  Cloud computing deals with heterogeneity and dynamicity at all levels and
therefore there is a need to manage resources in such an environment and
properly allocate them. Resource planning and scheduling requires a proper
understanding of arrival patterns and scheduling of resources. Study of
workloads can aid in proper understanding of their associated environment.
Google has released its latest version of cluster trace, trace version 2.1 in
November 2014.The trace consists of cell information of about 29 days spanning
across 700k jobs. This paper deals with statistical analysis of this cluster
trace. Since the size of trace is very large, Hive which is a Hadoop
distributed file system (HDFS) based platform for querying and analysis of Big
data, has been used. Hive was accessed through its Beeswax interface. The data
was imported into HDFS through HCatalog. Apart from Hive, Pig which is a
scripting language and provides abstraction on top of Hadoop was used. To the
best of our knowledge the analytical method adopted by us is novel and has
helped in gaining several useful insights. Clustering of jobs and arrival time
has been done in this paper using K-means++ clustering followed by analysis of
distribution of arrival time of jobs which revealed weibull distribution while
resource usage was close to zip-f like distribution and process runtimes
revealed heavy tailed distribution.
