The Experts below are selected from a list of 1530 Experts worldwide ranked by ideXlab platform
Kate Keahey - One of the best experts on this subject based on the ideXlab platform.
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Traffic sensitive live migration of virtual machines
Future Generation Computer Systems, 2017Co-Authors: Umesh Deshpande, Kate KeaheyAbstract:Abstract In this paper we address the problem of network contention between the migration Traffic and the Virtual Machine (VM) application Traffic for the live migration of co-located Virtual Machines. When VMs are migrated with pre-copy, they run at the source host during the migration. Therefore the VM applications with predominantly Outbound Traffic contend with the outgoing migration Traffic at the source host. Similarly, during post-copy migration, the VMs run at the destination host. Therefore the VM applications with predominantly inbound Traffic contend with the incoming migration Traffic at the destination host. Such contention increases the total migration time of the VMs and degrades the performance of the VM application. Here, we propose a tra ffi c-sensitive live VM migration technique to reduce the contention of migration Traffic with the VM application Traffic. It uses a combination of pre-copy and post-copy techniques for the migration of the co-located VMs (those located on the same source host), instead of relying on any single pre-determined technique for the migration of all the VMs. We base the selection of migration techniques on the VMs’ network Traffic profiles so that the direction of migration Traffic complements the direction of the most VM application Traffic. We have implemented a prototype of Traffic-sensitive migration on the KVM/QEMU platform. In the evaluation, we compare Traffic-sensitive migration against the approaches that use only pre-copy or only post-copy for VM migration. We show that our approach minimizes the network contention for migration, thus reducing the total migration time and the application degradation.
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Traffic sensitive live migration of virtual machines
IEEE ACM International Symposium Cluster Cloud and Grid Computing, 2015Co-Authors: Umesh Deshpande, Kate KeaheyAbstract:In this paper we address the problem of network contention between the migration Traffic and the VM application Traffic for the live migration of co-located Virtual Machines (VMs). When VMs are migrated with pre-copy, they run at the source host during the migration. Therefore the VM applications with predominantly Outbound Traffic contend with the outgoing migration Traffic at the source host. Similarly, during post-copy migration, the VMs run at the destination host. Therefore the VM applications with predominantly inbound Traffic contend with the incoming migration Traffic at the destination host. Such a contention increases the total migration time of the VMs and degrades the performance of VM application. Here, we propose Traffic-sensitive live VM migration technique to reduce the contention of migration Traffic with the VM application Traffic. It uses a combination of pre-copy and post-copy techniques for the migration of the co-located VMs, instead of relying upon any single pre-determined technique for the migration of all the VMs. We base the selection of migration techniques on VMs' network Traffic profiles so that the direction of migration Traffic complements the direction of the most VM application Traffic. We have implemented a prototype of Traffic-sensitive migration on the KVM/QEMU platform. In the evaluation, we compare Traffic-sensitive migration against the approaches that use only pre-copy or only post-copy for VM migration. We show that our approach minimizes the network contention for migration, thus reducing the total migration time and the application degradation.
Umesh Deshpande - One of the best experts on this subject based on the ideXlab platform.
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Traffic sensitive live migration of virtual machines
Future Generation Computer Systems, 2017Co-Authors: Umesh Deshpande, Kate KeaheyAbstract:Abstract In this paper we address the problem of network contention between the migration Traffic and the Virtual Machine (VM) application Traffic for the live migration of co-located Virtual Machines. When VMs are migrated with pre-copy, they run at the source host during the migration. Therefore the VM applications with predominantly Outbound Traffic contend with the outgoing migration Traffic at the source host. Similarly, during post-copy migration, the VMs run at the destination host. Therefore the VM applications with predominantly inbound Traffic contend with the incoming migration Traffic at the destination host. Such contention increases the total migration time of the VMs and degrades the performance of the VM application. Here, we propose a tra ffi c-sensitive live VM migration technique to reduce the contention of migration Traffic with the VM application Traffic. It uses a combination of pre-copy and post-copy techniques for the migration of the co-located VMs (those located on the same source host), instead of relying on any single pre-determined technique for the migration of all the VMs. We base the selection of migration techniques on the VMs’ network Traffic profiles so that the direction of migration Traffic complements the direction of the most VM application Traffic. We have implemented a prototype of Traffic-sensitive migration on the KVM/QEMU platform. In the evaluation, we compare Traffic-sensitive migration against the approaches that use only pre-copy or only post-copy for VM migration. We show that our approach minimizes the network contention for migration, thus reducing the total migration time and the application degradation.
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Traffic sensitive live migration of virtual machines
IEEE ACM International Symposium Cluster Cloud and Grid Computing, 2015Co-Authors: Umesh Deshpande, Kate KeaheyAbstract:In this paper we address the problem of network contention between the migration Traffic and the VM application Traffic for the live migration of co-located Virtual Machines (VMs). When VMs are migrated with pre-copy, they run at the source host during the migration. Therefore the VM applications with predominantly Outbound Traffic contend with the outgoing migration Traffic at the source host. Similarly, during post-copy migration, the VMs run at the destination host. Therefore the VM applications with predominantly inbound Traffic contend with the incoming migration Traffic at the destination host. Such a contention increases the total migration time of the VMs and degrades the performance of VM application. Here, we propose Traffic-sensitive live VM migration technique to reduce the contention of migration Traffic with the VM application Traffic. It uses a combination of pre-copy and post-copy techniques for the migration of the co-located VMs, instead of relying upon any single pre-determined technique for the migration of all the VMs. We base the selection of migration techniques on VMs' network Traffic profiles so that the direction of migration Traffic complements the direction of the most VM application Traffic. We have implemented a prototype of Traffic-sensitive migration on the KVM/QEMU platform. In the evaluation, we compare Traffic-sensitive migration against the approaches that use only pre-copy or only post-copy for VM migration. We show that our approach minimizes the network contention for migration, thus reducing the total migration time and the application degradation.
Kevin Warwick - One of the best experts on this subject based on the ideXlab platform.
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1 Multi-Level Planning for Semi-Autonomous Vehicles in Traffic Scenarios based on Separation
2016Co-Authors: Rahul Kala, Kevin WarwickAbstract:The planning of semi-autonomous vehicles in Traffic scenarios is a relatively new problem that contributes towards the goal of making road travel by vehicles free of human drivers. An algorithm needs to ensure optimal real time planning of multiple vehicles (moving in either direction along a road), in the presence of a complex obstacle network. Unlike other approaches, here we assume that speed lanes are not present and that different lanes do not need to be maintained for inbound and Outbound Traffic. Our basic hypothesis is to carry forward the planning task to ensure that a sufficient distance is maintained by each vehicle from all other vehicles, obstacles and road boundaries. We present here a 4-layer planning algorithm that consists of road selection (for selecting the individual roads of traversal to reach the goal), pathway selection (a strategy to avoid and/or overtake obstacles, road diversions and other blockages), pathway distribution (to select the position of a vehicle at every instance of time in a pathway), and trajectory generation (for generating a curve, smooth enough, to allow for the maximum possible speed). Cooperation between vehicles is handled separately at the different levels, the aim being to maximize the separation between vehicles. Simulated results exhibit behaviours of smooth, efficient and safe driving of vehicles in multiple scenarios; along with typical vehicle behaviours including following and overtaking
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Multi-level planning for semi-autonomous vehicles in Traffic scenarios based on separation maximization
Journal of Intelligent and Robotic Systems: Theory and Applications, 2013Co-Authors: Rahul Kala, Kevin WarwickAbstract:The planning of semi-autonomous vehicles in Traffic scenarios is a relatively new problem that contributes towards the goal of making road travel by vehicles free of human drivers. An algorithm needs to ensure optimal real time planning of multiple vehicles (moving in either direction along a road), in the presence of a complex obstacle network. Unlike other approaches, here we assume that speed lanes are not present and that different lanes do not need to be maintained for inbound and Outbound Traffic. Our basic hypothesis is to carry forward the planning task to ensure that a sufficient distance is maintained by each vehicle from all other vehicles, obstacles and road boundaries. We present here a 4-layer planning algorithm that consists of road selection (for selecting the individual roads of traversal to reach the goal), pathway selection (a strategy to avoid and/or overtake obstacles, road diversions and other blockages), pathway distribution (to select the position of a vehicle at every instance of time in a pathway), and trajectory generation (for generating a curve, smooth enough, to allow for the maximum possible speed). Cooperation between vehicles is handled separately at the different levels, the aim being to maximize the separation between vehicles. Simulated results exhibit behaviours of smooth, efficient and safe driving of vehicles in multiple scenarios; along with typical vehicle behaviours including following and overtaking.
George Pavlou - One of the best experts on this subject based on the ideXlab platform.
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Making Outbound Route Selection Robust to Egress Point Failure
2020Co-Authors: Mina Amin, George Pavlou, Michael Howarth, {m Amin, M Howarth, G Pavlou}@eimAbstract:Abstract. Offline inter-domain Outbound Traffic Engineering (TE) can be formulated as an optimization problem whose objective is to determine primary egress points for Traffic exiting a domain. However, when egress point failures happen, congestion may occur if secondary egress points are not carefully determined. In this paper, we formulate a bi-level Outbound TE problem in order to make Outbound route selection robust to egress point failures. We propose a tabu search heuristic to solve the problem and compare the performance to three alternative approaches. Simulation results demonstrate that the tabu search heuristic achieves the best performance in terms of our optimization objectives and also keeps Traffic disruption to a minimum
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a robustness approach to inter autonomous system Outbound Traffic engineering
International Conference on Communications, 2006Co-Authors: Kinhon Ho, Stylianos Georgoulas, Mina Amin, George PavlouAbstract:Inter-AS Outbound Traffic Engineering (TE) aims to control the flow of Traffic exiting an AS so as to optimize inter-AS TE objectives such as load balancing among multiple downstream ASes. The inter-AS Traffic matrix is fundamental input for Outbound TE and its accuracy is essential for the target performance to be achieved. However, deriving an accurate Traffic matrix is far from trivial. This paper proposes a robust approach for Outbound TE to manage Traffic demand uncertainty through Scenario-based Robust Optimization. The objective of this robust Outbound TE is to minimize the worst-case maximum inter-AS link utilization across a set of inter-AS Traffic matrices while minimizing the performance deviation from the optimal solutions. Simulation results reveal that the robust Outbound TE is capable of achieving reasonably good performance under all the given Traffic matrices while non-robust approaches are not.
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an integrated network management framework for inter domain Outbound Traffic engineering
Lecture Notes in Computer Science, 2006Co-Authors: Mina Amin, Michael Howarth, George PavlouAbstract:This paper proposes an integrated network management framework for inter-domain Outbound Traffic engineering. The framework consists of three functional blocks (monitoring, optimization and implementation) to make the Outbound Traffic engineering adaptive to network condition changes such as inter-domain Traffic demand variation, inter-domain routing changes and link failures. The objective is to keep the inter-domain link utilization balanced under any of these changes while reducing service disruptions and reconfiguration overheads. Simulation results demonstrate that the proposed framework can achieve better load balancing with less service disruptions and re-configuration overheads in comparison to alternative approaches.
Rahul Kala - One of the best experts on this subject based on the ideXlab platform.
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1 Multi-Level Planning for Semi-Autonomous Vehicles in Traffic Scenarios based on Separation
2016Co-Authors: Rahul Kala, Kevin WarwickAbstract:The planning of semi-autonomous vehicles in Traffic scenarios is a relatively new problem that contributes towards the goal of making road travel by vehicles free of human drivers. An algorithm needs to ensure optimal real time planning of multiple vehicles (moving in either direction along a road), in the presence of a complex obstacle network. Unlike other approaches, here we assume that speed lanes are not present and that different lanes do not need to be maintained for inbound and Outbound Traffic. Our basic hypothesis is to carry forward the planning task to ensure that a sufficient distance is maintained by each vehicle from all other vehicles, obstacles and road boundaries. We present here a 4-layer planning algorithm that consists of road selection (for selecting the individual roads of traversal to reach the goal), pathway selection (a strategy to avoid and/or overtake obstacles, road diversions and other blockages), pathway distribution (to select the position of a vehicle at every instance of time in a pathway), and trajectory generation (for generating a curve, smooth enough, to allow for the maximum possible speed). Cooperation between vehicles is handled separately at the different levels, the aim being to maximize the separation between vehicles. Simulated results exhibit behaviours of smooth, efficient and safe driving of vehicles in multiple scenarios; along with typical vehicle behaviours including following and overtaking
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Multi-level planning for semi-autonomous vehicles in Traffic scenarios based on separation maximization
Journal of Intelligent and Robotic Systems: Theory and Applications, 2013Co-Authors: Rahul Kala, Kevin WarwickAbstract:The planning of semi-autonomous vehicles in Traffic scenarios is a relatively new problem that contributes towards the goal of making road travel by vehicles free of human drivers. An algorithm needs to ensure optimal real time planning of multiple vehicles (moving in either direction along a road), in the presence of a complex obstacle network. Unlike other approaches, here we assume that speed lanes are not present and that different lanes do not need to be maintained for inbound and Outbound Traffic. Our basic hypothesis is to carry forward the planning task to ensure that a sufficient distance is maintained by each vehicle from all other vehicles, obstacles and road boundaries. We present here a 4-layer planning algorithm that consists of road selection (for selecting the individual roads of traversal to reach the goal), pathway selection (a strategy to avoid and/or overtake obstacles, road diversions and other blockages), pathway distribution (to select the position of a vehicle at every instance of time in a pathway), and trajectory generation (for generating a curve, smooth enough, to allow for the maximum possible speed). Cooperation between vehicles is handled separately at the different levels, the aim being to maximize the separation between vehicles. Simulated results exhibit behaviours of smooth, efficient and safe driving of vehicles in multiple scenarios; along with typical vehicle behaviours including following and overtaking.
