Usage Report hotel

• Period: May 01 – May 31, 2014
• Hostname: hotel.futuregrid.org
• Services: nimbus
• 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: May 01 – May 31, 2014

• Cloud(IaaS): nimbus

• Hostname: hotel

• 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: May 01 – May 31, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel

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: May 01 – May 31, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel

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: May 01 – May 31, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel

Distribution

Figure 5: VM count by wall time

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

• Period: May 01 – May 31, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel

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: May 01 – May 31, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by project

Project	Value
fg-54:Investigating cloud computing as a solution for analyzing particle physics data	298
fg-314:User-friendly tools to play with cloud platforms	60
fg-418:Course: Cloud Computing Class - fourth edition	45
fg-97:FutureGrid and Grid‘5000 Collaboration	31
fg-217:Cloud Computing In Education	25
fg-82:FG General Software Development	23
fg-367:Optimize rapid deployment and updating of VM images at the remote compute cluster	22
fg-13:FutureGrid Systems Development and Prototyping	18
fg-257:Particle Physics Data analysis cluster for ATLAS LHC experiment	11
fg-9:Distributed Execution of Kepler Scientific Workflow on Future Grid	10
fg-224:Nimbus Auto Scale	5
fg-150:SC11: Using and Building Infrastructure Clouds for Science	3
fg-341:Course: Parallel Computing	3
fg-394:Hydroinformatics on the Cloud	3
fg-42:SAGA	2
fg-213:Course: Cloud Computing class - second edition	2

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: May 01 – May 31, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by project leader

Projectleader	Value
Randall Sobie	298
Massimo Canonico	107
Mauricio Tsugawa	31
Željko Šeremet	25
Gregor von Laszewski	23
Jan Balewski	22
Sharif Islam	18
Doug Benjamin	11
Ilkay Altintas	10
Pierre Riteau	5
John Bresnahan	3
Wilson Rivera	3
Kate Keahey	3
Shantenu Jha	2

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: May 01 – May 31, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by institution

Institution	Value
University of Victoria	298
University of Piemonte Orientale, Computer Science Department	105
Indiana University	41
University of Florida	31
University of Mostar	25
Massachusetts Institute of Technology, Laboratory for Nuclear Sc	22
Duke University	11
UCSD	10
University of Chicago	5
University of Chicago, Computation Institute	3
Nimbus	3
University of Puerto Rico, Electrical and Computer Emgineering D	3
Louisiana State University	2
University of Piemonte Orientale	2

Figure 9: Wall time (hours) by project leader

This chart illustrates proportionate total run times by project leader.

• Period: May 01 – May 31, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel

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 (hotel)

This column chart represents VMs count among systems.

• Period: May 01 – May 31, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel

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

This column chart represents wall time among systems.

• Period: May 01 – May 31, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel