Understanding the characteristics
and patterns of workloads within a Cloud computing environment is critical in
order to improve resource management and operational conditions while Quality
of Service guarantees are maintained. Simulation models based on realistic
parameters are also urgently needed for investigating the impact of these
workload characteristics on new system designs and operation policies.
Unfortunately there is a lack of analyses to support the development of
workload models that capture the inherent diversity of users and tasks, largely
due to the limited availability of Cloud trace logs as well as the complexity
in analyzing such systems. In this paper we present a comprehensive analysis of
the workload characteristics derived from a production Cloud datacenter that
features over 900 users submitting approximately 25 million tasks over a time
period of a month. Our analysis focuses on exposing and quantifying the
diversity of behavioral patterns for users and tasks, as well as identifying model
parameters and their values for the simulation of the workload created by such
components. Our derived model is implemented by extending the capabilities of
the Cloud Sim framework and is further validated through empirical comparison
and statistical hypothesis tests. We illustrate several examples of this work's
practical applicability in the domain of resource management and
energy-efficiency.
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