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The authors of [8] and [9] evaluate auto-scaling mechanisms which consider both user performance requirements and pricing for a workload characterized by jobs with deadlines.
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Moreover, they do not explicitly consider SLP for complex workloads characterized by burstiness.
According to [4, 7, 8], there are five workload patterns in the cloud computing environments: Static workload is characterized by a constant number of requests per minute.
Static workload is characterized by a constant number of requests per minute.
Nurse workload was characterized by m TISS-28 = 18 (IQR = 7) and Time of nurse's care of 190 min (IQR = 74).
The submitted workload is characterized by the following metrics: Budget Min, Budget Max : the minimum/maximum budget the user is willing to spend for executing the workload's jobs.
The arrival rates used in the experiments of a given workload type are different since each of the workloads is characterized by jobs with different execution times.
Specifically, the five workload scenarios are characterized by medium variability and extremely bursty session arrivals, i.e., S C V a =3 and I a =10,000.
Each task is characterized by its workload variation.
As mentioned in "Overview" section, each application a is characterized by a workload scenario, denoted by W a. We assume a planning horizon divided into T equal sized resource allocation intervals.
All informants described a tendency towards more challenging work situation characterized by increased workload and higher work pressure with less time available.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com