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Grant Mcalister

dblp:385/4545 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%

Topics — the 8 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing
autoscaling
0.812024
Resource Management in Aurora Serverless · Proc. VLDB Endow. 2024
Cloud and datacenter computing
cluster resource management and scheduling
0.812024
Resource Management in Aurora Serverless · Proc. VLDB Endow. 2024
Cloud and datacenter computing › virtualization › virtual machine migration
live migration
0.812024
Resource Management in Aurora Serverless · Proc. VLDB Endow. 2024
Cloud and datacenter computing
resource management
0.812024
Resource Management in Aurora Serverless · Proc. VLDB Endow. 2024
Cloud and datacenter computing
serverless computing
0.812024
Resource Management in Aurora Serverless · Proc. VLDB Endow. 2024
Cloud and datacenter computing › resource allocation
workload allocation
0.812024
Resource Management in Aurora Serverless · Proc. VLDB Endow. 2024
Cloud and datacenter computing › resource management
resource oversubscription
0.212024
Resource Management in Aurora Serverless · Proc. VLDB Endow. 2024
Cloud and datacenter computing
virtualization
0.212024
Resource Management in Aurora Serverless · Proc. VLDB Endow. 2024

Methods — techniques the papers use, named apart from their topics

token bucket rate regulation · 0.8reactive control · 0.8hierarchical decision-making · 0.8
YearPublicationVenuePosition
2024 Resource Management in Aurora Serverless
abstract
Amazon Aurora Serverless is an on-demand, autoscaling configuration for Amazon Aurora with full MySQL and PostgreSQL compatibility. It automatically offers capacity scale-up/down (i.e., vertical scaling) based on a customer database application's needs. For customers with time-varying workloads, it offers cost savings compared to provisioned Aurora or other alternatives due to its agile and granular scaling and its usage-based charging model. This paper describes the key ideas underlying Aurora Serverless's resource management. To help meet its goals, Aurora Serverless adapts and fine tunes well-established ideas related to resource over-subscription; reactive control informed by recent measurements; distributed & hierarchical decision-making; and innovations in the DB engine, OS, and hypervisor for efficiency. Perhaps the most challenging goal is to offer a consistent resource elasticity experience while operating hosts at high degrees of utilization. Aurora Serverless implements several novel ideas for striking a balance between these opposing needs. Its technique for mapping workloads to hosts ensures that, in the common case, there is adequate spare capacity within a host to support fast scale-up for a workload. In the rare event this is not so, it live migrates workloads to ensure seamless scale-up. Its load distribution strategy is characterized by "unbalancing" of load across hosts to enable agile live migrations. Finally, it employs a token bucket-based rate regulation mechanism to prevent a growing workload from saturating its host faster than live migration-based remedial actions.
Bradley Barnhart, Marc Brooker, Daniil Chinenkov, Tony Hooper, Jihoun Im, Prakash Chandra Jha 0003, Tim Kraska, Ashok Kurakula, Grant Mcalister, Arjun Muthukrishnan, Aravinthan Narayanan, Douglas Terry, Bhuvan Urgaonkar, Jiaming Yan
Proc. VLDB Endow.10