Paper Info
Title
CEDULE+: Resource Management for Burstable Cloud Instances Using Predictive Analytics
Abstract
Nearly all principal cloud providers now provide burstable instances in their offerings. The main attraction of this type of instance is that it can boost its performance for a limited time to cope with workload variations. Although burstable instances are widely adopted, it is not clear how to efficiently manage them to avoid waste of resources. In this article, we use predictive data analytics to optimize the management of burstable instances. We design CEDULE+, a data-driven framework that enables efficient resource management for burstable cloud instances by analyzing the system workload and latency data. CEDULE+ selects the most profitable instance type to process incoming requests and controls CPU, I/O, and network usage to minimize the resource waste without violating Service Level Objectives (SLOs). CEDULE+ uses lightweight profiling and quantile regression to build a data-driven prediction model that estimates system performance for all combinations of instance type, resource type, and system workload. CEDULE+ is evaluated on Amazon EC2, and its efficiency and high accuracy are assessed through real-case scenarios. CEDULE+ predicts application latency with errors less than 10%, extends the maximum performance period of a burstable instance up to 2.4 times, and decreases deployment costs by more than 50%.
Year
DOI
Venue
2021
10.1109/TNSM.2020.3039942
IEEE Transactions on Network and Service Management
Keywords
DocType
Volume
Burstable instance,Cloud,Scheduling,AWS,Credit depletion period,Resource credit,Data-driven predictive analytics
Journal
18
Issue
ISSN
Citations 
1
1932-4537
1
PageRank 
References 
Authors
0.35
0
4
Name
Order
Citations
PageRank
Riccardo Pinciroli1123.62
ahsan ali252.09
Feng Yan316318.78
Evgenia Smirni41857161.97