![](bgc.png)
negotiation points from A to H by positioning them
outside the acceptable region. An approach that
proposes such negotiation points as feasible, can
lead to a high customer dissatisfaction.
Figure 9 presents the price-based indicator
varying the workload plan. The figure shows that for
workload plan {100 r/s, 100 r/s}
is always
negative because the customer is perceiving a too
high price for the required workload plan.
7 CONCLUSIONS
We exploited capacity planning to support Cloud
providers in bilateral automatic negotiation of high-
level QoS parameters and prices of PaaS services.
The technique aims at achieving high satisfaction
levels for both providers and customers. To this end,
we propose a heuristic approach for the dynamic
evaluation of a non-additive utility function and the
acceptable region that takes into account information
about application performance and the availability of
resources and a cost-based price model for
resources.
Through an experimental analysis we
demonstrate that the proposed solution leads the
provider to accurately predict the utility that can be
gained by a contract and to avoid the stipulation of
contracts under conditions that conduct to
unprofitable revenues or customer dissatisfaction.
Further research aiming to improve our approach
regards the investigation of a progressive resource
allocation policy based on the effective incoming
workload of hosted applications and their
performance in order to better exploit data center
resources.
Finally, we are investigating an integrative
negotiation strategy based on time-based decision
functions for the proposed utility model able to
quickly reach an agreement with high satisfaction
levels for both providers and customers.
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