Scholarlink

<p>Cloud-based high-performance computing (HPC) is reshaping the computational landscape by offering scalable, flexible, and cost-effective alternatives to traditional supercomputing infrastructures. Despite its growing influence across scientific and industrial domains, adoption remains limited by persistent challenges in four critical dimensions: performance, portability, productivity, and security. This study presents a systematic literature review (SLR) of peer-reviewed journal publications from 2018 to 2024, aimed at synthesizing recent advancements and identifying unresolved research gaps in cloud-based HPC. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive literature search was conducted across the Scopus and Web of Science databases, yielding an initial pool of 77 studies. After rigorous screening and eligibility assessment, 51 high-quality articles were selected for in-depth analysis. The analysis reveals that intelligent workload scheduling, dynamic resource management strategies, and high-speed RDMA-enabled interconnects play a critical role in mitigating performance bottlenecks and enhancing scalability in cloud-based HPC. However, workload portability remains a major challenge due to architectural heterogeneity, proprietary APIs, and vendor lock-in. Recent efforts in multi-cloud orchestration, declarative provisioning, and platform-agnostic standards offer promising pathways to address these issues. Productivity enhancements are being driven by the integration of automated workflow tools, advanced decision-support systems, and user-friendly interfaces, which lower barriers for non-specialist users. In the security domain, advancements such as hardware-based trusted execution environments, robust encryption techniques, and fine-grained access control mechanisms are strengthening the protection of sensitive HPC workloads in shared cloud infrastructures. Despite these advancements, significant gaps persist. Key areas requiring further research include real-time performance tuning through autonomous optimization techniques, privacy-preserving computation frameworks, and the development of sustainable, energy-efficient HPC architectures. Addressing these challenges will require coordinated efforts across academia, industry stakeholders, and cloud service providers. This review offers a comprehensive, thematically organized synthesis of the current state-of-the-art and serves as a foundational resource for advancing secure, portable, and efficient cloud-based HPC.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Performance, portability, productivity, and security in HPC cloud: a systematic literature review

  • 期刊论文

  • 发表日期：

    2025年7月29日

查看全文
  • Mandeep Kumar,
  • Gagandeep Kaur,
  • Prashant Singh Rana

摘要

Cloud-based high-performance computing (HPC) is reshaping the computational landscape by offering scalable, flexible, and cost-effective alternatives to traditional supercomputing infrastructures. Despite its growing influence across scientific and industrial domains, adoption remains limited by persistent challenges in four critical dimensions: performance, portability, productivity, and security. This study presents a systematic literature review (SLR) of peer-reviewed journal publications from 2018 to 2024, aimed at synthesizing recent advancements and identifying unresolved research gaps in cloud-based HPC. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive literature search was conducted across the Scopus and Web of Science databases, yielding an initial pool of 77 studies. After rigorous screening and eligibility assessment, 51 high-quality articles were selected for in-depth analysis. The analysis reveals that intelligent workload scheduling, dynamic resource management strategies, and high-speed RDMA-enabled interconnects play a critical role in mitigating performance bottlenecks and enhancing scalability in cloud-based HPC. However, workload portability remains a major challenge due to architectural heterogeneity, proprietary APIs, and vendor lock-in. Recent efforts in multi-cloud orchestration, declarative provisioning, and platform-agnostic standards offer promising pathways to address these issues. Productivity enhancements are being driven by the integration of automated workflow tools, advanced decision-support systems, and user-friendly interfaces, which lower barriers for non-specialist users. In the security domain, advancements such as hardware-based trusted execution environments, robust encryption techniques, and fine-grained access control mechanisms are strengthening the protection of sensitive HPC workloads in shared cloud infrastructures. Despite these advancements, significant gaps persist. Key areas requiring further research include real-time performance tuning through autonomous optimization techniques, privacy-preserving computation frameworks, and the development of sustainable, energy-efficient HPC architectures. Addressing these challenges will require coordinated efforts across academia, industry stakeholders, and cloud service providers. This review offers a comprehensive, thematically organized synthesis of the current state-of-the-art and serves as a foundational resource for advancing secure, portable, and efficient cloud-based HPC.