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2 Autonomic Computing Concepts
The logistic domain application was designed to respect self-* capabilities related
to healing, configuration optimization and protection. Such approach is of inter-
est on at least two levels: one related to things (or their virtualization as agents)
and services overlay responsible for large scale business systems behaviour
orchestration.
The overall paradigm able to cover the needs of such environment is inspired
by the research area of Autonomic Computing, which has greatly evolved over
the course of the last ten years the with thecommon understanding on how to
realize systems with self-managing (covering all previous self-* phases) capabil-
ities. The main steps of such feature pack are inspired in its high-level design
by the MAPE-K loop, which is one key conceptual aspect of the Autonomic
Computing field. The MAPE-K autonomic loop (Monitor, Analyze, Plan, Exe-
cute and Knowledge) represents a blueprint for the design of autonomic systems
where a managed element is coordinated by a loop structured in 4 phases and a
common knowledge [
6
,
10
]. The common known image depicting the concept is
presented in Fig.
1
.
Fig. 1.
Autonomic systems base architecture [
6
,
10
].
The MAPE-K loop is structured in 4 correlated phases [
6
,
10
]:
-
Monitoring:
The monitoring component is in charge to observe and manage
the different sources of relevant data (named sensors here) that provide infor-
mation regarding the way how system performs. In the current context, sen-
sors can capture the current consumption of critical resources but also other
performance metrics (such as the number of processed requests in a time win-
dow and the request process latency). The monitoring granularity is usually
specified by rules. Sensors can also generate notifications when changes to the
system configuration happen and a reaction is expected.
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