167 research outputs found
Green spine switch management for datacenter networks
Energy consumption for datacenter has grown significantly and the trend is still growing due to the increasing popularity of cloud computing. Datacenter networks (DCNs), however, are starting to consume a greater portion of overall energy in comparison to servers used in datacenters due to advanced virtualization techniques. On the other hand, devices in a DCN often remain under-utilized. There are various DCN architectures. This paper proposes an approach called Green Spine Switch Management System (GSSMS) for Spine-Leaf topology based DCNs. The objective of the approach is to reduce energy consumption used by the network for a Spine-Leaf topology-based datacenter. The primary idea of GSSMS is to monitor the dynamic workload and only keep Spine switches that are necessary for handling the current network traffic. We have developed an adaptive management system to control the number of Spine switches in a Spine-Leaf DCN for efficient energy consumption. Further, we hav
Datacenter Traffic Control: Understanding Techniques and Trade-offs
Datacenters provide cost-effective and flexible access to scalable compute
and storage resources necessary for today's cloud computing needs. A typical
datacenter is made up of thousands of servers connected with a large network
and usually managed by one operator. To provide quality access to the variety
of applications and services hosted on datacenters and maximize performance, it
deems necessary to use datacenter networks effectively and efficiently.
Datacenter traffic is often a mix of several classes with different priorities
and requirements. This includes user-generated interactive traffic, traffic
with deadlines, and long-running traffic. To this end, custom transport
protocols and traffic management techniques have been developed to improve
datacenter network performance.
In this tutorial paper, we review the general architecture of datacenter
networks, various topologies proposed for them, their traffic properties,
general traffic control challenges in datacenters and general traffic control
objectives. The purpose of this paper is to bring out the important
characteristics of traffic control in datacenters and not to survey all
existing solutions (as it is virtually impossible due to massive body of
existing research). We hope to provide readers with a wide range of options and
factors while considering a variety of traffic control mechanisms. We discuss
various characteristics of datacenter traffic control including management
schemes, transmission control, traffic shaping, prioritization, load balancing,
multipathing, and traffic scheduling. Next, we point to several open challenges
as well as new and interesting networking paradigms. At the end of this paper,
we briefly review inter-datacenter networks that connect geographically
dispersed datacenters which have been receiving increasing attention recently
and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial
ATP: a Datacenter Approximate Transmission Protocol
Many datacenter applications such as machine learning and streaming systems
do not need the complete set of data to perform their computation. Current
approximate applications in datacenters run on a reliable network layer like
TCP. To improve performance, they either let sender select a subset of data and
transmit them to the receiver or transmit all the data and let receiver drop
some of them. These approaches are network oblivious and unnecessarily transmit
more data, affecting both application runtime and network bandwidth usage. On
the other hand, running approximate application on a lossy network with UDP
cannot guarantee the accuracy of application computation. We propose to run
approximate applications on a lossy network and to allow packet loss in a
controlled manner. Specifically, we designed a new network protocol called
Approximate Transmission Protocol, or ATP, for datacenter approximate
applications. ATP opportunistically exploits available network bandwidth as
much as possible, while performing a loss-based rate control algorithm to avoid
bandwidth waste and re-transmission. It also ensures bandwidth fair sharing
across flows and improves accurate applications' performance by leaving more
switch buffer space to accurate flows. We evaluated ATP with both simulation
and real implementation using two macro-benchmarks and two real applications,
Apache Kafka and Flink. Our evaluation results show that ATP reduces
application runtime by 13.9% to 74.6% compared to a TCP-based solution that
drops packets at sender, and it improves accuracy by up to 94.0% compared to
UDP
On energy consumption of switch-centric data center networks
Data center network (DCN) is the core of cloud computing and accounts for 40% energy
spend when compared to cooling system, power distribution and conversion of the whole data center
(DC) facility. It is essential to reduce the energy consumption of DCN to esnure energy-efficient (green)
data center can be achieved. An analysis of DC performance and efficiency emphasizing the effect of
bandwidth provisioning and throughput on energy proportionality of two most common switch-centric
DCN topologies: three-tier (3T) and fat tree (FT) based on the amount of actual energy that is turned
into computing power are presented. Energy consumption of switch-centric DCNs by realistic
simulations is analyzed using GreenCloud simulator. Power related metrics were derived and adapted
for the information technology equipment (ITE) processes within the DCN. These metrics are
acknowledged as subset of the major metrics of power usage effectiveness (PUE) and data center
infrastructure efficiency (DCIE), known to DCs. This study suggests that despite in overall FT consumes
more energy, it spends less energy for transmission of a single bit of information, outperforming 3T
Stateless Flow-Zone Switching Using Software-Defined Addressing
The trend toward cloudification of communication networks and services, with user data and applications stored and processed in data centers, pushes the limits of current Data Center Networks (DCNs), requiring improved scalability, resiliency, and performance. Here we consider a DCN forwarding approach based on software-defined addressing (SDA), which embeds semantics in the Medium Access Control (MAC) address and thereby enables new forwarding processes. This work presents Flow-Zone Switching (FZS), a loop-free location-based source-routing solution that eliminates the need for forwarding tables by embedding routing instructions and flow identifiers directly in the flow-zone software-defined address. FZS speeds the forwarding process, increasing the throughput and reducing the latency of QoS-sensitive flows while reducing the capital and operational costs of switching. This paper presents details of FZS and a performance evaluation within a complete DCN.This work was supported in part by the H2020 Europe/Taiwan Joint Action 5G-DIVE under Grant 859881, in part by the Spanish State Research Agency through the TRUE5G Project under Grant PID2019-108713RB-C52/AEI/10.13039/501100011033, and in part by the
Comunidad de Madrid through the Project TAPIR-CM under Grant S2018/TCS-4496
Enabling Scalable and Sustainable Softwarized 5G Environments
The fifth generation of telecommunication systems (5G) is foreseen to play a fundamental
role in our socio-economic growth by supporting various and radically new vertical
applications (such as Industry 4.0, eHealth, Smart Cities/Electrical Grids, to name
a few), as a one-fits-all technology that is enabled by emerging softwarization solutions
\u2013 specifically, the Fog, Multi-access Edge Computing (MEC), Network Functions Virtualization
(NFV) and Software-Defined Networking (SDN) paradigms. Notwithstanding
the notable potential of the aforementioned technologies, a number of open issues
still need to be addressed to ensure their complete rollout. This thesis is particularly developed
towards addressing the scalability and sustainability issues in softwarized 5G
environments through contributions in three research axes: a) Infrastructure Modeling
and Analytics, b) Network Slicing and Mobility Management, and c) Network/Services Management
and Control. The main contributions include a model-based analytics approach
for real-time workload profiling and estimation of network key performance indicators
(KPIs) in NFV infrastructures (NFVIs), as well as a SDN-based multi-clustering approach
to scale geo-distributed virtual tenant networks (VTNs) and to support seamless
user/service mobility; building on these, solutions to the problems of resource consolidation,
service migration, and load balancing are also developed in the context of 5G.
All in all, this generally entails the adoption of Stochastic Models, Mathematical Programming,
Queueing Theory, Graph Theory and Team Theory principles, in the context
of Green Networking, NFV and SDN
Fast ReRoute on Programmable Switches
Highly dependable communication networks usually rely on some kind of Fast Re-Route (FRR) mechanism which allows to quickly re-route traffic upon failures, entirely in the data plane. This paper studies the design of FRR mechanisms for emerging reconfigurable switches. Our main contribution is an FRR primitive for programmable data planes, PURR, which provides low failover latency and high switch throughput, by avoiding packet recirculation. PURR tolerates multiple concurrent failures and comes with minimal memory requirements, ensuring compact forwarding tables, by unveiling an intriguing connection to classic ``string theory'' (i.e., stringology), and in particular, the shortest common supersequence problem. PURR is well-suited for high-speed match-action forwarding architectures (e.g., PISA) and supports the implementation of a broad variety of FRR mechanisms. Our simulations and prototype implementation (on an FPGA and a Tofino switch) show that PURR improves TCAM memory occupancy by a factor of 1.5x-10.8x compared to a naïve encoding when implementing state-of-the-art FRR mechanisms. PURR also improves the latency and throughput of datacenter traffic up to a factor of 2.8x-5.5x and 1.2x-2x, respectively, compared to approaches based on recirculating packets
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