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Automated Control in Cloud Computing: Challenges and Opportunities

Automated Control in Cloud Computing: Challenges and Opportunities. Harold C. Lim, Shivnath Babu , Jeffrey S. Chase, and Sujay S. Parekh ACM’s First Workshop on Automated Control for Datacenters and Clouds, 2009, Barcelona, Spain. Presenter: Ramya Pradhan , Fall 2012, UCF.

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Automated Control in Cloud Computing: Challenges and Opportunities

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  1. Automated Control in Cloud Computing: Challenges and Opportunities Harold C. Lim, ShivnathBabu, Jeffrey S. Chase, and Sujay S. Parekh ACM’s First Workshop on Automated Control for Datacenters and Clouds, 2009, Barcelona, Spain. Presenter: RamyaPradhan, Fall 2012, UCF.

  2. Outline of the presentation • Research problem • Proposed solution • Evaluation of the proposed solution • Strengths • Limitations • Potential extensions

  3. Research Problem Guest’s clients IaaS provider Guest using IaaS How to adaptively provision resources?

  4. Challenges • Decoupling control • Cloud controller • arbitrate resource requests, select guest VM placements • Application controller • determine physical resources needed and communicate to cloud controller • Control granularity • Coarsesensor and actuator information. • Noisy sensor measurement • CPU utilization as percentage of VM usage • work-conserving scheduler gives noisy measurement

  5. Proposed solution • A feedback driven application control implemented at the guest’s end. • Guest application controllers or slice controllers. • IaaS provider provides sensors and actuators to enable control policies. • Slice controllers use APIs to collect coarse-grained information from sensors and actuators. • Solution: A control technique, proportional thresholding, for coarse-grained actuators with a wide range of actuator values.

  6. Proportional thresholding If incoming accumulated sensor value>high threshold, - then request resources - sethigh thresholdtoaccumulated sensor value high threshold low threshold If incoming accumulated sensor value <low threshold, - then release resources - setlow thresholdtoaccumulated sensor value

  7. Why proportional thresholding? • Parameters to tune: CPU entitlement and utilization • Tuned using: an integral control • control effort is proportional to the integral of the error • well-suited for coarse-grained actuators • actuators have a dynamic target range • steady state error is zero

  8. Evaluation of proportional thresholding • Horizontally scalable web service • Automat (control interface) • Open Resource Control Architecture (underlying architecture and resource leasing mechanism) • Hyperic HQ (gathers CPU utilization) • Sensor measurement • average CPU utilization on all leased VMs • experiments start with one VM • Additional VMs are obtained using • proportional thresholding • static thresholding • integral control

  9. Evaluation of proportional thresholding • Synthetic workload • time 0: 1000 threads, time 10: 1650 threads, time 40: 1000 threads • Proportional thresholding vs. integral control

  10. Evaluation of proportional thresholding • Synthetic workload • time 0: 1000 threads, 15: 1650 threads, 30: 3200 threads, 45: 2450 threads • Proportional thresholding vs. static thresholding

  11. Strengths • Utilizes accumulated actuator error to better adapt to dynamic resource provisioning. • Suitable for coarse-grained sensorinformation provided by cloud providers. • Shows self-constraint capability. • Performs better resource allocation than integral control and control using static thresholding.

  12. Limitations • A key parameter, integral gain, in the equation for integral control is empirically determined. • May become application specific • Limited to 3 VMs. • Discussion only on horizontal clusters.

  13. Possible Extensions • Extend to include more VMs. • Extend to include vertical clusters. • Analyze application of proportional thresholding to at least one target system that needs complex models for integral gain. • shows feasibility of the proposed method

  14. Thank you!

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