2017
DOI: 10.1016/j.future.2016.07.014
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Composable architecture for rack scale big data computing

Abstract: 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 const… Show more

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Cited by 52 publications
(27 citation statements)
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“…[6] evaluates the impact of data-centric workloads on the design of DC. Their observation suggests heterogeneity in the DC, in which running a job on the most cost-efficient server reduces the overall cost.…”
Section: Related Workmentioning
confidence: 99%
“…[6] evaluates the impact of data-centric workloads on the design of DC. Their observation suggests heterogeneity in the DC, in which running a job on the most cost-efficient server reduces the overall cost.…”
Section: Related Workmentioning
confidence: 99%
“…With this technology, the cost of hardware maintenance and acquisition can be reduced and, as a consequence, the service delivery cost can also be reduced. According to Reference , this design approach “ offers the potential advantage of enabling continuous peak workload performance while minimizing resource fragmentation for fast evolving heterogeneous workloads .”…”
Section: Introductionmentioning
confidence: 99%
“…Resource disaggregation can be implemented physically or logically at different scales i.e. rack-scale, pod-scale or DC-scale [27]. The composition of logical hosts from disaggregated resources adopts virtualization technologies and other software-oriented techniques to abstract the control plane of physical resources from their data plane and for control, orchestration and monitoring.…”
Section: Introductionmentioning
confidence: 99%