The Experts below are selected from a list of 12951 Experts worldwide ranked by ideXlab platform
Yanfeng Ouyang - One of the best experts on this subject based on the ideXlab platform.
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dynamic snow plow fleet management under uncertain demand and Service Disruption
IEEE Transactions on Intelligent Transportation Systems, 2016Co-Authors: Leila Hajibabai, Yanfeng OuyangAbstract:It is sometimes challenging to plan winter maintenance operations in advance because snow storms are stochastic with respect to, e.g., start time, duration, impact area, and severity. In addition, maintenance trucks may not be readily available at all times due to stochastic Service Disruptions. A stochastic dynamic fleet management model is developed to assign available trucks to cover uncertain snow plowing demand. The objective is to simultaneously minimize the cost for truck deadheading and repositioning, as well as to maximize the benefits (i.e., level of Service) of plowing. The problem is formulated into a dynamic programming model and solved using an approximate dynamic programming algorithm. Piecewise linear functional approximations are used to estimate the value function of system states (i.e., snow plow trucks location over time). We apply our model and solution approach to a snow plow operation scenario for Lake County, Illinois. Numerical results show that the proposed algorithm can solve the problem effectively and outperforms a rolling-horizon heuristic solution.
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dynamic snow plow fleet management under uncertain demand and Service Disruption
Transportation Research Board 94th Annual MeetingTransportation Research Board, 2015Co-Authors: Leila Hajibabai, Yanfeng OuyangAbstract:It is sometimes challenging to plan winter maintenance operations in advance because snow storms are stochastic with respect to, e.g. start time, duration, impact area, and severity. Besides, maintenance trucks may not be readily available at all times due to stochastic Service Disruptions. A stochastic dynamic fleet management model is developed to assign available trucks to cover uncertain snow plowing demand. The objective is to simultaneously minimize the cost for truck deadheading and repositioning, as well as to maximize the benefits (i.e., level of Service) of plowing. The problem is formulated into a dynamic programming model and solved using an approximate dynamic programming (ADP) algorithm. Piece-wise linear functional approximations are used to estimate the value function of system states (i.e., snow plow trucks location over time). The authors apply their model and solution approach to a snow plow operation scenario for Lake County, Illinois. Numerical results show that the proposed algorithm can solve the problem effectively and outperforms a rolling-horizon heuristic solution.
Reus Bernhard - One of the best experts on this subject based on the ideXlab platform.
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Towards model checking real-world software-defined networks
'Springer Science and Business Media LLC', 2020Co-Authors: Klimis Vassilis, Parisis George, Reus BernhardAbstract:In software-defined networks (SDN), a controller program is in charge of deploying diverse network functionality across a large number of switches, but this comes at a great risk: deploying buggy controller code could result in network and Service Disruption and security loopholes. The automatic detection of bugs or, even better, verification of their absence is thus most desirable, yet the size of the network and the complexity of the controller makes this a challenging undertaking. In this paper, we propose MOCS, a highly expressive, optimised SDN model that allows capturing subtle real-world bugs, in a reasonable amount of time. This is achieved by (1) analysing the model for possible partial order reductions, (2) statically pre-computing packet equivalence classes and (3) indexing packets and rules that exist in the model. We demonstrate its superiority compared to the state of the art in terms of expressivity, by providing examples of realistic bugs that a prototype implementation of MOCS in Uppaal caught, and performance/scalability, by running examples on various sizes of network topologies, highlighting the importance of our abstractions and optimisations
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Model checking software-defined networks with flow entries that time out
IEEE digital library, 2020Co-Authors: Klimis Vasileios, Parisis George, Reus BernhardAbstract:Software-defined networking (SDN) enables advanced operation and management of network deployments through (virtually) centralised, programmable controllers, which deploy network functionality by installing rules in the flow tables of network switches. Although this is a powerful abstraction, buggy controller functionality could lead to severe Service Disruption and security loopholes, motivating the need for (semi-)automated tools to find, or even verify absence of, bugs. Model checking SDNs has been proposed in the literature, but none of the existing approaches can support dynamic network deployments, where flow entries expire due to timeouts. This is necessary for automatically refreshing (and eliminating stale) state in the network (termed as soft-state in the network protocol design nomenclature), which is important for scaling up applications or recovering from failures. In this paper, we extend our model (MoCS) to deal with timeouts of flow table entries, thus supporting soft state in the network. Optimisations are proposed that are tailored to this extension. We evaluate the performance of the proposed model in UPPAAL using a load balancer and firewall in network topologies of varying siz
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Model Checking Software-Defined Networks with Flow Entries that Time Out
2020Co-Authors: Klimis Vasileios, Parisis George, Reus BernhardAbstract:Software-defined networking (SDN) enables advanced operation and management of network deployments through (virtually) centralised, programmable controllers, which deploy network functionality by installing rules in the flow tables of network switches. Although this is a powerful abstraction, buggy controller functionality could lead to severe Service Disruption and security loopholes, motivating the need for (semi-)automated tools to find, or even verify absence of, bugs. Model checking SDNs has been proposed in the literature, but none of the existing approaches can support dynamic network deployments, where flow entries expire due to timeouts. This is necessary for automatically refreshing (and eliminating stale) state in the network (termed as soft-state in the network protocol design nomenclature), which is important for scaling up applications or recovering from failures. In this paper, we extend our model (MoCS) to deal with timeouts of flow table entries, thus supporting soft state in the network. Optimisations are proposed that are tailored to this extension. We evaluate the performance of the proposed model in UPPAAL using a load balancer and firewall in network topologies of varying size
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Towards Model Checking Real-World Software-Defined Networks (version with appendix)
2020Co-Authors: Klimis Vasileios, Parisis George, Reus BernhardAbstract:In software-defined networks (SDN), a controller program is in charge of deploying diverse network functionality across a large number of switches, but this comes at a great risk: deploying buggy controller code could result in network and Service Disruption and security loopholes. The automatic detection of bugs or, even better, verification of their absence is thus most desirable, yet the size of the network and the complexity of the controller makes this a challenging undertaking. In this paper we propose MOCS, a highly expressive, optimised SDN model that allows capturing subtle real-world bugs, in a reasonable amount of time. This is achieved by (1) analysing the model for possible partial order reductions, (2) statically pre-computing packet equivalence classes and (3) indexing packets and rules that exist in the model. We demonstrate its superiority compared to the state of the art in terms of expressivity, by providing examples of realistic bugs that a prototype implementation of MOCS in UPPAAL caught, and performance/scalability, by running examples on various sizes of network topologies, highlighting the importance of our abstractions and optimisations
Leila Hajibabai - One of the best experts on this subject based on the ideXlab platform.
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dynamic snow plow fleet management under uncertain demand and Service Disruption
IEEE Transactions on Intelligent Transportation Systems, 2016Co-Authors: Leila Hajibabai, Yanfeng OuyangAbstract:It is sometimes challenging to plan winter maintenance operations in advance because snow storms are stochastic with respect to, e.g., start time, duration, impact area, and severity. In addition, maintenance trucks may not be readily available at all times due to stochastic Service Disruptions. A stochastic dynamic fleet management model is developed to assign available trucks to cover uncertain snow plowing demand. The objective is to simultaneously minimize the cost for truck deadheading and repositioning, as well as to maximize the benefits (i.e., level of Service) of plowing. The problem is formulated into a dynamic programming model and solved using an approximate dynamic programming algorithm. Piecewise linear functional approximations are used to estimate the value function of system states (i.e., snow plow trucks location over time). We apply our model and solution approach to a snow plow operation scenario for Lake County, Illinois. Numerical results show that the proposed algorithm can solve the problem effectively and outperforms a rolling-horizon heuristic solution.
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dynamic snow plow fleet management under uncertain demand and Service Disruption
Transportation Research Board 94th Annual MeetingTransportation Research Board, 2015Co-Authors: Leila Hajibabai, Yanfeng OuyangAbstract:It is sometimes challenging to plan winter maintenance operations in advance because snow storms are stochastic with respect to, e.g. start time, duration, impact area, and severity. Besides, maintenance trucks may not be readily available at all times due to stochastic Service Disruptions. A stochastic dynamic fleet management model is developed to assign available trucks to cover uncertain snow plowing demand. The objective is to simultaneously minimize the cost for truck deadheading and repositioning, as well as to maximize the benefits (i.e., level of Service) of plowing. The problem is formulated into a dynamic programming model and solved using an approximate dynamic programming (ADP) algorithm. Piece-wise linear functional approximations are used to estimate the value function of system states (i.e., snow plow trucks location over time). The authors apply their model and solution approach to a snow plow operation scenario for Lake County, Illinois. Numerical results show that the proposed algorithm can solve the problem effectively and outperforms a rolling-horizon heuristic solution.
Jasmina Panovskagriffiths - One of the best experts on this subject based on the ideXlab platform.
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Disruption of a primary health care domestic violence and abuse Service in two london boroughs interrupted time series evaluation
BMC Health Services Research, 2020Co-Authors: Jasmina Panovskagriffiths, Alex Hardip Sohal, Peter Martin, Estela Barbosa Capelas, Medina Johnson, Annie Howell, Natalia Lewis, Gene FederAbstract:BACKGROUND Domestic violence and abuse (DVA) is experienced by about 1/3 of women globally and remains a major health concern worldwide. IRIS (Identification and Referral to Improve Safety of women affected by DVA) is a complex, system-level, training and support programme, designed to improve the primary healthcare response to DVA. Following a successful trial in England, since 2011 IRIS has been implemented in eleven London boroughs. In two boroughs the Service was disrupted temporarily. This study evaluates the impact of that Service Disruption. METHODS We used anonymised data on daily referrals received by DVA Service providers from general practices in two IRIS implementation boroughs that had Service Disruption for a period of time (six and three months). In line with previous work we refer to these as boroughs B and C. The primary outcome was the number of daily referrals received by the DVA Service provider across each borough over 48 months (March 2013-April 2017) in borough B and 42 months (October 2013-April 2017) in borough C. The data were analysed using interrupted-time series, non-linear regression with sensitivity analyses exploring different regression models. Incidence Rate Ratio (IRR), 95% confidence intervals and p-values associated with the Disruption were reported for each borough. RESULTS A mixed-effects negative binomial regression was the best fit model to the data. In borough B, the Disruption, lasted for about six months, reducing the referral rate significantly (p = 0.006) by about 70% (95%CI = (23,87%)). In borough C, the three-month Service Disruption, also significantly (p = 0.005), reduced the referral rate by about 49% (95% CI = (18,68%)). CONCLUSIONS Disrupting the IRIS Service substantially reduced the rate of referrals to DVA Service providers. Our findings are evidence in favour of continuous funding and staffing of IRIS as a system level programme.
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Disruption of a primary health care domestic violence and abuse Service in two london boroughs interrupted time series evaluation
BMC Health Services Research, 2020Co-Authors: Jasmina Panovskagriffiths, Alex Hardip Sohal, Peter Martin, Estela Barbosa Capelas, Medina Johnson, Annie Howell, Natalia Lewis, Gene FederAbstract:Domestic violence and abuse (DVA) is experienced by about 1/3 of women globally and remains a major health concern worldwide. IRIS (Identification and Referral to Improve Safety of women affected by DVA) is a complex, system-level, training and support programme, designed to improve the primary healthcare response to DVA. Following a successful trial in England, since 2011 IRIS has been implemented in eleven London boroughs. In two boroughs the Service was disrupted temporarily. This study evaluates the impact of that Service Disruption. We used anonymised data on daily referrals received by DVA Service providers from general practices in two IRIS implementation boroughs that had Service Disruption for a period of time (six and three months). In line with previous work we refer to these as boroughs B and C. The primary outcome was the number of daily referrals received by the DVA Service provider across each borough over 48 months (March 2013–April 2017) in borough B and 42 months (October 2013–April 2017) in borough C. The data were analysed using interrupted-time series, non-linear regression with sensitivity analyses exploring different regression models. Incidence Rate Ratio (IRR), 95% confidence intervals and p-values associated with the Disruption were reported for each borough. A mixed-effects negative binomial regression was the best fit model to the data. In borough B, the Disruption, lasted for about six months, reducing the referral rate significantly (p = 0.006) by about 70% (95%CI = (23,87%)). In borough C, the three-month Service Disruption, also significantly (p = 0.005), reduced the referral rate by about 49% (95% CI = (18,68%)). Disrupting the IRIS Service substantially reduced the rate of referrals to DVA Service providers. Our findings are evidence in favour of continuous funding and staffing of IRIS as a system level programme.
Zhiquan Luo - One of the best experts on this subject based on the ideXlab platform.
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reconfiguration with no Service Disruption in multifiber wdm networks
Journal of Lightwave Technology, 2005Co-Authors: Mohamed Saad, Zhiquan LuoAbstract:In a wavelength division multiplexing (WDM)-based network, lightpaths are established between router pairs to form a virtual topology residing on top of the underlying physical topology. The ability to reconfigure its virtual topology upon dynamically changing traffic patterns has been identified as one of the most important features of WDM-based networks. Given a multifiber WDM network with limited fiber and wavelength resources, an existing virtual topology, and a new set of traffic demands, this paper addresses the problem of finding the new virtual topology that maximizes the carried traffic of connections, while absolutely guaranteeing that ongoing connections are not disrupted. We introduce conditions under which the new virtual topology has the intrinsic property of no Service Disruption. Then, we use these conditions to formulate the reconfiguration problem as an integer linear program (ILP). We also present a heuristic reconfiguration algorithm that is based on partitioning the traffic demands, so as to maintain wavelength loads as balanced as possible, followed by solving a sequence of single-wavelength problems. We theoretically verify the correctness of the algorithm, and illustrate its efficiency in terms of solution quality and computational cost via numerical experiments.
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reconfiguration with no Service Disruption in multifiber wdm networks based on lagrangean decomposition
International Conference on Communications, 2003Co-Authors: Mohamed Saad, Zhiquan LuoAbstract:In a WDM based network, lightpaths are established between router pairs to form a virtual topology residing on top of the underlying physical topology. The ability to reconfigure its virtual topology upon dynamically changing traffic patterns has been identified as one of the most important features of WDM based networks. Compared to previously reported reconfiguration studies, we provide contributions along two different directions. First, we address the problem of finding the new virtual topology that maximizes the number of successfully established lightpaths, while guaranteeing absolutely no Service Disruptions. Second, based on a Lagrangean decomposition approach, we demonstrate that optimal and near-optimal virtual topologies can be obtained by considering only one wavelength in the formulation, leading to a reconfiguration algorithm that scales to an arbitrarily large number of wavelengths. Computational results confirm the high efficiency of the proposed algorithm.