Small is better

Abstract

Public clouds have become a popular platform for building Internet-scale applications. Using virtualization, public cloud services grant customers full control of guest operating systems and applications, while service providers still retain the management of their host infrastructure. Because applications built with public clouds are often highly sensitive to response time, infrastructure builders strive to reduce the latency of their data center's internal network. However, most existing solutions require modification to the software stack controlled by guests. We introduce a new host-centric solution for improving latency in virtualized cloud environments. In this approach, we extend a classic scheduling principle-Shortest Remaining Time First-from the virtualization layer, through the host network stack, to the network switches. Experimental and simulation results show that our solution can reduce median latency of small flows by 40%, with improvements in the tail of almost 90%, while reducing throughput of large flows by less than 3%.

Toward Ultra-Low Latency SSDs: Analyzing the Impact on Data-Intensive Workloads

“…High IOPS requires high QD for massive internal parallelism [17], while low QD should be maintained for low latencies. On the other hand, the consistent low latency feature enables ULL SSDs to keep a low queue depth relatively easily and not suffer the long tail problem [18]. Data center storage systems will benefit most from this unique feature to enable predictably fast services.…”

PriorityMeister

“…HULL [1] addresses the problem of delays from long network switch queues by rate limiting and shifting the queueing to the end hosts. Xu et al [23] also address this problem, but do so using network prioritization. Both papers allow low bandwidth workloads to quickly pass through the network switch, but do not address how to deal with higher bandwidth workloads with different end-to-end latency SLOs.…”