Availability Of Cloud Services Research Articles

An Analysis on Security Issues and Research Challenges in Cloud Computing

Cloud computing has completely transformed how businesses handle, store, and process data and applications. However, using cloud services brings several security issues that need to be resolved to guarantee the availability, confidentiality, and integrity of critical data. This abstract overviews vital aspects of cloud computing security and highlights emerging trends and research directions. Essential challenges of cloud computing security include ensuring data privacy and confidentiality, maintaining data integrity and trustworthiness, and addressing compliance with regulatory requirements. Identity and access management (IAM) remains a critical area, focusing on enhancing authentication mechanisms and access controls to mitigate the risks of unauthorized access and insider threats. Additionally, active research areas include securing cloud orchestration and management platforms, resilience and availability of cloud services, and addressing the unique security considerations of cloud native technologies. Interdisciplinary collaboration between researchers, industry practitioners, and policymakers is essential to develop innovative security solutions and best practices for cloud computing environments. By addressing these challenges and advancing the state of the art in cloud security, organizations can leverage the benefits of cloud computing while mitigating associated risks and ensuring the protection of sensitive data and resources. In summary, this review paper provides a holistic understanding of cloud computing security, offering insights into current practices, challenges, and future directions for ensuring the confidentiality, integrity, and availability of cloud based systems and data.

Securing cloud-enabled smart cities by detecting intrusion using spark-based stacking ensemble of machine learning algorithms

<abstract> <p>With the use of cloud computing, which provides the infrastructure necessary for the efficient delivery of smart city services to every citizen over the internet, intelligent systems may be readily integrated into smart cities and communicate with one another. Any smart system at home, in a car, or in the workplace can be remotely controlled and directed by the individual at any time. Continuous cloud service availability is becoming a critical subscriber requirement within smart cities. However, these cost-cutting measures and service improvements will make smart city cloud networks more vulnerable and at risk. The primary function of Intrusion Detection Systems (IDS) has gotten increasingly challenging due to the enormous proliferation of data created in cloud networks of smart cities. To alleviate these concerns, we provide a framework for automatic, reliable, and uninterrupted cloud availability of services for the network data security of intelligent connected devices. This framework enables IDS to defend against security threats and to provide services that meet the users' Quality of Service (QoS) expectations. This study's intrusion detection solution for cloud network data from smart cities employed Spark and Waikato Environment for Knowledge Analysis (WEKA). WEKA and Spark are linked and made scalable and distributed. The Hadoop Distributed File System (HDFS) storage advantages are combined with WEKA's Knowledge flow for processing cloud network data for smart cities. Utilizing HDFS components, WEKA's machine learning algorithms receive cloud network data from smart cities. This research utilizes the wrapper-based Feature Selection (FS) approach for IDS, employing both the Pigeon Inspired Optimizer (PIO) and the Particle Swarm Optimization (PSO). For classifying the cloud network traffic of smart cities, the tree-based Stacking Ensemble Method (SEM) of J48, Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) are applied. Performance evaluations of our system were conducted using the UNSW-NB15 and NSL-KDD datasets. Our technique is superior to previous works in terms of sensitivity, specificity, precision, false positive rate (FPR), accuracy, F1 Score, and Matthews correlation coefficient (MCC).</p> </abstract>

Machine learning job failure analysis and prediction model for the cloud environment

Reliable and accessible cloud applications are essential for the future of ubiquitous computing, smart appliances, and electronic health. Owing to the vastness and diversity of the cloud, a most cloud services, both physical and logical services have failed. Using currently accessible traces, we assessed and characterized the behaviors of successful and unsuccessful activities. We devised and implemented a method to forecast which jobs will fail. The proposed method optimizes cloud applications more efficiently in terms of resource usage. Using Google Cluster, Mustang, and Trinity traces, which are publicly available, an in-depth evaluation of the proposed model was conducted. The traces were also fed into several different machine learning models to select the most reliable model. Our efficiency analysis proves that the model performs well in terms of accuracy, F1-score, and recall. Several factors, such as failure of forecasting work, design of scheduling algorithms, modification of priority criteria, and restriction of task resubmission, may increase cloud service dependability and availability.

A review on cloud security issues and solutions

Cloud computing provides computing resources, platforms, and applications as a service in a flexible, cost-effective, and efficient way. Cloud computing has integrated with industry and many other fields in recent years, which prompted researchers to look into new technologies. Cloud users have moved their applications, data and services to the Cloud storage due to the availability and scalability of Cloud services. Cloud services and applications are provided through the Internet-based on a pay-per-use model. Plenty of security issues are created due to the migration from local to remote computing for both Cloud users and providers. This paper discusses an overview of Cloud computing, as well as a study of security issues at various levels of Cloud computing. The article also provides a complete review of security issues with their existing solutions for a better understanding of specific open research issues.

Protecting Data and Queries in Cloud-Based Scenarios

The availability of cloud services offered by different providers brings several advantages to users and companies, facilitating the storage, sharing, and processing of data. At the same time, the adoption of cloud services brings new security and privacy risks and challenges. As a matter of fact when leveraging cloud-based services for data storage and processing, data owners loose direct control on their data. Data and queries over them could then be at risk for both potentially improper exposure, compromising their confidentiality, or tampering, compromising their integrity. In this paper, we discuss the main issues to be addressed for guaranteeing data security and privacy in cloud-based storage and processing. We illustrate the different challenges to be considered and the research directions toward their solutions.

Gone with the clouds: Estimating the electricity and water footprint of digital data services in Europe

Cloud and data services have experienced a constant increase in demand ever since their availability. Whether for computing of complex models, digital data storage, blockchain transactions or digital service hosting, digital data is having an increasingly important role in society. However, with the increasing presence of data centers, not only the availability and quality of cloud and data services increases, but also the demand for electricity and water by the digital services industry. This work aims to provide an estimation for the current and future water consumption of digital data services in Europe from 2022 to 2030. The projection considers European trends for population, data services consumption, technological development, data transmission and energy consumption among other factors. Publicly available data was combined with literature research to extrapolate the development of digital data usage in Europe until 2030. The results demonstrate that from 273.4 to 820.1 million cubic meters of water and from 56.3 to 169 Terawatt-hour of electricity will be consumed yearly in Europe in 2030 for internet usage.

A Self-Adaptive Trajectory Optimization Algorithm Using Fuzzy Logic for Mobile Edge Computing System Assisted by Unmanned Aerial Vehicle

The advancement of the Internet of Things (IoT) and the availability of wide cloud services have led to the horizon of edge computing paradigm which demands for processing the data at the edge of the network. The development of 5G technology has led to the increased usage of IoT-based devices and the generation of a large volume of data followed by increased data traffic, which is difficult to process by the mobile edge computing (MEC) platform. The latest inventions related to unmanned aerial vehicles (UAVs) helps to assist and replace the edge servers used for MEC. In the present work, the objective is to develop self-adaptive trajectory optimization algorithm (STO) which is a multi-objective optimization algorithm used to solve the vital objectives associated with the above scenario of a UAV-assisted MEC system. The objectives identified are minimizing the energy consumed by the MEC and minimizing the process emergency indicator, where the process emergency indicator implies the urgency level of a particular process. Finding the optimal values for these conflicting objectives will help to further efficiently apply UAV for MEC systems. A self-adaptive multi-objective differential evolution-based trajectory optimization algorithm (STO) is proposed, where a pool of trial vector generation strategies is extended. The strategies and the crossover rate associated with a differential evolution (DE) algorithm are self-adapted using fuzzy systems to improve the population diversity. The experimentation is planned to be conducted on hundreds of IoT device instances considered to be fixed on the ground level and to evaluate the performance of the proposed algorithm for a single unmanned aerial vehicle-assisted mobile edge computing system.

Generating an environmental awareness system for learning using IoT technology

Plants have numerous beneficial effects on mental health, well-being, and indoor air quality. Nonetheless, these effects are not sufficiently well addressed in educational contexts. The increasing availability of sensors, networks, and cloud services can facilitate real-time measurements to perform data analysis on plants and the environment in which they coexist with students and teachers. This article addresses the use of plants and Internet-of-Things (IoT) technology to educate students (and teachers) on the benefits of using plants in indoor learning contexts (i.e., classrooms, study rooms, offices, libraries). The contribution of this research is threefold: first, a theoretical framework to design learning activities targeting environmental awareness is presented; second, an IoT system (Spike) specifically designed to monitor plants in learning spaces is described and evaluated; third, alternative learning activities and paths for learning using plants and IoT technology are described from a teachers’ and students’ perspective. These results show the potential of IoT technology to teach and to promote environmental awareness.

Improving mobile cloud computing service efficiency using energy competent and federated learning process

AbstractMobile cloud computing (MCC) helps to make effective communication that is used to transmit various information like voice, data, and video. The MCC environments support different real‐time applications, including gaming and virtual reality implications. During the data transmission, energy conservation is one of the major issues for retaining the application span incorporating distributed and personalized computing and resource sharing. Ability‐sustained energy competent method (ASECM) is introduced to overcome these issues and manage energy efficiency. The ASECM approach analyzes the session span and energy requirements for managing the applications' energy factor. In addition, the federated learning and computation offloading process is incorporated to retain the energy factor for ensuring reliable services. The effective utilization of the offloading process is used to meet the application deadline and energy‐sufficient operations, preventing failures. In the recommendation process, cloud service availability with energy management constraints is considered for reducing energy expenditures. This process is nonrecurrent as the user/application requirements rely on diverse resources over different energy management techniques. The performance is measured using energy efficacy, utilization, application span, and failures.

A Fault Tolerant Elastic Resource Management Framework Toward High Availability of Cloud Services

Cloud computing has become inevitable for every digital service which has exponentially increased its usage. However, a tremendous surge in cloud resource demand stave off service availability resulting into outages, performance degradation, load imbalance, and excessive power-consumption. The existing approaches mainly attempt to address the problem by using multi-cloud and running multiple replicas of a virtual machine (VM) which accounts for high operational-cost. This paper proposes a Fault Tolerant Elastic Resource Management (FT-ERM) framework that addresses aforementioned problem from a different perspective by inducing high-availability in servers and VMs. Specifically, (1) an online failure predictor is developed to anticipate failure-prone VMs based on predicted resource contention; (2) the operational status of server is monitored with the help of power analyser, resource estimator and thermal analyser to identify any failure due to overloading and overheating of servers proactively; and (3) failure-prone VMs are assigned to proposed fault-tolerance unit composed of decision matrix and safe box to trigger VM migration and handle any outage beforehand while maintaining desired level of availability for cloud users. The proposed framework is evaluated and compared against state-of-the-arts by executing experiments using two real-world datasets. FT-ERM improved the availability of the services up to 34.47% and scales down VM-migration and power-consumption up to 88.6% and 62.4%, respectively over without FT-ERM approach.

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.

Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method

Cloud computing facilitates the users with on-demand services over the Internet. The services are accessible from anywhere at any time. Despite the valuable services, the paradigm is, also, prone to security issues. A Distributed Denial of Service (DDoS) attack affects the availability of cloud services and causes security threats to cloud computing. Detection of DDoS attacks is necessary for the availability of services for legitimate users. The topic has been studied by many researchers, with better accuracy for different datasets. This article presents a method for DDoS attack detection in cloud computing. The primary objective of this article is to reduce misclassification error in DDoS detection. In the proposed work, we select the most relevant features, by applying two feature selection techniques, i.e., the Mutual Information (MI) and Random Forest Feature Importance (RFFI) methods. Random Forest (RF), Gradient Boosting (GB), Weighted Voting Ensemble (WVE), K Nearest Neighbor (KNN), and Logistic Regression (LR) are applied to selected features. The experimental results show that the accuracy of RF, GB, WVE, and KNN with 19 features is 0.99. To further study these methods, misclassifications of the methods are analyzed, which lead to more accurate measurements. Extensive experiments conclude that the RF performed well in DDoS attack detection and misclassified only one attack as normal. Comparative results are presented to validate the proposed method.

Failure Free Cloud Computing Architectures

Cloud computing has gained popularity over the years, some organizations are using some form of cloud computing to enhance their business operations while reducing infrastructure costs and gaining more agility by deploying applications and making changes to applications easily. Cloud computing systems just like any other computer system are prone to failure, these failures are due to the distributed and complex nature of the cloud computing platforms. Cloud computing systems need to be built for failure to ensure that they continue operating even if the cloud system has an error. The errors should be masked from the cloud users to ensure that users continue accessing the cloud services and this intern leads to cloud consumers gaining confidence in the availability and reliability of cloud services. In this paper, we propose the use of N-Modular redundancy to design and implement failure-free clouds.

Economic Denial of Sustainability Attacks Mitigation in the Cloud

Cyber security is one of the most attention seeking issues with the increasing advancement of technology specifically when the network availability is threaten by attacks such as Denial of Service attacks (DoS), Distributed DoS attacks (DDoS), and Economic Denial of Sustainability (EDoS). The loss of the availability and accessibility of cloud services have greater impacts than those in the traditional enterprises networks. This paper introduces a new technique to mitigate the impacts of attacks which is called Enhanced DDoS-Mitigation System (Enhanced DDoS-MS) that helps in overcoming the determined security gap. The proposed technique is evaluated experimentally and the result shows that the proposed method adds lower delays as a result of the enhanced security. The paper also suggests some future directions to improve the proposed framework.

Proactive Fault-Tolerance Technique to Enhance Reliability of Cloud Service in Cloud Federation Environment

Cloud federation is a new computing paradigm that has paved the way for cloud service providers (CSPs) to offer their unused resources (virtual machine) to other CSPs when their resource demands are low. Federation also allows CSPs to outsource their resource requests to other CSPs when their computing resources’ demands are high. Thus, in cloud federation environment reliability and availability of services offered by service providers increase as the CSPs are able to share their resources among themselves. Moreover, to maintain the reliability and availability of cloud services offered through federation, it is important that the computational environment of member CSPs within the federation is fault tolerant. Therefore, there is a need for fault tolerant system to guarantee cloud service reliability and availability in cloud federation environment. In this article, we propose a proactive fault tolerance system that preempts faults within the federation on the basis of CPU temperature. The fault tolerance system within the federation is modeled as a multi-objective optimization problem of maximizing profit and minimizing migration cost while redistributing resources (virtual machine) from faulty CSPs to non-faulty CSPs within the federation. To address this issue, we have also proposed an algorithm called Preference Based Fault Management (PBFM) to manage the federation in the event of faults. We perform extensive experiments to evaluate the effectiveness of our proposed mechanism and compare it with two other mechanisms MCAFM (Migration Cost Assured Fault Management) and PAFM (Profit Assured Fault Management). Results show that our proposed mechanism PBFM yields an optimized solution to the general problem of profit and migration cost trade-off in presence of faulty CSPs.

Analysis of Job Failure and Prediction Model for Cloud Computing Using Machine Learning.

Modern applications, such as smart cities, home automation, and eHealth, demand a new approach to improve cloud application dependability and availability. Due to the enormous scope and diversity of the cloud environment, most cloud services, including hardware and software, have encountered failures. In this study, we first analyze and characterize the behaviour of failed and completed jobs using publicly accessible traces. We have designed and developed a failure prediction model to determine failed jobs before they occur. The proposed model aims to enhance resource consumption and cloud application efficiency. Based on three publicly available traces: the Google cluster, Mustang, and Trinity, we evaluate the proposed model. In addition, the traces were also subjected to various machine learning models to find the most accurate one. Our results indicate a significant correlation between unsuccessful tasks and requested resources. The evaluation results also revealed that our model has high precision, recall, and F1-score. Several solutions, such as predicting job failure, developing scheduling algorithms, changing priority policies, or limiting re-submission of tasks, can improve the reliability and availability of cloud services.

Root-Of-Trust for Continuous Integration and Continuous Deployment Pipeline in Cloud Computing

Cloud computing has gained significant use over the last decade due to its several benefits, including cost savings associated with setup, deployments, delivery, physical resource sharing across virtual machines, and availability of on-demand cloud services. However, in addition to usual threats in almost every computing environment, cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm. Furthermore, since there are a growing number of attacks directed at cloud environments (including dictionary attacks, replay code attacks, denial of service attacks, rootkit attacks, code injection attacks, etc.), customers require additional assurances before adopting cloud services. Moreover, the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches. In this study, the model based on the root of trust for continuous integration and continuous deployment is proposed, instead of only relying on a single sign-on authentication method that typically uses only id and password. The underlying study opted hardware security module by utilizing the Trusted Platform Module (TPM), which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers. The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.

Minimized False Alarm Predictive Threshold for Cloud Service Providers

Background: Cloud Security is a strong hindrance which discourages organisations to move toward cloud despite huge benefits. Distributed denial of service attacks operated via distributed systems compromise availability of cloud services which cause limited resources for authentic users and high expense for cloud service users and business owners. Objective: Techniques to identify distributed denial of service attacks with minimized false positives are highly required to ensure availability of cloud services to genuine users. Scarcity of solution which can detect denial of service attacks with minimum false positives and reduced detection delay has motivated us to compare classification algorithms for detection of distributed denial of service attacks with minimum false positive rate. Methods: Classification of incoming requests and outgoing responses using machine learning algorithms is a quite effective way of detection and prevention. We designed a performance tuned support vector machine algorithm with features of F-hold cross validation strategy. Results: F-hold crosses validation strategy, which can detect denial of service packets with 99.89% accuracy. Conclusion: This system ensures economic sustainability for business owners and limited resources mitigation for authenticated and valid cloud users.

Modelling cloud service latency and availability using a deep learning strategy

Low latency and high availability in cloud services give users satisfactory response time and guarantee stability to request they make to services that are hosted in the cloud, thus increasing the usability and reliability of cloud services. On the other hand, high latencies and poor availability will cost businesses their customers due customers dissatisfaction, thus losing customers to competitors. This situation noticeable in e-commerce businesses where real-time response and decision making within seconds are critical for business service delivery. Therefore, latency and availability are important parameters in the Service Level Agreement for cloud users when choosing Cloud Services Providers (CSPs). But the challenge for businesses is that they do not have their in–house mechanism that can accurately predict the required latency and availability for their requirements. Companies only rely on the CSPs tools for estimating resource requirements, which is biased towards the CSPs business model. In this paper, we developed a deep learning algorithm that predicts the latency and availability of cloud services using real-time live data from three CSPs. We designed and implemented experiments on Amazon Web Services, Alibaba Cloud and Tencent Cloud in Beijing University of Posts and Telecommunications to run compute instances across the United States, Europe and Asia Pacific regions. In each cloud platform, five servers were used that resulted in 30,815,100 invocations of http and ping operations for 6 weeks. The algorithm used the data on hourly, daily and weekly basis as historical network data to predict latency and availability. We used MATLABs deep learning toolbox for the implementation of our algorithm and the results showed that the prediction is usually above 90% accurate as compared with the data obtained. The results also revealed that latency performance depends on the locations of users and the availability depends on number of availability zones used.

Adaptive Neuro Fuzzy Interference and PNN Memory Based Grey Wolf Optimization Algorithm for Optimal Load Balancing

In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. For providing optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. Also, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. Finally, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.