添加解锁的示意图
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\OT1/ptm/m/n/10 ence and Tech-nol-ogy In-no-va-tion Co-op-er-a-tion" (Grant
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\label{fig:overview}
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\end{figure}
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To address the challenges of storage-level I/O contention and expensive runtime computations, we propose a layered distributed retrieval framework. As illustrated in Fig. \ref{fig:overview}, the system architecture is composed of four primary processing components: (1) \emph{requst interface}, (2) \emph{index manager}, (3) \emph{I/O coordinator}, (4) \emph{parallel executors}, and (5) \emph{adaptive tuner}.
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To address the challenges of storage-level I/O contention and expensive runtime computations, we propose a layered distributed retrieval framework. As illustrated in Fig. \ref{fig:overview}, the system architecture is composed of four primary processing components: (1) \emph{requst interface}, (2) \emph{index manager}, (3) \emph{I/O coordinator}, (4) \emph{data loader}, and (5) \emph{adaptive tuner}.
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The $\emph{requst interface}$ serves as the system entry point. It is responsible for accepting concurrent spatio-temporal retrievals. The $\emph{index manager}$ acts as the planner of the system, interacting with the metadata storage. It translates logical spatio-temporal predicates into physical storage locations using a dual-layer inverted index. The $\emph{I/O coordinator}$ serves as the traffic control layer. It detects spatial overlaps among concurrent reading plans to identify potential I/O conflicts and applies the hybrid concurrency-aware protocol to reorder or merge conflicting requests. Finally, the $\emph{parallel executors}$ interface with the distributed file system or object store to read the pixel data. What's more, \emph{adaptive tuner} optimizes the execution parameters in the background.
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The $\emph{requst interface}$ serves as the system entry point. It is responsible for accepting concurrent spatio-temporal retrievals. The $\emph{index manager}$ acts as the planner of the system, interacting with the metadata storage. It translates logical spatio-temporal predicates into physical storage locations using a dual-layer inverted index. The $\emph{I/O coordinator}$ serves as the traffic control layer. It detects spatial overlaps among concurrent reading plans to identify potential I/O conflicts and applies the hybrid concurrency-aware protocol to reorder or merge conflicting requests. Finally, the $\emph{data loader}$ interface with the distributed file system or object store to read the pixel data. What's more, \emph{adaptive tuner} optimizes the execution parameters in the background.
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\section{I/O-aware Indexing Structure}\label{sec:Index}
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This section introduces the details of the indexing structure for spatio-temporal range retrieval over RS data.
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@@ -270,7 +270,7 @@ Existing window-based I/O systems successfully reduce the I/O cost to the size o
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In contrast, our I/O-aware indexing approach fundamentally alters this trade-off. By materializing the grid-to-pixel mapping in the I2G table, we effectively shift the computational burden from retrieval time to ingestion time. Although the two-phase lookup (G2I and I2G) introduces a slight overhead compared to simple tree traversals, $C_{meta}$ remains in the order of milliseconds—orders of magnitude smaller than disk I/O latency. Since the precise pixel windows are pre-calculated and stored, the runtime geospatial computation is effectively eliminated, i.e., $C_{geo} = 0$. The system retains the minimal I/O cost characteristic of window-based approaches, fetching only relevant byte ranges. Therefore, our design achieves the theoretical minimum for both computation and I/O components within the retrieval execution critical path.
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\section{Hybrid Concurrency-Aware I/O Coordination}\label{sec:CC}
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In this section, we propose a hybrid coordination mechanism that adaptively employs either lock-free non-deterministic execution or deterministic coordinated scheduling based on the real-time contention level of spatio-temporal workloads.
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In this section, we propose a hybrid coordination mechanism that adaptively employs either non-deterministic execution or deterministic coordinated scheduling based on the real-time contention level of spatio-temporal workloads. Fig.~\ref{fig:cc} provides an overview of the entire coordination workflow.
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\begin{figure*}
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\centering
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@@ -309,19 +309,19 @@ The system utilizes a rule-based assignment mechanism similar to HDCC \cite{Hong
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\end{enumerate}
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\subsection{Deterministic Coordinated and Non-deterministic Execution}
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When $\sigma \ge \tau$, the system switches to a deterministic path to mitigate storage-level contention and I/O amplification, as shown in Fig.~\ref{fig:cc}. To coordinate concurrent access to shared storage resources, we introduce a Global I/O Plan Queue that enforces a deterministic ordering over all admitted I/O plans. Each windowed access $(img, w)$ derived from incoming retrievals is inserted into this queue according to a predefined policy, such as FIFO based on arrival time or lexicographic ordering by $(timestamp, RetrievalID)$.
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When $\sigma \ge \tau$, the system switches to a deterministic path to mitigate storage-level contention and I/O amplification, as shown in Fig.~\ref{fig:cc}. To coordinate concurrent access to shared storage resources, we introduce a Global I/O Plan Queue that enforces a globally consistent ordering over all admitted I/O plans. Each windowed access $(img, w)$ derived from incoming retrievals is inserted into this queue according to a predefined policy, such as FIFO based on arrival time or lexicographic ordering by $(timestamp, RetrievalID)$. This design is inspired by deterministic scheduling in systems such as Calvin~\cite{Thomson12Calvin}, but differs fundamentally in scope: ordering is imposed on window-level I/O operations rather than on transactions. As a result, accesses to the same image region across different retrievals follow a globally consistent order, preventing uncontrolled interleaving and reducing storage-layer contention. The deterministic ordering also provides a stable foundation for subsequent I/O coordination and sharing.
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This design is inspired by deterministic scheduling in systems such as Calvin, but differs fundamentally in its scope: the ordering is imposed on window-level I/O operations rather than on transactions. As a result, accesses to the same image region across different retrievals follow a globally consistent order, preventing uncontrolled interleaving of reads and reducing contention at the storage layer. The deterministic ordering also provides a stable foundation for subsequent I/O coordination and sharing.
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The coordination mechanism operates through three stages within each scheduling interval:
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The core of our approach lies in coordinating concurrent windowed reads at the image level. Windows originating from different retrievals may overlap spatially, be adjacent, or even be identical. Executing these requests independently would lead to redundant reads and excessive I/O amplification.
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\emph{Stage 1: Global De-duplication.} The system extracts all windowed access pairs $(img, w)$ from admitted retrievals and inserts them into a global window set $\mathcal{W}_{total}$. If multiple retrievals request the same pixel window $w$ from image $img$, only one unique entry is retained, preventing redundant retrieval of overlapping spatial grids.
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To address this, the system performs three coordination steps within each scheduling interval. Stage 1: Global De-duplication. The system first extracts all windowed access pairs $(img, w)$ from the admitted retrievals and inserts them into a global window set ($\mathcal{W}_{total}$). If multiple retrievals $Q_1, Q_2, ..., Q_n$ request the same pixel window $w$ from image $img$, the system retains only one unique entry in $\mathcal{W}_{total}$. This stage ensures that any specific byte range is identified as a single logical requirement, effectively preventing the redundant retrieval of overlapping spatial grids. Stage 2: Range Merging. After de-duplication, the system analyzes the physical disk offsets of all unique windows in $\mathcal{W}_{total}$. Following the principle of improving access locality, windows that are physically contiguous or separated by a gap smaller than a threshold $\theta$ are merged into a single read. Stage 3: Dispatching. This stage maintains a mapping between the physical byte-offsets in the buffer and the logical window requirements of each active retrieval. Each retrieval $Q_i$ receives only the exact pixel windows $w \in Plan(Q_i)$ it originally requested. This is achieved via zero-copy memory mapping where possible, or by slicing the shared system buffer into local thread-wise structures. This ensures that while the physical I/O is shared to reduce amplification, the logical execution of each retrieval remains independent and free from irrelevant data interference.
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\emph{Stage 2: Range Merging.} After de-duplication, the system analyzes physical disk offsets of all unique windows in $\mathcal{W}_{total}$. Windows that are physically contiguous or separated by a gap smaller than threshold $\theta$ are merged into a single read operation, improving access locality and reducing IOPS.
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For example, when $Q_1$ requests grids $\{1, 2\}$ and $Q_2$ requests grids $\{2, 3\}$, Stage 1 identifies the unique requirement set $\{1, 2, 3\}$. Stage 2 then merges these into a single contiguous I/O operation covering the entire range $[1, 3]$. In Stage 3, the dispatcher identifies memory offsets corresponding to grids $1$ and $2$ within the buffer and maps these slices to the private cache of $Q_1$. For $Q_2$, similarly, the dispatcher extracts and delivers slices for grids $2$ and $3$ to $Q_2$.
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\emph{Stage 3: Dispatching.} The dispatcher maintains mappings between physical byte offsets in the shared buffer and logical window requirements of each retrieval. Each retrieval $Q_i$ receives only the exact pixel windows $w \in Plan(Q_i)$ it originally requested, achieved via zero-copy memory mapping or buffer slicing. This ensures that while physical I/O is shared to reduce amplification, logical execution of each retrieval remains independent and free from irrelevant data interference.
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Through these mechanisms, concurrent retrievals collaboratively share I/O, and the execution unit becomes a coordinated window read rather than an isolated request. Importantly, this coordination operates entirely at the I/O planning level and does not require any form of locking or transaction-level synchronization.
|
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For example, when $Q_1$ requests grids $\{1, 2\}$ and $Q_2$ requests grids $\{2, 3\}$, Stage 1 identifies the unique requirement set $\{1, 2, 3\}$. Stage 2 merges these into a single contiguous I/O operation covering $[1, 3]$. In Stage 3, the dispatcher extracts memory offsets for grids $1$ and $2$ and delivers them to $Q_1$, while extracting and delivering grids $2$ and $3$ to $Q_2$. Through these mechanisms, concurrent retrievals collaboratively share I/O without requiring transaction-level synchronization.
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When contention remains below the threshold ($\sigma < \tau$), the system prioritizes low latency over merging efficiency by adopting an optimistic dispatch mechanism, as shown in Fig.~\ref{fig:cc}. Instead of undergoing heavy-weight sorting, I/O plans are immediately offloaded to the execution engine. By utilizing thread-local sublists, each thread independently handles its byte-range requests.
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When contention remains below the threshold ($\sigma < \tau$), the system prioritizes low latency by adopting optimistic dispatch, as shown in Fig.~\ref{fig:cc}. Instead of incurring coordination overhead, I/O plans are immediately offloaded to the execution engine with thread-local sublists, each independently handling its byte-range requests. This adaptive switching ensures that the system operates optimistically under low contention while engaging deterministic coordination to prevent thrashing when overlap becomes significant.
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\subsection{Optimistic Read Execution and Completion}
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Once a coordinated window read is scheduled, the system issues the corresponding byte-range I/O request immediately. Read execution is fully optimistic: there is no validation phase, no abort, and no rollback. This is enabled by the immutability of remote-sensing imagery and by the deterministic ordering of I/O plans, which together ensure consistent and repeatable read behavior.
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@@ -330,6 +330,20 @@ A retrieval is considered complete when all windows in its I/O plan have been se
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Overall, this concurrency-aware I/O coordination mechanism reinterprets concurrency control as a problem of coordinating shared I/O flows. By operating at the granularity of windowed reads and leveraging deterministic ordering and optimistic execution, it effectively reduces redundant I/O and improves scalability for multi-user spatio-temporal retrieval workloads.
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\subsection{Why Coordination Works}
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\begin{figure}
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\centering
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\includegraphics[width=3.0in]{fig/unlock.png}
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\caption{Illustration of retrieval execution models for overlapping spatio-temporal range queries}
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\label{fig:unlock}
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\end{figure}
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Fig.~\ref{fig:unlock} illustrates the fundamental distinction between non-deterministic and deterministic execution models, demonstrating how our coordination mechanism ``unlocks'' concurrent I/O performance under high contention.
|
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As shown in Fig.~\ref{fig:unlock}(a), when multiple overlapping retrievals $Q_1, Q_2, Q_3$ execute independently without coordination, they compete for the same underlying storage resources. This uncoordinated access pattern leads to severe I/O contention: $Q_2$ and $Q_3$ must wait for $Q_1$ to release locks on shared image regions, resulting in unpredictable serialization and degraded throughput. More critically, each retrieval independently performs identical geospatial computations and issues separate read requests for overlapping spatial regions. The timeline on the right of Fig.~\ref{fig:unlock}(a) reveals the consequences: redundant disk seeks, repeated lock acquisitions, and duplicated data transfers. This execution model suffers computational redundancy caused by repeated coordinate transformations, and I/O amplification due to multiple physical reads of the same byte ranges. Under high concurrency, this uncoordinated thrashing leads to super-linear latency growth.
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Fig.~\ref{fig:unlock}(b) illustrates how our deterministic coordination transforms competitive I/O into collaborative I/O. Instead of treating retrievals as isolated transactions, the system admits overlapping retrievals as a unified batch (enclosed by the red bounding box) and enforces a deterministic execution order. By merging overlapping window requests from $Q_1, Q_2, Q_3$ into a single globally-ordered I/O plan, the system converts $N$ concurrent random reads into one sequential scan. As shown in the timeline of Fig.~\ref{fig:unlock}(b), the coordinated approach requires only one lock acquisition and one contiguous disk read, eliminating seek overhead and reducing rotational latency in spinning disks. This serialization advantage is particularly significant for parallel file systems where small random reads suffer from metadata amplification. What's more, the deterministic scheduler analyzes all admitted retrievals and identifies overlapping spatial windows before issuing any I/O. When multiple retrievals request the same pixel region, the system retains only one unique entry in the global I/O plan, effectively collapsing logical demand $N$ into physical execution $1 \le k \le N$.
|
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\section{I/O Stack Tuning}\label{sec:Tuning}
|
||||
We first describe the I/O stack tuning problem and then propose the surrogate-assisted GMAB algorithm to solve the problem.
|
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