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Scientific experiments and modern applications are generating large amounts of data every day. Most organizations utilize In-house servers or Cloud resources to manage application data and workload. The traditional database management…

Quantum computing is moving swiftly from theoretical to practical applications, making it crucial to establish a significant quantum advantage. Despite substantial investments, access to quantum devices is still limited, with users facing…

The suitability of cloud computing has been studied by several authors to run scientific applications. However, the unpredictable performance fluctuations in these environments hinders the migration of scientific applications to cloud…

Rapid Large Language Model (LLM) advancements are fueling autonomous Multi-Agent System (MAS) development. However, current frameworks often lack flexibility, resource awareness, model diversity, and autonomous tool creation. This paper…

Problem Definition: Allocating sufficient capacity to cloud services is a challenging task, especially when demand is time-varying, heterogeneous, contains batches, and requires multiple types of resources for processing. In this setting,…

We introduce a distributed resource allocation framework for the Quantum Internet that relies on feedback-based, fully decentralized coordination to serve multiple co-existing applications. We develop quantum network control algorithms…

The Industrial Internet of Things (IIoT) is an emerging paradigm to make industrial operations more efficient and intelligent by deploying a massive number of wireless devices to industry scenes. However, due to the limited computing…

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…

Interference is a major performance bottleneck in Heterogeneous Network (HetNet) due to its multi-tier topological structure. We propose almost blank resource block (ABRB) for interference control in HetNet. When an ABRB is scheduled in a…

The Network Function Virtualization (NFV) is very promising for efficient provisioning of network services and is attracting a lot of attention. NFV can be implemented in commercial off-the-shelf servers or Physical Machines (PMs), and many…

AI data centers are increasingly becoming tightly coupled compute--energy systems, where workload placement, cooling demand, electricity procurement, storage operation, and carbon emissions interact over time. This paper studies…

We propose a novel job scheduling approach for homogeneous cluster computing platforms. Its key feature is the use of virtual machine technology to share fractional node resources in a precise and controlled manner. Other VM-based…

Heterogeneous computing systems provide high performance and energy efficiency. However, to optimally utilize such systems, solutions that distribute the work across host CPUs and accelerating devices are needed. In this paper, we present a…

Quantum Reservoir Computing (QRC) has emerged as a strong pa- radigm for Noisy Intermediate-Scale Quantum (NISQ) machine learning, ena- bling the processing of temporal data with minimal training overhead by exploi- ting the…

This paper presents an in-depth examination of checkpoint-restart mechanisms in High-Performance Computing (HPC). It focuses on the use of Distributed MultiThreaded CheckPointing (DMTCP) in various computational settings, including both…

Generative large language models (LLMs) have garnered significant attention due to their exceptional capabilities in various AI tasks. Traditionally deployed in cloud datacenters, LLMs are now increasingly moving towards more accessible…

Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as Markov decision processes and compactly represented through dynamic decision Bayesian networks.…

In a grid with a significant share of renewable generation, operators will need additional tools to evaluate the operational risk due to the increased volatility in load and generation. The computational requirements of the forward…

We propose an approach to utilize idle computational resources of supercomputers. The idea is to maintain an additional queue of low-priority non-parallel jobs and execute them in containers, using container migration tools to break the…

Kubernetes has become the foundation of modern cloud-native infrastructure, yet its management remains complex and fragmented. Administrators must navigate a vast API surface, manage heterogeneous workloads, and coordinate tasks across…