Low latency via redundancy

Low latency is critical for interactive networked applications. But while we know how to scale systems to increase capacity, reducing latency --- especially the tail of the latency distribution --- can be much more difficult. In this paper, we argue that the use of redundancy is an effective way to convert extra capacity into reduced latency. By initiating redundant operations across diverse resources and using the first result which completes, redundancy improves a system's latency even under exceptional conditions. We study the tradeoff with added system utilization, characterizing the situations in which replicating all tasks reduces mean latency. We then demonstrate empirically that replicating all operations can result in significant mean and tail latency reduction in real-world systems including DNS queries, database servers, and packet forwarding within networks

Inside Dropbox: Understanding Personal Cloud Storage Services

Personal cloud storage services are gaining popularity. With a rush of providers to enter the market and an increasing of- fer of cheap storage space, it is to be expected that cloud storage will soon generate a high amount of Internet traffic. Very little is known about the architecture and the perfor- mance of such systems, and the workload they have to face. This understanding is essential for designing efficient cloud storage systems and predicting their impact on the network. This paper presents a characterization of Dropbox, the leading solution in personal cloud storage in our datasets. By means of passive measurements, we analyze data from four vantage points in Europe, collected during 42 consecu- tive days. Our contributions are threefold: Firstly, we are the first to study Dropbox, which we show to be the most widely-used cloud storage system, already accounting for a volume equivalent to around one third of the YouTube traffic at campus networks on some days. Secondly, we characterize the workload typical users in different environments gener- ate to the system, highlighting how this reflects on network traffic. Lastly, our results show possible performance bot- tlenecks caused by both the current system architecture and the storage protocol. This is exacerbated for users connected far from control and storage data-center

Server selection on the internet using passive probing

This paper describes a server selection mechanism for connection oriented services based on passive probing. The criterion of selection is the quality of service expected from each server, expressed as a function of availability and response time. Measures from previous connections to servers made by local clients are used to continuously update a QoS database which the prediction algorithm uses to compute the response time expected in subsequent connections. The forecasting approach is mainly based on prior measurements of TCP connection establishment time. The maximum segment size in a connection is also considered. The proposed metric is compared with other ones normally used to measure network proximity. Results show that the proposed server selection mechanism achieves a reduction of response time of over 50 percent compared with a random selection mechanism