Usage Report sierra

• Period: March 01 – March 31, 2014
• Hostname: sierra.futuregrid.org
• Services: nimbus, openstack, eucalyptus
• 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: March 01 – March 31, 2014

• Cloud(IaaS): nimbus, openstack, eucalyptus

• Hostname: sierra

• 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: March 01 – March 31, 2014
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

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: March 01 – March 31, 2014
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

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: March 01 – March 31, 2014
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

Distribution

Figure 5: VM count by wall time

This chart illustrates usage patterns of VM instances in terms of running wall time.

• Period: March 01 – March 31, 2014
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

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: March 01 – March 31, 2014
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

VMs count by project

Project	Value
fg-298:FRIEDA: Flexible Robust Intelligent Elastic Data Management	250
fg-367:Optimize rapid deployment and updating of VM images at the remote compute cluster	150
fg-371:Characterizing Infrastructure Cloud Performance for Scientific Computing	133
fg-174:RAIN: FutureGrid Dynamic provisioning Framework	113
fg-54:Investigating cloud computing as a solution for analyzing particle physics data	41
fg-224:Nimbus Auto Scale	37
fg-40:Inca	30
fg-1:Peer-to-peer overlay networks and applications in virtual networks and virtual clusters	16
fg-172:Cloud-TM	15
fg-389:Investigating the Apache Big Data Stack	14
fg-244:Course: Data Center Scale Computing	10
fg-165:The VIEW Project	10
fg-384:Graph/network analysis Resource manager	8
fg-372:Mobile Device Computation Offloading over SocialVPNs	8
fg-362:Course: Cloud Computing and Storage (UF)	8
fg-175:GridProphet, A workflow execution time prediction system for the Grid	3
fg-316:Course: Cloud Computing Class - third edition	2
fg-82:FG General Software Development	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: March 01 – March 31, 2014
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

VMs count by project leader

Projectleader	Value
Lavanya Ramakrishnan	250
Jan Balewski	150
Theron Voran	133
Gregor von Laszewski	115
Randall Sobie	41
Pierre Riteau	37
Shava Smallen	30
Renato Figueiredo	24
Paolo Romano	15
ibrahim hallac	14
Dirk Grunwald	10
Shiyong Lu	10
Tirtha Bhattacharjee	8
Andy Li	8
Thomas Fahringer	3
Massimo Canonico	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: March 01 – March 31, 2014
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

VMs count by institution

Institution	Value
Lawrence Berkeley National Lab	250
Massachusetts Institute of Technology, Laboratory for Nuclear Sc	150
University of Colorado at Boulder, Computer Science Department	133
Indiana University	115
University of Victoria	41
University of Chicago	37
UC San Diego	30
University of Florida	16
INESC ID	15
Firat University, Computer Science Department	14
Wayne State University	10
Univ. of Colorado	10
University of Florida, Electrical and Computer Engineering	8
University of Florida, Department of Electrical and Computer Eng	8
Virginia Bioinformatics Institute, Virginia Polytechnic Institut	8
University of Innsbruck	3
University of Piemonte Orientale, Computer Science Department	2

Figure 9: Wall time (hours) by project leader

This chart illustrates proportionate total run times by project leader.

• Period: March 01 – March 31, 2014
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

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 (sierra)

This column chart represents VMs count among systems.

• Period: March 01 – March 31, 2014
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

Figure 11: Wall time (hours) by systems (compute nodes) in Cluster (sierra)

This column chart represents wall time among systems.

• Period: March 01 – March 31, 2014
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra