[Storage-research-list] [Extended deadline] Call for Papers: SDDCS
2018, March 25, Williamsburg, VA, USA
by George Amvrosiadis
We invite authors to submit papers to the Fourth Software-Defined Data
Computing and Storage workshop (SDDCS) which will be co-located with
ACM ASPLOS 2018 and held on March 25, 2018 in Williamsburg, VA, USA.
* Overview
Data center and cloud computing infrastructure is becoming
increasingly software-defined. Although such infrastructure consists
of tightly interconnected computing, networking, and storage
components, these resources are typically studied independently. For
example, studies focused on computing or networking often overlook the
properties of storage devices, and vice versa. Overall infrastructure
performance often decreases due to miscommunication and
misconfiguration of different resources. Software-defined
methodologies offer an opportunity to bridge this gap and deliver high
performance, efficiency, and reliability. Making any infrastructure
"software defined" requires significant community efforts. The SDDCS
workshop provides the forum for multidisciplinary research spanning
computing architecture, networking, storage systems and devices, as
well as applications.
SDDCS aims to bring together industry and academia to jointly explore
recent progress related to performance bottleneck discoveries and to
bridging the gap between computing and storage in the software-defined
context. We particularly encourage contributions containing highly
novel ideas, new approaches, and/or groundbreaking results.
Conference web-site: https://sddcs.github.io/2018/sddcs2018.html
* Topics
Topics of interest in SDDCS include but are not limited to:
- Software-defined memory systems for cloud computing
- Software-defined non-volatile devices
- Convergent design for computing and storage
- Non-volatile storage support for network transmission
- Storage deduplication for remote cloud backups
- Data collection and analytics for system optimization
- Dynamic workload redistribution and scheduling
- In-memory processing
- Near-data-computing
- Cross-layer coordination in data centers
- Storage and network virtualization
- Security for software-defined schemes
- Programmable interfaces for convergent design
- User studies and experiences of real-world applications (e.g., graph
processing, deep learning, database, etc)
* Submission Instructions
Submitted papers must be no longer than 8 single-spaced 8.5" x 11"
pages, including figures, tables, and references; two-column format,
using 10-point type on 12-point (single-spaced) leading; and a text
block 6.5" wide x 9" deep. Author names and affiliations should appear
on the title page.
The submitted papers should present original theoretical and/or
experimental research in any of the areas listed above that has not
been previously published, accepted for publication, or is not
currently under review by another conference or journal.
The accepted papers will be published in the workshop proceedings of
ACM ASPLOS 2018 and available in the ACM Digital Library. Selected
(extended) papers will b recommended for fast-track processing in ACM
Transactions on Storage (TOS).
* Important Dates:
Paper submission due: February 1, 2018, 11:59pm AoE
Notification to authors: February 20, 2018
Final paper files due: March 10, 2018
* Submission Site:
https://easychair.org/conferences/?conf=sddcs18
* Workshop Organizer
General Co-chairs:
- Evgenia Smirni, College of William and Mary
- Yu Hua, Huazhong University of Science and Technology
Program Co-chairs:
- Bo Wu, Colorado School of Mines
- Vasily Tarasov, IBM Research
Publication Chair:
- Xing Lin, NetApp
Publicity Chair:
- George Amvrosiadis, Carnegie Mellon University
Web Chair:
- Pengfei Zuo, Huazhong University of Science and Technology
Program Committee:
- Vaneet Aggarwal, Purdue University
- Bharath Balasubramanian, AT&T Labs Research
- Feng Chen, Louisiana State University
- Chris Gniady, University of Arizona
- Song Jiang, University of Texas, Arlington
- Mahmut Kandemir, Pennsylvania State University
- Scott Klasky, Oak Ridge National Laboratory
- Jay Lofstead, Sandia National Laboratories
- Darrell Long, University of California, Santa Cruz
- Ao Ma, University of Wisconsin-Madison
- Rajesh Panta, AT&T Labs Research
- Marco Paolieri, University of Southern California
- Lukas Rupprecht, IBM Research - Almaden
- Philip Shilane, DellEMC
- Emina Soljanin, Rutgers University
- Alan Sussman, University of Maryland
- Ravi Tandon, University of Arizona
- Peter Varman, Rice University
- Youjip Won, Hanyang University
- Yuan Xie, University of California, Santa Barbara
- Ming Zhao, Arizona State University
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5 years, 4 months
[Storage-research-list] Call for book chapters "Smart Data:
State-of-the-Art and Perspectives in Computing and Applications",
Taylor & Francis
by Kuan-Ching Li
*** Apologies if multiple copies of this call are received ****
----------------------
Call for Book Chapters
----------------------
Smart Data: State-of-the-Art and Perspectives in Computing and Applications
(Chapman & Hall/ CRC Big Data Series)
CRC Press, Taylor & Francis Group, USA
Important Dates
* Proposal Submission: February 1, 2018*
* Proposal (Acceptance/Rejection): February 15, 2018
* Sample Chapter (Acceptance/Rejection): April 15, 2018
* Complete Chapter Submission (to editors): June 15, 2018
* Submission of Chapters (to publisher): July 1, 2018
* Publication Time: Q4/2018 (estimated)
Big Data is being generated around us at 24/7 basis, from daily business,
custom use, engineering, science activities, sensory data collected from
IoTs and CPS systems, among others. Storing and owing only such massive
amount of data is meaningless, as the key point is to identify, locate and
extract valuable knowledge from Big Data to forecast and services support,
improving quality of service and society’s value. Such extracted valuable
knowledge is usually referred to Smart Data that is vital in providing
suitable decision in highly on-demand business, science and engineering
applications.
How to select Smart Data from Big Data, unlocking value in massive
datasets? Advanced Big Data modeling and analytics are indispensable for
discovering the underlying structure from retrieved data to acquire Smart
Data, whereas novel computing theories as well advanced mining and learning
techniques are fundamentally important to the search of such intelligent
decision and predicative services support.
In this book, it is intended to invite scholars, experts and successful
case participating members to contribute discussions on topics for smart
data mining and management as well as applications. Not only smart data
computing algorithms and architectures from the computer point of view, but
also smart data applications in business issues aspects, industrial aspects
and related areas, it is equally well suitable for data analysts in
business and industry.
* Topics
Topics include, but are not limited to, the following:
Track 1: Data Science and Its Foundations
- Foundational Theories for Data Science
- Theoretical Models for Big Data
- Foundational Algorithms and Methods for Big Data
- Interdisciplinary Theories and Models for Smart Data
- Data Classification and Taxonomy
- Data Metrics and Metrology
Track 2: Smart Data Infrastructure and Systems
- Programming Models/Environments for Cluster/Cloud/Edge/BigData Computing
- High Performance/Throughtput Platforms for Smart/Big Data Computing
- Cloud Computing, Edge Computing and Fog Computing for Smart/Big Data
- System Architecture and Infrastructure of Smart/Big Data
- New Programming Models for Smart/Big Data beyond Hadoop/MapReduce
- Smart Data Appliance
- Smart Data Ecosystems
Track 3: Big Data Storage and Management
- Smart Data Collection, Transformation and Transmission
- Big Data Integration and Cleaning for Smart Data
- Uncertainty and Incompleteness Handling in Smart/Big Data
- Quality Management of Smart/Big Data
- Smart Data Storage Models
- Query and Indexing Technologies
- Distributed File Systems
- Distributed Database Systems
- Large-Scale Graph/Document Databases
Track 4: Smart Data Processing and Analytics
- Smart Data Search, Mining and Drilling from Big Data
- Semantic Integration and Fusion of Multi-Source Heterogeneous Big Data
- In-Memory/Streaming/Graph-Based Computing for Smart/Big Data
- Brain-Inspired/Nature-Inspired Computing for Smart/Big Data
- Distributed Representation Learning of Smart Data
- Machine Learning/Deep Learning for Smart/Big Data
- Applications of Conventional Theories (e.g., Fuzzy Set, Rough Set) in
Smart/Big Data
- New Models, Algorithms, and Methods for Smart/Big Data Processing and
Analytics
- Exploratory Data Analysis
- Visualization Analytics for Big Data
- Smart/Big Data Aided Decision-Marking
Track 5: Smart/Big Data Applications
- Smart/Big Data Applications in Science, Internet, Finance,
Telecommunications, Business, Medicine, Healthcare, Government,
Transportation, Industry, Manufacture
- Smart/Big Data Applications in Government and Public Sectors
- Smart/Big Data Applications in Enterprises
- Security, Privacy and Trust in Smart/Big Data
- Smart/Big Data Opening and Sharing
- Smart/Big Data Exchange and Trading
- Data as a Service (DaaS)
- Standards for Smart/Big Data
- Case Studies of Smart/Big Data Applications
- Practices and Experiences of Smart/Big Data Project Deployments
- Ethic Issues on Smart/Big Data Applications
* Proposal submission
A proposal for book chapter is needed from prospective authors before the
proposal *submission due date*, describing the objective, scope and
structure of the proposed chapter (no more than 5 pages). Acceptance of
chapter proposals will be communicated to lead chapter authors after a
formal double-blind review process, to ensure relevance, quality and
originality. The submission of chapter proposals should be sent directly
via email to editors.
* Book Editors
Kuan-Ching Li, Providence University, Taiwan, kuancli(a)gm.pu.edu.tw
Qingchen Zhang, St. Francis Xavier University, Canada, qzhang(a)stfx.ca
Laurence T. Yang, St. Francis Xavier University, Canada, ltyang(a)gmail.com
Beniamino Di Martino, Universita' della Campania "Luigi Vanvitelli", Italy,
beniamino.dimartino(a)unicampania.it
* Additional Information
Inquiries and chapter proposal submissions can be forwarded electronically
by email, to:
Qingchen Zhang (email: qzhang(a)stfx.ca), cc'ied to kuancli(a)gm.pu.edu.tw,
ltyang(a)gmail.com and beniamino.dimartino(a)unicampania.it
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5 years, 4 months
[Storage-research-list] CFP: P-RECS'18, June 11, 2018, Tempe, AZ,
USA
by Carlos Maltzahn
# P-RECS '18 Call for Papers
First International Workshop on Practical Reproducible Evaluation of
Computer Systems.
June 11, 2018. In conjunction with HPDC'18 <http://hpdc.org/2018/ <http://hpdc.org/2018/>>. In
cooperation with SIGHPC (pending).
Independent evaluation of experimental results in the area of computer
and networking systems is a challenging task. Recreating the
environment where an experiment originally ran is commonly considered
impractical or even impossible. This workshop will focus heavily on
practical, actionable aspects of reproducibility in broad areas of
computational science and data exploration, with special emphasis on
issues in which community collaboration can be essential for adopting
novel methodologies, techniques and frameworks aimed at addressing
some of the challenges we face today. The workshop will bring together
researchers and experts to share experiences and advance the state of
the art in the reproducible evaluation of computer systems, featuring
contributed papers and invited talks.
## Topics
We expect submissions from topics such as, but not limited to:
* Experiment dependency management.
* Software citation and persistence.
* Data versioning and preservation.
* Provenance of data-intensive experiments.
* Tools and techniques for incorporating provenance into publications.
* Automated experiment execution and validation.
* Experiment portability for code, performance, and related metrics.
* Experiment discoverability for re-use.
* Cost-benefit analysis frameworks for reproducibility.
* Usability and adaptability of reproducibility frameworks into already-established domain-specific tools.
* Long-term artifact archiving for future reproducibility.
* Frameworks for sociological constructs to incentivize paradigm shifts.
* Policies around publication of articles/software.
* Blinding and selecting artifacts for review while maintaining history.
* Reproducibility-aware computational infrastructure.
## Submission
Submit via EasyChair
<https://easychair.org/conferences/?conf=precs18 <https://easychair.org/conferences/?conf=precs18>>. We look for two
categories of submissions:
* **Position papers**. This category is for papers whose goal is to
propose solutions (or scope the work that needs to be done) to
address some of the issues outlined above. We hope that a research
agenda comes out of this and that we can create a community that
meets yearly to report on our status in addressing these problems.
* **Experience papers**. This category consists of papers reporting
on the authors' experience in automating one or more
experimentation pipelines. The committee will look for submissions
reporting on their experience: what worked? What aspects of
experiment automation and validation are hard in your domain? What
can be done to improve the tooling for your domain? As part of the
submission, authors need to provide a URL to the automation
service they use (e.g., [TravisCI](https://travis-ci.org <https://travis-ci.org/>),
[GitLabCI](https://about.gitlab.com/gitlab-ci/ <https://about.gitlab.com/gitlab-ci/>),
[CircleCI](https://circleci.com <https://circleci.com/>),
[Jenkins](https://jenkins-ci.org <https://jenkins-ci.org/>), etc.) so reviewers can verify
that there is one or more automated pipelines associated to the
submission.
### Format
Authors are invited to submit manuscripts in English not exceeding 5
pages of content. The 5-page limit includes figures, tables and
appendices, but does not include references, for which there is no
page limit. Submissions must use the [ACM Master
Template](https://www.acm.org/publications/proceedings-template <https://www.acm.org/publications/proceedings-template>)
(please use the `sigconf` format with default options).
### Proceedings
The proceedings will be archived in both the ACM Digital Library and
IEEE Xplore through SIGHPC.
### Tools
These tools can be optionally used used to automate your experiments:
[CWL](http://commonwl.org <http://commonwl.org/>),
[Popper](https://github.com/systemslab/popper <https://github.com/systemslab/popper>),
[ReproZip](http://reprozip.org <http://reprozip.org/>), [Sciunit](http://sciunit.run <http://sciunit.run/>),
[Sumatra](https://github.com/open-research/sumatra <https://github.com/open-research/sumatra>).
## Important Dates
* Submissions due: April 2, 2018
* Acceptance notification: April 30, 2018
* Camera-ready paper submission: May 6, 2018
* Workshop: June 11, 2018
## Organizers
* Ivo Jimenez, UC Santa Cruz
* Carlos Maltzahn, UC Santa Cruz
* Jay Lofstead, Sandia National Laboratories
## Program Committee
* Divyashri Bhat, UMass Amherst
* Michael Crusoe, Project Lead, Common Workflow Language project
* Anja Feldmann, TU Berlin
* Todd Gamblin, LLNL
* Mike Heroux, Sandia National Laboratories
* Torsten Hoefler, ETH Zürich
* Neil Chue Hong, Software Sustainability Institute / University of
Edinburgh, UK
* Dan Katz, NCSA
* Kate Keahey, Argonne National Lab / ChameleonCloud
* Ignacio Laguna, LLNL
* Arnaud Legrand, Bâtiment IMAG
* Reed Milewicz, Sandia National Laboratories
* Robert Ricci, University of Utah / CloudLab
* Victoria Stodden, UIUC
* Violet R. Syrotiuk, ASU
* Michela Taufer, University of Delaware
* Michael Zink, UMass Amherst
## Contact
Please address workshop questions to <ivo(a)cs.ucsc.edu <mailto:ivo@cs.ucsc.edu>>.
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5 years, 4 months
[Storage-research-list] Call for Papers: MLCS 2018, June 12, Tempe,
AZ, USA
by George Amvrosiadis
We invite authors to submit papers to the **First** workshop on
Machine Learning for Computing Systems (MLCS) which will be co-located
with ACM HPDC 2018 and held on June 12, 2018 in Tempe, AZ, USA.
* Overview
As the HPC community rapidly approaches the era of exascale machines,
the complexity of problems such as monitoring, troubleshooting, and
design also increases. Large HPC facilities already produce terabytes
of data each day, ranging from low-level hardware telemetry and system
logs, to troubleshooting tickets. Current administration tools tend to
focus on designing filters for well-defined system events, restricting
them to only detect behaviors previously known to be interesting.
These tools will never find new, previously unknown modes of behavior
automatically, or adapt to changes in the system. Meanwhile, machine
learning techniques are uniquely suited for characterizing and
extracting knowledge from large and complex datasets.
Recently, machine learning techniques are also used to better
understand and analyze HPC machines and facilities. Interdisciplinary
research at the intersection of machine learning and HPC has already
produced advances in memory error mitigation, datacenter cooling,
system log analysis, job scheduling, and many other areas. As the
machine learning community focuses on human-understandable models,
these models become extremely attractive for HPC-related decision
support and development of data-driven tools to assist of human
experts. Additionally, HPC-related problems are often relevant to
open machine learning research areas, such as anomaly detection within
near-natural language text in logs, and there is a definite need for
collaboration between HPC domain experts and statistical modeling /
machine learning experts.
For these reasons, we are organizing the 1st Workshop on Machine
Learning for Computing Systems (MLCS). MLCS 2018 will provide a
much-needed opportunity for cutting-edge research ideas to be shared,
and bring together researchers across the disciplines of machine
learning, systems design, systems monitoring, HPC resilience, hardware
architecture, data science, statistics, and applied mathematics to
address a shared goal of better and more efficient use and monitoring
of HPC machines and facilities.
Conference web-site: https://mlcsworkshop.weebly.com/
* Topics
Working from our premise that the deluge of HPC monitoring data
necessitates a move toward data-driven intelligent modeling, we
solicit contributions including, but not limited to:
1. Use of machine learning or data science to better understand:
- Hardware faults and errors
- Software errors
- Telemetry data (temperature, voltages, cooling apparatus)
- Power consumption
- Facilities / building control
- Job scheduling
- Filesystem logs
- Network logs
- Syslog or console logs
- Error detection and correction
- Resilience and fault tolerance
- Failure troubleshooting / assistance of human experts
- Assistance of non-expert users
- HPC system security
2. Use of interpretable machine learning models for HPC-related decision support
- Including user/human-subject studies
3. Modeling techniques incorporating human expert knowledge along with
knowledge extracted from data
- Use of these models to evaluate, confirm, or refute human assumptions
4. New or improved machine learning models particularly suited for HPC problems
5. Tools, at any stage of development, using data-driven technologies
for some aspect of systems monitoring or design
6. Experience reports detailing successes and failures of machine
learning applied to HPC
7.Formulations of unsolved data-related HPC problems with the
potential for machine learning
- Especially including the public release of HPC-related datasets for
use by the community
* Submission Instructions
We are soliciting full papers, short work-in-progress, experience, or
position papers, and poster abstracts:
- Submitted full papers must be no longer than 8 single-spaced 8.5" x
11" pages, including figures, tables, and references; in the ACM
format (two-column format, using 10-point type on 12-point
(single-spaced) leading; and a text block 6.5" wide x 9" deep). Author
names and affiliations should appear on the title page.
- Submitted short work-in-progress, experience, or position papers
must be no longer than 4 single-spaced 8.5" x 11" pages, including
figures, tables, and references; in the ACM format (two-column format,
using 10-point type on 12-point (single-spaced) leading; and a text
block 6.5" wide x 9" deep). Author names and affiliations should
appear on the title page.
- Submitted poster abstracts must be no longer than 2 single-spaced
8.5" x 11" pages, including figures, tables, and references; in the
ACM format (two-column format, using 10-point type on 12-point
(single-spaced) leading; and a text block 6.5" wide x 9" deep). Author
names and affiliations should appear on the title page.
The submitted papers should present original theoretical and/or
experimental research in any of the areas listed above that has not
been previously published, accepted for publication, or is not
currently under review by another conference or journal.
The accepted papers will be published in the workshop proceedings of
ACM HPDC 2018 and available in the ACM Digital Library.
* Important Dates:
Paper submissions due: April 9, 2018, 11:59pm AoE
Notification to authors: May 9, 2018
Final paper files due: May 12, 2018
* Submission Site:
https://easychair.org/conferences/?conf=mlcs18
* Workshop Organizers
Chair:
- Elisabeth Baseman, Los Alamos National Laboratory, USA
Organizing Committee:
- George Amvrosiadis, Carnegie Mellon University, USA
- Huiping Cao, New Mexico State University, USA
Program Committee:
- Medha Bhadkamkar, Nimble Storage, USA
- Sean Blanchard, Los Alamos National Laboratory, USA
- John Daly, Department of Defense, USA
- Nathan DeBardeleben, Los Alamos National Laboratory, USA
- Kurt Ferreira, Sandia National Laboratories, USA
- Todd Gamblin, Lawrence Livermore National Laboratory, USA
- Chuan Hu, Microsoft, USA
- Satyajayant Misra, New Mexico State University, USA
- Frank Mueller, North Carolina State University, USA
- Nicole Nichols, Pacific Northwest National Laboratory, USA
- Aleatha Parker-Wood, Center for Advanced Machine Learning at Symantec, USA
- J. Ray Scott, Pittsburgh Supercomputing Center, USA
- Feng Yan, University of Nevada, Reno, USA
- Mai Zheng, New Mexico State University, USA
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5 years, 4 months