Outin, Edouard, et al. " Enhancing cloud energy models for - - PowerPoint PPT Presentation

Outin, Edouard, et al. "Enhancing cloud energy models for optimizing datacenters efficiency." Cloud and Autonomic Computing (ICCAC), 2015 International Conference on. IEEE, 2015.

Reviewed by Cristopher Flagg December 6, 2017

Objective

• Minimize Energy Consumption
• Maintain SLA requirements
• Nontrivial Multi-Objective Optimization Problem

○ Genetic algorithm to optimize Cloud energy consumption ○ Machine learning to improve fitness function

Fitness Function - Research Questions

• Depends on the underlying model
• RQ1. Do differences exist between the energy simulation

based on hardware specifications and the real data that can be observed?

• RQ2. Could we use machine learning techniques at

runtime to improve the simulation accuracy?

Problem Statement

• Simulation used to model datacenter consumption
• Accuracy of simulation drives accuracy of modeling
• Models used in "Analysis" step of MAPE-K
• Based on Standard Performance Evaluation Corporation

(SPEC) benchmarks of power consumption

Problem Statement - CloudSim

• “to provide a generalized and extensible simulation

framework that enables modeling, simulation, and experimentation of emerging Cloud computing infrastructures and application services”

• Energy model is based on the host CPU utilization

Problem Statement - GreenCloud

• Packet level simulator with a strong emphasis on

networking and energy awareness.

• Independent energy models for each type of resource

(e.g. CPU, RAM, disk, network).

• Determining coefficients for models is complex and can

not be approximated.

Problem Statement - SimGrid

• Study the behavior of large-scale distributed systems such

as Grids, Clouds, HPC or P2P systems

• SURF Energy Plugin enables accounting for computation

time and dissipated energy

• Assumes energy consumption is linear with the CPU

utilization

Problem Statement - iCanCloud

• Predict the trade-offs between cost and performance of a

given set of applications executed in a specific hardware

• Supports modeling hardware energy consumption of a

system such as CPUs, memories, disks, PSUs.

• Based on predefined collections of applications

Problem Statement - Summary

• Simulators used in classical analysis step of a MAPE-K
• Analysis step uses hard coded "static" rules, also called

Event-Condition-Action (ECA) engines

• This paper uses and manipulates simulators instead of the

ECA engine.

Problem Statement - Experimental Protocol

• Google Scholar to identify most cited simulator (CloudSim)
• Simulators based on the spec.org values for the DELL

PowerEdge R620

• Request the PDU metrics for this server through SNMP
• Stress tools to mimic variable server utilization (stress-ng)
• Two experiments on fresh Ubuntu Server 14.04.2 LTS

Problem Statement - Bare Metal

• No hypervisor - Directly

stressing host operating system

• Average energy

consumption over 120 seconds interval

Problem Statement - Hypervisor and VM

• KVM hypervisor with

single large Ubuntu VM

• When idle,

non-negligible gap between spec.org and measured value

Problem Statement - RQ1 Revisited

• CloudSim simulation values not very accurate (based on

the spec.org data)

• Cannot rely on the CPU metric to predict the Watts

consumed.

Approach

• Monitor managed elements of Cloud infrastructure.
• Analysis determines changes needed to bring the system

in the ideal state ○ more energy-efficient ○ no SLA violations ○ High performance

Approach

Approach

• Genetic algorithm manipulates a Cloud configuration

instanced as a model

• Fitness function designed to evaluate the energy

consumption (goal of paper)

• Plan and execute changes from best instance

Approach - Cloud Model

• Model inspired by previous experiments
• Model is mapping of

○ virtual machine placement ○ SLA constraints ○ different hosts load

• Allows mutations, crossovers and validity checks

Approach - Cloud Model

• Uses KMF modeling framework (modeling.kevoree.org)
• Utilizes model generators
• Stores time series of models

Approach - Energy Consumption Model

• OpenStack Ceilometer compute agent on each node
• Forwards all the metrics to central agent for aggregation
• Uses machine learning mechanisms to design a new

energy model for the Cloud datacenter

• Train our model beforehand

Approach - Energy Consumption Model

Detailed sequence of actions performed by every compute node agent:

• On the server, monitor CPU utilization, RAM usage, volume of read and

writes on the disk and volume of network data received and sent.

• With the PDU we get the corresponding energy consumed by the server
• Every second we retrieve the metrics from the server and the PDU
• Metrics collector stores tuple (%cpu, %ram, read, writes, recv, sent, Watts)

Approach - Energy Consumption Model

Approach - Energy Consumption Model

• Multivariate Adaptive Regression Spline
• Predict the values of a continuous dependent variable from a set of

independent variables

• Does not assume any particular type or class of relationship (e.g., linear,

logistic, etc.) between the predictor variables and the dependent variable

• Etotal = ∑' predict(host) + Enetwork
• Network usage does not change with proportional to traffic load, is related to
• topology. Model assumes this is a static value

Experimental Protocol - Validation

• Gather sparse data for predictions, representing different

utilization levels of the server’s hardware (i.e. CPU, RAM, disk, network)

• Cloud infrastructure mimics random / variable workloads
• Stress-ng used to consume server resources

Experimental Protocol - Sample Data

• Training data gathered for a given host node

Experimental Protocol - Energy model results

Ehost is the total energy consumption of a given host

• cpu refers to the current host CPU utilization
• ram refers to the current host RAM usage
• sent denotes the volume of network sent data (in Kb)

Conclusion - Analysis of Results

?

Conclusion - Analysis of Results

The results look promising as we get an average error

• f 3,8% between the effectively measured values and the

predicted ones which improve the accuracy comparing to

• CloudSim. This result permits to answer positively to RQ2

Conclusion - Threats to Validity

• Disk I/O NOT dominant features in the prediction equation

computed by the MARS algorithm ○ Volume of disk operations was quite constant ○ Pure sequential disk access is not realistic

• NO live migration energy overhead considered

Conclusion - Questions

• CloudSim is CPU only. Greencloud takes drives and ram

into account as well, but not reviewed

• No results, no analysis of missing results