Usage Report hotel

• Period: October 01 – December 23, 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 – December 23, 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 – December 23, 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 – December 23, 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 – December 23, 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 – December 23, 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 – December 23, 2013
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by project

Project	Value
fg-82:FG General Software Development	1459
fg-54:Investigating cloud computing as a solution for analyzing particle physics data	1232
Others	249
fg-364:Course: EEL6871 Autonomic Computing	221
fg-362:Course: Cloud Computing and Storage (UF)	129
fg-371:Characterizing Infrastructure Cloud Performance for Scientific Computing	110
fg-224:Nimbus Auto Scale	96
fg-172:Cloud-TM	53
fg-367:Optimize rapid deployment and updating of VM images at the remote compute cluster	29
fg-97:FutureGrid and Grid‘5000 Collaboration	28
fg-47:Parallel scripting using cloud resources	21
fg-213:Course: Cloud Computing class - second edition	19
fg-381:Authentication of Mobile Cloud Computing	14
fg-340:Research: Parallel Computing for Machine Learning	8
fg-341:Course: Parallel Computing	5
fg-175:GridProphet, A workflow execution time prediction system for the Grid	5
fg-372:Mobile Device Computation Offloading over SocialVPNs	5
fg-130:Optimizing Scientific Workflows on Clouds	3
fg-391:Topics in Parallel Computation	2
fg-201:ExTENCI Testing, Validation, and Performance	1
fg-10:TeraGrid XD TIS(Technology Insertion Service) Technology Evaluation Laboratory	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: October 01 – December 23, 2013
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by project leader

Projectleader	Value
Gregor von Laszewski	1459
Randall Sobie	1232
Others	249
Meng Han	221
Andy Li	129
Theron Voran	110
Pierre Riteau	96
Paolo Romano	53
Jan Balewski	29
Mauricio Tsugawa	28
Michael Wilde	21
Massimo Canonico	19
Shane Green	14
Wilson Rivera	13
Thomas Fahringer	5
Renato Figueiredo	5
Weiwei Chen	3
Heru Suhartanto	2
Preston Smith	1
John Lockman	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: October 01 – December 23, 2013
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by institution

Institution	Value
Indiana University	1459
University of Victoria	1232
Others	249
University of Florida, ACIS	221
University of Florida, Department of Electrical and Computer Eng	129
University of Colorado at Boulder, Computer Science Department	110
University of Chicago	96
INESC ID	53
Massachusetts Institute of Technology, Laboratory for Nuclear Sc	29
University of Florida	28
Argonne National Laboratory	21
University of Piemonte Orientale	19
Colorado Technical University, Computer Science and Engineering	14
University of Puerto Rico, Electrical and Computer Emgineering D	13
University of Florida, Electrical and Computer Engineering	5
University of Innsbruck	5
University of Southern California	3
Universitas Indonesia, Faculty of Computer Science	2
University of Texas at Austin	1
Purdue University	1

Figure 9: Wall time (hours) by project leader

This chart illustrates proportionate total run times by project leader.

• Period: October 01 – December 23, 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 – December 23, 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 – December 23, 2013
• Cloud(IaaS): nimbus
• Hostname: hotel