Distributed shared logs simplify the implementation and interoperation of data stores. This paper addresses a simple question: Is it feasible to build a single, datacenter-wide distributed shared log that can support all the data stores running in a datacenter? We answer in the affirmative by presenting RingWorld, a scalable log based on a ring of programmable switches that can sustain tens of billions of appends per second while maintaining low latency. We hope the design of RingWorld will propel the adoption of shared logs as a core part of datacenter infrastructure.

The introduction of computational resources at the network edge allows application designers to offload computation from clients and/or servers, thereby reducing response latency and backbone bandwidth. More fundamentally, edge-computing moves applications from a client-server model to a client-edge-server model. While this is an attractive paradigm for many use cases, it raises the question of how to design client-edge-server systems so they can tolerate edge failures and client mobility. This is particularly challenging when edge processing is strongly stateful. In this paper we propose a design for meeting this challenge called the Client-Edge-Server for Stateful Network Applications (CESSNA).

TCP incast congestion happens in many-to-one communication workflow patterns that frequently arise in large-scale datacenter applications such as web search, social networks, and cluster-based storage systems. Incast congestion can severely degrade the performance of applications. This paper studies the effectiveness of randomizing the TCP retransmission timeout (RTO) in mitigating the impact of incast. Our design is based on the observation that under incast, retransmitted packets also get synchronized due to the use of similar RTOs by the senders. Using analysis and experimental evaluation, we show that there exists a tradeoff between the randomization interval (from which the RTO values are picked) and the number of senders involved in incast. Motivated by this insight, we propose three algorithms (TDA, MAA, and FSA) for the dynamic adaptation of the randomization interval that rely on (a) successive timeouts, (b) explicit knowledge of the level of multiplexing, and/or (c) the knowledge of flow sizes (i.e., large interval for long flows and a small interval for short flows), respectively. Our results show that these algorithms improve goodput by 1.5x-11x for up to 64 senders and provide greater improvement for larger number of senders. The proposed algorithms can be readily deployed as they do not require any changes in switches or applications.

Data center operators face extreme challenges in simultaneously providing low latency for short flows, high throughput for long flows, and high burst tolerance. We propose a buffer management strategy that addresses these challenges by isolating short and long flows into separate buffers, sizing these buffers based on flow requirements, and scheduling packets to meet different flow-level objectives. Our design provides new opportunities for performance improvements that complement transport layer optimisations.

For provisioning large-scale online applications such as web search, social networks and advertisement systems, data centers face extreme challenges in providing low latency for short flows (that result from end-user actions) and high throughput for background flows (that are needed to maintain data consistency and structure across massively distributed systems). We propose L2DCT, a practical data center transport protocol that targets a reduction in flow completion times for short flows by approximating the Least Attained Service (LAS) scheduling discipline, without requiring any changes in application software or router hardware, and without adversely affecting the long flows. L2DCT can co-exist with TCP and works by adapting flow rates to the extent of network congestion inferred via Explicit Congestion Notification (ECN) marking, a feature widely supported by the installed router base. Though L2DCT is deadline unaware, our results indicate that, for typical data center traffic patterns and deadlines and over a wide range of traffic load, its deadline miss rate is consistently smaller compared to existing deadline-driven data center transport protocols. L2DCT reduces the mean flow completion time by up to 50% over DCTCP and by up to 95% over TCP. In addition, it reduces the completion for 99th percentile flows by 37% over DCTCP. We present the design and analysis of L2DCT, evaluate its performance, and discuss an implementation built upon standard Linux protocol stack.