12,020 research outputs found
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
The Making of Cloud Applications An Empirical Study on Software Development for the Cloud
Cloud computing is gaining more and more traction as a deployment and
provisioning model for software. While a large body of research already covers
how to optimally operate a cloud system, we still lack insights into how
professional software engineers actually use clouds, and how the cloud impacts
development practices. This paper reports on the first systematic study on how
software developers build applications in the cloud. We conducted a
mixed-method study, consisting of qualitative interviews of 25 professional
developers and a quantitative survey with 294 responses. Our results show that
adopting the cloud has a profound impact throughout the software development
process, as well as on how developers utilize tools and data in their daily
work. Among other things, we found that (1) developers need better means to
anticipate runtime problems and rigorously define metrics for improved fault
localization and (2) the cloud offers an abundance of operational data,
however, developers still often rely on their experience and intuition rather
than utilizing metrics. From our findings, we extracted a set of guidelines for
cloud development and identified challenges for researchers and tool vendors
Online Fault Classification in HPC Systems through Machine Learning
As High-Performance Computing (HPC) systems strive towards the exascale goal,
studies suggest that they will experience excessive failure rates. For this
reason, detecting and classifying faults in HPC systems as they occur and
initiating corrective actions before they can transform into failures will be
essential for continued operation. In this paper, we propose a fault
classification method for HPC systems based on machine learning that has been
designed specifically to operate with live streamed data. We cast the problem
and its solution within realistic operating constraints of online use. Our
results show that almost perfect classification accuracy can be reached for
different fault types with low computational overhead and minimal delay. We
have based our study on a local dataset, which we make publicly available, that
was acquired by injecting faults to an in-house experimental HPC system.Comment: Accepted for publication at the Euro-Par 2019 conferenc
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
Cloud engineering is search based software engineering too
Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; ‘SBSE in the cloud’. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of ‘SBSE for the cloud’, formulating cloud computing challenges in ways that can be addressed using SBSE
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