Point Objectives (RPO), ensuring that critical data is not lost during an unexpected outage. Additionally, the framework leverages automated failover mechanisms to achieve low Recovery Time Objectives (RTO), allowing businesses to restore operations quickly after an interruption. Cloud orchestration tools such as AWS Elastic Disaster Recovery or Azure Site Recovery are utilized to automate disaster recovery processes, reducing manual intervention and improving the speed of recovery. The framework also incorporates regular testing of disaster recovery plans, using simulation tools to identify weaknesses and optimize response times. For SMEs, cost-effectiveness and ease of management are crucial. The framework emphasizes a pay-as-you-go model for cloud resources, allowing businesses to scale their disaster recovery solutions as they grow without incurring excessive upfront costs. By providing continuous monitoring and proactive threat detection, this disaster recovery framework ensures that SMEs can maintain uninterrupted business operations on cloud platforms, thereby enhancing resilience and mitigating the financial and operational risks associated with data loss and system downtime.

API management and cloud integration model for SMEs

In the rapidly evolving technological landscape, Small and Medium-sized Enterprises (SMEs) face significant challenges in maintaining efficient and scalable IT systems. This review outlines a strategic model for API management and cloud integration designed to enhance system performance and optimize cloud-native features. The proposed model focuses on the redesign and optimization of Application Programming Interfaces (APIs) to leverage cloud capabilities effectively, addressing common issues faced by SMEs in system integration and performance. The model begins with a comprehensive assessment of existing API architectures to identify inefficiencies and performance bottlenecks. It emphasizes the need for a cloud-native approach, which includes the adoption of microservices architecture and serverless computing. By redesigning APIs to align with cloud-native principles, the model aims to achieve greater scalability, flexibility, and responsiveness in handling varying workloads. Key components of the model include API versioning strategies, improved security protocols, and enhanced data management practices. The integration of cloud-native features such as containerization, orchestration, and automated scaling ensures that APIs are optimized for performance and reliability. This approach not only streamlines system operations but also reduces the overhead associated with traditional server-based architectures. Furthermore, the model incorporates best practices for continuous integration and continuous deployment (CI/CD), facilitating seamless updates and maintenance of APIs. It also includes monitoring and analytics tools to provide real-time insights into API performance and user interactions, enabling proactive management and optimization. By implementing this strategic model, SMEs can achieve significant improvements in system performance, reduce latency, and enhance overall cloud integration. The model supports the efficient utilization of cloud resources, leading to cost savings and improved operational efficiency. This approach positions SMEs to better compete in the digital economy by leveraging advanced technologies and optimizing their IT infrastructure.

Optimizing Cloud Resource Utilization with ANN-Based VM Placement and Prediction

Optimizing Cloud Resource Utilization with ANN-Based VM Placement and Prediction

CNN-LSTM is all you Need for Efficient Resource Allocation in Cloud Computing

Many organizations have embraced cloud computing in recent years to provide new services, easily expand their IT resources, and reduce the cost of their IT infrastructure. This has been made possible through the implementation of resource allocation strategies by cloud service providers. One of the major challenges during resource allocation is to minimize power consumption while ensuring the required Service Level Agreement (SLA). To solve this problem, a new approach to efficiently allocate resources in cloud computing while optimizing energy consumption and guaranteeing the required service level agreement has been proposed. The main idea of this proposal is to leverage the CNN-LSTM architecture to accurately predict resource utilization in order to make the appropriate resource allocation decision. The proposed solution was validated in two steps: step 1) a comprehensive set of statistical performance analysis and step 2) an intensive simulation of the solution for resource allocation using cloudSim Plus tool. The results of the experimentation demonstrated that the proposed solution can help cloud service providers achieve energy savings while guaranteeing the required SLA.

An Analysis of Managing the Cloud: Obstacles and Solutions for Efficient Administration and Protection

Cloud management offers possibilities and difficulties to enterprises looking for effective management and strong data security. This essay examines the challenges associated with cloud administration and suggests workarounds for these issues. Assuring data security, upholding legal compliance, cutting expenses, and overseeing intricate multi-cloud setups are important obstacles. The use of cloud-native monitoring and governance tools, the implementation of extensive data backup and recovery plans, the use of automation for resource provisioning and administration, and the implementation of strong security measures like encryption and access restrictions are all necessary for finding solutions. Organizations may improve the security of their important data assets and expedite their cloud administration procedures by tackling these challenges with suitable solutions. Ensuring data security is a major concern in cloud administration. Organizations are concerned about data breaches, illegal access, and industry regulatory compliance when they move critical data to the cloud. Organizations need to have strong security mechanisms like identity management, access restrictions, and encryption in place to lessen these risks. Furthermore, implementing a defense-in-depth strategy that incorporates several security tiers might improve cloud environments' overall resilience. Upholding legal requirements and industry norms is another challenge for cloud managers. To secure sensitive data and uphold client confidence, businesses in regulated sectors like healthcare and finance are subject to strict compliance regulations. Adherence to frameworks like HIPAA, GDPR, and PCI DSS, as well as thorough policies and frequent audits are necessary to achieve compliance in the cloud. Organizations may minimize the risk of non-compliance penalties and expedite compliance processes by putting governance tools and compliance automation systems into place. For enterprises using cloud resources, cost optimization is another major concern. Pay-as-you-go cloud services might result in overspending and financial restrictions due to improper resource utilization.

Nonlinear Analysis of EDF Scheduling Framework for Resource Distribution in Cloud Computing

In modern times, the field of information technology is significantly shifting towards cloud computing. This innovative technology involves the permanent storage of resources on servers, which clients access via the internet. Typically, a cloud comprises a collection of resources known as virtual machines, capable of managing both computational tasks and storage needs. As the demand for cloud services continues to rise, the scheduling of these resources presents a considerable challenge. Scheduling refers to the workflow management necessary to complete tasks within the system. An efficient scheduler adapts its scheduling policy dynamically in response to changing conditions and task requirements. Cloud computing represents a recently developed technology where resources, whether physical or virtual machines, are permanently stored on servers and accessed by users through the internet. The cloud offers various service models, including Platform as a Service (PaaS), Software as a Service (SaaS), and Infrastructure as a Service (IaaS). In this context, IaaS refers to a set of resources, including virtual machines, that provide computational and storage services. Given the increasing adoption of cloud technology, resource scheduling has become a critical task. Hence, optimizing the scheduling of requests to ensure the best utilization of cloud resources is essential. This paper introduces a Nonlinear Analysis of EDF Scheduling Framework for resource allocation that considers deadlines and processing times when assigning resources to specific jobs. The performance metrics, including Average Turnaround Time, Average Waiting Time, and Average Deadline Violation, show a significant improvement when compared to traditional scheduling models such as First-Come, First-Served (FCFS), the Shortest Job First (SJF), and Simple Earliest Deadline First (EDF) models.

Efficient resource allocation in cloud computing environments using AI-driven predictive analytics

This paper proposes an innovative AI-driven approach for efficient resource allocation in cloud computing environments using predictive analytics. The study addresses the critical challenge of optimizing resource utilization while maintaining high quality of service in dynamic cloud infrastructures. A hybrid predictive model combining XGBoost and LSTM networks is developed to forecast workload patterns across various time horizons. The model leverages historical data from a large-scale cloud environment, encompassing 1000 servers and over 52 million data points. A dynamic resource scaling algorithm is introduced, which integrates the predictive model outputs with real-time system state information to make proactive allocation decisions. The proposed framework incorporates advanced techniques such as workload consolidation, resource oversubscription, and elastic resource pools to maximize utilization efficiency. Experimental results demonstrate significant improvements in key performance indicators, including increasing resource utilization from 65% to 83%, reducing SLA violation rates from 2.5% to 0.8%, and enhancing energy efficiency, with PUE improving from 1.4 to 1.18. Comparative analysis shows that the proposed model outperforms existing prediction accuracy and resource allocation efficiency methods. The study contributes to the field by presenting a comprehensive, AI-driven solution that addresses the complexities of modern cloud environments and paves the way for more intelligent and autonomous cloud resource management systems.

An Oversubscription and Service Pricing Exploitation-Based Profit Maximization Framework for Industry Cloud Resource Management

An Oversubscription and Service Pricing Exploitation-Based Profit Maximization Framework for Industry Cloud Resource Management