626 research outputs found
Achieving Ultra-Low Latency in 5G Millimeter Wave Cellular Networks
The IMT 2020 requirements of 20 Gbps peak data rate and 1 millisecond latency
present significant engineering challenges for the design of 5G cellular
systems. Use of the millimeter wave (mmWave) bands above 10 GHz --- where vast
quantities of spectrum are available --- is a promising 5G candidate that may
be able to rise to the occasion.
However, while the mmWave bands can support massive peak data rates,
delivering these data rates on end-to-end service while maintaining reliability
and ultra-low latency performance will require rethinking all layers of the
protocol stack. This papers surveys some of the challenges and possible
solutions for delivering end-to-end, reliable, ultra-low latency services in
mmWave cellular systems in terms of the Medium Access Control (MAC) layer,
congestion control and core network architecture
Why (and How) Networks Should Run Themselves
The proliferation of networked devices, systems, and applications that we
depend on every day makes managing networks more important than ever. The
increasing security, availability, and performance demands of these
applications suggest that these increasingly difficult network management
problems be solved in real time, across a complex web of interacting protocols
and systems. Alas, just as the importance of network management has increased,
the network has grown so complex that it is seemingly unmanageable. In this new
era, network management requires a fundamentally new approach. Instead of
optimizations based on closed-form analysis of individual protocols, network
operators need data-driven, machine-learning-based models of end-to-end and
application performance based on high-level policy goals and a holistic view of
the underlying components. Instead of anomaly detection algorithms that operate
on offline analysis of network traces, operators need classification and
detection algorithms that can make real-time, closed-loop decisions. Networks
should learn to drive themselves. This paper explores this concept, discussing
how we might attain this ambitious goal by more closely coupling measurement
with real-time control and by relying on learning for inference and prediction
about a networked application or system, as opposed to closed-form analysis of
individual protocols
Dataplane Specialization for High-performance OpenFlow Software Switching
OpenFlow is an amazingly expressive dataplane program-
ming language, but this expressiveness comes at a severe
performance price as switches must do excessive packet clas-
sification in the fast path. The prevalent OpenFlow software
switch architecture is therefore built on flow caching, but
this imposes intricate limitations on the workloads that can
be supported efficiently and may even open the door to mali-
cious cache overflow attacks. In this paper we argue that in-
stead of enforcing the same universal flow cache semantics
to all OpenFlow applications and optimize for the common
case, a switch should rather automatically specialize its dat-
aplane piecemeal with respect to the configured workload.
We introduce ES WITCH , a novel switch architecture that
uses on-the-fly template-based code generation to compile
any OpenFlow pipeline into efficient machine code, which
can then be readily used as fast path. We present a proof-
of-concept prototype and we demonstrate on illustrative use
cases that ES WITCH yields a simpler architecture, superior
packet processing speed, improved latency and CPU scala-
bility, and predictable performance. Our prototype can eas-
ily scale beyond 100 Gbps on a single Intel blade even with
complex OpenFlow pipelines
Boosting Distributed Machine Learning Training Through Loss-tolerant Transmission Protocol
Distributed Machine Learning (DML) systems are utilized to enhance the speed
of model training in data centers (DCs) and edge nodes. The Parameter Server
(PS) communication architecture is commonly employed, but it faces severe
long-tail latency caused by many-to-one "incast" traffic patterns, negatively
impacting training throughput. To address this challenge, we design the
\textbf{L}oss-tolerant \textbf{T}ransmission \textbf{P}rotocol (LTP), which
permits partial loss of gradients during synchronization to avoid unneeded
retransmission and contributes to faster synchronization per iteration. LTP
implements loss-tolerant transmission through \textit{out-of-order
transmission} and \textit{out-of-order Acknowledges (ACKs)}. LTP employs
\textit{Early Close} to adjust the loss-tolerant threshold based on network
conditions and \textit{Bubble Filling} for data correction to maintain training
accuracy. LTP is implemented by C++ and integrated into PyTorch. Evaluations on
a testbed of 8 worker nodes and one PS node demonstrate that LTP can
significantly improve DML training task throughput by up to 30x compared to
traditional TCP congestion controls, with no sacrifice to final accuracy.Comment: This paper will be published on IWQoS 2023. Preview version onl
Performance Comparison of Dual Connectivity and Hard Handover for LTE-5G Tight Integration in mmWave Cellular Networks
MmWave communications are expected to play a major role in the Fifth
generation of mobile networks. They offer a potential multi-gigabit throughput
and an ultra-low radio latency, but at the same time suffer from high isotropic
pathloss, and a coverage area much smaller than the one of LTE macrocells. In
order to address these issues, highly directional beamforming and a very
high-density deployment of mmWave base stations were proposed. This Thesis aims
to improve the reliability and performance of the 5G network by studying its
tight and seamless integration with the current LTE cellular network. In
particular, the LTE base stations can provide a coverage layer for 5G mobile
terminals, because they operate on microWave frequencies, which are less
sensitive to blockage and have a lower pathloss. This document is a copy of the
Master's Thesis carried out by Mr. Michele Polese under the supervision of Dr.
Marco Mezzavilla and Prof. Michele Zorzi. It will propose an LTE-5G tight
integration architecture, based on mobile terminals' dual connectivity to LTE
and 5G radio access networks, and will evaluate which are the new network
procedures that will be needed to support it. Moreover, this new architecture
will be implemented in the ns-3 simulator, and a thorough simulation campaign
will be conducted in order to evaluate its performance, with respect to the
baseline of handover between LTE and 5G.Comment: Master's Thesis carried out by Mr. Michele Polese under the
supervision of Dr. Marco Mezzavilla and Prof. Michele Zorz
Incast mitigation in a data center storage cluster through a dynamic fair-share buffer policy
Incast is a phenomenon when multiple devices interact with only one device at a given time. Multiple storage senders overflow either the switch buffer or the single-receiver memory. This pattern causes all concurrent-senders to stop and wait for buffer/memory availability, and leads to a packet loss and retransmission—resulting in a huge latency. We present a software-defined technique tackling the many-to-one communication pattern—Incast—in a data center storage cluster. Our proposed method decouples the default TCP windowing mechanism from all storage servers, and delegates it to the software-defined storage controller. The proposed method removes the TCP saw-tooth behavior, provides a global flow awareness, and implements the dynamic fair-share buffer policy for end-to-end I/O path. It considers all I/O stages (applications, device drivers, NICs, switches/routers, file systems, I/O schedulers, main memory, and physical disks) while achieving the maximum I/O throughput. The policy, which is part of the proposed method, allocates fair-share bandwidth utilization for all storage servers. Priority queues are incorporated to handle the most important data flows. In addition, the proposed method provides better manageability and maintainability compared with traditional storage networks, where data plane and control plane reside in the same device
The End of a Myth: Distributed Transactions Can Scale
The common wisdom is that distributed transactions do not scale. But what if
distributed transactions could be made scalable using the next generation of
networks and a redesign of distributed databases? There would be no need for
developers anymore to worry about co-partitioning schemes to achieve decent
performance. Application development would become easier as data placement
would no longer determine how scalable an application is. Hardware provisioning
would be simplified as the system administrator can expect a linear scale-out
when adding more machines rather than some complex sub-linear function, which
is highly application specific.
In this paper, we present the design of our novel scalable database system
NAM-DB and show that distributed transactions with the very common Snapshot
Isolation guarantee can indeed scale using the next generation of RDMA-enabled
network technology without any inherent bottlenecks. Our experiments with the
TPC-C benchmark show that our system scales linearly to over 6.5 million
new-order (14.5 million total) distributed transactions per second on 56
machines.Comment: 12 page
F-DCTCP: Fair Congestion Control for SDN-Based Data Center Networks
Network congestion control and management has long been a major issue in providing low-latency, high throughput and high link-utilisation. In particular, Data Center Network (DCN) environments, often dominated by partition-aggregate workloads, suffer performance collapse due to TCP Incast caused by inadequate congestion control parameters. This paper proposes a step in solving this problem. In particular, we describe a novel framework to overcome and control congestion in DCNs based on Software Defined Networking (SDN). We propose a native SDN-based congestion control mechanism, termed Fair Data Center TCP (F-DCTCP). As we shall see, the experimental results show that F-DCTCP outperforms all previous proposals by providing the best combined overall performance in terms of throughout, fairness and flow completion times, making it highly attractive for next generation networks
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