October 28, 2013
Oct. 28 -- Cloud computing promises unlimited, cost-effective and agile computing resources for users. However, this new computing paradigm also poses a unique set of challenges to both cloud providers and users. On the one hand, cloud providers need to ensure that resources being provided are highly available and deliver high performance, while optimizing cloud infrastructure to reduce their operational costs. On the other hand, cloud users need to ensure that their applications receive the best performance from the cloud, while maintaining their budgetary constraints and the terms of any Service Level Agreements (SLAs) they have with their cloud providers.
Given the scale of cloud deployment, systematic analytical approaches are critically needed to provide insights to both providers and users to achieve their respective goals. For instance, cloud providers need to constantly be aware of the running status and/or anomalies in functionality from their cloud, to be able to quickly fix any issues that may arise, to adjust physical resource allocations to ensure that their customers get best performance, or plan which services to offer to get the best return on investment.
Similarly, cloud users need to understand the workload to be deployed into the cloud, plan the deployment in a cost-effective way, or ascertain the flexibility and service quality provided by different cloud environments and use this to decide their deployment strategy. Analytics can play a pivotal role in all these scenarios. By gathering insights from the large amount of data from the cloud, both cloud providers and consumers can develop analytical approaches to achieving their respective objectives in spite of the scale that clouds provide.
The purpose of this workshop is to provide a forum for researchers in the related fields to exchange ideas, and share their experiences in developing analytics to better deploy, operate and use the cloud. Specifically, we seek and wish to foster research contributions that draw on statistical analysis, analytical modeling, and machine learning to develop novel solutions in this problem area.
Topics of Interest
Topics of interest include, but are not limited to, the following:
• Cloud workload measurement and analysis
• Workload behavior modeling
• Analytics for application deployment in cloud
• Performance modeling of cloud applications
• Cloud performance benchmarking
• Resource utilization optimization
• Tracing and problem identification in cloud systems
• Log and monitoring data analysis
• Problem diagnosis and troubleshooting
• Security and intrusion detection
• Reliability engineering, fault management, and disaster recovery
• Design and implementation of analytics systems
• Business optimization in cloud operations
The IWCA workshop invites authors to submit original and unpublished work. Papers should not exceed 6 pages in IEEE style (single-spaced 2-column text using 10-point size type on A4 paper). Authors should submit a PostScript (level 2) or PDF file that will print on a PostScript printer.
• Electronic submission only via the IWCA14 Submission site
• All selected papers will be peer-reviewed
• For each accepted paper, at least one author is required to register and present the paper at the workshop
• All accepted papers will be published with IEEE Xplore.
• We will submit all accepted workshop papers for possible publication in a special issue of the International Journal on Big Data Intelligence.
Paper submission due: November 24, 2013
Notification of acceptance: December 22, 2013
Final camera-ready papers due: January 17, 2014
Shu Tao (IBM T J Watson Research)
Rich Wolski (UCSB)
Rahul Singh (IBM T J Watson Research)
Theophilus Benson (Duke University)
Yanpei Chen (Cloudera)
Yuan Chen (HP Labs)
David Irwin (UMass, Amherst)
Thilo Kielmann (VU University, Amsterdam)
Ningfang Mi (Northeastern University)
Lavanya Ramakrishnan (Lawrence Berkeley National Lab)
Prashant Shenoy (UMass, Amherst)
Christopher Charles Stewart (Ohio State University)
Evgenia Smirni (William and Mary)
Chunqiang Tang (Facebook)
Jon Weissman (University of Minnesota)
Timothy Wood (George Washington University)
Lydia Chen (IBM Zurich Research)
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