Related papers: Workload Intelligence: Punching Holes Through the …
Modern cloud-native systems increasingly rely on multi-cluster deployments to support scalability, resilience, and geographic distribution. However, existing resource management approaches remain largely reactive and cluster-centric,…
While scheduling and dispatching of computational workloads is a well-investigated subject, only recently has Google provided publicly a vast high-resolution measurement dataset of its cloud workloads. We revisit dispatching and scheduling…
The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, security and privacy concerns caused by billions of connected wireless devices and typically zillions bytes of data they…
The Dynamic Scalability of resources, a problem in Infrastructure as a Service (IaaS) has been the hotspot for research and industry communities. The heterogeneous and dynamic nature of the Cloud workloads depends on the Quality of Service…
Dynamic nature of the cloud environment has made distributed resource management process a challenge for cloud service providers. The importance of maintaining the quality of service in accordance with customer expectations as well as the…
Cloud Computing is an emerging area for accessing computing resources. In general, Cloud service providers offer services that can be clustered into three categories: SaaS, PaaS and IaaS. This paper discusses the Cloud workload analysis.…
Large Language Model (LLM) workloads have distinct prefill and decode phases with different compute and memory requirements which should ideally be accounted for when scheduling input queries across different LLM instances in a cluster.…
Human involvement is critical in training and deploying AI systems in high-stakes defence and security contexts. However, real-time interaction is impractical in HPC environments due to compute intensity and resource constraints. We present…
The equitable assessment of individual contribution in teams remains a persistent challenge, where conflict and disparity in workload can result in unfair performance evaluation, often requiring manual intervention - a costly and…
Business Intelligence (BI) has gained a new lease of life through Cloud computing as its demand for unlimited hardware and platform resources expandability is fulfilled by the Cloud elasticity features. BI can be seamlessly deployed on the…
Cloud data centers face increasing pressure to reduce operational energy consumption as big data workloads continue to grow in scale and complexity. This paper presents a workload aware and energy efficient scheduling framework that…
Human-machine Interface (HMI) is critical for safety during automated driving, as it serves as the only media between the automated system and human users. To enable a transparent HMI, we first need to know how to evaluate it. However, most…
Hybrid local--cloud agents enrich user requests with context from persistent working state before delegating capability-intensive subtasks to a cloud language model (CLM). While this enrichment can improve task success, it also exposes…
Cloud computing is an established technology allowing users to share resources on a large scale, never before seen in IT history. A cloud system connects multiple individual servers in order to process related tasks in several environments…
In many working environments, users have to solve complex problems relying on large and multi-source data. Such problems require several experts to collaborate on solving them, or a single analyst to reconcile multiple complementary…
Isolation is a critical property for shared infrastructure to limit exposure and interference among simultaneous running workloads. Cloud providers use different isolation mechanisms such as full Virtual Machines, microVMs, Linux…
Static, long-lived credentials for workload authentication create untenable security risks that violate Zero-Trust principles. This paper presents a multi-cloud framework using Workload Identity Federation (WIF) and OpenID Connect (OIDC)…
Learned indexes fit machine learning (ML) models to the data and use them to make query operations more time and space-efficient. Recent works propose using learned spatial indexes to improve spatial query performance by optimizing the…
The cloud computing paradigm underlines data center and telecommunication infrastructure design. Heavily leveraging virtualization, it slices hardware and software resources into smaller software units for greater flexibility of…
Very High Throughput satellites typically provide multibeam coverage, however, a common problem is that there can be a mismatch between the capacity of each beam and the traffic demand: some beams may fall short, while others exceed the…