Software defined environments: An introduction

Li, C.-S.; Brech, B. L.; Crowder, S.; Dias, Daniel M.; Franke, Hubertus; Hogstrom, Matt; Lindquist, D. A.; Pacifici, G.; Pappe, Stefan; Rajaraman, B.; Rao, Josyula R.; Ratnaparkhi, R. P.; Smith, Ronan J.; Williams, Michael D.

doi:10.1147/jrd.2014.2298134

Cited by 58 publications

(25 citation statements)

References 16 publications

(15 reference statements)

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“…For instance, solutions such as FlowVisor [166], [111], [167], FlowN [168], NVP [112], OpenVirteX [169], [170], IBM SDN VE [171], [172], RadioVisor [173], AutoVFlow [174], eXtensible Datapath Daemon (xDPd) [175], [176], optical transport network virtualization [177], and version-agnostic OpenFlow slicing mechanisms [178], have been recently proposed, evaluated and deployed in real scenarios for on-demand provisioning of virtual networks.…”

Section: Layer Iii: Network Hypervisorsmentioning

confidence: 99%

“…IBM has also recently proposed SDN VE [171], [172], another commercial and enterprise-class network virtualization platform. SDN VE uses OpenDaylight as one of the building blocks of the so-called Software-Defined Environments (SDEs), a trend further discussed in Section V. This solution also offers a complete implementation framework for network virtualization.…”

Section: Commercial Multi-tenant Network Hypervisorsmentioning

confidence: 99%

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Software-Defined Networking: A Comprehensive Survey

et al. 2015

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Abstract-The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-Defined Networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution.In this paper we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound APIs, network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms -with a focus on aspects such as resiliency, scalability, performance, security and dependability -as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

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Section: Layer Iii: Network Hypervisorsmentioning

confidence: 99%

Section: Commercial Multi-tenant Network Hypervisorsmentioning

confidence: 99%

Software-Defined Networking: A Comprehensive Survey

et al. 2015

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“…Software defined infrastructure (Kandiraju, et al, 2014;Li, et al, 2014;Alba, et al, 2014;Truong & Dustdar, 2014) is a new concept for architecting IT infrastructure. It composes IT infrastructure from commodity hardware, managed by a software-stack above it.…”

Section: Software-defined Storagementioning

confidence: 99%

Intelligent Software-Defined Storage With Deep Traffic Modeling for Cloud Storage Service

Huang¹,

Huang²,

Chen³

2016

STSC

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The advent of cloud computing, big data, and mobile computing has created a fast-growing demand for storage. Cloud service providers are looking for cost-effective storage solutions as an alternative to traditional, high-cost, embeddedsystems-based storage to meet the needs of newly emerging applications, such as messaging, video streaming, data analytics, etc. In particular, they are facing the challenge of lowering costs while still accommodating multi-workloads on a single instance of storage without compromising workload performance requirements. Software-defined storage (SDS) is a new generation of storage system. Unlike traditional embedded-systems-based storage, the SDS uses a software-stack above commodity hardware to provide more valuable and cost-effective features. To meet the challenges cloud service providers are facing, this paper introduces the architecture of a new SDS platform called Federator. It also argues that the architecture of an SDS platform should have three main characteristics: 1. separation of the control and data pathways, 2. self-configuration of storage resources, and 3. RESTful APIs for new business extension. This paper specifically introduces the storage I/O traffic modeling supported by Federator. With this capability, storage performance metrics are generated by using Long-Short Term Memory (LSTM). This prediction capability is important to a self-configurable SDS to meet performance requirements.

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“…Recently, the need for increased agility and flexibility has accelerated the introduction of software defined environments (which include software defined networking, storage, and compute) where the control and management planes of these resources are decoupled from the data planes so that they are no longer vertically integrated as in traditional compute, storage or switch systems and can be deployed anywhere within a datacenter [4].…”

Section: Introductionmentioning

confidence: 99%

“…This configuration allows maximal amount of sharing and flexibility to address the complete spectrum of potential workloads. The broad deployment of Software Defined Environments (SDE) within cloud datacenters is facilitating the disaggregation among the management planes, control planes, and data planes within servers, switches and storage [4].…”

Section: Introductionmentioning

confidence: 99%

Composable architecture for rack scale big data computing

Franke

Parris

et al. 2017

Future Generation Computer Systems

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Keywords:Big data platforms, Composable system architecture, Disaggregated datacenter architecture, composable datacenter, software defined environments, software defined networking. Abstract:The rapid growth of cloud computing, both in terms of the spectrum and volume of cloud workloads, necessitate re-visiting the traditional rack-mountable servers based datacenter design. Next generation datacenters need to offer enhanced support for: (i) fast changing system configuration requirements due to workload constraints, (ii) timely adoption of emerging hardware technologies, and (iii) maximal sharing of systems and subsystems in order to lower costs. Disaggregated datacenters, constructed as a collection of individual resources such as CPU, memory, disks etc., and composed into workload execution units on demand, are an interesting new trend that can address the above challenges. In this paper, we demonstrated the feasibility of composable systems through building a rack scale composable system prototype using PCIe switch. Through empirical approaches, we develop assessment of the opportunities and challenges for leveraging the composable architecture for rack scale cloud datacenters with a focus on big data and NoSQL workloads. In particular, we compare and contrast the programming models that can be used to access the composable resources, and developed the implications for the network and resource provisioning and management for rack scale architecture.

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Software defined environments: An introduction

Cited by 58 publications

References 16 publications

Software-Defined Networking: A Comprehensive Survey

Software-Defined Networking: A Comprehensive Survey

Intelligent Software-Defined Storage With Deep Traffic Modeling for Cloud Storage Service

Composable architecture for rack scale big data computing

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