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Providing In-network Support to Coflow Scheduling
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0001-7734-1653
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0002-9446-8143
Queen Mary University, United Kingdom.
Brown University, United States.
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2021 (English)In: Proceedings of the 2021 IEEE Conference on Network Softwarization: Accelerating Network Softwarization in the Cognitive Age, NetSoft 2021, IEEE, 2021, p. 235-243, article id 9492530Conference paper, Published paper (Refereed)
Abstract [en]

Many emerging distributed applications, including big data analytics, generate a number of flows that concurrently transport data across data center networks. To improve their performance, it is required to account for the behavior of a collection of flows, i.e., coflows, rather than individual. State-of-the-art solutions allow for a near-optimal completion time by continuously reordering the unfinished coflows at the end-host, using network priorities. This paper shows that dynamically changing flow priorities at the end host, without taking into account in-flight packets, can cause high-degrees of packet re-ordering, thus imposing pressure on the congestion control and potentially harming network performance in the presence of switches with shallow buffers. We present pCoflow, a new solution that integrates end-host based coflow ordering with in-network scheduling based on packet history. Our evaluation shows that pCoflow improves in CCT upon state-of-the-art solutions by up to 34% for varying load.

Place, publisher, year, edition, pages
IEEE, 2021. p. 235-243, article id 9492530
Keywords [en]
Coflow, Datacenter Networks, P4, Dataplane Programming
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-81893DOI: 10.1109/NetSoft51509.2021.9492530Scopus ID: 2-s2.0-85112096351ISBN: 9781665405225 (print)OAI: oai:DiVA.org:kau-81893DiVA, id: diva2:1510664
Conference
7th IEEE International Conference on Network Softwarization, NetSoft 2021, Virtual, Online, 28 June 2021 - 2 July 2021, 170657
Note

Article part of Hernandez Benet's doctoral thesis (2021) Traffic Management in Software-Defined Data Center Networks as manuscript, now published.

Available from: 2020-12-16 Created: 2020-12-16 Last updated: 2022-03-17Bibliographically approved
In thesis
1. Traffic Management in Software-Defined Data Center Networks
Open this publication in new window or tab >>Traffic Management in Software-Defined Data Center Networks
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Traffic management in data centers is paramount to improving network and application performance, thereby improving the quality of service by reducing network congestion, packet loss, and latency. However, the deployment and configuration of traffic management techniques are challenging due to diverse data-center traffic characteristics, large data center topologies, and the interplay of different protocols at the routing, transport, and link layer. Software-Defined Networking (SDN) emerges as a new paradigm towards a centralized network configuration and traffic management by decoupling the control plane from forwarding devices. Despite its holistic view of the network, data centers are commonly interconnected by traditional networks that use standard routing protocols. It is therefore essential to achieve interoperability with legacy systems, end-to-end traffic management, and to avoid the cumbersome, time-consuming, and error-prone configuration process of data-center edge network devices.

In this thesis, we aim to improve traffic management and its configuration for software-defined data center networks. To achieve this objective, we provide novel approaches that enhance the control plane as well as leverage novel concepts of data plane programmability.

At the control plane, we first propose different mechanisms that enable the fast restoration of network connectivity after a virtual machine migration. Second, we suggest a network management automation framework that extends layer 2 connectivity to the tenants' services hosted across geo-distributed data centers. Moreover, we provide high-level policy-based mechanisms that make network configuration and traffic management simpler for data-center operators. At the data plane, we develop MP-HULA that load-balances multipath connections across least-congested paths. MP-HULA leverages advanced data plane mechanisms to rank multiple paths according to congestion metrics and uses that information for fine-grained load-balancing decisions considering transport layer information. To improve flowlet-based load-balancing decisions, we propose FlowDyn, which efficiently estimates round-trip time using programmable telemetry data. Finally, we present pCoflow, an in-network support mechanism that uses advanced programmable scheduling primitives to effectively avoid reordering for data-parallel applications even when there are flow priority changes due to global coflow scheduling updates.

Abstract [en]

Traffic management in data centers is paramount to improving network and application performance, thereby improving the quality of service by reducing network congestion, packet loss, and latency. However, the deployment and configuration of traffic management techniques are challenging due to diverse data center traffic characteristics, large data center topologies, and the interplay of different protocols at the routing, transport, and link layer. Software-Defined Networking (SDN) emerges as a new paradigm towards a centralized network configuration and traffic management by decoupling the control plane from forwarding devices.

In this thesis, we aim to improve traffic management and its configuration for software-defined data center networks by providing novel approaches that enhance the control plane as well as leveraging novel concepts of data plane programmability. At the control plane, we provide solutions such as high-level based policy framework, automation of layer 2 services, and fast restoration of network connectivity for virtual machine migration, that make network configuration and traffic management simpler for data-center operators. At the data plane, we propose approaches to improve load balancing in data centers and an in-network support mechanism to avoid reordering for data-parallel applications even when there are flow priority changes due to global coflow scheduling updates.

 

Place, publisher, year, edition, pages
Karlstad: Karlstads universitet, 2021. p. 69
Series
Karlstad University Studies, ISSN 1403-8099 ; 2021:15
Keywords
SDN, data center, load-balancing, traffic management, EVPN, big data, programmable data plane
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-83671 (URN)978-91-7867-210-3 (ISBN)978-91-7867-220-2 (ISBN)
Public defence
2021-06-03, Zoom, 13:00 (English)
Opponent
Supervisors
Note

Article 8 part of doctoral thesis as manuscript, now published.

Available from: 2021-05-12 Created: 2021-04-16 Last updated: 2021-09-27Bibliographically approved

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Hernandez Benet, CristianKassler, Andreas

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