AutoCompBD: Autonomic Computing and Big Data platforms

doi:10.1007/s00500-017-2739-8

AutoCompBD: Autonomic Computing and Big Data platforms

2017-07-26

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DOI：10.1007/s00500-017-2739-8

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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.

Keyword:

Scheduling algorithms

Resource management

Fault tolerance

Big Data

Heterogeneous distributed systems

AutoCompBD: Autonomic Computing and Big Data platforms

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