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Generalizing QoS-Aware Memory Bandwidth Allocation to Multi-Socket Cloud Servers
KTH. Univ Lisbon, INIESC ID, Lisbon, Portugal..
Univ Lisbon, INIESC ID, Lisbon, Portugal..
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0002-6779-7435
2021 (English)In: 2021 Ieee 14Th International Conference On Cloud Computing (Cloud 2021) / [ed] Ardagna, CA Chang, C Daminai, E Ranjan, R Wang, Z Ward, R Zhang, J Zhang, W, Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 551-557Conference paper, Published paper (Refereed)
Abstract [en]

Although the problem of QoS-aware resource allocation is not new, novel hardware-based resource allocation mechanisms have recently become available in commodity cloud servers and enabled a new generation of QoS-aware resource allocation approaches. Unfortunately, to the best of our knowledge, existing proposals are by design tailored to single-socket architectures only. In many warehouse scale data centers, dual-socket (or even larger) machines already constitute the largest share of hosts. This paper presents the full design and implementation of BALM, a QoS-aware memory bandwidth allocation technique for multi-socket architectures. BALM combines commodity bandwidth allocation mechanisms originally designed for single-socket with a novel adaptive cross-socket page migration scheme. Our evaluation with a large and dynamic set of real applications co-located on a dual-socket machine shows that BALM can overcome the efficiency limitations of state-of-the-art. BALM delivers substantial throughput gains to bandwidth-intensive best-effort applications, while ensuring marginal SID violation windows to latency-critical applications.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2021. p. 551-557
Series
IEEE International Conference on Cloud Computing, ISSN 2159-6182
Keywords [en]
QoS-aware resource allocation, Cloud computing, Multi-socket systems
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-309298DOI: 10.1109/CLOUD53861.2021.00071ISI: 000749430800059Scopus ID: 2-s2.0-85119378639OAI: oai:DiVA.org:kth-309298DiVA, id: diva2:1641284
Conference
IEEE 14th International Conference on Cloud Computing (CLOUD), SEP 05-10, 2021, ELECTR NETWORK, Chicago, IL, USA
Note

QC 20220301

Part of proceedings ISBN: 978-1-6654-0060-2

Available from: 2022-03-01 Created: 2022-03-01 Last updated: 2023-03-06Bibliographically approved

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Vlassov, Vladimir

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Citation style
  • apa
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