Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.13396

ShoreKeeper: Accelerating Iterative Data Analytics in the Cloud via Distributed Recovery-Cost-Aware Caching

Jongseop Lee·Minsu Sun·Xinyue Ma·Atul Sandur·Myeongjae Jeon
View original source ↗Read source document ↗Export metadata ↓

Abstract

Large-scale data-intensive workloads powered by iterative graph or machine learning processing have an insatiable demand for memory due to the continuous generation of new intermediate data. Although existing caching strategies strive to keep performance-critical data in memory, they often fail in practice because they are limited to node-local memory management and assume high node availability. In this paper, we introduce ShoreKeeper, a framework that maximizes execution efficiency by treating cluster-wide memory as a unified, elastic pool. ShoreKeeper employs a global optimization model that maps distributed dataflows into a single graph abstraction to minimize the recovery cost of data evicted from cache across the cluster. To handle memory working sets and cluster capacities that fluctuate over time, ShoreKeeper employs a graph-pruning technique that permits the fast refinement of the caching plan. These optimizations allow ShoreKeeper to work seamlessly whether deployed on highly available VMs or cost-effective preemptible instances in the cloud. Our experiments show that ShoreKeeper consistently outperforms state-of-the-art systems.

Explore related subjects

cs.DC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jongseop Lee, Minsu Sun, Xinyue Ma, Atul Sandur, Myeongjae Jeon. 2026-10-09. ShoreKeeper: Accelerating Iterative Data Analytics in the Cloud via Distributed Recovery-Cost-Aware Caching. https://arxiv.org/abs/2610.13396

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Byzantine-linearizable Single-Writer Multi-Reader Register in the GPU-Inspired Shared Memory Model

The construction of fault-tolerant atomic registers is a fundamental problem in distributed computing. GPUs, widely used in scientific computing and machine learning, face Byzantine thread behaviour, while their shared memory lacks access control, allowing any thread to modify shared objects. This raises the question of whether atomic register semantics can be maintained when any thread, including the designated writer, may behave Byzantine. We study this problem in the GPU-inspired shared-memory model of Georgiou et al. and present implementations of a Byzantine-linearizable single-writer multi-reader (SWMR) register. CAReg uses constant memory with a quantifiable probability of invalid reads, while SnapObject maintains a bounded window of authenticated writes, eliminating invalid reads and tolerating up to \(n-1\) Byzantine processes through lock-free and wait-free variants. Our results demonstrate how GPU phase-based, lock-step execution can partially compensate for the absence of access control.

cs.DC↗
arXiv2026

A Self-Defining Operator for Distributed Applications

To operate distributed applications with high availability, operators must rapidly diagnose and mitigate any incident. LLM-based agents can help, but unlike experienced human operators they do not improve as they handle more incidents. Each investigation starts from scratch, re-deriving likely causes and re-testing hypotheses even when a similar fault was resolved in the past. We present a self-defining operator (SDO) that improves with time by updating its operational behavior and memory. When SDO resolves an incident, it distills that experience into playbooks, which describe reusable diagnostic instructions for future incidents with similar faults, and detectors, scripts that take application telemetry as input and output relevant playbook(s). SDO continuously runs the detectors and invokes LLMs only when one fires, so its token cost scales with incident count rather than with operational time. Across 11 open-source microservice applications, SDO cuts resolution time by 52% on recurring faults while keeping per-incident cost low. SDO is open source at https://github.com/uw-syfi/sdo.

cs.DC↗
arXiv2026

Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving

Large Language Model (LLM) inference is driving a growing share of datacenter carbon emissions. Although carbon-aware scheduling can reduce these emissions in geo-distributed datacenters by exploiting the spatiotemporal variations in grid carbon intensity, the latency-sensitive nature of LLM serving constrains this design space. While prior studies investigate the carbon savings achievable through spatial scheduling, they oversimplify the dynamics of modern inference engines, such as queueing and continuous batching, which strongly shape inference latency. We address this issue by designing and evaluating a latency-centric, geo-distributed inference simulator, calibrated with hardware-grounded latency and power measurements, that reproduces these dynamics and enables the study of carbon savings under strict service-level objectives (SLOs). We then formulate a series of SLO- and carbon-aware schedulers of increasing sophistication, from per-request heuristics to batch-level optimization: (i) a greedy heuristic, (ii) an integer linear programming (ILP)-based scheduler jointly assigning batches of requests, (iii) an iterative ILP-based solution projecting its own assignments back into the load estimates to capture load coupling, and (iv) a facility mixed-integer linear programming (MILP) scheduler pricing the energy of batched requests collectively rather than in isolation. Across three LLMs under MLPerf SLOs and varying system loads, the MILP-based option offers the best carbon-SLO balance, reducing carbon emissions by up to 60% relative to the round-robin policy, with an average SLO violation of 3%. Our characterization further shows that savings grow with the spread of carbon intensity across datacenters and shrink as system load increases, while SLO attainment depends on how frequently schedulers receive datacenters' load updates.

cs.DC↗