Fundamental bounds on the accuracy of network performance measurements
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Hot or not: revealing hidden services by their clock skew
Proceedings of the 13th ACM conference on Computer and communications security
Adaptivity metric and performance for restart strategies in web services reliable messaging
WOSP '08 Proceedings of the 7th international workshop on Software and performance
On fast and accurate detection of unauthorized wireless access points using clock skews
Proceedings of the 14th ACM international conference on Mobile computing and networking
An improved clock-skew measurement technique for revealing hidden services
SS'08 Proceedings of the 17th conference on Security symposium
Towards a theory for securing time synchronization in wireless sensor networks
Proceedings of the second ACM conference on Wireless network security
Spectroscopy of traceroute delays
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
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Packet delay and loss traces are frequently used by network engineers, as well as network applications, to analyze network performance. The clocks on the end-systems used to measure the delays, however, are not always synchronized, and this lack of synchronization reduces the accuracy of these measurements. Therefore, estimating and removing relative skews and offsets from delay measurements between sender and receiver clocks are critical to the accurate assessment and analysis of network performance. In this paper we introduce a linear programming based algorithm to estimate the clock skew in network delay measurements and compare it with three other algorithms. We show that our algorithm has the time complexity of $O(N)$, leaves the delay after the skew removal positive, and is robust in the sense that the error margin of the skew estimate is independent of the magnitude of the skew. We use traces of real Internet delay measurements to assess the algorithm, and compare its performance to that of three other algorithms. Furthermore, we show through simulation that our algorithm is unbiased, and that the sample variance of the skew estimates is better(smaller) than existing algorithms.