Academic Cloud Computing Research: Five Pitfalls and Five Opportunities

This discussion paper argues that there are five fundamental pitfalls, which can restrict academics from conducting cloud computing research at the infrastructure level, which is currently where the vast majority of academic research lies. Instead academics should be conducting higher risk research, in order to gain understanding and open up entirely new areas. We call for a renewed mindset and argue that academic research should focus less upon physical infrastructure and embrace the abstractions provided by clouds through five opportunities: user driven research, new programming models, PaaS environments, and improved tools to support elasticity and large-scale debugging. The objective of this paper is to foster discussion, and to define a roadmap forward, which will allow academia to make longer-term impacts to the cloud computing community.Comment: Accepted and presented at the 6th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud'14

Technical Report on Deploying a highly secured OpenStack Cloud Infrastructure using BradStack as a Case Study

Cloud computing has emerged as a popular paradigm and an attractive model for providing a reliable distributed computing model.it is increasing attracting huge attention both in academic research and industrial initiatives. Cloud deployments are paramount for institution and organizations of all scales. The availability of a flexible, free open source cloud platform designed with no propriety software and the ability of its integration with legacy systems and third-party applications are fundamental. Open stack is a free and opensource software released under the terms of Apache license with a fragmented and distributed architecture making it highly flexible. This project was initiated and aimed at designing a secured cloud infrastructure called BradStack, which is built on OpenStack in the Computing Laboratory at the University of Bradford. In this report, we present and discuss the steps required in deploying a secured BradStack Multi-node cloud infrastructure and conducting Penetration testing on OpenStack Services to validate the effectiveness of the security controls on the BradStack platform. This report serves as a practical guideline, focusing on security and practical infrastructure related issues. It also serves as a reference for institutions looking at the possibilities of implementing a secured cloud solution.Comment: 38 pages, 19 figures

Effective Management of Hybrid Workloads in Public and Private Cloud Platforms.

As organizations increasingly adopt hybrid cloud architectures to meet their diverse computing needs, managing workloads across on-premises and on multiple cloud environments has become a critical challenge. This thesis explores the concept of hybrid workload management through the implementation of Azure Arc, a cutting-edge solution offered by Microsoft Azure. The primary objective of this study is to investigate how azure Arc enables efficient resource utilization and scalability for hybrid workloads. The research methodology involves a comprehensive analysis of the key features and functionalities of Azure Arc, coupled with practical experimentation in a simulated hybrid environment. The thesis begins by examining the fundamental principles of hybrid cloud computing and the associated workload management challenges. It then introduces Azure Arc as a novel approach that extends Azure control to on-premises and multi-cloud systems. The architecture, components, and integration mechanisms of Azure Arc are presented in detail, highlighting its ability to centralize management, enforce governance policies, and streamline operational tasks. This thesis contributes to the understanding of hybrid workload management by exploring the capabilities of Azure Arc. It provides valuable insights into the benefits of adopting this technology for organizations seeking to optimize resource utilization, streamline operations, and scale their workloads efficiently across on-premises and multi-cloud environments. The research findings serve as a foundation for further advancements in hybrid cloud computing and workload management strategies

Effective Management of Hybrid Workloads in Public and Private Cloud Platforms

As organizations increasingly adopt hybrid cloud architectures to meet their diverse computing needs, managing workloads across on-premises and on multiple cloud environments has become a critical challenge. This thesis explores the concept of hybrid workload management through the implementation of Azure Arc, a cutting-edge solution offered by Microsoft Azure. The primary objective of this study is to investigate how Azure Arc enables efficient resource utilization and scalability for hybrid workloads. The research methodology involves a comprehensive analysis of the key features and functionalities of Azure Arc, coupled with practical experimentation in a simulated hybrid environment. The thesis begins by examining the fundamental principles of hybrid cloud computing and the associated workload management challenges. It then introduces Azure Arc as a novel approach that extends Azure control to on-premises and multi-cloud systems. The architecture, components, and integration mechanisms of Azure Arc are presented in detail, highlighting its ability to centralize management, enforce governance policies, and streamline operational tasks. This thesis contributes to the understanding of hybrid workload management by exploring the capabilities of Azure Arc. It provides valuable insights into the benefits of adopting this technology for organizations seeking to optimize resource utilization, streamline operations, and scale their workloads efficiently across on-premises and multi-cloud environments. The research findings serve as a foundation for further advancements in hybrid cloud computing and workload management strategies

An infrastructure service recommendation system for cloud applications with real-time QoS requirement constraints

The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant launch of new cloud services and capabilities almost every month by both big (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g., Rackspace and Ninefold), decision makers (e.g., application developers and chief information officers) are likely to be overwhelmed by choices available. The decision-making problem is further complicated due to heterogeneous service configurations and application provisioning QoS constraints. To address this hard challenge, in our previous work, we developed a semiautomated, extensible, and ontology-based approach to infrastructure service discovery and selection only based on design-time constraints (e.g., the renting cost, the data center location, the service feature, etc.). In this paper, we extend our approach to include the real-time (run-time) QoS (the end-to-end message latency and the end-to-end message throughput) in the decision-making process. The hosting of next-generation applications in the domain of online interactive gaming, large-scale sensor analytics, and real-time mobile applications on cloud services necessitates the optimization of such real-time QoS constraints for meeting service-level agreements. To this end, we present a real-time QoS-aware multicriteria decision-making technique that builds over the well-known analytic hierarchy process method. The proposed technique is applicable to selecting Infrastructure as a Service (IaaS) cloud offers, and it allows users to define multiple design-time and real-time QoS constraints or requirements. These requirements are then matched against our knowledge base to compute the possible best fit combinations of cloud services at the IaaS layer. We conducted extensive experiments to prove the feasibility of our approach