Usage Report hotel

• Period: October 01 – October 31, 2013
• 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: October 01 – October 31, 2013

• 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: October 01 – October 31, 2013
• 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: October 01 – October 31, 2013
• 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: October 01 – October 31, 2013
• 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: October 01 – October 31, 2013
• 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: October 01 – October 31, 2013
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by project

Project	Value
fg-82:FG General Software Development	535
fg-54:Investigating cloud computing as a solution for analyzing particle physics data	389
Others	209
fg-364:Course: EEL6871 Autonomic Computing	199
fg-362:Course: Cloud Computing and Storage (UF)	112
fg-371:Characterizing Infrastructure Cloud Performance for Scientific Computing	73
fg-172:Cloud-TM	52
fg-224:Nimbus Auto Scale	48
fg-367:Optimize rapid deployment and updating of VM images at the remote compute cluster	29
fg-47:Parallel scripting using cloud resources	21
fg-97:FutureGrid and Grid‘5000 Collaboration	18
fg-381:Authentication of Mobile Cloud Computing	14
fg-213:Course: Cloud Computing class - second edition	7
fg-372:Mobile Device Computation Offloading over SocialVPNs	5
fg-175:GridProphet, A workflow execution time prediction system for the Grid	4
fg-130:Optimizing Scientific Workflows on Clouds	3
fg-340:Research: Parallel Computing for Machine Learning	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: October 01 – October 31, 2013
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by project leader

Projectleader	Value
Gregor von Laszewski	535
Randall Sobie	389
Others	209
Meng Han	199
Andy Li	112
Theron Voran	73
Paolo Romano	52
Pierre Riteau	48
Jan Balewski	29
Michael Wilde	21
Mauricio Tsugawa	18
Shane Green	14
Massimo Canonico	7
Renato Figueiredo	5
Thomas Fahringer	4
Weiwei Chen	3
Wilson Rivera	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: October 01 – October 31, 2013
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by institution

Institution	Value
Indiana University	535
University of Victoria	389
Others	209
University of Florida, ACIS	199
University of Florida, Department of Electrical and Computer Eng	112
University of Colorado at Boulder, Computer Science Department	73
INESC ID	52
University of Chicago	48
Massachusetts Institute of Technology, Laboratory for Nuclear Sc	29
Argonne National Laboratory	21
University of Florida	18
Colorado Technical University, Computer Science and Engineering	14
University of Piemonte Orientale	7
University of Florida, Electrical and Computer Engineering	5
University of Innsbruck	4
University of Southern California	3
University of Puerto Rico, Electrical and Computer Emgineering D	2

Figure 9: Wall time (hours) by project leader

This chart illustrates proportionate total run times by project leader.

• Period: October 01 – October 31, 2013
• 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: October 01 – October 31, 2013
• 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: October 01 – October 31, 2013
• Cloud(IaaS): nimbus
• Hostname: hotel