Elascale: Autoscaling and Monitoring as a Service

TL;DR

Elascale is a flexible, extendable cloud autoscaling and monitoring platform that uses Elasticsearch for performance data analysis, capable of adapting resources dynamically for various applications without system dependencies.

Contribution

We introduce Elascale, a novel, extendable autoscaling and monitoring framework that can be applied to any cloud software system without dependencies.

Findings

Elascale effectively manages resource scaling for IoT applications.

The system supports both reactive and proactive scaling algorithms.

Elascale's architecture enables easy integration with diverse cloud environments.

Abstract

Auto-scalability has become an evident feature for cloud software systems including but not limited to big data and IoT applications. Cloud application providers now are in full control over their applications' microservices and macroservices; virtual machines and containers can be provisioned or deprovisioned on demand at runtime. Elascale strives to adjust both micro/macro resources with respect to workload and changes in the internal state of the whole application stack. Elascale leverages Elasticsearch stack for collection, analysis and storage of performance metrics. Elascale then uses its default scaling engine to elastically adapt the managed application. Extendibility is guaranteed through provider, schema, plug-in and policy elements in the Elascale by which flexible scalability algorithms, including both reactive and proactive techniques, can be designed and implemented for various technologies, infrastructures and software stacks. In this paper, we present the architecture and initial implementation of Elascale; an instance will be leveraged to add auto-scalability to a generic IoT application. Due to zero dependency to the target software system, Elascale can be leveraged to provide auto-scalability and monitoring as-a-service for any type of cloud software system.