Change search
ReferencesLink to record
Permanent link

Direct link
Window-based Cost-effective Auto-scaling Solution with Optimized Scale-in Strategy
KTH, School of Information and Communication Technology (ICT).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Auto-scaling is a major way of minimizing the gap between the demand and the availability of the computing resources for the applications with dynamic workloads. Even though a lot of effort has been taken to address the requirement of auto-scaling for the distributed systems, most of the available solutions are application-specific and consider only on fulfilling the application level requirements.

Today, with the pay-as-you-go model of cloud computing, many different price plans have been offered by the cloud providers which leads the resource price to become an important decision-making criterion at the time of auto-scaling. One major step is using the spot instances which are more advantageous in the aspect of cost for elasticity. However, using the spot instances for auto-scaling should be handled carefully to avoid its drawbacks since the spot instances can be terminated at any time by the infrastructure providers. Despite the fact that some cloud providers such as Amazon Web Services and Google Compute Engine have their own auto-scaling solutions, they do not follow the goal of cost-effectiveness.

In this work, we introduce our auto-scaling solution that is targeted for middle-layers in-between the cloud and the application, such as Karamel. Our work combines the aspect of minimizing the cost of the deployment with maintaining the demand for the resources. Our solution is a rule-based system that is built on top of resource utilization metrics as a more general metric for workloads. Further, the machine terminations and the billing period of the instances are taken into account as the cloud source events. Different strategies such as window based profiling, dynamic event profiling, and optimized scale-in strategy have been used to achieve our main goal of providing a cost-effective auto-scaling solution for cloud-based deployments. With the help of our simulation methodology, we explore our parameter space to find the best values under different workloads. Moreover, our cloud-based experiments show that our solution performs much more economically compare to the available cloud-based auto-scaling solutions. 

Place, publisher, year, edition, pages
2016. , 73 p.
TRITA-ICT-EX, 2016:124
Keyword [en]
auto-scaling, customer-centric, cost-effective, cloud
National Category
Computer Systems
URN: urn:nbn:se:kth:diva-194210OAI: diva2:1038805
Subject / course
Computer Science
Educational program
Master of Science - Distributed Computing
Available from: 2016-11-07 Created: 2016-10-19 Last updated: 2016-11-07Bibliographically approved

Open Access in DiVA

fulltext(3135 kB)17 downloads
File information
File name FULLTEXT01.pdfFile size 3135 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
School of Information and Communication Technology (ICT)
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 17 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 17 hits
ReferencesLink to record
Permanent link

Direct link