Autonomic Aspects of IoT Based Systems: A Logistics Domain Scheduling Example - Interoperability and Open-Source Solutions for the Internet of Things - page 153

a systematic engineering approach that integrates both control-loop approaches
with decentralized agent inspired approaches.
Of course that MAPE-K loop only represents a vision that leaves lower level
details of the architecture purposely unspecified (i.e., they do not impose con-
straints on the implementation). Each individual analysis of requirements should
define a reference conceptual architecture for the runtime platform which we here
describe and that follows the MAPE-K loop design approach. The details and
implementation of this conceptual architecture might be deployment dependent
at detail level. The only purpose of this section is to provide a high-level intu-
ition of the systems that will compose the architecture, which is required in order
to identify the actors that are involved in the requirement specification of the
logistic application.
Our focus here is to provide an adequate toolset for monitoring part of the
MAPE-K loop.
2.2 Application
Autonomic computing and its extensions in the area of autonomic networking
find its most representative application area until now in telecommunication
and networks management. One of the most representative problems addressed
is the one of congestion control. Designing ecient congestion control scheme
is, therefore, a very relevant issue to improve the control of network congestion
and to fulfill data transmission effectively. The main diculty in designing such
scheme lies in the large propagation delay in transmission that usually leads to a
mismatch between the network resources and the amount of admitted tra c. The
crucial issue of the network control is that we should adapt the controllable flows
to the changing network environment, so as to achieve the goal of data transfer
and to alleviate network congestion. Congestion is the result of an issue between
the network resources capacity and the amount of trac for transmission. But
congestion problems can easily be scaled to any other trac capacity problems
or even to economic value chains such as logistic ones.
Another use case, relevant for the Internet of Things area, known as virtual-
ization as a service layer, is the one of service engineering, more specific the one
of self-verifying service based systems [ 11 ]. Service systems are under the chal-
lenge of continuous service availability and quality risk and may benefit from
the use of autonomic control where knowledge of probabilistic failure models
supports the continuous adaptation during runtime.
Based on those examples, it is visible that an autonomic computing approach
generates large benefits to IT governance systems. Foundational methodologies
such as ITIL (IT Infrastructure Library) [ 12 ] may find a coherent technological
mirror in self-management features.
Autonomic computing provides an architecture that enables systems,
including networked software systems, to be flexible, dynamic, and adaptable.
It provides technologies to offset the inherently increased complexity as grids
expand the domain of computing. For example, self-managing autonomic sys-
tems can help optimize process performance and manage workloads across the