The Experts below are selected from a list of 76713 Experts worldwide ranked by ideXlab platform
Orbandan - One of the best experts on this subject based on the ideXlab platform.
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Remote Network labs
Computer Communication Review, 2010Co-Authors: Liuhuan, OrbandanAbstract:Network Equipment is difficult to configure correctly. To minimize configuration errors, Network administrators typically build a smaller scale test lab replicating the production Network and test ...
Liuhuan - One of the best experts on this subject based on the ideXlab platform.
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Remote Network labs
Computer Communication Review, 2010Co-Authors: Liuhuan, OrbandanAbstract:Network Equipment is difficult to configure correctly. To minimize configuration errors, Network administrators typically build a smaller scale test lab replicating the production Network and test ...
D. J. Ford - One of the best experts on this subject based on the ideXlab platform.
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Understanding Network Equipment Reliability
BT Technology Journal, 1998Co-Authors: D. J. FordAbstract:This paper reviews the concepts and techniques that have been adopted by BT in order to understand Network Equipment reliability. The ideas presented are largely generic, in the sense that they can be applied irrespective of whether the Equipment is intended for a core Network or an access Network application. In particular, the paper examines the relationship between out-turn reliability and the reliability that might be guaranteed by an Equipment manufacturer. The relevance of confidence limits to reliability modelling and in-service reliability monitoring is also discussed, and ideas presented on how reliability monitoring can be applied to resilient Networks. It is shown that in competitive situations, there are good reasons why reliability modellers should use failure rate data whose upper confidence limit is 50%. In contrast, it is shown that for practical reasons, in-service monitoring schemes should not be based on specific confidence levels.
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Understanding Network Equipment reliability : Local access technologies
Bt Technology Journal, 1998Co-Authors: D. J. FordAbstract:This paper reviews the concepts and techniques that have been adopted by BT in order to understand Network Equipment reliability. The ideas presented are largely generic, in the sense that they can be applied irrespective of whether the Equipment is intended for a core Network or an access Network application. In particular, the paper examines the relationship between out-turn reliability and the reliability that might be guaranteed by an Equipment manufacturer. The relevance of confidence limits to reliability modelling and in-service reliability monitoring is also discussed, and ideas presented on how reliability monitoring can be applied to resilient Networks. It is shown that in competitive situations, there are good reasons why reliability modellers should use failure rate data whose upper confidence limit is 50%. In contrast, it is shown that for practical reasons, in-service monitoring schemes should not be based on specific confidence levels.
Dan Orban - One of the best experts on this subject based on the ideXlab platform.
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Remote Network labs: an on-demand Network cloud for configuration testing
ACM SIGCOMM Computer Communication Review, 2010Co-Authors: Dan OrbanAbstract:Network Equipment is difficult to configure correctly. To minimize configuration errors, Network administrators typically build a smaller scale test lab replicating the production Network and test out their configuration changes before rolling out the changes to production. Unfortunately, building a test lab is expensive and the test Equipment is rarely utilized. In this paper, we present Remote Network Labs, which is aimed at leveraging the expensive Network Equipment more efficiently and reducing the cost of building a test lab. Similar to a server cloud such as Amazon EC2, a user could request Network Equipment remotely and connect them through a GUI or web services interface. The Network Equipment is geographically distributed, allowing us to reuse test Equipment anywhere. Beyond saving costs, Remote Network Labs brings about many additional benefits, including the ability to fully automate Network configuration testing.
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WREN - Remote Network labs: an on-demand Network cloud for configuration testing
Proceedings of the 1st ACM workshop on Research on enterprise networking - WREN '09, 2009Co-Authors: Dan OrbanAbstract:Network Equipment is difficult to configure correctly. To minimize configuration errors, Network administrators typically build a smaller scale test lab replicating the production Network and test out their configuration changes before rolling out the changes to production. Unfortunately, building a test lab is expensive and the test Equipment is rarely utilized. In this paper, we present Remote Network Labs, which is aimed at leveraging the expensive Network Equipment more efficiently and reducing the cost of building a test lab. Similar to a server cloud such as Amazon EC2, a user could request Network Equipment remotely and connect them through a GUI or web services interface. The Network Equipment is geographically distributed, allowing us to reuse test Equipment anywhere. Beyond saving costs, Remote Network Labs brings about many additional benefits, including the ability to fully automate Network configuration testing.
Tijani Chahed - One of the best experts on this subject based on the ideXlab platform.
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Energy efficiency of a Network Equipment per service category
2018Co-Authors: Wilfried Yoro, Mamdouh El Tabach, Taoufik En-najjary, Azeddine Gati, Tijani ChahedAbstract:We investigate in this paper the assessment of the energy efficiency of the service categories provided by a Network Equipment. We consider five service categories, namely, Streaming, Web, Download, other data services and Voice. We consider two scenarios: a case when the Network Equipment has no sleep mode feature, and another one when it is put into sleep mode during idle periods. We share the responsibility of the service categories in the power consumption of the Network Equipment, which represents the share of the power consumption imputed to each service category, and then compute the energy efficiency as the ratio of the traffic throughput to the imputed power consumption. The power consumption of a Network Equipment is composed of two components: a variable component that is consumed proportionally to its load, and a fixed component consumed irrespective of the traffic. We share the former among the service categories proportionally to their traffic proportions. The latter is shared according to the scenario. In the first one, it is shared with our Shapley-based model introduced in a previous work [1]. In the second scenario, we introduce a new sharing model, also based on Shapley value, which takes into account the sleep mode feature. Our results, applied to a real dataset extracted from an operational Network in Europe, show that the former sharing model is better than the sleep-mode-oriented model in terms of fairness, although it does not consider the sleep mode feature of the Equipment
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ICC - Energy Efficiency of a Network Equipment Per Service Category
2018 IEEE International Conference on Communications (ICC), 2018Co-Authors: Wilfried Yoro, Mamdouh El Tabach, Taoufik En-najjary, Azeddine Gati, Tijani ChahedAbstract:We investigate in this paper the assessment of the energy efficiency of the service categories provided by a Network Equipment. We consider five service categories, namely, Streaming, Web, Download, other data services and Voice. We consider two scenarios: a case when the Network Equipment has no sleep mode feature, and another one when it is put into sleep mode during idle periods. We share the responsibility of the service categories in the power consumption of the Network Equipment, which represents the share of the power consumption imputed to each service category, and then compute the energy efficiency as the ratio of the traffic throughput to the imputed power consumption. The power consumption of a Network Equipment is composed of two components: a variable component that is consumed proportionally to its load, and a fixed component consumed irrespective of the traffic. We share the former among the service categories proportionally to their traffic proportions. The latter is shared according to the scenario. In the first one, it is shared with our Shapley-based model introduced in a previous work [1]. In the second scenario, we introduce a new sharing model, also based on Shapley value, which takes into account the sleep mode feature. Our results, applied to a real dataset extracted from an operational Network in Europe, show that the former sharing model is better than the sleep-mode-oriented model in terms of fairness, although it does not consider the sleep mode feature of the Equipment.