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?

ChallengesDecoupling control

Cloud controller arbitrate resource requests, select guest VM placements

Application controller determine physical resources needed and communicate

to cloud controller

Control granularityCoarse sensor and actuator information.Noisy sensor measurement

CPU utilization as percentage of VM usage work-conserving scheduler gives noisy measurement

Proposed solutionA 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 thresholdingIf incoming accumulated sensor value > high threshold, - then request resources- set high threshold to accumulated sensor valuehigh

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 thresholdingSynthetic workload

time 0: 1000 threads, time 10: 1650 threads, time 40: 1000 threads

Proportional thresholding vs. integral control

Evaluation of proportional thresholdingSynthetic 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!