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Toward Next-generation Data Centers: Principles of Software-Defined “Hardware” Infrastructures and Resource Disaggregation
KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS. Ericsson Research.ORCID iD: 0000-0003-4088-7884
2019 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

The cloud is evolving due to additional demands introduced by new technological advancements and the wide movement toward digitalization. Therefore, next-generation data centers (DCs) and clouds are expected (and need) to become cheaper, more efficient, and capable of offering more predictable services.

Aligned with this, we examine the concept of software-defined “hardware” infrastructures (SDHI) based on hardware resource disaggregation as one possible way of realizing next-generation DCs. We start with an overview of the functional architecture of a cloud based on SDHI. Following this, we discuss a series of use-cases and deployment scenarios enabled by SDHI and explore the role of each functional block of SDHI’s architecture, i.e., cloud infrastructure, cloud platforms, cloud execution environments, and applications.

Next, we propose a framework to evaluate the impact of SDHI on techno-economic efficiency of DCs, specifically focusing on application profiling, hardware dimensioning, and total cost of ownership (TCO). Our study shows that combining resource disaggregation and software-defined capabilities makes DCs less expensive and easier to expand; hence they can rapidly follow the exponential demand growth. Additionally, we elaborate on technologies behind SDHI, its challenges, and its potential future directions.

Finally, to identify a suitable memory management scheme for SDHI and show its advantages, we focus on the management of Last Level Cache (LLC) in currently available Intel processors. Aligned with this, we investigate how better management of LLC can provide higher performance, more predictable response time, and improved isolation between threads. More specifically, we take advantage of LLC’s non-uniform cache architecture (NUCA) in which the LLC is divided into “slices,” where access by the core to which it closer is faster than access to other slices. Based upon this, we introduce a new memory management scheme, called slice-aware memory management, which carefully maps the allocated memory to LLC slices based on their access time latency rather than the de facto scheme that maps them uniformly. Many applications can benefit from our memory management scheme with relatively small changes. As an example, we show the potential benefits that Key-Value Store (KVS) applications gain by utilizing our memory management scheme. Moreover, we discuss how this scheme could be used to provide explicit CPU slicing – which is one of the expectations of SDHI  and hardware resource disaggregation.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2019. , p. 102
Series
TRITA-EECS-AVL ; 2019:39
Keywords [en]
cloud computing, next-generation data centers, resource disaggregation, TCO, last level cache
National Category
Communication Systems
Research subject
Information and Communication Technology
Identifiers
URN: urn:nbn:se:kth:diva-249618ISBN: 978-91-7873-169-5 (print)OAI: oai:DiVA.org:kth-249618DiVA, id: diva2:1304795
Presentation
2019-05-10, Sal B, Electrum, KTH, Kistagången 16, Kista., Stockholm, 09:30 (English)
Opponent
Supervisors
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Note

QC 20190415

Available from: 2019-04-15 Created: 2019-04-13 Last updated: 2019-04-17Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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