AutoCompBD: Autonomic Computing and Big Data platforms

AutoCompBD: Autonomic Computing and Big Data platforms The amount of data collected or generated by ICT systems is growing exponentially (today we reached a Petabyte Era and will soon enter the ExaScale one). Processing and storing ever-larger volumes of data introduces new challenges, and consequently, we need to constantly develop new technological means to face them. Massive parallel processing platforms are the answer and are already being developed over distributed systems (i.e., over cloud or fog computing). However, the problem is that such platforms need to support a wide variety of applications, coming with different processing requirements. Thus, self-* behavior is a must in this context, referring to self-managing characteristics of distributed computing resources, their capability to adapt to unpredictable changes while hiding intrinsic complexity to operators and users. This special issue is dedicated to dissemination and evaluation of advances in Autonomic Computing and Big Data platforms, supported by large-scale distributed systems (LSDS). Autonomic Computing is facilitated by self-management capabilities that modern LSDS introduce, such as self-configuration, self-healing, self-optimization, and self-protection properties. In LSDS, an important characteristic is dependability (defined in terms of reliability, availability, safety and security of the operating system). Increased dependability means the system has to be able to detect, recover, and tolerate every possible deviation from its normal operation, and a wide area of Autonomic Computing research is today dedicated to this subject. The models used in the development of systems with dependability capabilities combine monitoring, scheduling, data management, security, and fault tolerance. The challenge is that in Big Data platforms applications and users, and even the distributed resources themselves, introduce unpredictable dynamic behavior. Autonomic Computing is considered one great challenge today faced by the IT industry, in need of finding good answers to how to conquer the growing complexity of large-scale systems and how to adequately cope with the many issues faced by truly Big Data processing. All these topics challenge today researchers, due to the strong dynamic behavior of the user communities and of resource collections they use. The special issue is oriented on computer and information advances aiming to develop and optimize advanced system software, networking, and data management components to cope with Big Data processing and the introduction of Autonomic Computing capabilities for the supporting large-scale platforms. We consider that our special issue comes with new and novel added value in the domain of Autonomic Computing and Big Data platforms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Soft Computing Springer Journals

AutoCompBD: Autonomic Computing and Big Data platforms

Loading next page...
 
/lp/springer_journal/autocompbd-autonomic-computing-and-big-data-platforms-3jGGgbHIOS
Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag GmbH Germany
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Control, Robotics, Mechatronics
ISSN
1432-7643
eISSN
1433-7479
D.O.I.
10.1007/s00500-017-2739-8
Publisher site
See Article on Publisher Site

Abstract

The amount of data collected or generated by ICT systems is growing exponentially (today we reached a Petabyte Era and will soon enter the ExaScale one). Processing and storing ever-larger volumes of data introduces new challenges, and consequently, we need to constantly develop new technological means to face them. Massive parallel processing platforms are the answer and are already being developed over distributed systems (i.e., over cloud or fog computing). However, the problem is that such platforms need to support a wide variety of applications, coming with different processing requirements. Thus, self-* behavior is a must in this context, referring to self-managing characteristics of distributed computing resources, their capability to adapt to unpredictable changes while hiding intrinsic complexity to operators and users. This special issue is dedicated to dissemination and evaluation of advances in Autonomic Computing and Big Data platforms, supported by large-scale distributed systems (LSDS). Autonomic Computing is facilitated by self-management capabilities that modern LSDS introduce, such as self-configuration, self-healing, self-optimization, and self-protection properties. In LSDS, an important characteristic is dependability (defined in terms of reliability, availability, safety and security of the operating system). Increased dependability means the system has to be able to detect, recover, and tolerate every possible deviation from its normal operation, and a wide area of Autonomic Computing research is today dedicated to this subject. The models used in the development of systems with dependability capabilities combine monitoring, scheduling, data management, security, and fault tolerance. The challenge is that in Big Data platforms applications and users, and even the distributed resources themselves, introduce unpredictable dynamic behavior. Autonomic Computing is considered one great challenge today faced by the IT industry, in need of finding good answers to how to conquer the growing complexity of large-scale systems and how to adequately cope with the many issues faced by truly Big Data processing. All these topics challenge today researchers, due to the strong dynamic behavior of the user communities and of resource collections they use. The special issue is oriented on computer and information advances aiming to develop and optimize advanced system software, networking, and data management components to cope with Big Data processing and the introduction of Autonomic Computing capabilities for the supporting large-scale platforms. We consider that our special issue comes with new and novel added value in the domain of Autonomic Computing and Big Data platforms.

Journal

Soft ComputingSpringer Journals

Published: Jul 26, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off