cloud computing research paper abstract

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David Rasmussen, MBA

An Overview of Cloud Computing: Fundamentals, Deployment Models, and Key Concepts

Cloud computing has transformed the way organizations access and utilize IT resources, offering numerous benefits such as cost savings, flexibility, and scalability. This article provides a comprehensive guide on cloud computing fundamentals, covering deployment and service models, key cloud computing concepts, and best practices for ensuring security, privacy, and reliability. The paper aims to serve as a valuable resource for those seeking to better understand and leverage this technology effectively.

I. Introduction

Cloud computing has revolutionized the way organizations access and utilize IT resources, enabling them to reduce costs, improve agility, and easily scale their infrastructure. With the widespread adoption of cloud services, it is crucial for professionals to understand the fundamentals of cloud computing. This article provides a comprehensive guide to the underlying concepts, deployment models, service models, and key concepts in cloud computing, including virtualization, scalability, and security.

II. Cloud Computing Deployment Models

A. Public Cloud

The public cloud model offers computing resources and services over the internet, which are shared among multiple users or organizations. This model provides on-demand access to a pool of resources, allowing users to quickly scale their infrastructure. Advantages of public cloud include cost-effectiveness, scalability, and flexibility. However, potential disadvantages include data security concerns and limited control over the underlying infrastructure.

B. Private Cloud

Private cloud refers to a cloud computing environment dedicated to a single organization, providing greater control over resources and security. While private clouds offer enhanced security and customization, they come with higher upfront costs and increased management responsibilities. Common use cases for private clouds include organizations with strict security requirements or those needing customization beyond what public cloud providers offer.

C. Hybrid Cloud

Hybrid cloud combines elements of both public and private clouds, allowing organizations to leverage the advantages of both models. This approach provides greater flexibility, as organizations can dynamically allocate resources between public and private clouds based on their needs. However, managing hybrid cloud environments can be complex, requiring expertise in both public and private cloud technologies.

D. Multi-Cloud

Multi-cloud refers to the use of multiple cloud providers for different services or applications within an organization. This strategy can offer increased flexibility, avoiding vendor lock-in, and allowing organizations to choose the best provider for each specific service. However, multi-cloud management can be complex, and organizations must carefully consider the interoperability of different cloud platforms.

III. Cloud Computing Service Models

A. Infrastructure as a Service (IaaS)

IaaS provides virtualized computing resources over the internet, such as virtual machines, storage, and networking. Examples of IaaS providers include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform. IaaS offers cost savings, as users only pay for the resources they use, and the ability to scale infrastructure quickly.

B. Platform as a Service (PaaS)

PaaS offers a development and deployment platform for building and managing applications without the complexity of managing the underlying infrastructure. Examples of PaaS providers include Heroku, Google App Engine, and Microsoft Azure App Service. PaaS enables faster application development and deployment by providing pre-built components and tools, allowing developers to focus on writing code.

C. Software as a Service (SaaS)

SaaS provides access to software applications over the internet, with the cloud provider managing the underlying infrastructure and software updates. Examples of SaaS providers include Salesforce, Microsoft Office 365, and Google Workspace. SaaS offers ease of use, as users can access applications from any device with an internet connection, and cost savings, as organizations do not need to invest in hardware or software licenses.

IV. Key Cloud Computing Concepts

A. Virtualization

Virtualization is the process of creating virtual instances of physical resources, such as servers, storage, and networks. Virtualization enables efficient resource utilization, allowing multiple virtual machines to run on a single physical server. This technology is crucial to cloud computing, as it provides the foundation for offering scalable and flexible computing resources.

B. Scalability and Elasticity

Scalability refers to the ability of a system to handle increased workload by adding more resources, while elasticity is the ability to add or remove resources dynamically based on demand. Both concepts are essential in cloud computing, as they enable organizations to efficiently allocate resources and adapt to changing needs. Techniques for achieving scalability and elasticity include horizontal scaling (adding more servers) and vertical scaling (increasing the capacity of existing servers).

C. High Availability and Fault Tolerance

High availability refers to the design of a system that ensures continuous operation, even in the event of component failures. Fault tolerance, on the other hand, is the ability of a system to continue functioning despite failures. In cloud computing, high availability and fault tolerance are essential to ensure the reliability of services. Strategies for achieving these goals include redundant infrastructure, load balancing, and automated failover mechanisms.

D. Cloud Security and Privacy

Security and privacy are critical concerns in cloud computing, as organizations often store sensitive data on shared infrastructure. Common security concerns include data breaches, unauthorized access, and denial of service attacks. To ensure security and privacy, cloud providers implement various best practices, such as strong encryption, identity and access management, and regular security audits. Additionally, organizations must also follow best practices to protect their data and applications in the cloud.

V. Conclusion

Understanding the fundamentals of cloud computing, including deployment models, service models, and key concepts, is essential for professionals seeking to effectively leverage this technology. Cloud computing offers numerous benefits, such as cost savings, flexibility, and scalability, making it an attractive option for many organizations. As cloud computing continues to evolve, professionals must stay informed about the latest trends and developments to ensure they can make informed decisions and maximize the value of cloud services.

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Cloud computing in agriculture: a bibliometric and network visualization analysis

• Published: 06 October 2022
• Volume 57 , pages 3849–3883, ( 2023 )

Cite this article

• Krunal K. Punjani   ORCID: orcid.org/0000-0002-9061-2339 1 , 2 ,
• Kala Mahadevan   ORCID: orcid.org/0000-0002-6368-5292 2 , 3 ,
• Angappa Gunasekaran 4 ,
• V. V. Ravi Kumar 2 , 5 &
• Sujata Joshi 2 , 6  

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This study is intended to quantitatively analyze the research trends in the domain of cloud computing in agriculture by conducting the first ever bibliometric analysis in this domain. This paper analyzed 565 research articles published during 2010–2021 in this area. Cloud computing in agriculture has been found to be a research area with promising growth with various technologies and applications. This bibliometric analysis highlights the research’s impact through several bibliometric techniques including trend analysis, citation analysis, key contributors, bibliographic coupling, keyword analysis, co-authorship analysis and co-citation analysis. Additionally, this study conducted a thematic analysis and determined four emerging themes in the domain of cloud computing in agriculture. The study adds to the academic body of knowledge in this domain by making several theoretical contributions to the existing literature and outlines relevant practical implications for the different stakeholders of cloud computing in agriculture. The study also charts a future research agenda in this research domain.

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Punjani, K.K., Mahadevan, K., Gunasekaran, A. et al. Cloud computing in agriculture: a bibliometric and network visualization analysis. Qual Quant 57 , 3849–3883 (2023). https://doi.org/10.1007/s11135-022-01535-1

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Intelligent monitoring: towards ai-assisted monitoring for cloud services.

Published March 19, 2024

By Anjaly Parayil , Senior Researcher Ayush Choure , Principal Researcher Fiza Husain , Research Fellow Avi Nayak , Senior Program Manager Piyali Jana , Principal Software Engineer Manager Rujia Wang , Principal Research Product Manager Chetan Bansal , Senior Principal Research Manager Saravan Rajmohan , Partner Director of AI and Applied Research

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In the evolving field of software development, professionals are increasingly adopting a modern approach known as service-oriented architecture to enhance the scalability and flexibility of their services and applications. Often utilizing a microservices approach, developers construct software as a collection of small, independently functioning services. This method is particularly advantageous for developing cloud-based software, as it offers numerous benefits over the traditional monolithic architectures, including the ability to separately develop, deploy, and scale individual components of an application. Nevertheless, this approach also introduces challenges, notably the difficulty of offline testing of these services, which can result in issues being discovered only after the software is in use—potentially leading to costly repairs and user dissatisfaction. This underscores the need for careful software deployment, ensuring the software is as free from bugs as possible before it is released.

Currently, the process of setting up monitoring for cloud services relies heavily on trial and error and the expertise of service managers, who must understand the system’s architecture, its dependencies, and the expectations outlined in service-level agreements (SLAs). Often, adjustments to the monitoring setup are made after the service has been launched, in response to emerging problems. This reactive approach can lead to inefficiencies and often misses critical monitoring checks until issues arise. It also creates redundant alerts that waste resources. At the same time, unoptimized monitoring systems may misdetect anomalous behavior, negatively affecting the user experience and potentially extending the time needed for system upgrades or migrations. To improve how we monitor large cloud computing systems, we need to better understand how these systems work. We can then determine how to decrease the number of missed detections while also reducing the number of unnecessary alerts.

Microsoft cloud monitor platforms

When they are properly configured, cloud monitors can help to meet monitoring requirements. At M365 Research , our intelligent monitoring projects tackle the challenges of managing monitor portfolios for large service families, ensuring high reliability and efficiency.

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AI Frontiers: Models and Systems with Ece Kamar

Ece Kamar explores short-term mitigation techniques to make these models viable components of the AI systems that give them purpose and shares the long-term research questions that will help maximize their value. 

Azure Monitor (opens in new tab) offers a comprehensive solution for collecting, analyzing, and responding to monitoring data across cloud and on-premises environments, supporting range of resources like applications, VMs, containers (including Prometheus metrics), databases, and security and networking events. However, while it excels in anomaly detection, root cause analysis, and time series analysis, introducing additional intelligence during the monitor setup could further improve its efficacy.

A closer look at monitors and incident detection at Microsoft

In our paper, “ Detection Is Better Than Cure: A Cloud Incidents Perspective (opens in new tab) ,” presented at ESEC/FSE 2023 (opens in new tab) , we tackled this problem by studying a year’s worth of production incidents at Microsoft to understand misdetection. The goal was to use our insights to inform improvements in data-driven monitoring.

We identified six primary reasons for misdetections, ranging from missing signals and monitors to improper monitor coverage and alerting logic, along with buggy monitors and inadequate documentation. Figure 1 (a) shows the distribution of incident misdetections across a broad range of categories. Notably, missing monitors and alerts constituted over 40 percent of all misdetections, indicating the complexity of determining what to monitor in cloud services. The second most common issue was improper or missing signals, suggesting a need to set up the signals on which new monitors are created. Additionally, approximately 10 percent of monitors had improper coverage, and about 13 percent had alerting logic that needed to be reevaluated. Figure 1 (b) shows that 27.25 percent of these misdetections led to outages, emphasizing the importance of accurately defining monitoring parameters.

Data-driven intelligent monitoring

Organizing monitor data.

Because there is no standardized approach to building monitors, monitor data often lacks structure. To address this, we defined a structure comprised of categories, or classes, for the different types of resources being monitored, as well as service-level objective (SLO) classes for their associated objectives. These classes capture the kinds of measurements users may want to perform over a resource.

In our paper, “ Intelligent Monitoring Framework for Cloud Services: A Data-Driven Approach (opens in new tab) ,” to be presented at ICSE 2024 (opens in new tab) , we propose a data-driven approach for developing this ontology. By leveraging LLMs together with a person-in-the-loop approach, we effectively extract signals from monitor metadata. This approach facilitates the incremental development of monitor ontology and ensures accuracy through human validation and refinement of the predicted results.

Breakdown of resource and SLO classes

In our analysis, we identified 13 major resource classes and nine SLO classes that correspond to the majority of monitors in our dataset, as shown in Figure 2.

We analyzed the distribution of SLO classes within each resource class to determine the relationship between them. We observed that the distribution varies across resource classes, suggesting that a specific subset of metric classes should be applied to each, as illustrated in Figure 3. This shows us that we can predict a service’s SLO classes by analyzing its associated resource classes.

Monitor recommendation model

We developed a deep learning framework that recommends certain monitors for specific services based on their properties. This model uses monitors that have a structured ontology as well as service properties to create the recommendation pipeline, as shown in Figure 4. It incorporates upstream and downstream dependencies and service components.

To identify patterns within the data, the model uses a prototypical learning network, which learns abstract representations of the classes, or prototypes. This approach allows the network to compare prototypes for classification, enabling stronger generalization capabilities. During the prediction stage, the model outputs the class that it identifies as the most probable, with custom thresholds ensuring the recommendations are of production quality. This is illustrated in Table 1.

Finally, to understand the importance and utility of the monitor’s recommendations and how engineers perceive them, we interviewed 11 Microsoft engineers who modified monitors from January to June 2023. We introduced the proposed ontology, asked if it was helpful, and solicited suggestions for new classes. The average rating for the ontology was 4.27 out of 5, indicating its usefulness.

Looking ahead

Developing an ontology for monitoring, alongside a recommendation framework to create performance monitors for cloud platforms, marks the initial steps towards tackling the complexities associated with monitor management. One planned project, called Monitor Scorecards, aims to systematically analyze monitor performance through incident reports, their downstream impact, resolution time, and coverage. This approach combines Bayesian statistics with time-series modeling to estimate monitor effectiveness, offering actionable insights into the monitor portfolio’s performance by classifying and quantifying both false positives and negatives. We hope these effectiveness assessments will enhance recommendation models’ training phase and improve the recommendations they make.

Acknowledgments

We would like to thank colleagues from the Azure Health Platform team, Microsoft Research, and the Data, Knowledge, and Intelligence (DKI) team, for contributing to this work.

Related publications

Detection is better than cure: a cloud incidents perspective, intelligent monitoring framework for cloud services: a data-driven approach, meet the authors.

Anjaly Parayil

Senior Researcher

Ayush Choure

Principal Researcher

Fiza Husain

Research Fellow

Senior Program Manager

Piyali Jana

Principal Software Engineer Manager

Principal Research Product Manager

Chetan Bansal

Senior Principal Research Manager

Saravan Rajmohan

Partner Director of AI and Applied Research

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