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Journal ArticleDOI

VL2: a scalable and flexible data center network

TLDR
VL2 is a practical network architecture that scales to support huge data centers with uniform high capacity between servers, performance isolation between services, and Ethernet layer-2 semantics and can be deployed today, and a working prototype is built.
Abstract
To be agile and cost effective, data centers must allow dynamic resource allocation across large server pools. In particular, the data center network should provide a simple flat abstraction: it should be able to take any set of servers anywhere in the data center and give them the illusion that they are plugged into a physically separate, noninterfering Ethernet switch with as many ports as the service needs. To meet this goal, we present VL2, a practical network architecture that scales to support huge data centers with uniform high capacity between servers, performance isolation between services, and Ethernet layer-2 semantics. VL2 uses (1) flat addressing to allow service instances to be placed anywhere in the network, (2) Valiant Load Balancing to spread traffic uniformly across network paths, and (3) end system--based address resolution to scale to large server pools without introducing complexity to the network control plane. VL2's design is driven by detailed measurements of traffic and fault data from a large operational cloud service provider. VL2's implementation leverages proven network technologies, already available at low cost in high-speed hardware implementations, to build a scalable and reliable network architecture. As a result, VL2 networks can be deployed today, and we have built a working prototype. We evaluate the merits of the VL2 design using measurement, analysis, and experiments. Our VL2 prototype shuffles 2.7 TB of data among 75 servers in 395 s---sustaining a rate that is 94% of the maximum possible.

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Book ChapterDOI

On Internet of Things Programming Models

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Journal ArticleDOI

An ACO-based multi-objective optimization for cooperating VM placement in cloud data center

TL;DR: The VM placement problem is formulated as a multi-objective optimization problem and an Ant Colony Optimization-based VM consolidation algorithm is proposed to find a solution in real-time to solve the problem.
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Kraken: Online and Elastic Resource Reservations for Cloud Datacenters

TL;DR: K Kraken is designed, a system that allows to dynamically update minimum guarantees for both network bandwidth and compute resources at runtime and achieves this through an online resource reservation scheme, which comes with provable optimality guarantees.
Proceedings ArticleDOI

Multipath TCP for datacenters: From energy efficiency perspective

TL;DR: MPTCP-D is presented, an energy-efficient variant of multipath TCP for datacenters that incorporates a novel congestion control algorithm that can provide energy efficiency by minimizing the flow completion time, and an extra subflow elimination mechanism that can preclude link-sharing subflows from increasing the host CPU power.
Proceedings ArticleDOI

ALB: Adaptive Load Balancing Based on Accurate Congestion Feedback for Asymmetric Topologies

TL;DR: ALB is proposed, an adaptive load-balancing mechanism based on accurate congestion feedback running at end hosts, which is resilient to asymmetry and achieves up to 7% and 40% better flow completion time (FCT) than CONGA and CLOVE-ECN under asymmetry.
References
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Journal ArticleDOI

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Journal ArticleDOI

The part-time parliament

TL;DR: The Paxon parliament's protocol provides a new way of implementing the state machine approach to the design of distributed systems.
Proceedings Article

The Art of Computer Systems Performance Analysis.

Raj Jain
TL;DR: The authors' goal is always to offer you an assortment of cost-free ebooks too as aid resolve your troubles.
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