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.

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

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

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 Coarse sensor and actuator information. Noisy sensor measurement CPU utilization as percentage of VM usage work-conserving scheduler gives noisy measurement

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.

Proportional thresholding If incoming accumulated sensor value > high threshold, - then request resources - set high threshold to accumulated sensor value high threshold low threshold If incoming accumulated sensor value < low threshold, - then release resources - set low threshold to accumulated sensor value

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

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

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

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

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

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.

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

Thank you!