The Experts below are selected from a list of 48 Experts worldwide ranked by ideXlab platform
Darshak Thakore - One of the best experts on this subject based on the ideXlab platform.
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HTTP Random Access and Live Content
2019Co-Authors: Craig Pratt, Barbara Stark, Darshak ThakoreAbstract:To accommodate Byte range requests for content that has data appended over time, this document defines semantics that allow a HTTP client and server to perform Byte-range GET and HEAD requests that start at an arbitrary Byte Offset within the representation and ends at an indeterminate Offset.
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HTTP Bytes-live Range Unit for Live Content
2016Co-Authors: Craig Pratt, Barbara Stark, Darshak ThakoreAbstract:To accommodate Byte range requests for content that has data appended over time, this document defines a new HTTP range unit named "Bytes- live". The "Bytes-live" range unit provides the ability for a client to specify a Byte range in a GET or HEAD request which starts at an arbitrary Byte Offset within the representation and ends at an indeterminate Offset, represented by "*".
Jean-yves Le Boudec - One of the best experts on this subject based on the ideXlab platform.
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On Packet Reordering in Time-Sensitive Networks
2020Co-Authors: Mohammadpour Ehsan, Jean-yves Le BoudecAbstract:Time-sensitive networks (IEEE TSN or IETF DetNet) may tolerate some packet reordering. Re-sequencing buffers are then used to provide in-order delivery, the parameters of which (timeout, buffer size) may affect worst-case delay and delay jitter. There is so far no precise understanding of per-flow reordering metrics nor of the dimensioning of re-sequencing buffers in order to provide worst-case guarantees, as required in such networks. First, we show that a previously proposed per-flow metric, reordering late time Offset (RTO), determines the timeout value. If the network is lossless, another previously defined metric, the reordering Byte Offset (RBO), determines the required buffer. If packet losses cannot be ignored, the required buffer may be larger than RBO, and depends on jitter, an arrival curve of the flow at its source, and the timeout. Then we develop a calculus to compute the RTO for a flow path; the method uses a novel relation with jitter and arrival curve, together with a decomposition of the path into non order-preserving and order-preserving elements. We also analyse the effect of re-sequencing buffers on worst-case delay, jitter and propagation of arrival curves. We show in particular that, in a lossless (but non order-preserving) network, re-sequencing is "for free", namely, it does not increase worst-case delay nor jitter, whereas in a lossy network, re-sequencing increases the worst-case delay and jitter. We apply the analysis to evaluate the performance impact of placing re-sequencing buffers at intermediate points and illustrate the results on two industrial test cases.Comment: 25 pages, 8 figures, submitte
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On Packet Reordering in Time-Sensitive Networks.
arXiv: Networking and Internet Architecture, 2020Co-Authors: Ehsan Mohammadpour, Jean-yves Le BoudecAbstract:Time-sensitive networks (IEEE TSN or IETF DetNet) may tolerate some packet reordering. Re-sequencing buffers are then used to provide in-order delivery, the parameters of which (timeout, buffer size) may affect worst-case delay and delay jitter. There is so far no precise understanding of per-flow reordering metrics nor of the dimensioning of re-sequencing buffers in order to provide worst-case guarantees, as required in such networks. First, we show that a previously proposed per-flow metric, reordering late time Offset (RTO), determines the timeout value. If the network is lossless, another previously defined metric, the reordering Byte Offset (RBO), determines the required buffer. If packet losses cannot be ignored, the required buffer may be larger than RBO, and depends on jitter, an arrival curve of the flow at its source, and the timeout. Then we develop a calculus to compute the RTO for a flow path; the method uses a novel relation with jitter and arrival curve, together with a decomposition of the path into non order-preserving and order-preserving elements. We also analyse the effect of re-sequencing buffers on worst-case delay, jitter and propagation of arrival curves. We show in particular that, in a lossless (but non order-preserving) network, re-sequencing is "for free", namely, it does not increase worst-case delay nor jitter, whereas in a lossy network, re-sequencing increases the worst-case delay and jitter. We apply the analysis to evaluate the performance impact of placing re-sequencing buffers at intermediate points and illustrate the results on two industrial test cases.
Ralucca Gera - One of the best experts on this subject based on the ideXlab platform.
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Making Sense of Email Addresses on Drives
Journal of Digital Forensics Security and Law, 2016Co-Authors: Neil C. Rowe, Riqui Schwamm, Michael Mccarrin, Ralucca GeraAbstract:Drives found during investigations often have useful information in the form of email addresses which can be acquired by search in the raw drive data independent of the file system. Using this data we can build a picture of the social networks that a drive owner participated in, even perhaps better than investigating their online profiles maintained by social-networking services because drives contain much data that users have not approved for public display. However, many addresses found on drives are not forensically interesting, such as sales and support links. We developed a program to filter these out using a Naive Bayes classifier and eliminated 73.3% of the addresses from a representative corpus. We show that the Byte-Offset proximity of the remaining addresses found on a drive, their word similarity, and their number of co-occurrences over a corpus are good measures of association of addresses, and we built graphs using this data of the interconnections both between addresses and between drives. Results provided several new insights into our test data.
Gera Ralucca - One of the best experts on this subject based on the ideXlab platform.
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Making Sense of Email Addresses on Drives
ADFSL, 2016Co-Authors: Rowe, Neil C., Schwamm Riqui, Mccarrin, Michael R., Gera RaluccaAbstract:Drives found during investigations often have useful information in the form of email addresses, which can be acquired by search in the raw drive data independent of the file system. Using these data, we can build a picture of the social networks in which a drive owner participated, even perhaps better than investigating their online profiles maintained by social-networking services, because drives contain much data that users have not approved for public display. However, many addresses found on drives are not forensically interesting, such as sales and support links. We developed a program to filter these out using a Naïve Bayes classifier and eliminated 73.3% of the addresses from a representative corpus. We show that the Byte-Offset proximity of the remaining addresses found on a drive, their word similarity, and their number of co-occurrences over a corpus are good measures of association of addresses, and we built graphs using this data of the interconnections both between addresses and between drives. Results provided several new insights into our test data
Craig Pratt - One of the best experts on this subject based on the ideXlab platform.
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HTTP Random Access and Live Content
2019Co-Authors: Craig Pratt, Barbara Stark, Darshak ThakoreAbstract:To accommodate Byte range requests for content that has data appended over time, this document defines semantics that allow a HTTP client and server to perform Byte-range GET and HEAD requests that start at an arbitrary Byte Offset within the representation and ends at an indeterminate Offset.
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HTTP Bytes-live Range Unit for Live Content
2016Co-Authors: Craig Pratt, Barbara Stark, Darshak ThakoreAbstract:To accommodate Byte range requests for content that has data appended over time, this document defines a new HTTP range unit named "Bytes- live". The "Bytes-live" range unit provides the ability for a client to specify a Byte range in a GET or HEAD request which starts at an arbitrary Byte Offset within the representation and ends at an indeterminate Offset, represented by "*".
