Cloud Operations Research Articles

Advanced Analysis of Scalability and Mobility in Cloud Servers using Logistic Regression Models.

With the rapid expansion of cloud computing, efficiently managing scalability and mobility has become paramount. This paper presents an in-depth examination of how logistic regression models can predict and optimize these two critical aspects of cloud infrastructure. By analyzing various cloud configurations, workload patterns, and system performance data, the logistic regression framework forecasts resource needs and identifies potential bottlenecks. The study incorporates real-world applications and case studies to demonstrate the practical benefits of logistic regression in enhancing cloud performance. Additionally, the paper compares logistic regression with other predictive models and explores future advancements in cloud optimization through AI-driven automation and real-time machine learning. The findings underscore the importance of predictive analytics in maintaining seamless cloud operations and provide actionable insights for cloud architects aiming to design more resilient and efficient cloud systems.

Leveraging Azure And AWS Advisor Scores for Improved Cloud Governance and Cost Management

Cloud governance and cost management become major concerns when shifting to cloud environments [Figure 1] Because cloud resources are dynamic, frameworks for evaluation and optimization that need the least amount of financial outlay are required. In this sense, cloud resource management has been made easier by built-in tools like Advisor scores, which present a list of recommendations for improved usage, on platforms like AWS and Azure. The finest practices in the areas of security, performance, dependability, and cost efficiency are highlighted by these scores. Furthermore, if a company utilizes these ratings consistently, it can discover opportunities for significant cost savings in addition to improving governance, policy, and regulatory compliance. This paper explores how Azure and AWS Advisor ratings may be used as useful tools to improve cloud governance and simplify cost management. By doing this, it aims to offer a critical perspective on how these technologies could change how organizations optimize their use of cloud resources. These ratings, when combined with governance frameworks, will encourage enterprises to manage cloud resources more proactively and methodically [12]. The organization will maintain compliance with industry standards thanks to this systematic examination, which will lead to unmatched security and dependability as well as cost control. Eventually, the company will be able to safely use AWS and Azure advisor scores to optimize cloud operations, maintaining the cloud's reliability, security, and efficiency while keeping it in line with strategic goals

Enterprise cloud resource optimization and management based on cloud operations

The so-called automated operation and maintenance refers to a large number of repetitive tasks in daily IT operations (from simple daily checks, configuration changes and software installation to organizational scheduling of the entire change process) from manual execution in the past to standardized, streamlined and automated operations. This article delves into the realm of enterprise cloud resource optimization and management, leveraging automated operations (autoOps) as a fundamental strategy. As industries like banking witness exponential growth and innovation in IT systems, the complexity of managing resources escalates. Automated operations have emerged as a critical component, transitioning from manual interventions to encompass standardization, workflow optimization, and architectural enhancements. Through real-world deployments and theoretical frameworks, it elucidates effective strategies for optimizing and governing enterprise cloud resources, thereby enhancing efficiency, security, and resilience in IT operations.

Enterprise cloud resource optimization and management based on cloud operations

The so-called automated operation and maintenance refers to a large number of repetitive tasks in daily IT operations (from simple daily checks, configuration changes and software installation to organizational scheduling of the entire change process) from manual execution in the past to standardized, streamlined and automated operations. This article delves into the realm of enterprise cloud resource optimization and management, leveraging automated operations (autoOps) as a fundamental strategy. As industries like banking witness exponential growth and innovation in IT systems, the complexity of managing resources escalates. Automated operations have emerged as a critical component, transitioning from manual interventions to encompass standardization, workflow optimization, and architectural enhancements. Through real-world deployments and theoretical frameworks, it elucidates effective strategies for optimizing and governing enterprise cloud resources, thereby enhancing efficiency, security, and resilience in IT operations.

Review of AI-driven Cloud Optimization

Cloud automation is the key to realization a fully-optimized performance of modern cloud platforms while cloud resources utilization. Resource allocation efficiency is valuable. We are however faced with increasing pressure for computational resources. The Long Short-Term Memory (LSTM) algorithms have found a great use case in the dynamic resource allocation problem when the problem is solved by the proactive provisioning of resources based on historical usage patterns taking advantage of recurrent neural networks. Furthermore, the concern over quality-of-service delivery (QoS) and energy efficiency is now almost as challenging for cloud providers when implementing the utilization of cloud resources, especially in a dynamic setting. Deep Reinforcement Learning (DRL) allows to pursue that end by developing agents, which might guide the work of optimizing resource allocation and reducing energy expenses at the same time. It improves the result and adaptability of the applications running on clouds. Artificial intelligence, in its diverse form, for the instance machine learning and optimization algorithms, brings in a great influence in the areas of cloud operations, resource management, and security. Furthermore, the 3rd generation of FPGAs (Reconfigurable Digital Computing-In-Memory or ReDCIM processors) and the bitwise parallelism via-memory core multipliers also improve the efficiency of computing in cloud environments. Adopting these inputting methodologies is a consequence of cloud systems achieving top performance, high scalability, and low costing. Keywords - Cloud Automation, Long Short-Term Memory (LSTM) algorithms, Deep Reinforcement Learning (DRL), Reconfigurable Digital Computing-In-Memory (ReDCIM) processors, Resource sAllocation, Energy Efficiency.

Anomaly Detection in Cloud Network: A Review

Cloud computing stands out as one of the fastest-growing technologies in the 21st century, offering enterprises opportunities to reduce costs, enhance scalability, and increase flexibility through rapid access to a shared pool of elastic computing resources. However, its security remains a significant challenge. As cloud networks grow in complexity and scale, the need for effective anomaly detection becomes crucial. Identifying anomalous behavior within cloud networks poses a challenge due to factors such as the voluminous data exchanged and the dynamic nature of underlying cloud infrastructures. Detecting anomalies helps prevent threats and maintain cloud operations. This literature review examines previous works in anomaly detection in the cloud that employ various strategies for anomaly detection, describes anomaly detection datasets, discusses the challenges of anomaly detection in cloud networks, and presents directions for future studies.

Challenges and Experiences in Designing Interpretable KPI-diagnostics for Cloud Applications

Automated root cause analysis of performance problems in modern cloud computing infrastructures is of a high technology value in the self-driving context. Those systems are evolved into large scale and complex solutions which are core for running most of today’s business applications. Hence, cloud management providers realize their mission through a “total” monitoring of data center flows thus enabling a full visibility into the cloud. Appropriate machine learning methods and software products rely on such observation data for real-time identification and remediation of potential sources of performance degradations in cloud operations to minimize their impacts. We describe the existing technology challenges and our experiences while working on designing problem root cause analysis mechanisms which are automatic, application agnostic, and, at the same time, interpretable for human operators to gain their trust. The paper focuses on diagnosis of cloud ecosystems through their Key Performance Indicators (KPI). Those indicators are utilized to build automatically labeled data sets and train explainable AI models for identifying conditions and processes “responsible” for misbehaviors. Our experiments on a large time series data set from a cloud application demonstrate that those approaches are effective in obtaining models that explain unacceptable KPI behaviors and localize sources of issues.

Adversarial Deep Learning based Dampster–Shafer data fusion model for intelligent transportation system

Intelligent Transportation Systems (ITS) have revolutionized transportation by incorporating advanced technologies for efficient and safe mobility. However, these systems face challenges ensuring security and resilience against adversarial attacks. This research addresses these challenges and introduces a novel Dampster–Shafer data fusion-based Adversarial Deep Learning (DS-ADL) Model for ITS in fog cloud environments. Our proposed model focuses on three levels of adversarial attacks: original image level, feature level, and decision level. Adversarial examples are generated at each level to evaluate the system’s vulnerability comprehensively. To enhance the system’s capabilities, we leverage the power of several vital components. Firstly, we employ Dempster–Shafer-based Multimodal Sensor Fusion, enabling the fusion of information from multiple sensors for improved scene understanding. This fusion approach enhances the system’s perception and decision-making abilities. For feature extraction and classification, we utilize ResNet 101, a deep learning architecture known for its effectiveness in computer vision tasks. We introduced a novel Monomodal Multidimensional Gaussian Model (MMGM-DD) based Adversarial Detection approach to detect adversarial examples. This detection mechanism enhances the system’s ability to identify and mitigate adversarial attacks in real-time. Additionally, we incorporate the Defensive Distillation method for adversarial training, which trains the model to be robust against attacks by exposing it to adversarial examples during the training process. To evaluate the performance of our proposed model, we utilize two datasets: Google Speech Command version 0.01 and the German Traffic Sign Recognition Benchmark (GTSRB). Evaluation metrics include latency delay and computation time (fog–cloud), accuracy, MSE, loss, and F-score for attack detection and defense. The results and discussions demonstrate the effectiveness of our Dampster–Shafer data fusion-based Adversarial Deep Learning Model in enhancing the robustness and security of ITS in fog–cloud environments. The model’s ability to detect and defend against adversarial attacks while maintaining low-latency fog–cloud operations highlights its potential for real-world deployment in ITS.

Privacy-preserving based encryption techniques for securing data in cloud computing environments

A successful strategy for comprehensive data processing and storage is cloud computing. Nevertheless, in cloud environments maintaining confidentiality for critical data remains one of the biggest concerns for end-users as well as service providers. Homomorphic encryption has been proved to be one of the efficient techniques to handle the data securely in cloud while keeping the data secret. The importance of privacy preserving encryption in cloud computing environments is discussed in this paper to justify the significance of these protocols within the IT Industry that has evolved through the availability of elastic, on-demand resources. This paper presents an overview of privacy-preserving encryption techniques that enhance data security in cloud computing. We explore various encryption methodologies, including Symmetric, Asymmetric, and homomorphic encryption, as well as Encryption Protocols like SMPC, ZKPs, PRE and Functional Encryption. These protocols ensure data confidentiality and regulatory compliance while addressing the challenges of maintaining data utility and minimising exposure risks. By proposing a comprehensive framework that integrates these techniques, we aim to provide a robust solution for safeguarding data privacy in cloud environments, ultimately fostering trust and confidence in cloud services. However, as cloud adoption continues to grow, optimising these encryption protocols for computational efficiency and adaptability remains essential to achieving seamless, secure cloud operations across industries.

Leveraging AI and ML to Enhance Devops Practices and Automate Cloud Operations

Leveraging AI and ML to Enhance Devops Practices and Automate Cloud Operations

Automated Security Assessments of Amazon Web Services Environments

Migrating enterprises and business capabilities to cloud platforms like Amazon Web Services (AWS) has become increasingly common. However, securing cloud operations, especially at large scales, can quickly become intractable. Customer-side issues such as service misconfigurations, data breaches, and insecure changes are prevalent. Furthermore, cloud-specific tactics and techniques paired with application vulnerabilities create a large and complex search space. Various solutions and modeling languages for cloud security assessments exist. However, no single one appeared sufficiently cloud-centered and holistic. Many also did not account for tactical security dimensions. This article, therefore, presents a domain-specific modeling language for AWS environments. When used to model AWS environments, manually or automatically, the language automatically constructs and traverses attack graphs to assess security. Assessments, therefore, require minimal security expertise from the user. The modeling language was primarily tested on four third-party AWS environments through securiCAD Vanguard, a commercial tool built around the AWS modeling language. The language was validated further by measuring performance on models provided by anonymous end users and a comparison with a similar open source assessment tool. As of March 2020, the modeling language could represent essential AWS structures, cloud tactics, and threats. However, the tests highlighted certain shortcomings. Data collection steps, such as planted credentials, and some missing tactics were obvious. Nevertheless, the issues covered by the DSL were already reminiscent of common issues with real-world precedents. Future additions to attacker tactics and addressing data collection should yield considerable improvements.

Compressed Geometric Arrays for Point Cloud Processing

The ever-increasing demand for 3D modeling in the emerging immersive applications has made point clouds an essential class of data for 3D image and video processing. Tree based structures are commonly used for representing point clouds where pointers are used to realize the connection between nodes. Tree-based structures significantly suffer from irregular access patterns for large point clouds. Memory access indirection in such structures is disruptive to bandwidth efficiency and performance. In this paper, we propose a point cloud representation format based on compressed geometric arrays (CGA). Then, we examine new methods for point cloud processing based on CGA. The proposed format enables a higher bandwidth efficiency via eliminating memory access indirections (i.e., pointer chasing at the nodes of tree) thereby improving the efficiency of point cloud processing. Our experimental results show that using CGA for point cloud operations achieves 1328x speed up, 1321x better bandwidth utilization, and 54% reduction in the volume of transferred data as compared to the state-of-the-art tree-based format from point cloud library (PCL).

A 2-Phase Strategy for Intelligent Cloud Operations

A 2-Phase Strategy for Intelligent Cloud Operations

Blueprint for Security: Designing Secure Cloud Architectures

The research paper delves into the imperative of robust security frameworks within cloud computing environments, addressing the prevalent challenges and vulnerabilities that accompany the cloud's expansive utilization across industries. This study underscores the necessity of foundational security principles, such as least privilege, role-based access control, and comprehensive data encryption, aiming to fortify cloud infrastructures against escalating cyber threats. The paper provides a thorough analysis of contemporary security practices, including network segmentation, the deployment of intrusion detection systems, and rigorous compliance protocols, to ensure resilient and secure cloud operations. Additionally, it evaluates cutting-edge tools and technologies, like automated security solutions and AI-driven threat detection systems, which are pivotal in enhancing security postures and mitigating risks in dynamic cloud ecosystems. Through a series of case studies, the paper highlights successful implementations and extracts critical lessons from historical security breaches, offering actionable insights and strategies to design inherently secure cloud architectures. This study not only synthesizes complex security requirements into actionable architecture blueprints but also anticipates future challenges and technological advancements, making it a vital resource for professionals and organizations aiming to leverage cloud computing securely and efficiently.

Application of cloud computing in banking and e-commerce and related security threats

Cloud computing is one of the most powerful inventions that has grabbed the curiosity of technologists all around the world. Cloud computing has many advantages, but it also has a slew of security risks that no organization can afford to ignore. For a successful Cloud Computing adoption in a corporation, proper planning and awareness of emerging risks, threats, vulnerabilities, and potential solutions are necessary. As a result, determining the most effective solution instructions to increase cloud security has become important for all cloud operations. In this research, we are investigating and assessing the most noteworthy network security and data security risks on cloud systems based on a literature review. Because many businesses have promoted and marketed virtual environments as the solution to current security concerns, a deeper look finds that virtualization adds extra software to the network system, which may have a negative influence on security if built and deployed poorly. Furthermore, data center hubs link their servers through software, which means that if something goes wrong, the final effect might be damaging to security. Consumers must rely on trust mechanisms since they have no control over the cloud's resources. This article examines several cloud computing applications in banking and e-commerce, as well as the security issues associated with them.

Dwell time estimation at intersections for improved vehicular micro cloud operations

Edge computing is becoming a major building block of next generation 5G/6G networks. However, infrastructure might not always be available because of slow deployment. At the same time, vehicular networks are becoming a reality now and cars are being equipped with a variety of short-range communication devices. The idea of vehicular micro clouds is to turn cars into (virtual) edge computing infrastructure. One of the challenging questions in this domain is to maintain data within and among such micro clouds. In this paper, we focus on this task and present a novel solution for such data exchange between vehicular micro clouds. For efficient operation, the dwell times of cars in such a micro cloud need to be known or accurately predicted. In an extensive study based on trace data, we investigate the distribution of dwell times of cars at intersections. We make use of this distribution as an input for designing an improved data exchange algorithm. As not all intersections are the same, adding additional variance further benefits the solution. We evaluated our algorithm in different vehicular densities, and we observed that we could maintain data 22–208% longer within the micro clouds using our new algorithm. Overall, our results show that our algorithm clearly outperforms previous solutions.

Carbon to Diamond: An Incident Remediation Assistant System From Site Reliability Engineers’ Conversations in Hybrid Cloud Operations

Conversational channels are changing the landscape of hybrid cloud service management. These channels are becoming important avenues for Site Reliability Engineers (SREs) %Subject Matter Experts (SME) to collaboratively work together to resolve an incident or issue. Identifying segmented conversations and extracting key insights or artefacts from them can help engineers to improve the efficiency of the incident remediation process by using information retrieval mechanisms for similar incidents. However, it has been empirically observed that due to the semi-formal behavior of such conversations (human language) the conversations are very unique in nature and also contain domain-specific terms. %It is important to identify the correct keywords and artefacts like symptoms, issue etc., present in the conversation chats. In this paper, we build a framework that taps into the conversational channels and uses various learning methods to (1) understand and extract key artefacts from conversations like diagnostic steps and resolution actions taken and (2) present an approach to identify past conversations about similar issues. Experimental results on our dataset show the efficacy of the methods used in our proposed system.

Atenea Exams, an IMS LTI Application to Solve Scalability Problems: A Study Case

In 2004, the Universitat Politècnica de Catalunya decided to change one of its critical and strategic information services: the online teaching support platform. An Open Source software, Moodle, was adopted and the previous proprietary software was abandoned. In 2020, the home confinement of the population due to the pandemic caused by COVID-19 has been a stress test for the entire university community, and very specifically by those responsible for providing the support of the online teaching support platform. The increase in activity and the prospect of the generalization of online assessment raised the concerns of possible scalability issues with the quiz functionality during the examination period. The solution applied involved using Moodle’s IMS LTI interoperability features to deploy a high-performance version of the Moodle Quizz Module As A Service (SaaS), to plug-and-play within the university’s LMS without drawing on its internal resources. This solution combined with a systems strategy, private cloud operations, internal communication and teacher training, solved this problem and successfully complete the assessment of the university course in a situation of confinement.

Cloud Operations

Cloud Operations

Enlightenments from Cloud Computing Technology to the Construction of Aviation and Space Information Equipment System

Cloud operation is a new operational mode brought by cloud computing technology in the military field. In the future aviation and space information warfare system confrontation, cloud operations will have the invisible power application and the combination of virtual and real combat operations. Based on the concept of cloud computing and cloud operation, this paper analyzes the construction requirements of aviation and space information equipment system. Based on cloud operation, aviation and space information equipment system is constructed. The paper discusses the enlightenment of cloud computing technology and cloud operation concept to the construction of aviation and space information equipment system. The revelations are Emphasizing the research on the winning mechanism of cloud operation, enhancing equipment fusion capability, focusing on reliability and safety, adhering to overall coordination, etc.