Usage Report hotel

• Period: September 01 – September 30, 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: September 01 – September 30, 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: September 01 – September 30, 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: September 01 – September 30, 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: September 01 – September 30, 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: September 01 – September 30, 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: September 01 – September 30, 2013
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by project

Project	Value
fg-54:Investigating cloud computing as a solution for analyzing particle physics data	761
fg-364:Course: EEL6871 Autonomic Computing	339
fg-362:Course: Cloud Computing and Storage (UF)	223
fg-172:Cloud-TM	150
Others	78
fg-217:Cloud Computing In Education	55
fg-82:FG General Software Development	40
fg-97:FutureGrid and Grid‘5000 Collaboration	26
fg-175:GridProphet, A workflow execution time prediction system for the Grid	17
fg-371:Characterizing Infrastructure Cloud Performance for Scientific Computing	17
fg-374:Course: Cloud and Distributed Computing	16
fg-213:Course: Cloud Computing class - second edition	11
fg-367:Optimize rapid deployment and updating of VM images at the remote compute cluster	10
fg-10:TeraGrid XD TIS(Technology Insertion Service) Technology Evaluation Laboratory	9
fg-150:SC11: Using and Building Infrastructure Clouds for Science	3
fg-130:Optimizing Scientific Workflows on Clouds	3
fg-136:JGC-DataCloud-2012 paper experiments	3
fg-1:Peer-to-peer overlay networks and applications in virtual networks and virtual clusters	2
fg-355:Course: Data Center Scale Computing Class	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: September 01 – September 30, 2013
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by project leader

Projectleader	Value
Randall Sobie	761
Meng Han	339
Andy Li	223
Paolo Romano	150
Others	78
Željko Šeremet	55
Gregor von Laszewski	40
Mauricio Tsugawa	26
Theron Voran	17
Thomas Fahringer	17
Philip Rhodes	16
Massimo Canonico	11
Jan Balewski	10
John Lockman	9
Mats Rynge	3
Weiwei Chen	3
John Bresnahan	3
Renato Figueiredo	2
Dirk Grunwald	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: September 01 – September 30, 2013
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by institution

Institution	Value
University of Victoria	761
University of Florida, ACIS	339
University of Florida, Department of Electrical and Computer Eng	223
INESC ID	150
Others	78
University of Mostar	55
Indiana University	40
University of Florida	28
University of Colorado at Boulder, Computer Science Department	17
University of Innsbruck	17
University of Mississippi, Department of Computer Science	16
University of Piemonte Orientale	11
Massachusetts Institute of Technology, Laboratory for Nuclear Sc	10
University of Texas at Austin	9
USC	3
Nimbus	3
University of Southern California	3
Univ. of Colorado, Boulder, Computer Science	1

Figure 9: Wall time (hours) by project leader

This chart illustrates proportionate total run times by project leader.

• Period: September 01 – September 30, 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: September 01 – September 30, 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: September 01 – September 30, 2013
• Cloud(IaaS): nimbus
• Hostname: hotel