Software defined infrastructures

Kandiraju, Gokul; Franke, Hubertus; Williams, Michael D.; Steinder, Małgorzata; Black, Steve

doi:10.1147/jrd.2014.2298133

Cited by 30 publications

(13 citation statements)

References 11 publications

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“…This paper does not focus on SDI and its core technologies, rather we assume that this capability is available while we focus on the evaluation of optimal scale of resource disaggregation under effective software definition. Interested readers are referred to [12]- [14] for discussions on SDI and its underlying components.…”

Section: B Software Defined Infrastructuresmentioning

confidence: 99%

Energy Efficient Placement of Workloads in Composable Data Center Networks

Ajibola

El-Gorashi

Elmirghani

2021

J. Lightwave Technol.

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This paper studies the energy efficiency of composable data center (DC) infrastructures over network topologies. Using a mixed integer linear programming (MILP) model, we compare the performance of disaggregation at rackscale and pod-scale over selected electrical, optical and hybrid network topologies relative to a traditional DC. Relative to a podscale DC, the results show that physical disaggregation at rackscale is sufficient for optimal efficiency when the optical network topology is adopted, and resource components are allocated in a suitable manner. The optical network topology also enables optimal energy efficiency in composable DCs. The paper also studies logical disaggregation of traditional DC servers over an optical network topology. Relative to physical disaggregation at rack-scale, logical disaggregation of server resources within each rack enables marginal fall in the total DC power consumption (TDPC) due to improved resource demands placement. Hence, an adaptable composable infrastructure that can support both in memory (access) latency sensitive and insensitive workloads is enabled. We also conduct a study of the adoption of micro-service architecture in both traditional and composable DCs. Our results show that increasing the modularity of workloads improves the energy efficiency in traditional DCs, but disproportionate utilization of DC resources persists. A combination of disaggregation and micro-services achieved up to 23% reduction in the TDPC of the traditional DC by enabling optimal resources utilization and energy efficiencies. Finally, we propose a heuristic for energy efficient placement of workloads in composable DCs which replicates the trends produced by the MILP model formulated in this paper. Index Terms-Composable infrastructures, energy efficient data centers, MILP, micro-services, optical networks, rack-scale data center, software defined infrastructures. I. INTRODUCTIONATA CENTERS are critical infrastructures that provide platforms driving wide adoption of digital technologies. These indispensable infrastructures provide computing resources needed to run public internet-facing applications and private enterprise-critical applications alike in environments that support the requirements of cloud computing and data analytics applications. The requirements of cloud computing and data analytic applications include on-demand resource provisioning, multitenant isolation, parallel computation, and security. Examples of such applications include web services,

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Section: B Software Defined Infrastructuresmentioning

confidence: 99%

Energy Efficient Placement of Workloads in Composable Data Center Networks

Ajibola

El-Gorashi

Elmirghani

2021

J. Lightwave Technol.

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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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“…Examples of such improvements are the introduction of server virtualization technologies, such as hypervisor & container technologies [10] and the move towards Software-Defined Infrastructures (SDI). With SDI the entire DC's infrastructure is abstracted, becoming software-defined and dynamically programmable [11].…”

Section: Introductionmentioning

confidence: 99%