Prediction Mechanisms for Monitoring State of Cloud Resources Using Markov Chain Model
To evaluate our mechanisms, we have considered a dataset that has been released by Google in May 2011. This dataset represents 29 days of status information about a cluster of 11k physical machines that are operated as a single unit. It contains a realistic mixture of workloads, as it was collected from a Cluster of nonhomogeneous machines. This Cluster was composed of three different platforms and a variety of memory/compute ratios. The platforms are; Type A includes 126 machines, Type B includes about 10K machines, and Type C includes 7950 machines with top configuration. We focused on the Type C platform as CPU and memory size measurements are normalized to the configuration of the largest machines. The exact machine configurations, exact numbers of CPU cores and bytes of memory, were normalized to the configuration of the largest machine. The dataset contains percentages of used resources by each task and requests to allocate these resources. We focused only on data that belongs to the usage of resources, which include measurements of CPU usage, memory space usage, and some other measurements. For simplicity, CPU, and memory measurements were focused on, where each file is organized as the following: the first column is the occurrence time, the second column is CPU measurements, and the third column is memory measurements.