The Experts below are selected from a list of 483 Experts worldwide ranked by ideXlab platform

Gross James - One of the best experts on this subject based on the ideXlab platform.

  • Transient Delay Bounds for Multi-Hop Wireless Networks
    2018
    Co-Authors: Champati, Jaya Prakash, Al-zubaidy Hussein, Gross James
    Abstract:

    In this article, we investigate the transient behavior of a sequence of packets/bits traversing a multi-hop wireless network. Our work is motivated by novel applications from the domain of process automation, Machine-Type Communication (MTC) and cyber-physical systems, where short messages are communicated and statistical guarantees need to be provided on a per-message level. In order to optimize such a network, apart from understanding the stationary system dynamics, an understanding of the short-term dynamics (i.e., transient behavior) is also required. To this end, we derive novel Wireless Transient Bounds (WTB) for end-to-end delay and Backlog in a multi-hop wireless network using stochastic network calculus approach. WTB depends on the Initial Backlog at each node as well as the instantaneous channel states. We numerically compare WTB with State-Of-The-Art Transient bounds (SOTAT), that can be obtained by adapting existing stationary bounds, as well as simulation of the network. While SOTAT and stationary bounds are not able to capture the short-term system dynamics well, WTB provides relatively tight upper bound and has a decay rate that closely matches the simulation. This is achieved by WTB only with a slight increase in the computational complexity, by a factor of O(T + N), where T is the duration of the arriving sequence and N is the number of hops in the network. We believe that the presented analysis and the bounds can be used as base for future work on transient network optimization, e.g., in massive MTC, critical MTC, edge computing and autonomous vehicle.Comment: 13 pages, 13 figures, joourna

Champati, Jaya Prakash - One of the best experts on this subject based on the ideXlab platform.

  • Transient Delay Bounds for Multi-Hop Wireless Networks
    2018
    Co-Authors: Champati, Jaya Prakash, Al-zubaidy Hussein, Gross James
    Abstract:

    In this article, we investigate the transient behavior of a sequence of packets/bits traversing a multi-hop wireless network. Our work is motivated by novel applications from the domain of process automation, Machine-Type Communication (MTC) and cyber-physical systems, where short messages are communicated and statistical guarantees need to be provided on a per-message level. In order to optimize such a network, apart from understanding the stationary system dynamics, an understanding of the short-term dynamics (i.e., transient behavior) is also required. To this end, we derive novel Wireless Transient Bounds (WTB) for end-to-end delay and Backlog in a multi-hop wireless network using stochastic network calculus approach. WTB depends on the Initial Backlog at each node as well as the instantaneous channel states. We numerically compare WTB with State-Of-The-Art Transient bounds (SOTAT), that can be obtained by adapting existing stationary bounds, as well as simulation of the network. While SOTAT and stationary bounds are not able to capture the short-term system dynamics well, WTB provides relatively tight upper bound and has a decay rate that closely matches the simulation. This is achieved by WTB only with a slight increase in the computational complexity, by a factor of O(T + N), where T is the duration of the arriving sequence and N is the number of hops in the network. We believe that the presented analysis and the bounds can be used as base for future work on transient network optimization, e.g., in massive MTC, critical MTC, edge computing and autonomous vehicle.Comment: 13 pages, 13 figures, joourna

Al-zubaidy Hussein - One of the best experts on this subject based on the ideXlab platform.

  • Transient Delay Bounds for Multi-Hop Wireless Networks
    2018
    Co-Authors: Champati, Jaya Prakash, Al-zubaidy Hussein, Gross James
    Abstract:

    In this article, we investigate the transient behavior of a sequence of packets/bits traversing a multi-hop wireless network. Our work is motivated by novel applications from the domain of process automation, Machine-Type Communication (MTC) and cyber-physical systems, where short messages are communicated and statistical guarantees need to be provided on a per-message level. In order to optimize such a network, apart from understanding the stationary system dynamics, an understanding of the short-term dynamics (i.e., transient behavior) is also required. To this end, we derive novel Wireless Transient Bounds (WTB) for end-to-end delay and Backlog in a multi-hop wireless network using stochastic network calculus approach. WTB depends on the Initial Backlog at each node as well as the instantaneous channel states. We numerically compare WTB with State-Of-The-Art Transient bounds (SOTAT), that can be obtained by adapting existing stationary bounds, as well as simulation of the network. While SOTAT and stationary bounds are not able to capture the short-term system dynamics well, WTB provides relatively tight upper bound and has a decay rate that closely matches the simulation. This is achieved by WTB only with a slight increase in the computational complexity, by a factor of O(T + N), where T is the duration of the arriving sequence and N is the number of hops in the network. We believe that the presented analysis and the bounds can be used as base for future work on transient network optimization, e.g., in massive MTC, critical MTC, edge computing and autonomous vehicle.Comment: 13 pages, 13 figures, joourna

Curcio, Mendeley E Data) - One of the best experts on this subject based on the ideXlab platform.

  • GLSP Instances
    2017
    Co-Authors: Curcio, Mendeley E Data)
    Abstract:

    There are 23 instances for OPL based models: - 5 instances with 3 products, 3 periods and low production capacity (cp=0.9) - 5 instances with 3 products, 3 periods and high production capacity (cp=0.75) - 5 instances with 3 products, 3 periods and low production capacity (cp=0.9) - 5 instances with 3 products, 3 periods and high production capacity (cp=0.75) - 3 instances with 7 products, 8 periods and low production capacity (cp=0.9) There are complementary instances for stochastic programming models: - For the two-stage stochastic programming with 10, 50, 100, 200 and 500 scenarios - For the multistage stochastic programming with 12, 20, 30, 39, 42, 110, 155, 240, 258, 399, 420 and 584 nodes - Remarks: 1) the number of nodes depends on the size of instances. 2) there are instances with small number of nodes that were not considered in the computational experiment. Characteristics of instances: --- General --- P -> Number of Products T -> Number of Periods // " "-> Coeficient of variation (for simulation purposes) M -> Number of Microperiods P_Cap -> Production capacity P_BigM -> "BigM" P_Productiontime -> Production time P_MinimumLot -> Minimum lotsize P_HoldingCost -> Holding cost P_ShortageCost -> Shortage cost P_InitialInventory -> Initial inventory P_InitialBacklog -> Initial Backlog P_SetupCost -> Setup costs P_SetupTime -> Setup time P_MaximummLot -> Maximum lotsize Microperiods2 -> Parameter relating microperiods to periods --- Two-stage stochastic programming parameters --- K -> Number of scenarios P_Probability -> Probability of scenario realization P_SetupTimeS -> Setup time per scenario (NOT USED) P_ProductiontimeS -> Production time per scenario (NOT USED) P_DemandS -> Demand realization for a given scenario --- Multistage stochastic programming parameters --- N -> Number of nodes P_Probability -> Probability of node realization Period -> Parameter relating a node to a period PreNode -> Parameter relating a node to its predecessor P_DemandS -> Demand realization for a given nod