Usage Report hotel

• Period: January 01 – March 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: January 01 – March 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: January 01 – March 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: January 01 – March 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: January 01 – March 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: January 01 – March 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: January 01 – March 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	821
fg-224:Nimbus Auto Scale	254
fg-97:FutureGrid and Grid‘5000 Collaboration	239
fg-172:Cloud-TM	147
fg-82:FG General Software Development	67
fg-314:User-friendly tools to play with cloud platforms	8
fg-239:Community Comparison of Cloud frameworks	7
fg-175:GridProphet, A workflow execution time prediction system for the Grid	6
fg-213:Course: Cloud Computing class - second edition	5
fg-9:Distributed Execution of Kepler Scientific Workflow on Future Grid	4
fg-150:SC11: Using and Building Infrastructure Clouds for Science	3
fg-371:Characterizing Infrastructure Cloud Performance for Scientific Computing	3
fg-374:Course: Cloud and Distributed Computing	3
fg-298:FRIEDA: Flexible Robust Intelligent Elastic Data Management	2
fg-165:The VIEW Project	2
fg-10:TeraGrid XD TIS(Technology Insertion Service) Technology Evaluation Laboratory	2
fg-362:Course: Cloud Computing and Storage (UF)	1
fg-401:Evaluation of HPC Applications on Cloud Resources	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: January 01 – March 31, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by project leader

Projectleader	Value
Randall Sobie	821
Pierre Riteau	254
Mauricio Tsugawa	239
Paolo Romano	147
Gregor von Laszewski	67
Massimo Canonico	13
Yong Zhao	7
Thomas Fahringer	6
Ilkay Altintas	4
Philip Rhodes	3
John Bresnahan	3
Theron Voran	3
Shiyong Lu	2
Lavanya Ramakrishnan	2
John Lockman	2
Brock Palen	1
Andy Li	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: January 01 – March 31, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by institution

Institution	Value
University of Victoria	821
University of Chicago	254
University of Florida	239
INESC ID	147
Indiana University	67
University of Piemonte Orientale, Computer Science Department	8
University of Electronic Science and Technology	7
University of Innsbruck	6
University of Piemonte Orientale	5
UCSD	4
Nimbus	3
University of Colorado at Boulder, Computer Science Department	3
University of Mississippi, Department of Computer Science	3
University of Texas at Austin	2
Lawrence Berkeley National Lab	2
Wayne State University	2
U of Michigan / Xsede, CAEN HPC	1
University of Florida, Department of Electrical and Computer Eng	1

Figure 9: Wall time (hours) by project leader

This chart illustrates proportionate total run times by project leader.

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