Taming Server Memory TCO with Multiple Software-Defined Compressed Tiers
Sandeep Kumar, Aravinda Prasad, and Sreenivas Subramoney

TL;DR
This paper introduces a multi-tier software-defined memory compression system that significantly reduces server memory TCO while maintaining or improving application performance through dynamic data placement.
Contribution
It proposes a novel multi-tier compressed memory architecture with a cost model and placement strategy, outperforming existing 2-tier solutions in TCO savings and performance.
Findings
Increases memory TCO savings by 22-40 percentage points.
Maintains or improves application performance by 2-10 percentage points.
Provides a dynamic data placement model guided by an analytical cost framework.
Abstract
Memory accounts for 33 - 50% of the total cost of ownership (TCO) in modern data centers. We propose a novel solution to tame memory TCO through the novel creation and judicious management of multiple software-defined compressed memory tiers. As opposed to the state-of-the-art solutions that employ a 2-Tier solution, a single compressed tier along with DRAM, we define multiple compressed tiers implemented through a combination of different compression algorithms, memory allocators for compressed objects, and backing media to store compressed objects. These compressed memory tiers represent distinct points in the access latency, data compressibility, and unit memory usage cost spectrum, allowing rich and flexible trade-offs between memory TCO savings and application performance impact. A key advantage with ntier is that it enables aggressive memory TCO saving opportunities by placing…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Distributed and Parallel Computing Systems · Advanced Data Storage Technologies
