Managing Energy Efficiency and Quality of Service in Cloud Applications Using a Distributed Monitoring System

Monica Vitali

Dip. Elettronica Informazione e BioingegneriaPolitecnico di Milano, Italy

MOTIVATION

monitor and improve Energy Efficiency in a cloud environment from an application perspective, while respecting the constraints established in the Service Level Agreement (QoS).

ISSUEMonitoring data are too BIG (volume and velocity) to be collected and analyzed in a centralized way.

GOALproposing a distributed and efficient methodology for monitoring the application and analyze the data.

The Model

Goal to Goal relations

influence existing between indicators (KPIs and GPIs)

Actions to Goal relationsthe effect of an action over a goal (positive or negative)

Monica Vitali, Barbara Pernici, and Una-May O’Reilly. "Learning a goal-oriented model for energy efficient adaptive applications in data centers." Information Sciences (2015).

Enables “what if” analysis and indirect improvement.

The Model (Goal Layer)

Goal to goal (metrics) relations are represented by an automatically learned Bayesian Network (BN).

Learn relations (correlation)

Learn directions (MMHC)

Learn Parameters (MAPE)

The Model (Goal Layer)

If executed in a centralized node, computation time is exponential with the number of monitored indicators

Example of BNClusters of relations

The Model (Goal Layer)

If executed in a centralized node, computation time is exponential with the number of monitored indicators

Example of BNClusters of relations

The Model (Goal Layer)

If executed in a centralized node, computation time is exponential with the number of monitored indicators

Example of BNClusters of relations

The Approach

Strengths the approach is dynamic, flexible, automatic

Weaknesses the sample time of the collection of monitoring data is limited by the amount of data, time for the analysis grows exponentially with the number of monitored indicators

Solution employ a distributed monitoring system and implement a distributed algorithm for data analysis

Distributed Monitoring

Data are saved where they are collectedNetwork usage reduction, improvement in the effectiveness of data collection

Server StorageVirtual Machine

Application activity

Monitoring Agent

CLOUD

DATA CENTER 1 DATA CENTER 2 Indicators collected and computed at three layers:VM, Host, Application

Two steps:Local analysis in each host and global analysis in a centralized node

Distributed LearningThe BN can be learned with a distributed algorithm exploiting the clusters of variables

Apply the BN learning for each VM

Learn relations between and with the hosts

metrics

Learn relations between and with the

application metrics

Distributed LearningMonitored indicators:4 indicators for each VM,2 indicators for each server

Initial configuration:2 servers,2 VMs on each server

Step:Increase the number of VMs

RESULTS:RT reduction between 62(81)% and 92(96)%

From exponential to linear behavior → scalability improvement

Managing Energy Efficiency and Quality of Service in Cloud Applications Using a Distributed Monitoring System

Monica Vitali

Dip. Elettronica Informazione e BioingegneriaPolitecnico di Milano, Italy