Usage Report sierra

• Period: July 01 – September 30, 2013
• Hostname: sierra.futuregrid.org
• Services: nimbus, openstack, eucalyptus
• Metrics: VMs count, Users count, Wall time (hours), Distribution by wall time, project, project leader, and institution, and systems

Histogram

Summary (Monthly)

Figure 1: Average monthly usage data (wall time (hour), launched VMs, users)

This mixed chart represents average monthly usage as to wall time (hour), the number of VM instances and active users.

• Period: July 01 – September 30, 2013

• Cloud(IaaS): nimbus, openstack, eucalyptus

• Hostname: sierra

• Metric:

  • Runtime (Wall time hours): Sum of time elapsed from launch to termination of VM instances
  • Count (VM count): The number of launched VM instances
  • User count (Active): The number of users who launched VMs

Summary (Daily)

Figure 2: Users count

This time series chart represents daily active user count for cloud services and shows historical changes during the period.

• Period: July 01 – September 30, 2013
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

Figure 3: VMs count

This time series chart represents the number of daily launched VM instances for cloud services and shows historical changes during the period.

• Period: July 01 – September 30, 2013
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

Figure 4: Wall time (hours)

This time series chart represents daily wall time (hours) for cloud services and shows historical changes during the period.

• Period: July 01 – September 30, 2013
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

Distribution

Figure 5: VM count by wall time

This chart illustrates usage patterns of VM instances in terms of running wall time.

• Period: July 01 – September 30, 2013
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

Figure 6: VMs count by project

This chart illustrates the proportion of launched VM instances by project groups. The same data in tabular form follows.

• Period: July 01 – September 30, 2013
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

VMs count by project

Project	Value
fg-40:Inca	700
fg-52:Cost-Aware Cloud Computing	615
fg-54:Investigating cloud computing as a solution for analyzing particle physics data	528
fg-172:Cloud-TM	505
fg-224:Nimbus Auto Scale	491
fg-214:Mining Interactions between Network Community Structure and Information Diffusion	277
fg-174:RAIN: FutureGrid Dynamic provisioning Framework	230
fg-298:FRIEDA: Flexible Robust Intelligent Elastic Data Management	177
fg-346:Course: Example Course On Advanced Cloud Computing	144
fg-82:FG General Software Development	133
fg-168:Next Generation Sequencing in the Cloud	90
fg-362:Course: Cloud Computing and Storage (UF)	36
fg-1:Peer-to-peer overlay networks and applications in virtual networks and virtual clusters	35
fg-316:Course: Cloud Computing Class - third edition	33
fg-372:Mobile Device Computation Offloading over SocialVPNs	31
fg-364:Course: EEL6871 Autonomic Computing	24
fg-315:Biome representational in silico karyotyping	17
fg-97:FutureGrid and Grid‘5000 Collaboration	15
fg-264:Course: 1st Workshop on bioKepler Tools and Its Applications	13
fg-367:Optimize rapid deployment and updating of VM images at the remote compute cluster	12
fg-132:Large scale data analytics	10
fg-175:GridProphet, A workflow execution time prediction system for the Grid	5
fg-374:Course: Cloud and Distributed Computing	4
fg-356:IPython pipelines for training life sciences researchers on NGS data analysis	3
fg-251:Course: Fall 2012 B534 Distributed Systems Graduate Course	3
fg-244:Course: Data Center Scale Computing	2
fg-314:User-friendly tools to play with cloud platforms	1
fg-180:STAMPEDE	1
fg-355:Course: Data Center Scale Computing Class	1
fg-69:Investigate provenance collection for MapReduce	1

Figure 7: VMs count by project leader

This chart also illustrates the proportion of launched VM instances by project Leader. The same data in tabular form follows.

• Period: July 01 – September 30, 2013
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

VMs count by project leader

Projectleader	Value
Shava Smallen	700
David Lowenthal	615
Randall Sobie	528
Paolo Romano	505
Pierre Riteau	491
Gregor von Laszewski	363
Yong-Yeol Ahn	277
Lavanya Ramakrishnan	177
Albert Elfstein	144
Jonathan Klinginsmith	90
Renato Figueiredo	66
Andy Li	36
Massimo Canonico	34
Meng Han	24
Aaron Lee	17
Mauricio Tsugawa	15
Ilkay Altintas	13
Jan Balewski	12
Yogesh Simmhan	10
Thomas Fahringer	5
Philip Rhodes	4
Todd Blevins	3
Dirk Grunwald	3
Judy Qiu	3
Jiaan Zeng	1
Dan Gunter	1

Figure 8: VMs count by institution

This chart illustrates the proportion of launched VM instances by Institution. The same data in tabular form follows.

• Period: July 01 – September 30, 2013
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

VMs count by institution

Institution	Value
Indiana University	733
UC San Diego	700
University of Arizona	615
University of Victoria	528
INESC ID	505
University of Chicago	491
Lawrence Berkeley National Lab	177
Indiana University, Computer Science Department	144
University of Florida	50
University of Florida, Department of Electrical and Computer Eng	36
University of Piemonte Orientale, Computer Science Department	34
University of Florida, Electrical and Computer Engineering	31
University of Florida, ACIS	24
Washington University at St Louis, School of Medicine, Departmen	17
UCSD	13
Massachusetts Institute of Technology, Laboratory for Nuclear Sc	12
University of Southern California	10
University of Innsbruck	5
University of Mississippi, Department of Computer Science	4
Indiana University, Depts of Biology and Molecular and Cellular	3
Univ. of Colorado	2
Computer Science	1
Univ. of Colorado, Boulder, Computer Science	1
LBNL	1

Figure 9: Wall time (hours) by project leader

This chart illustrates proportionate total run times by project leader.

• Period: July 01 – September 30, 2013
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

System information

System information shows utilization distribution as to VMs count and wall time. Each cluster represents a compute node.

Figure 10: VMs count by systems (compute nodes) in Cluster (sierra)

This column chart represents VMs count among systems.

• Period: July 01 – September 30, 2013
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

Figure 11: Wall time (hours) by systems (compute nodes) in Cluster (sierra)

This column chart represents wall time among systems.

• Period: July 01 – September 30, 2013
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra