github.com/PKUHPC/CraneSched
PKUHPC/CraneSched
A distributed scheduling system for HPC and AI workloads (⭐ 134)
arxiv.org/abs/2310.03522v1
Putting a Padlock on Lambda -- Integrating vTPMs into AWS Firecracker
When software services use cloud providers to run their workloads, they place implicit trust in the cloud provider, without an explicit trust relationship. One way to achieve such explicit trust in a computer system is to use a hardware Trusted Platf...
www.bing.com/ck/a?!&&p=2f8be264d7b81879969bb97a357b725dd95771d4bbc5961df6f55fb27a59aba0JmltdHM9MTc3Mjc1NTIwMA&ptn=3&ver=2&hsh=4&fclid=18afbc35-d9eb-67b2-214d-ab21d87d66c1&u=a1aHR0cHM6Ly9hYm91dC5mYi5jb20vbmV3cy8yMDI2LzAyL21ldGEtYW1kLXBhcnRuZXItbG9uZ3Rlcm0tYWktaW5mcmFzdHJ1Y3R1cmUtYWdyZWVtZW50Lw&ntb=1
Meta and AMD Partner for Longterm AI Infrastructure Agreement
Feb 24, 2026 · We’re announcing a long-term agreement with AMD to power our AI infrastructure with up to 6GW of AMD Instinct GPUs, helping us build a flexible, resilient tech stack for our AI workloads.
arxiv.org/abs/2110.09221v1
A Serverless Distributed Ledger for Enterprises
Enterprises have been attracted by the capability of blockchains to provide a single source of truth for workloads that span companies, geographies, and clouds while retaining the independence of each party's IT operations. However, so far production...
arxiv.org/abs/2507.17769v1
PolyServe: Efficient Multi-SLO Serving at Scale
Advances in Large Language Models (LLMs) have led to a surge of LLM-powered applications. These applications have diverse token-generation latency requirements. As a result, simply classifying workloads as latency-sensitive (LS) or best-effort (BE) o...
arxiv.org/abs/2504.15247v1
Lance: Efficient Random Access in Columnar Storage through Adaptive Structural E…
The growing interest in artificial intelligence has created workloads that require both sequential and random access. At the same time, NVMe-backed storage solutions have emerged, providing caching capability for large columnar datasets in cloud stor...
arxiv.org/abs/2010.02989v1
Sharon: Shared Online Event Sequence Aggregation
Streaming systems evaluate massive workloads of event sequence aggregation queries. State-of-the-art approaches suffer from long delays caused by not sharing intermediate results of similar queries and by constructing event sequences prior to their a...
arxiv.org/abs/1803.01296v1
Scout: An Experienced Guide to Find the Best Cloud Configuration
Finding the right cloud configuration for workloads is an essential step to ensure good performance and contain running costs. A poor choice of cloud configuration decreases application performance and increases running cost significantly. While Baye...
github.com/gardener/landscaper
gardener/landscaper
Development of Landscaper - A deployer for K8S workloads with integrated data flow engine. (⭐ 60)
arxiv.org/abs/2510.16025v1
Resource Estimation of CGGI and CKKS scheme workloads on FracTLcore Computing Fa…
Cornami Mx2 accelerates of Fully Homomorphic Encryption (FHE) applications, enabled by breakthrough work [1], which are otherwise compute limited. Our processor architecture is based on the systolic array of cores with in-memory compute capability an...
www.bing.com/ck/a?!&&p=b1d549720b672c9bb508528facc01088a48ba3100cc7ea019e96602f7be5b997JmltdHM9MTc3MjY2ODgwMA&ptn=3&ver=2&hsh=4&fclid=0128f519-7dca-67ee-0e62-e20a7c40660c&u=a1aHR0cHM6Ly9jYXRhbG9nLndvcmtzaG9wcy5hd3Mvd2VsbC1hcmNoaXRlY3RlZC10b29sL2VuLVVT&ntb=1
Well-Architected Tool - catalog.workshops.aws
The AWS Well-Architected Tool , available at no cost in the AWS Management Console , provides a mechanism for regularly evaluating workloads, identifying high-risk issues, and recording …
www.reddit.com/r/pcmasterrace/comments/1mt3tj1/the_nvidia_ai_gpu_black_market_investigating/
THE NVIDIA AI GPU BLACK MARKET | Investigating Smuggling, Corruption, & Gove…
GamersNexus investigative report >NVIDIA (NVDA) GPUs have become so in-demand for so-called "AI" workloads that a black market has emerged around them. Where there's prohibition, there's smuggling...
arxiv.org/abs/2406.08746v1
The AHA-Tree: An Adaptive Index for HTAP Workloads
In this demo, we realize data indexes that can morph from being write-optimized at times to being read-optimized at other times nonstop with zero-down time during the workload transitioning. These data indexes are useful for HTAP systems (Hybrid Tran...
arxiv.org/abs/2510.15122v1
NEMO: Faster Parallel Execution for Highly Contended Blockchain Workloads (Full…
Following the design of more efficient blockchain consensus algorithms, the execution layer has emerged as the new performance bottleneck of blockchains, especially under high contention. Current parallel execution frameworks either rely on optimisti...
www.bing.com/ck/a?!&&p=0966ab80acd0e670593a0484c9f0a7d4e8c60044006263fe44ab594e4288226bJmltdHM9MTc3MjU4MjQwMA&ptn=3&ver=2&hsh=4&fclid=0a8b6059-3e35-65da-0c24-774b3f4c6483&u=a1aHR0cHM6Ly9jYXRhbG9nLndvcmtzaG9wcy5hd3Mvd2VsbC1hcmNoaXRlY3RlZC10b29sL2VuLVVT&ntb=1
Well-Architected Tool - catalog.workshops.aws
The AWS Well-Architected Tool , available at no cost in the AWS Management Console , provides a mechanism for regularly evaluating workloads, identifying high-risk issues, and recording …
arxiv.org/abs/2510.00183v2
Lattica: A Decentralized Cross-NAT Communication Framework for Scalable AI Infer…
The rapid expansion of distributed Artificial Intelligence (AI) workloads beyond centralized data centers creates a demand for new communication substrates. These substrates must operate reliably in heterogeneous and permissionless environments, wher...
arxiv.org/abs/2602.04100v2
SPPAM: Signature Pattern Prediction and Access-Map Prefetcher
The discrepancy between processor speed and memory system performance continues to limit the performance of many workloads. To address the issue, one effective and well studied technique is cache prefetching. Many prefetching designs have been propos...
arxiv.org/abs/2409.19488v1
A House United Within Itself: SLO-Awareness for On-Premises Containerized ML Inf…
This paper tackles the challenge of running multiple ML inference jobs (models) under time-varying workloads, on a constrained on-premises production cluster. Our system Faro takes in latency Service Level Objectives (SLOs) for each job, auto-distill...
arxiv.org/abs/2306.01404v1
Reducing Large Adaptation Spaces in Self-Adaptive Systems Using Machine Learning
Modern software systems often have to cope with uncertain operation conditions, such as changing workloads or fluctuating interference in a wireless network. To ensure that these systems meet their goals these uncertainties have to be mitigated. One...
github.com/ray-project/ray
ray-project/ray
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads. (⭐ 41585)