Network support for resource disaggregation in next-generation datacenters

Han, Sangjin; Egi, Norbert; Panda, Aurojit; Ratnasamy, Sylvia; Shi, Guangyu; Shenker, Scott

doi:10.1145/2535771.2535778

Cited by 131 publications

(85 citation statements)

References 17 publications

(22 reference statements)

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“…A detailed study of the impacts of network bandwidth and latency of a composable datacenter for executing in-memory workloads such as GraphLab [16], MemcacheD [17] and Pig [18] was reported in [19]. When the remote memory is configured to contain 75% of the working set, it was found through simulation that the application level degradation was minimal (less than 10%) when network bandwidth is 40 Gb/s and the latency is less than s  10 [20]. There has been an ongoing effort to reconcile big data and big compute environments, such as the LLGrid at MIT Lincoln Lab [21].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Composable architecture for rack scale big data computing

Franke

Parris

et al. 2017

Future Generation Computer Systems

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show abstract

Section: Related Workmentioning

confidence: 99%

“…The authors of [4] were one of the first to discuss resource disaggregation on a broad perspective. Lately, further work has been done to understand required technical components to realize resource disaggregation, such as [5][10] [11]. Today's most tangible realization of a disaggregated system is seen in Intel's rack scale design (RSD) [12] which is part of the foundation of the first disaggregated system available in the market [13].…”

Section: Related Workmentioning

confidence: 99%

“…In the center of this architecture change is the concept of hardware resource disaggregation [4], whose flexibility not only brings new functional opportunities, but it is also seen as a promising step towards reduced total cost of ownership (TCO). There have been a set of early studies looking to application performance under this new architecture [4] [5]. Although these studies showed that migrating certain applications can result in a decrease in performance, they have also pointed out that redesigning of applications with this architecture in mind could boost back application performance.…”

Section: Introductionmentioning

confidence: 99%

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Techno-economic framework for cloud infrastructure: a cost study of resource disaggregation

Mahloo

Soares

Roozbeh

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-The rapid growth of data and high-dependency of industries on using data put lots of focus on the computing facilities. Increasing the efficiency and re-architecting the underlying infrastructure of datacenters, has become a major priority. The total cost of owning and running a datacenter (DC) is affected by many parameters, which until recently were ignored as their impact on the business economy was negligible. However, that is not the case anymore, as in the new era of digital economy every penny counts. The market is too aggressive to ignore anything. Hence, the economic efficiency becomes vital for cloud infrastructure providers despite their size. This article presents a framework to assess cloud infrastructure economic efficiency, taking into account three main aspects: application profiling, hardware dimensioning and total cost of ownership (TCO). Moreover, it presents a cost study of deploying the emerging concept of disaggregated hardware architecture in DCs based on the proposed framework. The study considers all the major cost categories incurred during the DC lifetime in terms of both capital and operational expenditures. A thorough cost comparison between a DC running on a disaggregated hardware architecture with one running on a traditional server-based hardware architecture is presented. The study demonstrates the evolution of the yearly cost over DC lifetime as well as a sensitivity analysis, allowing to understand how to minimize the cost of running cloud. Results show that, lifecycle management cost is one of the main differentiators between two technologies. Moreover, it is shown that in the presence of heterogeneous workloads, having a DC based on a fully disaggregated hardware brings high savings (more than 40% depending on the applications) compared to the traditional hardware architectures independent of the hardware set-up.Index Terms-datacenter cost, disaggregated hardware, total cost of ownership, hardware pools, reconfigurable hardware

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“…There is considerable interest and effort in improving data center network architectures [6,7], however, efforts lack modularity and flexibility to deliver required performance for disaggregated data centers [8][9][10]. Traditional data centers have a relatively static computing infrastructure [11], normally a number of servers, and each with a set number of CPUs and fixed amount of memory.…”

Section: Introductionmentioning

confidence: 99%