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Datacenters today waste CPU and memory, as resources demanded by applications often fail to match the resources available on machines. This leads to stranded resources because one resource that runs out prevents placing additional applications that could consume the other resources. Unusable stranded resources result in reduced utilization of servers, and wasted money and energy. Quicksand is a new framework and runtime system that unstrands resources by providing developers with familiar, high-level abstractions (e.g., data structures, batch computing). Internally Quicksand decomposes them into resource proclets, granular units that each primarily consume resources of one type. Inspired by recent granular programming models, Quicksand decouples consumption of resources as much as possible. It splits, merges, and migrates resource proclets in milliseconds, so it can use resources on any machine, even if available only briefly. Evaluation of our prototype with four applications shows that Quicksand uses stranded resources effectively; that Quicksand reacts to changing resource availability and demand within milliseconds, increasing utilization; and that porting applications to Quicksand requires moderate effort.
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Unleashing True Utility Computing with Quicksand
Today's clouds are inefficient: their utilization of resources like CPUs, GPUs, memory, and storage is low. This inefficiency occurs because applications consume resources at variable rates and ratios, while clouds offer resources at fixed rates and ratios. This mismatch of offering and consumption styles prevents fully realizing the utility computing vision. We advocate for fungible applications, that is, applications that can distribute, scale, and migrate their consumption of different resources independently while fitting their availability across different servers (e.g., memory at one server, CPU at another). Our goal is to make use of resources even if they are transiently available on a server for only a few milliseconds. We are developing a framework called Quicksand for building such applications and unleashing the utility computing vision. Initial results using Quicksand to implement a DNN training pipeline are promising: Quicksand saturates resources that are imbalanced across machines or rapidly shift in quantity.
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- Award ID(s):
- 2045170
- PAR ID:
- 10491357
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400701955
- Page Range / eLocation ID:
- 196 to 205
- Format(s):
- Medium: X
- Location:
- Providence, RI, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3593856.3595893
