Utilization Of Cloud Resources Research Articles

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.

Optimizing Task Scheduling in Cloud Computing Environments using Hybrid Swarm Optimization

Cloud computing has revolutionized the Information Technology (IT) landscape by offering on-demand access to a shared pool of computing resources over the internet. Effective task scheduling is pivotal in optimizing resource utilization and enhancing the overall performance of cloud systems. Tasks are allocated to virtual machines (VMs) based on a server's workload capacity, aiming to minimize traffic congestion and waiting times. Although Particle Swarm Optimization (PSO) is currently the most effective algorithm for task scheduling in cloud environments, this study introduces a Hybrid Swarm Optimization (HSO) algorithm that combines the strengths of PSO and Salp Swarm Optimization (SSO). The proposed hybrid algorithm addresses the challenges associated with task scheduling in cloud computing. The performance of the HSO algorithm is evaluated using the CloudSim simulator and compared against traditional scheduling algorithms. Simulation results indicate that the hybrid PSO-SSO algorithm outperforms existing methods regarding makespan time, cloud throughput, and task execution efficiency. Consequently, the hybrid approach significantly improves resource utilization and overall system performance in cloud computing environments.

An optimized algorithm for resource utilization in cloud computing based on the hybridization of meta-heuristic algorithms

An optimized algorithm for resource utilization in cloud computing based on the hybridization of meta-heuristic algorithms

An efficient load balancing technique using CAViaR-HHO enabled VM migration and replica management in cloud computing

Cloud computing is immense technology that offers distributed resources to a number of users who are present throughout the world. Cloud model is comprised of numerous virtual machines (VMs) and physical machines (PMs) to carry out user tasks effectively in a parallel manner but in some cases, the demand of the users may be high that resulting in the overloading of PMs and this condition deteriorates the performance of cloud network. For achieving effective virtualization in the cloud paradigm, energy and resource utilization are major properties that should be handled effectively and such properties are accomplished through effective management of workload by distributing load equivalently among VMs. By doing so, resource utilization of the network is enhanced and it only requires minimum energy to process the tasks. Numerous load-balancing algorithms have been introduced earlier to maintain load in a cloud environment, nevertheless, they are devoid of mitigating the number of task migrations. Hence, this research proposes an effective load balancing algorithm and replica management method using the proposed Conditional Autoregressive Value at risk by Regression Quantiles-Horse Herd Optimization (CAViaR-HHO) model. Here, the load is computed by considering some factors like Central Processing Unit (CPU), Million Instructions per Second (MIPS), bandwidth, memory, and frequency. VM migration and replica migration is effectively carried out using the proposed CAViaR-HHO model. Meanwhile, the developed method is devised by integration of Conditional Autoregressive Value at risk by Regression Quantiles (CAViaR) with Horse Herd Optimization Algorithm (HOA). However, the proposed CAViaR-HHO has achieved a load with a minimum value of 0.109, capacity with a maximum value of 0.591, resource utilization with a maximum value of 0.467, and minimum cost of 0.344. Using setup-1, when the number of tasks is 500, the capacity of the proposed method is 5.58%, 3.89%, 2.87%, 1.52%, and 0.67% higher when compared to the existing approaches namely, C-FDLA, K-means clustering + LB, Adaptive starvation threshold, EIMORM, and Dynamic replica creation method.

An Efficient Task Scheduling in a Cloud Computing Environment using Hybrid Swarm Optimization

Information technology (IT) services are available on demand over the Internet under the current computing standard known as cloud computing. A work is mapped with an available resource in the cloud environment to get a good result. Cloud computing has emerged as a transformative technology that provides on-demand access to a shared pool of computing resources over the internet. Effective task scheduling plays a crucial role in optimizing resource utilization and improving overall system performance in cloud environments. According to a server's workload capacity, tasks are distributed across its virtual machines (VMs) via task scheduling. The server's workload is planned out to reduce traffic and wait times. The best algorithm currently in use to schedule a work to an existing resource in a cloud setting is particle swarm optimization (PSO). PSO schedules the task for an available resource to cut down on computational costs. In order to address job scheduling challenges in the cloud environment, a hybrid swarm optimizations (HSO) algorithm—a combination of PSO and salp swarm optimization (SSO)—is suggested in this paper. The hybrid algorithm leverages the strengths of both PSO and SSO to achieve better optimization results. The proposed method is evaluated using the CloudSim simulator and compared with traditional scheduling algorithms. Simulation results demonstrate the superiority of the hybrid PSO-SSO algorithm in terms of makespan time, cloud throughput, and task execution efficiency. The findings suggest that the hybrid approach can significantly enhance resource utilization and performance in cloud computing environments.

A Novel Hybrid Model for Docker Container Workload Prediction

The emergence of containers dramatically simplifies and facilitates the development and deployment of applications. More and more enterprises deploy their applications on the container cloud platform. For cloud service providers, an effective container workload prediction method is a must to achieve efficient utilization of cloud resources. However, the existing methods are either rarely based on container load characteristics or cannot make accurate real-time predictions. In this paper, we propose a Docker container workload proactive prediction method using a hybrid model combining triple exponential smoothing and long short-term memory (LSTM), which not only can capture both short-term and long-term dependencies in container resource time series but also smooth the container resource utilization data. In order to improve the prediction accuracy of the hybrid model, those two single models are combined using the mean absolute percentage error (MAPE) method. Besides, we design a real-time Docker workload prediction system for the hybrid model. Our experiments show that the mean absolute percentage error of the hybrid model is decreased by an average of 3.24%, 12.18%, 13.42%, 43.45%, and 50.69% compared with the LSTM, the triple exponential smoothing, ES-ARIMA, Bayesian Ridge Regression and BiLSTM with an acceptable time and computational cost overhead.

DisguisedNets: Secure Image Outsourcing for Confidential Model Training in Clouds

Large training data and expensive model tweaking are standard features of deep learning with images. As a result, data owners often utilize cloud resources to develop large-scale complex models, which also raises privacy concerns. Existing cryptographic solutions for training deep neural networks (DNNs) are too expensive, cannot effectively utilize cloud GPU resources, and also put a significant burden on client-side pre-processing. This article presents an image disguising approach: DisguisedNets, which allows users to securely outsource images to the cloud and enables confidential, efficient GPU-based model training. DisguisedNets uses a novel combination of image blocktization, block-level random permutation, and block-level secure transformations: random multidimensional projection (RMT) or AES pixel-level encryption (AES) to transform training data. Users can use existing DNN training methods and GPU resources without any modification to training models with disguised images. We have analyzed and evaluated the methods under a multi-level threat model and compared them with another similar method—InstaHide. We also show that the image disguising approach, including both DisguisedNets and InstaHide, can effectively protect models from model-targeted attacks.

A fault tolerance aware green IoT workflow scheduling algorithm for multi-dimensional resource utilization in sustainable cloud computing

In recent times, the distributed cloud computing landscape has witnessed a remarkable surge in processing vast amounts of data and the crucial need to maintain service level agreements (SLAs) between providers and consumers. In this dynamic environment, cloud resources are inherently multidimensional, encompassing computing machines and communication connections susceptible to failures and energy-related considerations. However, given two-objective energy and reliability optimization, existing time allocation policies focus primarily on optimizing a single intent, which leads to system degradation in environments that generate problematic constraints from executable processing units. To handle the shortcomings of the application placement policies, we suggest a solution in the form of a three-phase bi-objective workflow scheduling issue called (Bi-OWSP). This three-phase approach aims to optimize workflow scheduling by simultaneously considering energy and reliability as two vital objectives. To achieve this, we employ two distinct algorithms. First is the stepwise dynamic voltage and frequency scale algorithm ensures energy efficiency by calculating optimal frequencies, reducing energy usage, and minimizing task mapping time. The second algorithm is the reliability-conscious heterogeneous fault tolerance approach, which emphasizes avoiding high-deficit servers and communication links to enhance system reliability. Furthermore, we introduce an energy-aware stepwise reliability maximization algorithm, which intelligently selects the best combination of task-server pairs to achieve energy minimization and reliability maximization. Through extensive simulation experiments on artificial and real-world workflow applications, we demonstrate the significance of Bi-OWSP in providing superior energy-reliability compensation solutions compared to competing algorithms.

DBSCAN inspired task scheduling algorithm for cloud infrastructure

Cloud computing in today's computing environment plays a vital role, by providing efficient and scalable computation based on pay per use model. To make computing more reliable and efficient, it must be efficient, and high resources utilized. To improve resource utilization and efficiency in cloud, task scheduling and resource allocation plays a critical role. Many researchers have proposed algorithms to maximize the throughput and resource utilization taking into consideration heterogeneous cloud environments. This work proposes an algorithm using DBSCAN (Density-based spatial clustering) for task scheduling to achieve high efficiency. The proposed DBScan-based task scheduling algorithm aims to improve user task quality of service and improve performance in terms of execution time, average start time and finish time. The experiment result shows proposed model outperforms existing ACO and PSO with 13% improvement in execution time, 49% improvement in average start time and average finish time. The experimental results are compared with existing ACO and PSO algorithms for task scheduling.

SWARM INTELLIGENCE OPTIMIZATION FOR RESOURCE ALLOCATION IN CLOUD COMPUTING ENVIRONMENTS

Cloud computing has emerged as a powerful paradigm for resource allocation due to its scalability and flexibility. Efficient resource allocation is critical for optimizing the performance and utilization of cloud resources. In this context, swarm intelligence optimization algorithms, such as Salp Swarm Optimization (SSO), have shown promising results in solving complex optimization problems. This paper presents a novel approach that utilizes SSO for resource allocation in cloud computing environments. The proposed approach aims to maximize resource utilization, minimize response time, and improve overall system performance. The SSO algorithm is used to dynamically allocate virtual machines (VMs) to physical hosts based on their resource demands and availability. Experimental results demonstrate that the proposed approach outperforms existing methods in terms of resource utilization and response time, thereby enhancing the efficiency of cloud computing environments.

Adaptive and Convex Optimization-Inspired Workflow Scheduling for Cloud Environment

Scheduling large-scale and resource-intensive workflows in cloud infrastructure is one of the main challenges for cloud service providers (CSPs). Cloud infrastructure is more efficient when virtual machines and other resources work up to their full potential. The main factor that influences the quality of cloud services is the distribution of workflow on virtual machines (VMs). Scheduling tasks to VMs depends on the type of workflow and mechanism of resource allocation. Scientific workflows include large-scale data transfer and consume intensive resources of cloud infrastructures. Therefore, scheduling of tasks from scientific workflows on VMs requires efficient and optimized workflow scheduling techniques. This paper proposes an optimised workflow scheduling approach that aims to improve the utilization of cloud resources without increasing execution time and execution cost.

Managing Identity and Access Management (IAM) in Amazon Web Services (AWS)

Identity and Access Management (IAM) plays a pivotal role within Amazon Web Services (AWS) by governing the utilization of cloud resources and enhancing the overall security of the AWS ecosystem. IAM allows AWS users to effectively manage resource access while ensuring a robust security framework. This exploration delves into the fundamental concepts that underpin IAM, shedding light on authentication and authorization processes, best practices, practical implementation scenarios, and the convergence of IAM and security, and provides a forward-looking perspective into emerging trends in the field. IAM's significance lies in its ability to control access for individual users and groups, roles, and policies, ensuring the principle of least privilege and minimizing security risks. Multi-factor authentication (MFA) is a key facet of IAM within AWS, offering an extra layer of security and identity verification. This enables organizations to fortify access control measures, safeguarding their AWS resources from potential threats. In addition to these foundational concepts, this exploration delves into practical implementations, elucidating how IAM can be leveraged to meet realworld security and compliance requirements. IAM's robust features enable users to define and manage access permissions with precision, catering to the specific needs of their organizations. Furthermore, this abstract introduces the IAM Query API, which provides a means to interact directly with AWS services. The IAM Query API supports GET and POST methods through APIs, offering developers and administrators a more streamlined approach to managing access to AWS resources. IAM is a linchpin in AWS security, ensuring that access to cloud resources is fine-tuned, well-protected, and compliant with best practices. The continued evolution of IAM, including integrating emerging technologies and security measures, promises to further enhance the security posture of AWS users and the cloud computing ecosystem.

Perspicacious Fuzzy Clustering Franchised by Grasshopper Optimization Dexterity for Robust Resource Scheduling with Effective Load Balancing in Cloud Precedent

Utilizing cloud resources become promising in encroachment of internet technology, countenancing everyone to use resources for little or no cost. It will be very important to have task scheduling for sharing resources in cloud environment. To maintain effective resource usage, cloud technology equally divides workload among shareable resources when it receives task requests. Machine learning and metaheuristic algorithms afford dynamic component for equitable task distribution in cloud paradigm. The current state-of-art unsupervised models-based load balancing arbitrarily selects centroid locations and struggles to achieve incorrect task requests. Using an optimization technique that takes inspiration from behavioral science, study aims to build well-balanced clustering model-based task scheduling system. In order to efficiently schedule tasks among virtual servers in cloud environment, this proposed work styles aids of perspicacious fuzzy and Grass Hopper algorithms. The results showed that PFC-GOD upsurges cloud resources usage while lowering make-span, execution time, and high balance load scheduling.

Cost-Minimized Computation Offloading of Online Multifunction Services in Collaborative Edge-Cloud Networks

Cloud Computing (CC) is powerful for the computation offloading of services, promoting the implementation of various modern applications. Mobile Edge Computing (MEC) can provide low-latency services utilizing edge servers locating in proximity to users. The combination of MEC and CC can give play to the dual advantages of both. However, it is a challenging problem to offload service requests to the collaborative edge-cloud networks aiming at minimizing costs due to the resource limitation of edge servers and the online feature of services. To address this issue, we mathematically model the service requests with multiple inter-connected functions. Then, the problem of computation offloading of multi-function service requests in collaborative edge-cloud networks is formulated to be an Integer Linear Programming (ILP) and is proved to be NP-hard. Furthermore, a Cost-minimized Computation Offloading with Reconfiguration (CCOR) algorithm is proposed to minimize the total cost of online services. Finally, simulation results show that the proposed CCOR algorithm can effectively reduce the cost of computation offloading with higher resource utilization of edge cloud compared with baseline algorithms.

Efficient and Privacy-Preserving Search Over Edge–Cloud Collaborative Entity in IoT

Limited by the storage and computing capacity of Internet of Things (IoT) devices, outsourcing encrypted entity data has become a prevalent trend. The existing IoT entity search methods lack the integration and utilization of both edge and cloud resources and the protection of user privacy. Besides, the traditional searchable encryption mode is inapplicable to state-time-varying entities in IoT. Therefore, in this article, an edge–cloud collaborative entity search method with privacy protection in IoT is proposed, fusing the advantages of edge and cloud resources to fulfill users’ needs for real-time search and privacy protection. Specifically, a secure search architecture and search method for edge–cloud collaboration is designed to support various needs of users, such as real-time search and global search. Then, an adaptive discrimination method for similar interested entities through attribute analysis and feature extraction is proposed to construct attribute-distinguished encrypted index and query vector groups, enabling efficient entity search meanwhile ensuring fast index update. Simulation results demonstrate that the proposed method with privacy protection can effectively improve the efficiency of entity search in IoT while safeguarding user privacy.

Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing

Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing

Energy Aware Job Scheduling and Simulation in a Cloud Datacenter

Virtualization technology is used by cloud systems for the users to utilize cloud resources through Virtual Machines.These VM’s process the task requests made by users. Ever since inefficient hardware utilization is the concernfor the future and the environment, efficient work load balancing and allocation of VMs helps to bring down thehardware usage and results to efficient working. That being said, this paper proposes task scheduling frameworkwhere the task will be assigned to a VMs running on the active hosts(servers) through preemption as required andclassification of the cloudlets. The algorithm that we have taken into consideration will categorize the cloudletsinto three distinct types and allocate them a VM based on first come, first served resource time in regards to thatparticular host. This in turn will reduce the energy consumption by having lesser machines running in the activestate meanwhile preserving efficient utilization of the active servers. Such kind of simulations are fairly achievedusing the CloudSim framework

A load-balanced hybrid heuristic for allocation of batch of tasks in cloud computing environment

PurposeCloud computing gives several on-demand infrastructural services by dynamically pooling heterogeneous resources to cater to users’ applications. The task scheduling needs to be done optimally to achieve proficient results in a cloud computing environment. While satisfying the user’s requirements in a cloud environment, scheduling has been proven an NP-hard problem. Therefore, it leaves scope to develop new allocation models for the problem. The aim of the study is to develop load balancing method to maximize the resource utilization in cloud environment.Design/methodology/approachIn this paper, the parallelized task allocation with load balancing (PTAL) hybrid heuristic is proposed for jobs coming from various users. These jobs are allocated on the resources one by one in a parallelized manner as they arrive in the cloud system. The novel algorithm works in three phases: parallelization, task allocation and task reallocation. The proposed model is designed for efficient task allocation, reallocation of resources and adequate load balancing to achieve better quality of service (QoS) results.FindingsThe acquired empirical results show that PTAL performs better than other scheduling strategies under various cases for different QoS parameters under study.Originality/valueThe outcome has been examined for the real data set to evaluate it with different state-of-the-art heuristics having comparable objective parameters.

Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health Monitoring

Modern real-time Structural Health Monitoring systems can generate a considerable amount of information that must be processed and evaluated for detecting early anomalies and generating prompt warnings and alarms about the civil infrastructure conditions. The current cloud-based solutions cannot scale if the raw data has to be collected from thousands of buildings. This paper presents a full-stack deployment of an efficient and scalable anomaly detection pipeline for SHM systems which does not require sending raw data to the cloud but relies on edge computation. First, we benchmark three algorithmic approaches of anomaly detection, i.e., Principal Component Analysis (PCA), Fully-Connected AutoEncoder (FC-AE), and Convolutional AutoEncoder (C-AE). Then, we deploy them on an edge-sensor, the STM32L4, with limited computing capabilities. Our approach decreases network traffic by $\approx8\cdot10^5\times$ , from 780KB/hour to less than 10 Bytes/hour for a single installation and minimize network and cloud resource utilization, enabling the scaling of the monitoring infrastructure. A real-life case study, a highway bridge in Italy, demonstrates that combining near-sensor computation of anomaly detection algorithms, smart pre-processing, and low-power wide-area network protocols (LPWAN) we can greatly reduce data communication and cloud computing costs, while anomaly detection accuracy is not adversely affected.

Peer to peer (P2P) and cloud computing on infrastructure as a service (IaaS) performance analysis

The resources of information technology and the availability of services on non-cloud network systems are limited. This constitutes problems for companies, especially in the efficient management of information technology. The high investment in infrastructure procurement is an obstacle in building centralized systems, including the adoption of cloud computing through Infrastructure as a Service (IaaS), as an elective solution. This research aims to analyze the performance of cloud servers on IaaS services using the parameters of cloud service availability, resource utilization, and throughput transfer which were implemented in companies engaged in the toll road concession sector. Furthermore, the results are expected to be a reference in supporting company decisions/policies related to cloud system adoption. The methodology involved the Network Development Life Cycle (NDLC), a system constituted by 6 (six) stages of management, namely user, proxy server, database, web service, monitoring service, and Remote Desktop Protocol (RDP). The results of cloud service availability indicate that the cloud system provides service availability (system interface, broad network access, and resource pooling). Furthermore, cloud systems have a significant performance on resource utilization (CPU) and throughput transfer parameters, while non-cloud systems only excel in response time and resource utilization (Memory) parameters. The overall result analysis based on this research scenario showed that the cloud system provides services according to user needs and has a better speed in data transmission, but has shortcomings in response time.