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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.
Workloads are characterized according to the connectivity, heterogeneity, and communication-to-cost ratio of the task graphs representing the application tasks.
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.
We review in Section 2 cloud measurement studies that help characterize those properties for specific cloud systems, followed by a review of workload characterizations and inference techniques that can be applied to QoS analysis.
In recent work, interworkload distances have been proposed as a way of characterizing workload similarity.
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