Usage Report sierra

• Period: October 01 – December 23, 2013
• 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: October 01 – December 23, 2013

• 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: October 01 – December 23, 2013
• 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: October 01 – December 23, 2013
• 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: October 01 – December 23, 2013
• 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: October 01 – December 23, 2013
• 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: October 01 – December 23, 2013
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra

VMs count by project

Project	Value
fg-174:RAIN: FutureGrid Dynamic provisioning Framework	2066
fg-224:Nimbus Auto Scale	835
fg-40:Inca	617
fg-54:Investigating cloud computing as a solution for analyzing particle physics data	236
fg-172:Cloud-TM	128
fg-298:FRIEDA: Flexible Robust Intelligent Elastic Data Management	82
fg-82:FG General Software Development	77
fg-389:Investigating the Apache Big Data Stack	74
fg-362:Course: Cloud Computing and Storage (UF)	70
fg-168:Next Generation Sequencing in the Cloud	58
fg-355:Course: Data Center Scale Computing Class	55
fg-367:Optimize rapid deployment and updating of VM images at the remote compute cluster	40
fg-10:TeraGrid XD TIS(Technology Insertion Service) Technology Evaluation Laboratory	35
fg-97:FutureGrid and Grid‘5000 Collaboration	27
fg-363:Course: Applied Cyberinfrastructure concepts	23
fg-214:Mining Interactions between Network Community Structure and Information Diffusion	15
fg-374:Course: Cloud and Distributed Computing	15
fg-316:Course: Cloud Computing Class - third edition	15
fg-244:Course: Data Center Scale Computing	14
fg-372:Mobile Device Computation Offloading over SocialVPNs	14
fg-384:Graph/network analysis Resource manager	12
fg-264:Course: 1st Workshop on bioKepler Tools and Its Applications	12
fg-334:Tutorial on Cloud Computing and Software-defined Networking	10
fg-371:Characterizing Infrastructure Cloud Performance for Scientific Computing	10
fg-364:Course: EEL6871 Autonomic Computing	8
fg-215:FuturGrid Directory Entry	8
fg-1:Peer-to-peer overlay networks and applications in virtual networks and virtual clusters	7
fg-341:Course: Parallel Computing	6
fg-380:FutureGrid Support for BigData MOOC	6
fg-233:CINET - A Cyber-Infrastructure for Network Science	5
fg-382:Reliability Analysis using Hadoop and MapReduce	5
fg-175:GridProphet, A workflow execution time prediction system for the Grid	5
fg-369:Testing of Network Facing Services for the Open Science Grid	4
fg-243:Applied Cyberinfrastructure concepts	4
fg-381:Authentication of Mobile Cloud Computing	3
fg-301:Course: Advanced Networking class University of Colorado	1
fg-314:User-friendly tools to play with cloud platforms	1
fg-315:Biome representational in silico karyotyping	1
fg-340:Research: Parallel Computing for Machine Learning	1
fg-69:Investigate provenance collection for MapReduce	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, openstack, eucalyptus
• Hostname: sierra

VMs count by project leader

Projectleader	Value
Gregor von Laszewski	2151
Pierre Riteau	835
Shava Smallen	617
Randall Sobie	236
Paolo Romano	128
Lavanya Ramakrishnan	82
ibrahim hallac	74
Andy Li	70
Dirk Grunwald	69
Jonathan Klinginsmith	58
Jan Balewski	40
John Lockman	35
Nirav Merchant	27
Mauricio Tsugawa	27
Renato Figueiredo	21
Massimo Canonico	16
Philip Rhodes	15
Yong-Yeol Ahn	15
Ilkay Altintas	12
Tirtha Bhattacharjee	12
Jose Fortes	10
Theron Voran	10
Meng Han	8
Wilson Rivera	7
Abhilash Koppula	6
Thomas Fahringer	5
Keith Bisset	5
Carl Walasek	5
Igor Sfiligoi	4
Shane Green	3
Aaron Lee	1
Jiaan Zeng	1
Eric Keller	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, openstack, eucalyptus
• Hostname: sierra

VMs count by institution

Institution	Value
Indiana University	2224
University of Chicago	835
UC San Diego	617
University of Victoria	236
INESC ID	128
Lawrence Berkeley National Lab	82
Firat University, Computer Science Department	74
University of Florida, Department of Electrical and Computer Eng	70
Univ. of Colorado, Boulder, Computer Science	55
Massachusetts Institute of Technology, Laboratory for Nuclear Sc	40
University of Texas at Austin	35
University of Florida	34
University of Arizona, Arizona Research Laboratories, School of	23
University of Piemonte Orientale, Computer Science Department	16
University of Mississippi, Department of Computer Science	15
University of Florida, Electrical and Computer Engineering	14
Univ. of Colorado	14
Virginia Bioinformatics Institute, Virginia Polytechnic Institut	12
UCSD	12
University of Florida, Advanced Computing and Information System	10
University of Colorado at Boulder, Computer Science Department	10
University of Florida, ACIS	8
University of Puerto Rico, Electrical and Computer Emgineering D	7
Indiana University, Community Grids Lab	6
University of the Sciences , Mathematics, Physics, and Statistic	5
University of Innsbruck	5
Virginia Tech	5
University of California San Diego, Physics Department	4
University of Arizona	4
Colorado Technical University, Computer Science and Engineering	3
Washington University at St Louis, School of Medicine, Departmen	1
University of Colorado	1
Computer Science	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, 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: October 01 – December 23, 2013
• 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: October 01 – December 23, 2013
• Cloud(IaaS): nimbus, openstack, eucalyptus
• Hostname: sierra