09-07-2012, 11:58 AM
Autonomic Computing Systems: Issues and Challenges
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Introduction
Data and programs in centralized applications are kept
at one site and this creates a bottleneck in performance
and availability of information in remote desktop computers.
Distributed systems have emerged to remove this
flaw. Advances in communication technology, especially
the Internet and increasing desktops power, have helped
distributed computing systems. Distributed Computing
Systems (DCSs) [31] consist of different computers connected
to each other and located at geographically remote
sites. Several flavours emerged such as Peer-to-Peer (P2P)
Computing [21], agents [36], and grid computing [24]. The
complexity of these systems has revolutionized their management.
Today, IT organizations have encountered growing
challenges in the management and maintenance of large
scale heterogeneous distributed computing systems because
these systems have to be active and available at all hours.
Autonomic Computing Architecture
In this section, we express how ACSs can be built. We
describe the necessary elements and set of tools that provide
the basic structure for an ACS. Software architectures
for AC are also explained.
Related Work
On March 8, 2001, Paul Horn presented a link between
pervasiveness and self-regulation in body ’s autonomic nervous
system and introduced ACSs to the National Academy
of Engineering at Harvard University. With choosing the
term autonomic, researchers attempted to make autonomic
capabilities in computer systems with the aim of decreasing
the cost of developing and managing them. [18] presents
a standard and quantitative definition of AC according to
quality mertics frameworks described in IEEE Std 1061-
1998. It also represents a quality metrics framework for
AC that contains three layers, openness and anticipatory
at first layer, self-awareness, context-awareness, and selfmanagement
as subsets of anticipatory at second layer, and
self-CHOP as subsets of self-management at third layer.
Conclusion
Current programming languages, methods, and management
tools are not adequate to handle the complexity,
scale, dynamism, heterogeneity, and uncertainty. Autonomic
Computing has gained world attention in recent
years for the reason of developing applications with selfmanaging
behavior. Automatic computing is a grand challenge
that requires advances in several fields such as software
architecture, learning and reasoning, modelling behavior,
policies, multi-agent systems, and knowledge base
design. In this paper, a survey of autonomic computing systems,
their importance, their architectures, and some challenges
was presented.