Samsung Advanced Institute of Technology

With the rapid growth in memory demands, the slowdown of DRAM device scaling, and the fluctuations in

DRAM prices, DRAM has become one of the critical resources in cloud computing systems and datacenters.

The compressed memory swap (CMS) is a promising technique that improves the effective memory capacity

of the underlying computer system by compressing and storing a subset of pages in memory instead of the

disk swap. While prior works have extensively investigated resource management techniques for workload

consolidation, they lack the capability of dynamically allocating cores, memory, andCMSto the consolidated

applications in a controlled and efficient manner.

To bridge this gap, this work presents the in-depth characterization of the impact of cores, memory, and

CMS on the QoS and throughput of the consolidated latency-critical (LC) and batch applications. Guided

by the characterization results, we propose COSMOS, a software-based runtime system for coordinated

management of cores, memory, and CMS for QoS-aware and efficient workload consolidation for memoryintensive

applications. COSMOS dynamically analyzes the characteristics of the consolidated applications

and allocates the resources to the consolidated applications in away that achieves high throughput with strong

QoS guarantees. Our quantitative evaluation based on a real system and widely-used memory-intensive

benchmarks demonstrates the effectiveness of COSMOS in that it robustly satisfies the QoS and achieves

high throughput across all the evaluated workload mixes and scenarios and significantly reduces the number

of explored system states.