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PerfEnforce: A Dynamic Scaling Engine for Analytics with Performance Guarantees

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Jennifer Ortiz, Brendan Lee, Magdalena Balazinska, Joseph L. Hellerstein

Databases · Submitted 31 May 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We find that perceptron learning outperforms the other two methods when making cluster scaling decisions.

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arXiv ID1605.09753 (version 1)
Pith IDPIOELOQW, Pith's permanent record of this paper and its review
CitationBrendan Lee, Jennifer Ortiz, Joseph L. Hellerstein, Magdalena Balazinska. "PerfEnforce: A Dynamic Scaling Engine for Analytics with Performance Guarantees." arXiv:1605.09753 (2016). https://pith.science/paper/PIOELOQW
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,222 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.10 · human participants 0.04 · controlled comparison 0.36 · randomized assignment 0.02 · states quantitative result 0.06 · compares with prior results 0.82 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In this paper, we present PerfEnforce, a scaling engine designed to enable cloud providers to sell performance levels for data analytics cloud services. PerfEnforce scales a cluster of virtual machines allocated to a user in a way that minimizes cost while probabilistically meeting the query runtime guarantees offered by a service level agreement. With PerfEnforce, we show how to scale a cluster in a way that minimally disrupts a user's query session. We further show when to scale the cluster using one of three methods: feedback control, reinforcement learning, or perceptron learning. We find that perceptron learning outperforms the other two methods when making cluster scaling decisions.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In this paper, we present PerfEnforce, a scaling engine designed to enable cloud providers to sell performance levels for data analytics cloud services. contribution 1.00 0.71 0.05 asserted 0.99
s1 PerfEnforce scales a cluster of virtual machines allocated to a user in a way that minimizes cost while probabilistically meeting the query runtime guarantees offered by a service level agreement. contribution 0.55 0.60 0.15 asserted 0.99
s2 With PerfEnforce, we show how to scale a cluster in a way that minimally disrupts a user's query session. contribution 0.94 0.89 0.06 asserted 1.00
s3 We further show when to scale the cluster using one of three methods: feedback control, reinforcement learning, or perceptron learning. method 0.75 0.83 0.07 asserted 0.99
s4 We find that perceptron learning outperforms the other two methods when making cluster scaling decisions. result 1.00 0.96 0.53 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.79.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In this paper, we present PerfEnforce, a scaling engine designed to enable cloud providers to sell performance levels for data analytics cloud services. PerfEnforce scales a cluster of virtual machines allocated to a user in a way that minimizes cost while probabilistically meeting the query runtime guarantees offered by a service level agreement. With PerfEnforce, we show how to scale a cluster in a way that minimally disrupts a user's query session. We further show when to scale the cluster using one of three methods: feedback control, reinforcement learning, or perceptron learning. We find that perceptron learning outperforms the other two methods when making cluster scaling decisions.

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1 arXiv paper cite this in 1 sentence

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it

Cites it as background 1 paper
  • More concretely, reinforcement learning has been applied to managing elastic clusters [ref] , scheduling [ref] , and physical design [ref] .

    Bao: Learning to Steer Query Optimizers · Related work · 0.89

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