The Experts below are selected from a list of 6120 Experts worldwide ranked by ideXlab platform

Alexandra Kolla - One of the best experts on this subject based on the ideXlab platform.

  • high throughput data center Topology Design
    Networked Systems Design and Implementation, 2014
    Co-Authors: Ankit Singla, Brighten P Godfrey, Alexandra Kolla
    Abstract:

    With high throughput networks acquiring a crucial role in supporting data-intensive applications, a variety of data center network topologies have been proposed to achieve high capacity at low cost. While this work explores a large number of Design points, even in the limited case of a network of identical switches, no proposal has been able to claim any notion of optimality. The case of heterogeneous networks, incorporating multiple line-speeds and port-counts as data centers grow over time, introduces even greater complexity. In this paper, we present the first non-trivial upper-bound on network throughput under uniform traffic patterns for any Topology with identical switches. We then show that random graphs achieve throughput surprisingly close to this bound, within a few percent at the scale of a few thousand servers. Apart from demonstrating that homogeneous Topology Design may be reaching its limits, this result also motivates our use of random graphs as building blocks for Design of heterogeneous networks. Given a heterogeneous pool of network switches, we explore through experiments and analysis, how the distribution of servers across switches and the interconnection of switches affect network throughput. We apply these insights to a real-world heterogeneous data center Topology, VL2, demonstrating as much as 43% higher throughput with the same equipment.

  • high throughput data center Topology Design
    arXiv: Networking and Internet Architecture, 2013
    Co-Authors: Ankit Singla, Brighten P Godfrey, Alexandra Kolla
    Abstract:

    With high throughput networks acquiring a crucial role in supporting data-intensive applications, a variety of data center network topologies have been proposed to achieve high capacity at low cost. While this literature explores a large number of Design points, even in the limited case of a network of identical switches, no proposal has been able to claim any notion of optimality. The case of heterogeneous networks, incorporating multiple line-speeds and port-counts as data centers grow over time, introduces even greater complexity. In this paper, we present the first non-trivial upper-bound on network throughput under uniform traffic patterns for any Topology with identical switches. We then show that random graphs achieve throughput surprisingly close to this bound, within a few percent at the scale of a few thousand servers. Apart from demonstrating that homogeneous Topology Design may be reaching its limits, this result also motivates our use of random graphs as building blocks to explore the Design of heterogeneous networks. Given a heterogeneous pool of network switches, through experiments and analysis, we explore how the distribution of servers across switches and the interconnection of switches affect network throughput. We apply these insights to a real-world heterogeneous data center Topology, VL2, demonstrating as much as 43% higher throughput with the same equipment.

Ankit Singla - One of the best experts on this subject based on the ideXlab platform.

  • high throughput data center Topology Design
    Networked Systems Design and Implementation, 2014
    Co-Authors: Ankit Singla, Brighten P Godfrey, Alexandra Kolla
    Abstract:

    With high throughput networks acquiring a crucial role in supporting data-intensive applications, a variety of data center network topologies have been proposed to achieve high capacity at low cost. While this work explores a large number of Design points, even in the limited case of a network of identical switches, no proposal has been able to claim any notion of optimality. The case of heterogeneous networks, incorporating multiple line-speeds and port-counts as data centers grow over time, introduces even greater complexity. In this paper, we present the first non-trivial upper-bound on network throughput under uniform traffic patterns for any Topology with identical switches. We then show that random graphs achieve throughput surprisingly close to this bound, within a few percent at the scale of a few thousand servers. Apart from demonstrating that homogeneous Topology Design may be reaching its limits, this result also motivates our use of random graphs as building blocks for Design of heterogeneous networks. Given a heterogeneous pool of network switches, we explore through experiments and analysis, how the distribution of servers across switches and the interconnection of switches affect network throughput. We apply these insights to a real-world heterogeneous data center Topology, VL2, demonstrating as much as 43% higher throughput with the same equipment.

  • high throughput data center Topology Design
    arXiv: Networking and Internet Architecture, 2013
    Co-Authors: Ankit Singla, Brighten P Godfrey, Alexandra Kolla
    Abstract:

    With high throughput networks acquiring a crucial role in supporting data-intensive applications, a variety of data center network topologies have been proposed to achieve high capacity at low cost. While this literature explores a large number of Design points, even in the limited case of a network of identical switches, no proposal has been able to claim any notion of optimality. The case of heterogeneous networks, incorporating multiple line-speeds and port-counts as data centers grow over time, introduces even greater complexity. In this paper, we present the first non-trivial upper-bound on network throughput under uniform traffic patterns for any Topology with identical switches. We then show that random graphs achieve throughput surprisingly close to this bound, within a few percent at the scale of a few thousand servers. Apart from demonstrating that homogeneous Topology Design may be reaching its limits, this result also motivates our use of random graphs as building blocks to explore the Design of heterogeneous networks. Given a heterogeneous pool of network switches, through experiments and analysis, we explore how the distribution of servers across switches and the interconnection of switches affect network throughput. We apply these insights to a real-world heterogeneous data center Topology, VL2, demonstrating as much as 43% higher throughput with the same equipment.

Kazuhiro Saitou - One of the best experts on this subject based on the ideXlab platform.

  • continuum structural Topology Design with genetic algorithms
    Computer Methods in Applied Mechanics and Engineering, 2000
    Co-Authors: Mark J Jakiela, Colin D Chapman, James Duda, Adenike Adewuya, Kazuhiro Saitou
    Abstract:

    The genetic algorithm (GA), an optimization technique based on the theory of natural selection, is applied to structural Topology Design problems. After reviewing the GA and previous research in structural Topology optimization, we describe a binary material/void Design representation that is encoded in GA chromosome data structures. This representation is intended to approximate a material continuum as opposed to discrete truss structures. Four examples, showing the broad utility of the approach and representation, are then presented. A fifth example suggests an alternate representation that allows continuously-variable material density. Concluding discussion suggests recommended uses of the technique and describes ongoing and possible future work.

Brighten P Godfrey - One of the best experts on this subject based on the ideXlab platform.

  • high throughput data center Topology Design
    Networked Systems Design and Implementation, 2014
    Co-Authors: Ankit Singla, Brighten P Godfrey, Alexandra Kolla
    Abstract:

    With high throughput networks acquiring a crucial role in supporting data-intensive applications, a variety of data center network topologies have been proposed to achieve high capacity at low cost. While this work explores a large number of Design points, even in the limited case of a network of identical switches, no proposal has been able to claim any notion of optimality. The case of heterogeneous networks, incorporating multiple line-speeds and port-counts as data centers grow over time, introduces even greater complexity. In this paper, we present the first non-trivial upper-bound on network throughput under uniform traffic patterns for any Topology with identical switches. We then show that random graphs achieve throughput surprisingly close to this bound, within a few percent at the scale of a few thousand servers. Apart from demonstrating that homogeneous Topology Design may be reaching its limits, this result also motivates our use of random graphs as building blocks for Design of heterogeneous networks. Given a heterogeneous pool of network switches, we explore through experiments and analysis, how the distribution of servers across switches and the interconnection of switches affect network throughput. We apply these insights to a real-world heterogeneous data center Topology, VL2, demonstrating as much as 43% higher throughput with the same equipment.

  • high throughput data center Topology Design
    arXiv: Networking and Internet Architecture, 2013
    Co-Authors: Ankit Singla, Brighten P Godfrey, Alexandra Kolla
    Abstract:

    With high throughput networks acquiring a crucial role in supporting data-intensive applications, a variety of data center network topologies have been proposed to achieve high capacity at low cost. While this literature explores a large number of Design points, even in the limited case of a network of identical switches, no proposal has been able to claim any notion of optimality. The case of heterogeneous networks, incorporating multiple line-speeds and port-counts as data centers grow over time, introduces even greater complexity. In this paper, we present the first non-trivial upper-bound on network throughput under uniform traffic patterns for any Topology with identical switches. We then show that random graphs achieve throughput surprisingly close to this bound, within a few percent at the scale of a few thousand servers. Apart from demonstrating that homogeneous Topology Design may be reaching its limits, this result also motivates our use of random graphs as building blocks to explore the Design of heterogeneous networks. Given a heterogeneous pool of network switches, through experiments and analysis, we explore how the distribution of servers across switches and the interconnection of switches affect network throughput. We apply these insights to a real-world heterogeneous data center Topology, VL2, demonstrating as much as 43% higher throughput with the same equipment.

Mark J Jakiela - One of the best experts on this subject based on the ideXlab platform.

  • continuum structural Topology Design with genetic algorithms
    Computer Methods in Applied Mechanics and Engineering, 2000
    Co-Authors: Mark J Jakiela, Colin D Chapman, James Duda, Adenike Adewuya, Kazuhiro Saitou
    Abstract:

    The genetic algorithm (GA), an optimization technique based on the theory of natural selection, is applied to structural Topology Design problems. After reviewing the GA and previous research in structural Topology optimization, we describe a binary material/void Design representation that is encoded in GA chromosome data structures. This representation is intended to approximate a material continuum as opposed to discrete truss structures. Four examples, showing the broad utility of the approach and representation, are then presented. A fifth example suggests an alternate representation that allows continuously-variable material density. Concluding discussion suggests recommended uses of the technique and describes ongoing and possible future work.