Usage Report sierra

• Period: July 01 – September 30, 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: July 01 – September 30, 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: July 01 – September 30, 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: July 01 – September 30, 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: July 01 – September 30, 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: July 01 – September 30, 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: July 01 – September 30, 2014
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

VMs count by project

Project	Value
fg-224:Nimbus Auto Scale	91
fg-174:RAIN: FutureGrid Dynamic provisioning Framework	91
fg-298:FRIEDA: Flexible Robust Intelligent Elastic Data Management	50
fg-362:Course: Cloud Computing and Storage (UF)	32
fg-1:Peer-to-peer overlay networks and applications in virtual networks and virtual clusters	28
fg-264:Course: 1st Workshop on bioKepler Tools and Its Applications	18
fg-389:Investigating the Apache Big Data Stack	17
fg-165:The VIEW Project	11
fg-405:Spring 2014 CSCI-B649 Cloud Computing MOOC for residential and online students	7
fg-432:2014 Topics in Parallel Computation	4
fg-172:Cloud-TM	4
fg-333:Intrusion Detection and Prevention for Infrastructure as a Service Cloud Computing System	3
fg-372:Mobile Device Computation Offloading over SocialVPNs	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: July 01 – September 30, 2014
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

VMs count by project leader

Projectleader	Value
Gregor von Laszewski	91
Pierre Riteau	91
Lavanya Ramakrishnan	50
Andy Li	32
Renato Figueiredo	30
Ilkay Altintas	18
ibrahim hallac	17
Shiyong Lu	11
Judy Qiu	7
Heru Suhartanto	4
Paolo Romano	4
Jessie Walker	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: July 01 – September 30, 2014
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

VMs count by institution

Institution	Value
University of Chicago	91
Indiana University	91
Lawrence Berkeley National Lab	50
University of Florida, Department of Electrical and Computer Eng	32
University of Florida	28
UCSD	18
Firat University, Computer Science Department	17
Wayne State University	11
Indiana University, School of Informatics and Computing	7
Universitas Indonesia, Faculty of Computer Science	4
INESC ID	4
University of Arkansas at Pine Bluff , Computer Science	3
University of Florida, Electrical and Computer Engineering	2

Figure 9: Wall time (hours) by project leader

This chart illustrates proportionate total run times by project leader.

• Period: July 01 – September 30, 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: July 01 – September 30, 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: July 01 – September 30, 2014
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra