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  1. Proceedings of the 2016 ACM Workshop on Cloud-Assisted Networking (CAN '16)
  2. A First Look at Bugs in OpenStack
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AggFlow: Scalable and Efficient Network Address Virtualization on Software Defined Networking
CleanG: A Clean-Slate EPC Architecture and ControlPlane Protocol for Next Generation Cellular Networks
Personal Data Management with the Databox: What's Inside the Box?
Understanding the Performance and Bottlenecks of Cloud-Routed Overlay Networks: A Case Study
How Close to the Edge?: Delay/Utilization Trends in MEC
Securebox: Toward Safer and Smarter IoT Networks
Architecting Multimedia Conferencing Service using SDN
Profiling and Grouping Users to Edge Resources According to User Interest Similarity
SecuSpot: Toward Cloud-Assisted Secure Multi-Tenant WiFi HotSpot Infrastructures
A Dynamic Programming Algorithm for Joint VNF Placement and Chaining
A First Look at Bugs in OpenStack
Hybrid Cloud Integration of Routing Control & Data Planes

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A First Look at Bugs in OpenStack

Content Provider ACM Digital Library
Author Garcia, Washington Benson, Theophilus
Abstract An increasing amount of popular services are utilizing cloud infrastructure due to its convenience, low cost, and scalability. However, as more services turn to cloud as a means of storing and delivering data to consumers, the faults of cloud infrastructure become more apparent. When cloud infrastructure fails, the consequences are disastrous, with failures making national headlines. Popular services such as Amazon, Quora, Netflix, and many social media sites all rely on cloud computing at their core. Although new cloud infrastructures have sprouted in recent years, there is limited knowledge about what type of bugs they contain, and how these bugs affect quality of service of cloud components. We propose a system that can automatically classify bug tickets using the natural language descriptions provided by developers. We then utilize this system to classify a random sub-sample of 30k OpenStack bugs, and reveal trends related to OpenStack releases, priority assignments, and project characteristics. For example, we find that existing issues make up over 70% of bugs in OpenStack modules, with over half of these bugs corresponding to reliability.
Starting Page 67
Ending Page 72
Page Count 6
File Format PDF
ISBN 9781450346733
DOI 10.1145/3010079.3010086
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-12-12
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Software bugs Cloud Machine learning Openstack
Content Type Text
Resource Type Article
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