Efficient Resource Management for Cloud Computing Environments Andrew J. Younge Golisano College of Computing and Information Sciences Rochester Institute of Technology 102 Lomb Memorial Drive Rochester, New York 14623

Outline Introduction Motivation Related Work Green Cloud Framework VM Scheduling & Management Minimal Virtual Machine Images Conclusion 2

What is Cloud Computing? “Computing may someday be organized as a public utility just as the telephone system is a public utility... The computer utility could become the basis of a new and important industry.” – John McCarthy, 1961 “Cloud computing is a large- scale distributed computing paradigm that is driven by economies of scale, in which a pool of abstracted, virtualized, dynamically scalable, managed computing power, storage, platforms, and services are delivered on demand to external customers over the Internet.” – Ian Foster,

Virtualization Virtual Machine (VM) is a software artifact that executes other software as if it was running on a physical resource directly. Typically uses a Hypervisor or VMM which abstracts the hardware from an Operating System 4

Cloud Computing Features of Clouds – Scalable – Enhanced Quality of Service (QoS) – Specialized and Customized – Cost Effective – Simplified User Interface 5

Data Center Power Consumption Currently it is estimated that servers consume 0.5%of the world’s total electricity usage. – Closer to 1.2% when data center systems are factored into the equation Server energy demand doubles every 4-6 years. This results in large amounts of CO2 produced by burning fossil fuels. What if we could reduce the energy used with minimal performance impact? 6

Related Work Scheduling on Cluster resources – Power aware – Thermal aware Data center design to reduce Power Usage Effectiveness (PUE) – Cooling systems – Rack design Little research in designing efficient Cloud data centers 7

Research Opportunities There are a number of areas to explore in order to conserve energy within a Cloud environment – Schedule VMs to conserve energy – Management of both VMs and underlying infrastructure – Minimize operating inefficiencies for non-essential tasks – Optimize data center design 8

Green Cloud Framework Virtual Machine Controls Scheduling Power Aware Thermal Aware Management VM Image Design Migration Dynamic Shutdown Data Center Design Server & Rack Placement Air Cond. & Recirculation Framework 9

VM scheduling on Multi-core Systems There is a nonlinear relationship between the number of processes used and power consumption We can schedule VMs to take advantage of this relationship in order to conserve power Power consumption curve on an Intel Core i7 920 Server (4 cores, 8 virtual cores with Hyperthreading) Scheduling 10

Power-aware Scheduling Schedule as many VMs at once on a multi-core node – Greedy scheduling algorithm – Keep track of cores on a given node – Match vm requirements with node capacity Scheduling 11

Node 170W VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM Node 105W Node 105W Node 105W Node 138W VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM VMVM Node 138W Node 138W Node 138W 485 Watts vs. 552 Watts 12 VS.

VM Management Monitor Cloud usage and load When load decreases: Live migrate VMs to more utilized nodes Shutdown unused nodes When load increases: Use WOL to start up waiting nodes Schedule new VMs to new nodes Management 13

Node 1 VM Node 2 Node 1 VM Node 2 Node 1 VM Node 2 (offline) VM Node 1 VM Node

Minimizing VM Instances Virtual machines are desktop-based – Lots of unwanted packages – Unneeded services Are multi-application oriented, not service oriented – Clouds are based off of a Service Oriented Architecture Need a custom lightweight Linux VM for service oriented science Need to keep VM image as small as possible to reduce network latency Management 15

Cloud Linux Image Start with Ubuntu 9.04 Remove all packages not required for base image – No X11 – No Window Manager – Minimalistic server install – Can load language support on demand (via package manager) Readahead profiling utility – Reorder boot sequence – Pre-fetch boot files on disk – Minimize CPU idle time due to I/O delay Optimize Linux kernel – Built for Xen DomU – No 3d graphics, no sound, minimalistic kernel – Build modules within kernel directly VM Image Design 16

Energy Savings Reduced boot times from 38 seconds to just 8 seconds – Watts is 2.08wh or.002kwh In a small Cloud where 100 images are created every hour – Saves.2kwh of 15.2c per kwh – At 15.2c per kwh this saves $ every year – In a production Cloud where 1000 images are created every minute – Saves 120kwh less every hour – At 15.2c per kwh this saves over 1 million dollars every year Image size from 4GB to 635MB – Reduces time to perform live-migration – Can do better VM Image Design 17

Conslusion Cloud computing is an emerging topic in Distributed Systems Need to conserve energy Green Cloud Framework – Power-aware scheduling of VMs – Advanced VM & server management – Specialized VM Instances Small energy savings result in a large impact 18

Future Work Combine concepts of both Power-aware and Thermal-aware scheduling to minimize both energy and temperature Integrated server, rack, and cooling strategies Further slim down the Designing the next generation of Cloud computing systems 19

Accomplishments [1] G. von Laszewski, L. Wang, A. Younge, and X. He, “Power- aware scheduling of virtual machines in dvfs-enabled clusters,” Rochester Institute of Technology, Tech. Rep., [2] G. von Laszewski, A. Younge, X. He, K. Mahinthakumar, and L. Wang, “Experiment and workflow management using cyberaide shell,” in 4 th International Workshop on Workflow Systems in e-Science (WSES 09) in conjunction with 9th IEEE International Symposium on Cluster Computing and the Grid. IEEE, [3] L. Wang, G. von Laszewski, A. Younge, X. He, M. Kunze, and J. Tao, “Cloud computing: a perspective study,” New Generation Computing, to appear in [4] G. von Laszewski, F. Wang, A. Younge, X. He, Z. Guo, and M. Pierce, “Cyberaide javascript: A javascript commodity grid kit,” in GCE08 at SC’08. Austin, TX: IEEE, Nov [Online]. Available: javascript/vonLaszewski- 08- javascript.pdf [5] G. von Laszewski, F. Wang, A. Younge, Z. Guo, and M. Pierce, “Javascript grid abstractions,” in Proceedings of the Grid Computing Environments 2007 at SC07, Reno, NV, Nov [Online]. Available: javascript/vonLaszewski- 07- javascript.pdf Accepted into 2009 International Summer School on Grid Computing in Nice, France. – Supported by US Department of Energy OSG stipend. Accepted as student volunteer for 2009 International Supercomputing Conference in Hamburg, Germany. First author on an extended abstract and poster accepted to the TeraGrid 2009 conference. 20

Progress Have some prior knowledge and research in Grid computing After attending Supercomputing 2008 in late November, I realized the future existed in Cloud computing Spend the past few months investigating Cloud computing research opportunities Identified Green IT work and realized it was applicable to Clouds Dedicated the past two months to researching Green computing research and developing a framework for Cloud infrastructure 21

Appendix 22

Cloud Computing Distributed Systems encompasses a wide variety of technologies Grid computing spans most areas and is becoming more mature. Clouds are an emerging technology, providing many of the same features as Grids without many of the potential pitfalls. From “Cloud Computing and Grid Computing 360-Degree Compared” 23

Minimal VM Image Easier to slim down a fully functional distro than to create one from scratch Selected Ubuntu Linux – Jaunty 9.04 – Minimal install site – Package management software (aptitude) – Continuing support Ubuntu Linux Cloud Ubuntu Vs. VM Image Design 24

VM Scheduling Implemented scheduler on OpenNebula system Replaced Round Robin scheduling system with Based on Algorithm Startup and Shutdown VM Management Easily added From “Opennebula: The open source virtual machine manager for cluster computing” 25

DVFS VM Scheduling 26