Usage Report sierra

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

VMs count by project

Project	Value
fg-40:Inca	484
fg-298:FRIEDA: Flexible Robust Intelligent Elastic Data Management	290
fg-54:Investigating cloud computing as a solution for analyzing particle physics data	284
fg-371:Characterizing Infrastructure Cloud Performance for Scientific Computing	209
fg-174:RAIN: FutureGrid Dynamic provisioning Framework	208
fg-367:Optimize rapid deployment and updating of VM images at the remote compute cluster	167
fg-362:Course: Cloud Computing and Storage (UF)	139
fg-82:FG General Software Development	124
fg-172:Cloud-TM	116
fg-389:Investigating the Apache Big Data Stack	45
fg-1:Peer-to-peer overlay networks and applications in virtual networks and virtual clusters	42
fg-224:Nimbus Auto Scale	37
fg-384:Graph/network analysis Resource manager	31
fg-165:The VIEW Project	31
fg-175:GridProphet, A workflow execution time prediction system for the Grid	11
fg-244:Course: Data Center Scale Computing	10
fg-382:Reliability Analysis using Hadoop and MapReduce	9
fg-372:Mobile Device Computation Offloading over SocialVPNs	8
fg-10:TeraGrid XD TIS(Technology Insertion Service) Technology Evaluation Laboratory	8
fg-316:Course: Cloud Computing Class - third edition	5
fg-136:JGC-DataCloud-2012 paper experiments	3

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, openstack, eucalyptus
• Hostname: sierra

VMs count by project leader

Projectleader	Value
Shava Smallen	484
Gregor von Laszewski	332
Lavanya Ramakrishnan	290
Randall Sobie	284
Theron Voran	209
Jan Balewski	167
Andy Li	139
Paolo Romano	116
Renato Figueiredo	50
ibrahim hallac	45
Pierre Riteau	37
Shiyong Lu	31
Tirtha Bhattacharjee	31
Thomas Fahringer	11
Dirk Grunwald	10
Carl Walasek	9
John Lockman	8
Massimo Canonico	5
Mats Rynge	3

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, openstack, eucalyptus
• Hostname: sierra

VMs count by institution

Institution	Value
UC San Diego	484
Indiana University	332
Lawrence Berkeley National Lab	290
University of Victoria	284
University of Colorado at Boulder, Computer Science Department	209
Massachusetts Institute of Technology, Laboratory for Nuclear Sc	167
University of Florida, Department of Electrical and Computer Eng	139
INESC ID	116
Firat University, Computer Science Department	45
University of Florida	42
University of Chicago	37
Wayne State University	31
Virginia Bioinformatics Institute, Virginia Polytechnic Institut	31
University of Innsbruck	11
Univ. of Colorado	10
University of the Sciences , Mathematics, Physics, and Statistic	9
University of Texas at Austin	8
University of Florida, Electrical and Computer Engineering	8
University of Piemonte Orientale, Computer Science Department	5
USC	3

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