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 warm data in low latency compressed tiers with a reasonable performance impact while simultaneously placing cold data in the best memory TCO saving tiers. We believe our work represents an important server system configuration and optimization capability to achieve the best SLA-aware performance per dollar for applications hosted in production data center environments. We present a comprehensive and rigorous analytical cost model for performance and TCO trade-off based on continuous monitoring of the application's data access profile. Guided by this model, our placement model takes informed actions to dynamically manage the placement and migration of application data across multiple software-defined compressed tiers. On real-world benchmarks, our solution increases memory TCO savings by 22% - 40% percentage points while maintaining performance parity or improves performance by 2% - 10% percentage points while maintaining memory TCO parity compared to state-of-the-art 2-Tier solutions.

翻译：内存占现代数据中心总拥有成本（TCO）的33%至50%。我们提出一种通过创新构建并精细管理多层软件定义压缩内存层级来驯服内存TCO的新方案。与现有采用“DRAM+单一压缩层”的2层架构不同，我们定义了由多种压缩算法、压缩对象内存分配器及存储介质共同实现的多层压缩内存层级。这些压缩内存层级在访问延迟、数据压缩比与单位内存使用成本维度上呈现差异化特征，从而在内存TCO节省与应用性能影响之间提供丰富灵活的权衡空间。ntier的关键优势在于：通过将温数据置于低延迟压缩层（在合理性能影响下），同时将冷数据置于最优内存TCO节省层，实现激进的内存TCO节省。我们认为，该工作为生产数据中心环境中的托管应用提供了重要的服务器系统配置与优化能力，以实现每美元成本下的最佳SLA感知性能。我们基于应用数据访问特征的持续监控，建立了涵盖性能与TCO权衡的综合且严谨的分析成本模型。在该模型指导下，我们的放置模型通过动态管理跨多个软件定义压缩层级的数据放置与迁移，采取精准决策。在真实基准测试中，与现有最先进的2层方案相比，我们的解决方案在保持性能不变的情况下将内存TCO节省提升22%-40个百分点，或在保持内存TCO相等的情况下将性能提升2%-10个百分点。