cs.DC · 2026-08-27 · No. 97

Distributed, Parallel, and Cluster Computing, 2026-08-27.

8 new papers in cs.DC. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

8 entries

01

Slasher: Power Flexibility for Cloud Datacenters

Liuzixuan Lin, Fiodar Kazhamiaka, Alok Gautam Kumbhare, Chaojie Zhang, Jaylen Wang, Hassan Khan, Rodrigo L. Assis,...

cs.DC · cs.OS · eess.SY

Datacenters consume many megawatts of power, and regularly encounter scenarios that require modulating their power draw. These scenarios include datacenter infrastructure failures, power grid failures, grid services, and more, spanning a diverse range of requirements in terms of the power magnitude, the scope of the reduction, the notice time, and other dimensions. To address these scenarios, we have built Slasher, a general system for...

arxiv.org/abs/2608.26021 · PDF
02

Scalable Multi-GPU Simulation of 3D Multicellular Growth with RNN-Based Workload Balancing

Matvey Moisseyev, Huijing Du, Dandan Zheng, Chi Zhang, Hongfeng Yu

cs.DC · cs.CE · cs.LG · math.OC

Detailed multicellular growth simulations based on subcellular element models (SEMs) can capture complex tissue development, but their element-level interactions impose substantial computational cost. This work presents a scalable multi-GPU framework for 3D multicellular growth simulation that combines GPU acceleration, spatial binning, domain decomposition, and workload-aware partitioning. Cell movement, growth, and division continuously...

arxiv.org/abs/2608.25890 · PDF
03

Quantum Blackhole Learning-Optimized Hadamard Neural Network Model for Dynamic Resource Reservation in Industry Clouds

Deepika Saxena, Hari Mohan Gaur, Ashutosh Kumar Singh, Anand Mohan

cs.DC

Accurate workload prediction and proactive resource reservation are crucial for industry clouds. However, the conventional machine learning (CML) models with limited learning capabilities often fail to predict diverse, high-dimensional workloads with sudden changes in resource demand, leading to excessive power consumption and resource management issues. In this context, this article proposes a novel Hadamard neural network with quantum...

arxiv.org/abs/2608.25754 · PDF
04

REE-TM: Reliable and Energy-Efficient Traffic Management Model for Diverse Cloud Workloads

Ashutosh Kumar Singh, Deepika Saxena, Volker Lindenstruth

cs.DC

Diversity of workload demands lays a critical impact on efficient resource allocation and management of cloud services. The existing literature has either weakly considered or overlooked the heterogeneous feature of job requests received from wide range of internet services users. To address this context, the proposed approach named Reliable and Energy Efficient Traffic Management (REE-TM) has exploited the diversity of internet traffic in...

arxiv.org/abs/2608.25747 · PDF
05

A Spatially-Aware Publish-Subscribe Middleware for IoT Applications

Philipp Ungrund, Kurt Rothermel, Sukanya Bhowmik

cs.DC

Spatial and proximity awareness are critical enablers for efficient communication in Cyber-Physical Systems (CPS) and the Internet of Things (IoT). However, the lack of suitable middleware support and standardized mechanisms for spatial awareness significantly limits the pervasiveness of location-dependent applications. In this paper, we present a novel approach that extends topic-based publish-subscribe, a dominant messaging model in this...

arxiv.org/abs/2608.25728 · PDF
06

An Oversubscription and Service Pricing Exploitation-Based Profit Maximization Framework for Industry Cloud Resource Management

Deepika Saxena, Ashutosh Kumar Singh

cs.DC

This article proposed a novel industry cloud resource management framework that exploits resource oversubscription and heterogeneous service pricing models to maximize profitability and operational efficiency for industry cloud providers. The framework proposes an adaptive ensemble machine learning driven prediction model for proactive estimation of resource utilization of Virtual Machines (VM)s based on previous resource utilization of...

arxiv.org/abs/2608.25712 · PDF
07

psRL: Efficient Training for Agentic AI via Training-Time Prefix Sharing

Mianjie Yu, Zizhao Mo, Huanyu Qu, Zhirong Qian, Huanle Xu, Cen Li, Zifeng Zhao, Zhi Zhou, Jinhua Zhou, Jun Xie, Chengzhong Xu

cs.DC

In modern agentic AI training, the system bottleneck is shifting from rollout to update. Emerging sampling strategies such as tree-structured and step-wise RL greatly increase training sample volume while incurring relatively low marginal rollout cost, causing the update phase to dominate the end-to-end execution time. Crucially, this shift exposes a new optimization opportunity, as production traces reveal substantial prefix redundancy...

arxiv.org/abs/2608.25683 · PDF
08

Hierarchical Shared Memory-Aware Optimization for TRSM on GPU Platforms

Xinzhe Chen, Haowei Li, Lijuan Hu, Wenjing Ma, Fangfang Liu

cs.DC

Triangular Solve with Multiple Right-hand Sides (TRSM) is a fundamental BLAS Level-3 operation that underpins LU/Cholesky decomposition, sparse direct solvers, and matrix inversion. In the left-side lower-triangular case studied in this paper, efficient GPU implementation remains challenging because forward substitution introduces strict row-wise dependencies, and shared memory is too scarce to hold both operand matrices for wide data types...

arxiv.org/abs/2608.25469 · PDF

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