Using Application Data for SLA-aware Auto-scaling in Cloud Environments

TL;DR

This paper explores application data-driven auto-scaling in cloud environments, proposing algorithms that leverage real-time sentiment analysis to improve SLA compliance and resource efficiency.

Contribution

It introduces novel auto-scaling algorithms based on application data and sentiment analysis, moving beyond traditional infrastructure metrics.

Findings

Reduced SLA violations by up to 95%

Decreased resource usage by up to 33%

Effective handling of rapid data stream changes

Abstract

With the establishment of cloud computing as the environment of choice for most modern applications, auto-scaling is an economic matter of great importance. For applications like stream computing that process ever changing amounts of data, modifying the number and configuration of resources to meet performance requirements becomes essential. Current solutions on auto-scaling are mostly rule-based using infrastructure level metrics such as CPU/memory/network utilization, and system level metrics such as throughput and response time. In this paper, we introduce a study on how effective auto-scaling can be using data generated by the application itself. To make this assessment, two algorithms are proposed that use a priori knowledge of the data stream and use sentiment analysis from soccer-related tweets, triggering auto-scaling operations according to rapid changes in the public sentiment about the soccer players that happens just before big bursts of messages. Our application-based auto-scaling was able to reduce the number of SLA violations by up to 95% and reduce resource requirements by up to 33%.