Cake: Enabling High-level SLOs on Shared Storage Systems

Abstract

Cake is a coordinated, multi-resource scheduler for shared distributed storage environments with the goal of achieving both high throughput and bounded latency. Cake uses a two-level scheduling scheme to enforce high-level service-level objectives (SLOs). Firstlevel schedulers control consumption of resources such as disk and CPU. These schedulers (1) provide mechanisms for differentiated scheduling, (2) split large requests into smaller chunks, and (3) limit the number of outstanding device requests, which together allow for effective control over multi-resource consumption within the storage system. Cake's second-level scheduler coordinates the first-level schedulers to map high-level SLO requirements into actual scheduling parameters. These parameters are dynamically adjusted over time to enforce high-level performance specifications for changing workloads. We evaluate Cake using multiple workloads derived from real-world traces. Our results show that Cake allows application programmers to explore the latency vs. throughput trade-off by setting different high-level performance requirements on their workloads. Furthermore, we show that using Cake has concrete economic and business advantages, reducing provisioning costs by up to 50% for a consolidated workload and reducing the completion time of an analytics cycle by up to 40%.

Distributed resource management across process boundaries

“…However, it relies mostly on centralized resource management whereas Wisp is fully decentralized. Enforcing high-level scheduling policies and fair sharing have been explored in the context of distributed storage systems [30,31,62,67,73,74]; however, they typically consider simpler execution structures (e.g., client to server) whereas Wisp focuses on a general DAG wherein individual processes lack end-to-end visibility. Lastly, several proposals exist for optimizing job completion times for DAGs of tasks in big-data systems [11,28,77,78].…”

Argus: A Multi-tenancy NoSQL store with workload-aware resource reservation

“…The study in [10] shows that isolation through virtual machines (VM) yields lower performance compared to the shared process model. A majority of current research in multi-tenance [4,5,7,11,12] focuses on the shared process model which our work targets as well.…”

Revenue Driven Resource Allocation for Virtualized Data Centers

“…Pesto [12] and Cake [23] focus on storage resource provisioning to meet administrator-specified storage SLOs. Xu et al's work [25] considered the profit-driven resource optimization problem and formalized it as an instance of the continuous knapsack problem which they solved using a greedy allocation algorithm.…”