Bridging the gap between applications and networks in data centers

Costa, Paolo

doi:10.1145/2433140.2433143

Cited by 11 publications

(9 citation statements)

References 30 publications

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“…CPU-to-CPU communication is managed by the top of memory rack electronic switch, MMC, and the MEXC, as communicating CPUs will interconnect through the remote memory modules, shared memory, they are using [41,42]. CPU-MEM rack communication is performed by mutual functionality between the OXCs, DMC blocks, and the MEXC.…”

Section: Siphmentioning

confidence: 99%

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Future Energy Efficient Data Centers With Disaggregated Servers

Ali

El-Gorashi

Lawey

et al. 2017

J. Lightwave Technol.

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Abstract-The popularity of the Internet and the demand for 24/7 services uptime is driving system performance and reliability requirements to levels that today's data centers can no longer support. This article examines the traditional monolithic conventional server (CS) design and compares it to a new design paradigm: the disaggregated server (DS) data center design. The DS design arranges data centers resources in physical pools, such as processing, memory, and IO module pools, rather than packing each subset of such resources into a single server box. In this work, we study energy efficient resource provisioning and virtual machine (VM) allocation in DS-based data centers compared to CS-based data centers.

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Section: Siphmentioning

confidence: 99%

“…Constraints (41) and (42) generate the CPU-IO and Memory-IO traffic matrices respectively, using the relevant variables.…”

Section: ) Communication Constraintsmentioning

confidence: 99%

Future Energy Efficient Data Centers With Disaggregated Servers

Ali

El-Gorashi

Lawey

et al. 2017

J. Lightwave Technol.

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“…NaaS can be seen as the ultimate connection between SDN and cloud computing. NaaS is a supplementary scheme for SDN; while SDN is responsible for packet forwarding and network administration, NaaS provides application-specific packet processing for cloud tenants [42].With NaaS schemes, the operators can control the bandwidth, routing, and QoS requirements of their data. Eventually, with SDN, operators can leverage current NaaS initiatives and build their own SDN infrastructure [44].…”

Section: Naas and Sdn Integrationmentioning

confidence: 99%

Software defined networking: State of the art and research challenges

et al. 2014

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Abstract-Plug-and-play information technology (IT) infrastructure has been expanding very rapidly in recent years. With the advent of cloud computing, many ecosystem and business paradigms are encountering potential changes and may be able to eliminate their IT infrastructure maintenance processes. Real-time performance and high availability requirements have induced telecom networks to adopt the new concepts of the cloud model: software-defined networking (SDN) and network function virtualization (NFV). NFV introduces and deploys new network functions in an open and standardized IT environment, while SDN aims to transform the way networks function. SDN and NFV are complementary technologies; they do not depend on each other. However, both concepts can be merged and have the potential to mitigate the challenges of legacy networks. In this paper, our aim is to describe the benefits of using SDN in a multitude of environments such as in data centers, data center networks, and Network as Service offerings. We also present the various challenges facing SDN, from scalability to reliability and security concerns, and discuss existing solutions to these challenges.

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“…For example, new Cisco ASIC and arista application switches have provided a programmable data plane. Additionally, existing switches have been extended with dedicated devices like sidecar [17], [18]. Sidecar is connected to a switch via a direct link and receives all traffic.…”

Section: Data Center Networkingmentioning

confidence: 99%

Exploiting Efficient and Scalable Shuffle Transfers in Future Data Center Networks

Guo

Xie

Zhou

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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Distributed computing systems like MapReduce in data centers transfer massive amount of data across successive processing stages. Such shuffle transfers contribute most of the network traffic and make the network bandwidth become a bottleneck. In many commonly used workloads, data flows in such a transfer are highly correlated and aggregated at the receiver side. To lower down the network traffic and efficiently use the available network bandwidth, we propose to push the aggregation computation into the network and parallelize the shuffle and reduce phases. In this paper, we first examine the gain and feasibility of the in-network aggregation with BCube, a novel server-centric networking structure for future data centers. To exploit such a gain, we model the in-network aggregation problem that is NP-hard in BCube. We propose two approximate methods for building the efficient IRS-based incast aggregation tree and SRS-based shuffle aggregation subgraph, solely based on the labels of their members and the data center topology. We further design scalable forwarding schemes based on Bloom filters to implement in-network aggregation over massive concurrent shuffle transfers. Based on a prototype and large-scale simulations, we demonstrate that our approaches can significantly decrease the amount of network traffic and save the data center resources. Our approaches for BCube can be adapted to other servercentric network structures for future data centers after minimal modifications.

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Bridging the gap between applications and networks in data centers

Cited by 11 publications

References 30 publications

Future Energy Efficient Data Centers With Disaggregated Servers

Future Energy Efficient Data Centers With Disaggregated Servers

Software defined networking: State of the art and research challenges

Exploiting Efficient and Scalable Shuffle Transfers in Future Data Center Networks

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