Characterization and Comparison of Cloud versus Grid Workloads
Venue
IEEE Cluster 2012
Publication Year
2012
Authors
Sheng Di, Derrick Kondo, Walfredo Cirne
BibTeX
Abstract
A new era of Cloud Computing has emerged, but the characteristics of Cloud load in
data centers is not perfectly clear. Yet this characterization is critical for the
design of novel Cloud job and resource management systems. In this paper, we
comprehensively characterize the job/task load and host load in a real-world
production data center at Google Inc. We use a detailed trace of over 25 million
tasks across over 12,500 hosts. We study the differences between a Google data
center and other Grid/HPC systems, from the perspective of both work load (w.r.t.
jobs and tasks) and host load (w.r.t. machines). In particular, we study the job
length, job submission frequency, and the resource utilization of jobs in the
different systems, and also investigate valuable statistics of machine’s maximum
load, queue state and relative usage levels, with different job priorities and
resource attributes. We find that the Google data center exhibits finer resource
allocation with respect to CPU and memory than that of Grid/HPC systems. Google
jobs are always submitted with much higher frequency and they are much shorter than
Grid jobs. As such, Google host load exhibits higher variance and noise.
