Towards vertically scalable spark applications

The dynamic provisioning of virtual machines (VMs) supported by many cloud computing infrastructures eases the scalability of software applications. Unfortunately, VMs are relatively slow to boot and public cloud providers do not allow users to vary their resources (vertical scalability) dynamically. To tackle both problems, a few years ago we presented a solution that combines the management of VMs with the use of containers specifically targeted to the efficient runtime management of the resources provisioned to Web applications. This paper borrows from this solution and addresses the problem of provisioning resources to big data, Spark applications at runtime. Spark does not allow for the runtime scalability of the resources associated with its executors, but resources must be provisioned statically. To tackle this problem, the paper describes a container-based version of Spark that supports the dynamic resizing of the memory and CPU cores associated with the different executors. The evaluation demonstrates the feasibility of the approach and identifies the trade-offs involved.