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ETD Abstract Container

Abstract Header

AWS Flap Detector: An Efficient way to detect Flapping Auto Scaling Groups on AWS Cloud

Chandrasekar, Dhaarini
http://orcid.org/0000-0002-6996-1905
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1456848058

Abstract Details

2016, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Today, large number of companies are migrating to the cloud, leaving behind the concept of maintaining traditional data centers and servers. The main reasons for this migration include reduced capital costs, reduced expenditure on infrastructure and ease of accessibility. With the increasing demand for Cloud Computing and the changing needs of users, a need to make the services on the cloud dynamic in nature is essential. However, dynamic services require constant costly updates and highly meticulous configurations. One such dynamic service offered by Amazon Web Services (AWS) is Auto Scaling Groups (ASGs). With this service, AWS facilitates automatic scale up and scale down on the count of servers (instance resources) based on the ASG policies and conditions set by the users. A small misconfiguration or a build failure associated with the Amazon Machine Image (AMI) could cause the dynamism to occur when not actually needed. Since users are charged for the instances by the hour, unnecessary costs occur even if the usage is for as less as a minute. This situation of unnecessary launch and termination of instances is termed as “flaps” and can be compared to oscillations in signals. To prevent energy dissipation in case of oscillating signals, damping of signals is performed. This is similar to the problem of flapping in ASGs. We have come up with a software called AWS Flap Detector as a solution to this problem. AWS Flap Detector efficiently detects and reports flapping Auto Scaling Groups and paves the way for correction. This in turn helps prevent unnecessary resource allocation and billing.
Paul Talaga, Ph.D. (Committee Chair)
Nan Niu, Ph.D. (Committee Member)
Karen Davis, Ph.D. (Committee Member)
57 p.
Computer Science
AWS Flap Detector; Flapping Auto Scaling Groups; Flaps in AWS Auto Scaling Groups; preventing unnecessary resource allocation on AWS; preventing unnecessary billing on AWS; Spend wisely on AWS Auto Scaling Groups

Recommended Citations

Citations

  • Chandrasekar, D. (2016). AWS Flap Detector: An Efficient way to detect Flapping Auto Scaling Groups on AWS Cloud [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1456848058

    APA Style (7th edition)

  • Chandrasekar, Dhaarini. AWS Flap Detector: An Efficient way to detect Flapping Auto Scaling Groups on AWS Cloud. 2016. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1456848058.

    MLA Style (8th edition)

  • Chandrasekar, Dhaarini. "AWS Flap Detector: An Efficient way to detect Flapping Auto Scaling Groups on AWS Cloud." Master's thesis, University of Cincinnati, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1456848058

    Chicago Manual of Style (17th edition)

ucin1456848058
1,394
© 2016, some rights reserved.Creative Commons License AWS Flap Detector: An Efficient way to detect Flapping Auto Scaling Groups on AWS Cloud by Dhaarini Chandrasekar is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.