Fractal: Automated Application Scaling

Masoud Koleini; Carlos Oviedo; Derek McAuley; Charalampos Rotsos; Anil Madhavapeddy; Thomas Gazagnaire; Magnus Skejgstad; Richard Mortier

arXiv · 1902.09636

Fractal: Automated Application Scaling

Masoud Koleini·Carlos Oviedo·Derek McAuley·Charalampos Rotsos·Anil Madhavapeddy·Thomas Gazagnaire·Magnus Skejgstad·Richard Mortier

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Abstract

To date, cloud applications have used datacenter resources through manual configuration and deployment of virtual machines and containers. Current trends see increasing use of microservices, where larger applications are split into many small containers, to be developed and deployed independently. However, even with the rise of the devops movement and orchestration facilities such as Kubernetes, there is a tendency to separate development from deployment. We present an exploration of a more extreme point on the devops spectrum: Fractal. Developers embed orchestration logic inside their application, fully automating the processes of scaling up and down. Providing a set of extensions to and an API over the Jitsu platform, we outline the design of Fractal and describe the key features of its implementation: how an application is self-replicated, how replica lifecycles are managed, how failure recovery is handled, and how network traffic is transparently distributed between replicas. We present evaluation of a self-scaling website, and demonstrate that Fractal is both useful and feasible.

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BibTeX RIS

Masoud Koleini, Carlos Oviedo, Derek McAuley, Charalampos Rotsos, Anil Madhavapeddy, Thomas Gazagnaire, Magnus Skejgstad, Richard Mortier. 2019-02-25. Fractal: Automated Application Scaling. https://arxiv.org/abs/1902.09636

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