Related papers: Towards Cloud Efficiency with Large-scale Workload…
AI workloads, often hosted in multi-tenant cloud environments, require vast computational resources but suffer inefficiencies due to limited tenant-provider coordination. Tenants lack infrastructure insights, while providers lack workload…
Smart databases are adopting artificial intelligence (AI) technologies to achieve {\em instance optimality}, and in the future, databases will come with prepackaged AI models within their core components. The reason is that every database…
How can applications be deployed on the cloud to achieve maximum performance? This question is challenging to address with the availability of a wide variety of cloud Virtual Machines (VMs) with different performance capabilities. The…
Cloud computing can and does mean different things to different people. The common characteristics most shares are on-demand secure access to metered services from nearly anywhere and dislocation of data from inside to outside the…
To improve customer experience, datacenter operators offer support for simplifying application and resource management. For example, running workloads of workflows on behalf of customers is desirable, but requires increasingly more…
Measuring performance-critical characteristics of application workloads is important both for developers, who must understand and optimize the performance of codes, as well as designers and integrators of HPC systems, who must ensure that…
Capability jobs (e.g., large, long-running tasks) and capacity jobs (e.g., small, short-running tasks) are two common types of workloads in high-performance computing (HPC). Different HPC systems are typically deployed to handle distinct…
The emergence of cloud computing has made dynamic provisioning of elastic capacity to applications on-demand. Cloud data centers contain thousands of physical servers hosting orders of magnitude more virtual machines that can be allocated…
We consider robust resource allocation of services in Clouds. More specifically, we consider the case of a large public or private Cloud platform that runs a relatively small set of large and independent services. These services are…
In cloud computing environment, load balancing is a key issue which is required to distribute the dynamic workload over multiple machines to make certain that no single machine is overloaded. In recent research, many organizations lose…
Cloud Computing offers virtualized computing, storage, and networking resources, over the Internet, to organizations and individual users in a completely dynamic way. These cloud resources are cheaper, easier to manage, and more elastic…
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…
With the advantages that cloud computing offers in terms of platform as a service, software as a service, and infrastructure as a service, data engineers and data scientists are able to leverage cloud computing for their ETL/ELT (extract,…
Cloud latency has critical influences on the success of cloud applications. Therefore, characterizing cloud network performance is crucial for analyzing and satisfying different latency requirements. By focusing on the cloud's outbound…
Big data benchmark suites must include a diversity of data and workloads to be useful in fairly evaluating big data systems and architectures. However, using truly comprehensive benchmarks poses great challenges for the architecture…
Fog computing offloads latency critical application services running on the Cloud in close proximity to end-user devices onto resources located at the edge of the network. The research in this paper is motivated towards characterising and…
Cloud Computing is the latest blooming technology in the era of Computer Science and Information Technology domain. There is an enormous pool of data centres, which are termed as Clouds where the services and associated data are being…
We present and formalize a general approach for profiling workload by leveraging only a priori available static metadata to supply appropriate resource needs. Understanding the requirements and characteristics of a workload's runtime is…
Mobile robotic manipulation--the ability of robots to navigate spaces and interact with objects--is a core capability of physical AI. Foundation models have led to breakthroughs in their performance, but at a significant computational cost.…
Modern multi-tenant, hardware-heterogeneous computing environments pose significant challenges for effective workload orchestration. Simple heuristics for assessing workload performance, such as CPU utilization or application-level metrics,…