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2017 IEEE International Conference on Cloud and Autonomic Computing (ICCAC)
http://www.autonomic-
The University of Arizona, Tucson, AZ, USA
September 18-22, 2017
Co-located with the IEEE International Conference on Self-Adaptive and
Self-Organizing Systems
=== Overview ===
Enterprise-scale cloud platforms and services systems, present common
and cross-cutting challenges in maximizing power efficiency and
performance while maintaining predictable and reliable behavior, and
at the same time responding appropriately to environmental and system
changes such as hardware failures and varying workloads. Autonomic
computing systems address the challenges in managing these
environments by integrating monitoring, decision-processing and
actuation capabilities to autonomously manage resources and
applications based on high-level policies.
Research in cloud and autonomic computing spans a variety of areas,
from distributed systems, computer architecture, middleware services,
databases and data-stores, networks, machine learning, and control
theory. The purpose of the Fifth International Conference on Cloud and
Autonomic Computing (ICCAC) is to bring together researchers and
practitioners across these disciplines to address the multiple facets
of cloud and autonomic computing.
Papers are solicited on a broad array of topics of relevance to cloud
and autonomic computing and their intersections, including those that
bear on connections and relationships among different research areas
or report on prototype systems or experiences. The ICCAC conference
seeks also papers on autonomic aspects of combining cloud computing
with fog and edge computing. The goal is to continue our international
forum focused on the latest research, applications, and technologies
aimed at making cloud and autonomic computing systems and services
easy to design, to deploy, and to implement, while also being
self-manageable, self-regulating and scalable with little involvement
of humans or system administrators.
=== Topics ===
Autonomic Cloud Computing
* Self-managing cloud services
* Autonomic resource and energy management in cloud computing
* Autonomic cloud applications and services
* Autonomic virtual cloud resources and services
* Cloud workload characterization and prediction
* Monitoring, modeling and analysis of cloud resources and services
* Anomaly behavior analysis of autonomic systems and services
* Autonomic aspects of combining cloud computing with fog and edge computing
Autonomics for Extreme Scales
* Large scale autonomic systems
* Self-optimizing and self-healing at petacomputing scale
* Self-managing middleware and tools for extreme scales
* Experiences in autonomic systems and applications at extreme scales
(peta/exa-computing)
Autonomic Computing Foundations and Design Methods
* Evaluation, validation and quality and correctness assessment of
autonomic loops
* Theoretical frameworks for modeling and analyzing autonomic
computing systems, control and decision theory
* Model-based design, software engineering, formal methods, testing,
programming languages and environments support
* Knowledge representation and visualization of behavior of autonomic
systems and services
Autonomic Computing Systems, Tools and Applications
* Self-protection techniques of computing systems, networks and applications
* Stochastic analysis and prediction of autonomic systems and applications
* Benchmarks and tools to evaluate and compare different architectures
to implement autonomic cloud systems
* High performance autonomic applications
* Self-* applications in science and engineering
* Self-* Human Machine Interface
* Autonomic systems and applications combining cloud computing with
fog computing and edge computing
=== Important Dates ===
Abstract registration: May 5, 2017
Papers submission deadline: May 19, 2017
Author notification: June 23, 2017
Camera-ready papers: July 21, 2017
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sent to . If you'd like to opt out of these
announcements, information on how to unsubscribe is available at the
bottom of this email.]
2017 IEEE International Conference on Cloud and Autonomic Computing (ICCAC)
http://www.autonomic-
The University of Arizona, Tucson, AZ, USA
September 18-22, 2017
Co-located with the IEEE International Conference on Self-Adaptive and
Self-Organizing Systems
=== Overview ===
Enterprise-scale cloud platforms and services systems, present common
and cross-cutting challenges in maximizing power efficiency and
performance while maintaining predictable and reliable behavior, and
at the same time responding appropriately to environmental and system
changes such as hardware failures and varying workloads. Autonomic
computing systems address the challenges in managing these
environments by integrating monitoring, decision-processing and
actuation capabilities to autonomously manage resources and
applications based on high-level policies.
Research in cloud and autonomic computing spans a variety of areas,
from distributed systems, computer architecture, middleware services,
databases and data-stores, networks, machine learning, and control
theory. The purpose of the Fifth International Conference on Cloud and
Autonomic Computing (ICCAC) is to bring together researchers and
practitioners across these disciplines to address the multiple facets
of cloud and autonomic computing.
Papers are solicited on a broad array of topics of relevance to cloud
and autonomic computing and their intersections, including those that
bear on connections and relationships among different research areas
or report on prototype systems or experiences. The ICCAC conference
seeks also papers on autonomic aspects of combining cloud computing
with fog and edge computing. The goal is to continue our international
forum focused on the latest research, applications, and technologies
aimed at making cloud and autonomic computing systems and services
easy to design, to deploy, and to implement, while also being
self-manageable, self-regulating and scalable with little involvement
of humans or system administrators.
=== Topics ===
Autonomic Cloud Computing
* Self-managing cloud services
* Autonomic resource and energy management in cloud computing
* Autonomic cloud applications and services
* Autonomic virtual cloud resources and services
* Cloud workload characterization and prediction
* Monitoring, modeling and analysis of cloud resources and services
* Anomaly behavior analysis of autonomic systems and services
* Autonomic aspects of combining cloud computing with fog and edge computing
Autonomics for Extreme Scales
* Large scale autonomic systems
* Self-optimizing and self-healing at petacomputing scale
* Self-managing middleware and tools for extreme scales
* Experiences in autonomic systems and applications at extreme scales
(peta/exa-computing)
Autonomic Computing Foundations and Design Methods
* Evaluation, validation and quality and correctness assessment of
autonomic loops
* Theoretical frameworks for modeling and analyzing autonomic
computing systems, control and decision theory
* Model-based design, software engineering, formal methods, testing,
programming languages and environments support
* Knowledge representation and visualization of behavior of autonomic
systems and services
Autonomic Computing Systems, Tools and Applications
* Self-protection techniques of computing systems, networks and applications
* Stochastic analysis and prediction of autonomic systems and applications
* Benchmarks and tools to evaluate and compare different architectures
to implement autonomic cloud systems
* High performance autonomic applications
* Self-* applications in science and engineering
* Self-* Human Machine Interface
* Autonomic systems and applications combining cloud computing with
fog computing and edge computing
=== Important Dates ===
Abstract registration: May 5, 2017
Papers submission deadline: May 19, 2017
Author notification: June 23, 2017
Camera-ready papers: July 21, 2017
******************************
(https://lists.mcs.anl.gov/
If you do not remember your password (which is needed to change these options), you can reset it using the "Unsubscribe or Edit Options" button at the bottom of the page.
(https://lists.mcs.anl.gov/
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