Elascale is a comprehensive autoscaling and monitoring solution that leverages both virtual machines and containers. It adjusts both microservices and macroservices using Docker Swarm for clustering microservices and Docker Machine for provisioning macroservices. Metrics are collected using Beats and consolidated in Elasticsearch, while insights are provided in Kibana. The autoscaling engine uses metrics like CPU and memory utilization to scale services up and down according to a replication factor formula to maintain performance as workload increases. Elascale was tested on an IoT application deployed in a smart city testbed.

1. Elascale: A Comprehensive Autoscaling and Monitoring Solution
Rajsimman Ravichandiran, Hamzeh Khazaei, Byungchul Park, Hadi Bannazadeh and Alberto Leon-Garcia
Electrical and Computer Engineering Department, University of Toronto, Canada
 Autoscalability: A required feature for today’s cloud
software systems
 Both Virtual Machines (VMs) and containers are
leveraged to offer solutions as micro/macroservices
 Scaling algorithms should support scalability for both
VMs and containers
 Elascale adjusts both micro and macroservices [1]
 Container: isolated process on host and shares kernel
space; does not contain full OS
 Microservice: application components that provide
single service
 Macroservice: worker nodes that host multiple
services
 Docker Swarm: tool for clustering and scheduling
distributed microservices
 Docker Machine: tool for provisioning and managing
macroservices across backend clouds
✧ Utilizes Docker Swarm for clustering and scheduling
services
✧ Uses Docker Machine to provision and manage
macroservices (Fig. 1)
✧ Beats (Metricbeat and Dockbeat) collect performance
metrics of micro/macroservices (Fig. 2)
✧ HTTPS encrypted communication channels to prevent
information leakage
✧ NGINX reverse proxy conceal Elasticsearch and
Kibana
✧ Elasticsearch consolidates statistics
✧ Informed insights from Kibana using adhoc queries
and visualizations
✧ UI to view dashboard and change configurations
✧ Source code on GitHub [2]
Architecture Views
1. Hamzeh Khazaei, Rajsimman Ravichandiran, Byungchul Park, Hadi Bannazadeh, Ali
Tizghadam and Alberto Leon-Garcia. Elascale: Autoscaling and Monitoring as a
Service. Accepted in the 27th Annual International Conference on Computer Science and
Software Engineering (CASCON), Sept 2017.
2. R. Ravichandiran, Elascale_scripts, (2017), GitHub repository,
https://github.com/RajsimmanRavi/Elascale_secure
Sample IoT Application
References:
Experimental Evaluation
Figure 1: Layered view
Figure 2: Component view
Figure 3: Sample IoT application deployed in SAVI Testbed
Elascale Architecture
Introduction Elascale Autoscaling Engine
✧ General-purpose, cloud and application agnostic monitoring and
autoscaling solution
✧ Autoscaling algorithms use statistics to scale up/down
micro/macroservices
✧ 𝒇 = 𝜶 ∙ 𝒄𝒑𝒖 𝒖𝒕𝒊𝒍 + 𝜷 ∙ 𝒎𝒆𝒎 𝒖𝒕𝒊𝒍 + 𝜸 ∙ 𝒏𝒆𝒕 𝒖𝒕𝒊𝒍 + 𝝀 ∙
𝒓𝒆𝒑 𝒇𝒂𝒄
𝒕
𝒓𝒆𝒑 𝒇𝒂𝒄
𝒄
✧ 𝒓𝒆𝒑 𝒇𝒂𝒄
𝒕
- target replication factor , 𝒓𝒆𝒑 𝒇𝒂𝒄
𝒄
- current replication factor
✧ 𝜶 + 𝜷 + 𝜸 + 𝝀 = 𝟏
Background
✧ Deployed as Docker services on swarm nodes
✧ Three layers containing different microservices (Fig. 3)
✧ Aggregate-sensor: virtual sensors and Kafka aggregator
✧ Edge-cloud: stream processor collects from aggregator, preprocesses and
sends to Core-cloud
✧ Core-cloud: Cassandra database stores data
✧ Experiment: If more sensors deployed, stream processor CPU
utilization increases
✧ Elascale automatically scales microservice to handle workload