Axiom Gateway
Join
Latest
Call for Papers: Vol. 42 closes 30 June●New: Quantum Security Summit registration open●Axiom Standard 7042-2024 now ratified●Grant cycle 2025 — $4.2M committed●Fellows election voting opens 15 July●Call for Papers: Vol. 42 closes 30 June●New: Quantum Security Summit registration open●Axiom Standard 7042-2024 now ratified●Grant cycle 2025 — $4.2M committed●Fellows election voting opens 15 July●
Digital Library

Research Archive

Search across 2.4 million peer-reviewed documents from journals, conferences, and standards.

Showing 2 of 2,418,902 results

Journal Article Subscription Cloud Computing

Multi-Cloud DevOps: Portability Architectures, Vendor Lock-in Mitigation Strategies, and Operational Complexity in Heterogeneous Cloud Environments

As organizations distribute workloads across multiple cloud providers to optimize cost, latency, regulatory compliance, and vendor risk, their DevOps pipelines must increasingly operate across heterogeneous cloud environments. Multi-cloud DevOps introduces significant engineering challenges: toolchain fragmentation, authentication model divergence, network topology complexity, and observability aggregation across provider-siloed data streams. This paper presents the first systematic empirical study of multi-cloud DevOps, examining 11 organizations operating production workloads across two or more major cloud providers. Through interviews (n=67), pipeline architecture analysis, and an industry survey (n=412), we identify and characterize three multi-cloud DevOps architectural patterns — Cloud-Agnostic Abstraction, Provider-Native Federation, and Workload Partitioning — and evaluate their trade-offs across portability, operational complexity, and performance dimensions. We find that the Cloud-Agnostic Abstraction pattern, typically implemented through Terraform with provider-agnostic modules and cloud-neutral container orchestration, achieves the highest portability score but incurs a 34% higher operational complexity rating than single-cloud environments. We introduce the Multi-Cloud Operational Overhead Index (MOOI) and provide a decision framework for selecting multi-cloud architecture patterns based on organizational maturity, compliance requirements, and engineering capacity.

Adaeze Okonkwu, Marcus Lindblom, Akihiro Watanabe, Clara Rodrigues· Jul 2020· 298 citations
Journal Article Open Access Bioinformatics

Single-Cell RNA Sequencing Data Analysis Pipelines: Scalable Dimensionality Reduction, Cell Type Clustering, and Trajectory Inference for Million-Cell Atlas Construction

Single-cell RNA sequencing (scRNA-seq) has transformed cell biology by enabling genome-wide transcriptomic profiling at single-cell resolution, but the computational pipelines required to process, normalize, cluster, and interpret datasets at the scale of million-cell atlases demand engineering solutions that go substantially beyond the academic prototype tools in common use. This paper presents ScaleSC, a horizontally scalable scRNA-seq analysis pipeline designed for cloud cluster deployment, and evaluates its performance on datasets ranging from 10,000 to 4.2 million cells against the Seurat, Scanpy, and RAPIDS cuML pipelines. ScaleSC implements distributed PCA using a randomized SVD algorithm that scales linearly with cell count on Apache Spark clusters, a GPU-accelerated UMAP implementation achieving 18x speedup over CPU UMAP at one million cells, and a graph-based clustering module supporting both Leiden and Louvain algorithms with adaptive resolution selection. On the 4.2-million-cell Human Cell Atlas bone marrow dataset, ScaleSC completes the full analysis pipeline in 47 minutes on a 32-node cluster, compared to 14.3 hours for Scanpy on the same hardware. Cell type assignment accuracy (benchmarked against expert-annotated ground truth labels) is 94.2 percent using ScaleSC marker-gene transfer, versus 91.8 percent for Seurat v4 label transfer. We release ScaleSC as open-source software with Docker-based deployment pipelines and cloud infrastructure templates for AWS, GCP, and Azure.

Chukwuebuka Aneke, Astrid Karlsson, Masashi Yamada, Amira El-Sayed· Jul 2020· 389 citations