ICDE

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\title{Elastic Query Scheduling for\\Disaggregated Storage Databases}

\author{\IEEEauthorblockN{First Last}
\IEEEauthorblockA{\textit{University of Example}\\[email protected]}
\and
\IEEEauthorblockN{Jane Doe}
\IEEEauthorblockA{\textit{Example Research Labs}\\[email protected]}
\and
\IEEEauthorblockN{John Smith}
\IEEEauthorblockA{\textit{University of Example}\\[email protected]}}
\maketitle

\begin{abstract}
Disaggregated storage architectures decouple compute and storage,
exposing new scheduling challenges: query plans must be aware of
per-object latency, bandwidth quotas, and shared-cache effects. Elastix
models these dimensions jointly and outperforms state-of-the-art
schedulers by 2.1$\times$ on TPC-DS at 1TB.
\end{abstract}

\begin{IEEEkeywords}
query optimization, disaggregated storage, scheduling
\end{IEEEkeywords}

\section{Introduction}
Disaggregation is the dominant cloud-database architecture; it introduces
latency and bandwidth variability that classical schedulers do not anticipate.

\section{Background}
Snowflake, Aurora, Socrates, BigQuery architectures.

\section{Problem Formulation}
We model scheduling as a constrained optimization over task placements
with stochastic per-object latencies.

\begin{equation}
  T(Q) = \max_{\tau \in Q} \text{lat}(\tau) + \sum_{c \in \text{cache}} \text{shared}(c).
\end{equation}

\section{System Design}
Elastix replaces the scheduler layer in existing query engines without
requiring changes to the optimizer or the storage layer.

\section{Evaluation}
\begin{table}[t]
\centering\small
\begin{tabular}{lcc}
\toprule
Scheduler & p50 (s) & p99 (s) \\
\midrule
FIFO              & 18.4 & 142 \\
Quanto            & 11.2 &  84 \\
\textbf{Elastix}  & \textbf{4.8} & \textbf{38} \\
\bottomrule
\end{tabular}
\caption{TPC-DS runtime on 1TB, 100 concurrent queries.}
\end{table}

\section{Related Work}
Cloud data warehouses, adaptive query scheduling.

\section{Conclusion}
Disaggregation-aware scheduling is a first-class concern and yields
substantial practical gains.

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