Collect statistics and analysis of google trace - open to bidding

Hello

I need someone to program in ipython and collect statistics from google trace

You need to download the trace

[url removed, login to view]

Delivers in 3 days (Thureday 4 August) which include:

ipython program using anaconda with clear comments and be as simple as possible

a report about the statistics and the analysis with tables and figures and details

Statistics include:

1. Degree of Parallelization (DOP)

We begin by studying the relationship between the degree of parallelism of a job and its final status. In Google’s cluster, a job consists of one or more tasks that typically execute the same binary with the same resource requirements and scheduling constraints (e.g. priority, scheduling-class, etc). Applications that need to run different types of tasks will usually execute them as separate jobs. For example, MapReduce applications would execute masters and workers as separate jobs. Generally, multi-task jobs are meant to have their tasks run simultaneously, where a single task can be running on a single machine at any point in time. A configuration parameter is available where a user can indicate if tasks must execute on different physical machines.

2. Requested Resources: We now study the relationship between the amount of resources requested by tasks in a job the final status of the job. In Google, tasks are submitted with values for requested CPU, memory, or disk space, where these values represent the maximum amount of resources a task is allowed to consume on a machine. However, tasks are sometimes permitted to use more than what they requested if resources are available; e.g. tasks may use free CPU cycles on a machine .

The following materials in the attachment can help you in collect statistics (they have the same statistics with results)

Some helpful references:

C. Reiss, J. Wilkes, and J. L. Hellerstein. Google cluster-usage traces: format + schema, 2011. [url removed, login to view]

J. Wilkes, “More Google cluster data,” Google research blog, Nov. 2011, Posted at [url removed, login to view] less

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