An empirical analysis of Amazon EC2 spot instance features affecting cost-effective resource procurement

Cheng Wang, Qianlin Liang, Bhuvan Urgaonkar

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Many cost-conscious public cloud workloads ("tenants") are turning to Amazon EC2's spot instances because, on average, these instances offer significantly lower prices (up to 10 times lower) than on-demand and reserved instances of comparable advertised resource capacities. To use spot instances effectively, a tenant must carefully weigh the lower costs of these instances against their poorer availability. Toward this, we empirically study four features of EC2 spot instance operation that a cost-conscious tenant may find useful to model. Using extensive evaluation based on historical spot instance data, we show shortcomings in the state-of-the-art modeling of these features that we overcome. As an extension to our prior work, we conduct data analysis on a rich dataset of the latest spot price traces collected from a variety of EC2 spot markets. Our analysis reveals many novel properties of spot instance operation, some of which offer predictive value whereas others do not. Using these insights, we design predictors for our features that offer a balance between computational efficiency (allowing for online resource procurement) and cost efficacy. We explore "case studies" wherein we implement prototypes of dynamic spot instance procurement advised by our predictors for two types of workloads. Compared to the state of the art, our approach achieves (i) comparable cost but much better performance (fewer bid failures) for a latency-sensitive in-memory Memcached cache and (ii) an additional 18% cost savings with comparable (if not better than) performance for a delay-tolerant batch workload.

Original languageEnglish (US)
Article number6
JournalACM Transactions on Modeling and Performance Evaluation of Computing Systems
Volume3
Issue number2
DOIs
StatePublished - Mar 2018

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Software
  • Safety, Risk, Reliability and Quality
  • Media Technology

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