No abstract

No abstract

The widely used Tor anonymity network is designed to enable low-latency anonymous communication. However, in practice, interactive communication on Tor-which accounts for over 90% of connections in the Tor network [1]-incurs latencies over 5x greater than on the direct Internet path. In addition, since path selection to establish a circuit in Tor is oblivious to Internet routing, anonymity guarantees can breakdown in cases where an autonomous system (AS) can correlate traffic across the entry and exit segments of a circuit.In this paper, we show that both of these shortcomings in Tor can be addressed with only client-side modifications, i.e., without requiring a revamp of the entire Tor architecture. To this end, we design and implement a new Tor client, LASTor. First, we show that LASTor can deliver significant latency gains over the default Tor client by simply accounting for the inferred locations of Tor relays while choosing paths. Second, since the preference for low latency paths reduces the entropy of path selection, we design LASTor's path selection algorithm to be tunable. A user can choose an appropriate tradeoff between latency and anonymity by specifying a value between 0 (lowest latency) and 1 (highest anonymity) for a single parameter. Lastly, we develop an efficient and accurate algorithm to identify paths on which an AS can correlate traffic between the entry and exit segments. This algorithm enables LASTor to avoid such paths and improve a user's anonymity, while the low runtime of the algorithm ensures that the impact on end-to-end latency of communication is low. By applying our techniques to measurements of real Internet paths and by using LASTor to visit the top 200 websites from several geographically-distributed end-hosts, we show that, in comparison to the default Tor client, LASTor reduces median latencies by 25% while also reducing the false negative rate of not detecting a potential snooping AS from 57% to 11%.

Abstract. Data center network operators have to continually monitor path latency to quickly detect and re-route traffic away from high-delay path segments. Existing latency monitoring techniques in data centers rely on either 1) actively sending probes from end-hosts, which is restricted in some cases and can only measure end-to-end latencies, or 2) passively capturing and aggregating traffic on network devices, which requires hardware modifications. In this work, we explore another opportunity for network path latency monitoring, enabled by software-defined networking. We propose SLAM, a latency monitoring framework that dynamically sends specific probe packets to trigger control messages from the first and last switches of a path to a centralized controller. SLAM then estimates the latency distribution along a path based on the arrival timestamps of the control messages at the controller. Our experiments show that the latency distributions estimated by SLAM are sufficiently accurate to enable the detection of latency spikes and the selection of low-latency paths in a data center.

Developers of cloud-connected mobile apps need to ensure the consistency of application and user data across multiple devices. Mobile apps demand different choices of distributed data consistency under a variety of usage scenarios. The apps also need to gracefully handle intermittent connectivity and disconnections, limited bandwidth, and client and server failures. The data model of the apps can also be complex, spanning inter-dependent structured and unstructured data, and needs to be atomically stored and updated locally, on the cloud, and on other mobile devices.In this paper we study several popular apps and find that many exhibit undesirable behavior under concurrent use due to inadequate treatment of data consistency. Motivated by the shortcomings, we propose a novel data abstraction, called a sTable, that unifies a tabular and object data model, and allows apps to choose from a set of distributed consistency schemes; mobile apps written to this abstraction can effortlessly sync data with the cloud and other mobile devices while benefiting from end-to-end data consistency. We build Simba, a data-sync service, to demonstrate the utility and practicality of our proposed abstraction, and evaluate it both by writing new apps and porting existing inconsistent apps to make them consistent. Experimental results show that Simba performs well with respect to sync latency, bandwidth consumption, server throughput, and scales for both the number of users and the amount of data.