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Industrial companies are challenged by the task of managing and leveraging the tremendous amount of data generated throughout engineering, production, and operational phases of their manufacturing plants. As technology progresses, the emergence of ...

Handheld LiDAR devices are revolutionizing 3D data capture, offering unprecedented accessibility and ease of use even for inexperienced users. These compact systems facilitate robust virtual reconstructions by extracting detailed geometric information ...

Remote procedure calls are the workhorse of distributed systems. However, as software engineering trends, such as micro-services and serverless computing, push applications towards ever finer-grained decompositions, the overhead of RPC-based ...

Accurately and efficiently detecting plagiarism is a critical component of high-quality education. Many plagiarism detection algorithms are being invented and deployed, but these services often come at a significant cost due to their computational ...

Cloud providers offer storage-optimized VMs equipped with locally attached storage to meet the high performance requirements of cloud users. However, current cloud providers cannot enable live migration for storage-optimized VMs due to the high resource ...

Serverless computing is changing the way in which we structure and deploy computations in Internet-scale edge systems. This paper presents Capybara, a new scalable and programmable distributed object store for storing and sharing serverless function data ...

Remote procedure calls are a major contributor to performance variance in distributed systems due to lack of isolation and contention on shared resources. We propose a novel split architecture for a popular RPC framework, gRPC. The design partitions RPC ...

Training a reinforcement learning model is critical for various AI tasks. However, determining the hardware resources required for training RL models is challenging due to the interaction between the CPU and GPU, and the variability that exists in the ...

Local communities engage production systems based on a set of agreed quality standards to which each member must adhere. Most of these systems, from local food production to renewable energy communities, require continuous and transparent monitoring of ...

IoT devices are commonly powered by batteries: even rechargeable ones wear out and must be replaced. Hence, in a future with billions of IoT devices, the disposal of their batteries represents a looming environmental disaster. Battery-free systems have ...

Post-deployment monitoring (PDM) occurs in the late stages of a DevSecOps (DSO) pipeline. Its role in DSO is critical in providing feedback loops on system performance leading to desirable changes achieving long-term system and application sustainment. ...

Small uncrewed aerial systems (sUAS) are growing in their use for commercial, scientific, recreational, and emergency management purposes. A critical part of a successful flight is a correctly tuned controller which manages the physics of the vehicle. ...

Affective feedback from social robots is a useful technique for communicating to people whether they are interacting “well” with the robot or not. However, some users, such as people with physical or cognitive difficulties, may not be able to interact in ...

As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile, machine learning (ML) has been ...

This research focuses on hyperparameter optimization in Convolutional Neural Networks (CNNs) for image classification tasks. The primary objective is to propose a novel KMA-LeNet algorithm for optimizing hyperparameters in the LeNet-5 model without ...

Cloud providers face issues with cost efficiency due to the inefficient utilization of resources within their clusters. To improve efficiency, cloud resource management systems use techniques such as autoscaling and overcommiting resources across users, ...

AI/ML techniques have been used to solve systems problems, but their applicability to customize solutions on-the-fly has been limited. Traditionally, any customization required manually changing the AI/ML model or modifying the code, configuration ...

Recent research on the integration between HPC and quantum computer was mostly focused on the software stack and quantum circuit compilation aspects, neglecting critical issues like HPC resource allocation and job scheduling given the scarcity of QPUs, ...

With the increasing maturity and scale of quantum hardware and its integration into HPC systems, there is a need to develop robust techniques for developing, characterizing, and benchmarking quantum-HPC applications and middleware systems. This requires ...

The growing demand for large-scale quantum computers is pushing research on Distributed Quantum Computing (DQC). Recent experimental efforts have demonstrated some of the building blocks for such a design. DQC systems are clusters of quantum processing ...