![](bg8.png)
execution we calculated the accuracy of the moni-
toring system, produce by dividing the number of
Metrics+Datapoints(received)
Metrics+Datapoints(expected)
. These executions were con-
ducted with a workload of 60 requests/min and for
each one of them we increased the number of users
(one per time). As we can observe in Figure 6 the
system does not fail under 90% of accuracy. The 10%
percent loss of metrics and datapoints is due to the
pulling frequency of the monitoring system.
9 CONCLUSIONS AND FUTURE
WORK
In this paper we have introduced a monitoring frame-
work, that covers three main layers of a SBA, i.e. in-
frastructure, service and workflow layers. To achieve
this we propose the corresponding layer-specific qual-
ity models that define a set of metrics that can be
monitored in each layer. Moreover, in the proposed
framework we introduce the metric aggregators for
each layer as the components that computes compos-
ite metrics values from the raw ones produced by the
monitoring sensors and stores them in a times-series
database, to be further processed by the event-pattern
detection component.
As for future work, we plan to introduce a com-
plex cross-layer adaptation engine that will efficiently
manage the SLO violations both reactively and pro-
actively by exploiting the detected critical event pat-
terns. Furthermore, a future objective is the cre-
ation of a Business quality model, with regard to the
Business functional layer which could include quality
terms related to the quality of the business processes
and higher level Key Performance Indicators (KPIs).
ACKNOWLEDGMENTS
This work is supported by CloudSocket project
15
that
has been funded within the European Commissions
H2020 Program under contract number 644690.
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