Usage Report hotel

• Period: November 13 – May 13, 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: November 13 – May 13, 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: November 13 – May 13, 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: November 13 – May 13, 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: November 13 – May 13, 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: November 13 – May 13, 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: November 13 – May 13, 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	2164
fg-82:FG General Software Development	593
fg-97:FutureGrid and Grid‘5000 Collaboration	350
fg-224:Nimbus Auto Scale	316
fg-172:Cloud-TM	147
fg-364:Course: EEL6871 Autonomic Computing	22
fg-371:Characterizing Infrastructure Cloud Performance for Scientific Computing	21
fg-362:Course: Cloud Computing and Storage (UF)	18
fg-404:Enhancing Usage of cloud Infrastructure	17
fg-213:Course: Cloud Computing class - second edition	17
fg-314:User-friendly tools to play with cloud platforms	17
fg-341:Course: Parallel Computing	8
fg-239:Community Comparison of Cloud frameworks	7
fg-175:GridProphet, A workflow execution time prediction system for the Grid	7
fg-340:Research: Parallel Computing for Machine Learning	6
fg-150:SC11: Using and Building Infrastructure Clouds for Science	6
fg-217:Cloud Computing In Education	6
fg-9:Distributed Execution of Kepler Scientific Workflow on Future Grid	5
fg-374:Course: Cloud and Distributed Computing	3
fg-10:TeraGrid XD TIS(Technology Insertion Service) Technology Evaluation Laboratory	3
fg-391:Topics in Parallel Computation	2
fg-165:The VIEW Project	2
fg-298:FRIEDA: Flexible Robust Intelligent Elastic Data Management	2
fg-372:Mobile Device Computation Offloading over SocialVPNs	2
fg-201:ExTENCI Testing, Validation, and Performance	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: November 13 – May 13, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by project leader

Projectleader	Value
Randall Sobie	2164
Gregor von Laszewski	593
Mauricio Tsugawa	350
Pierre Riteau	316
Paolo Romano	147
Massimo Canonico	34
Meng Han	22
Theron Voran	21
Andy Li	18
Rahul Limbole	17
Wilson Rivera	14
Yong Zhao	7
Thomas Fahringer	7
John Bresnahan	6
Željko Šeremet	6
Ilkay Altintas	5
John Lockman	3
Philip Rhodes	3
Renato Figueiredo	2
Lavanya Ramakrishnan	2
Heru Suhartanto	2
Shiyong Lu	2
Brock Palen	1
Preston Smith	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: November 13 – May 13, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel

VMs count by institution

Institution	Value
University of Victoria	2164
Indiana University	593
University of Florida	350
University of Chicago	316
INESC ID	147
University of Florida, ACIS	22
University of Colorado at Boulder, Computer Science Department	21
University of Florida, Department of Electrical and Computer Eng	18
University of Piemonte Orientale	17
University of Piemonte Orientale, Computer Science Department	17
Veermata Jijabai Technological Institute Mumbai, Computer Scienc	17
University of Puerto Rico, Electrical and Computer Emgineering D	14
University of Innsbruck	7
University of Electronic Science and Technology	7
University of Mostar	6
Nimbus	6
UCSD	5
University of Texas at Austin	3
University of Mississippi, Department of Computer Science	3
University of Florida, Electrical and Computer Engineering	2
Wayne State University	2
Lawrence Berkeley National Lab	2
Universitas Indonesia, Faculty of Computer Science	2
Purdue University	1
U of Michigan / Xsede, CAEN HPC	1

Figure 9: Wall time (hours) by project leader

This chart illustrates proportionate total run times by project leader.

• Period: November 13 – May 13, 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: November 13 – May 13, 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: November 13 – May 13, 2014
• Cloud(IaaS): nimbus
• Hostname: hotel