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Modern distributed systems can benefit from the availability of large-scale and heterogeneous computing infrastructures. However, the complexity and dynamic nature of these environments also call for self-adaptation abilities, as guaranteeing efficient ...

Optimizing the performance of complex systems has always been a central issue for the control theory community. However, ideas and tools from this field often require very precise assumptions and extensive tuning to perform well, making them unsuited ...

In this paper, we explore the use of Graph Neural Networks (GNNs) for anomaly anticipation in high performance computing (HPC) systems. We propose a GNN-based approach that leverages the structure of the HPC system (particularly, the physical proximity ...

This paper presents a novel methodology based on first principles of statistics and statistical learning for anomaly detection in industrial processes and IoT environments. We present a 5-level analytical pipeline that cleans, smooths, and eliminates ...

The performance of distributed applications implemented using microservice architecture depends heavily on the configuration of various parameters, which are hard to tune due to large configuration search space and inter-dependence of parameters. While ...

We propose an incremental change detection method for data center (DC) energy efficiency metrics and consider its application to the power usage efficiency (PUE) metric. In recent years, there is an increasing focus on the sustainability of DCs and PUE ...

This paper proposes an auto-profiling tool for OSCAR, an open-source platform able to support serverless computing in cloud and edge environments. The tool, named OSCAR-P, is designed to automatically test a specified application workflow on different ...

We are pleased to welcome you to the 2023 ACM Workshop on Artificial Intelligence for Performance Modeling, Prediction, and Control - AIPerf'23.

In its first edition, AIPerf intends to foster the usage of AI (such as probabilistic methods, machine ...

When one hears the word Metaverse, it is automatically associated with millions of users, immersive experiences and its potential to change our lives. But, what enables the Metaverse to function at such a scale? This talk will present the different ...

Due to the proliferation of inference tasks on mobile devices, state-of-the-art neural architectures are typically designed using Neural Architecture Search (NAS) to achieve good tradeoffs between machine learning accuracy and inference latency. While ...

Cyber-Physical Systems (CPS) rely on sensing to control and optimize their operation. Nevertheless, sensing itself is prone to errors that can originate at several stages, from sampling to communication. In this context, several systems adopt ...

Predicting the performance and energy consumption of computing hardware is critical for many modern applications. This will inform procurement decisions, deployment decisions, and autonomic scaling. Existing approaches to understanding the performance ...

Model transformation languages are special-purpose languages, which are designed to define transformations as comfortably as possible, i.e., often in a declarative way. Typically, developers create their transformations based on small input models which ...

The Blockchain 2.0 era integrated with smart contract along with its platforms and applications have experienced explosive growth in recent years. However, many smart contracts deployed in practice are prone to errors and cannot be modified due to the ...

Sentiment analysis is an important natural language processing task that seeks to extract contextual subjective information and perform classification. CNN and Bi-LSTM are two of the most popular models in text-based sentiment analysis. However, with ...

Point-of-Interest (POI) recommendation recommends different personalized services to interested users, which are widely used in people's daily life. However, with the massive increase in users and POIs, the POI recommendation system faces the following ...

According to the "national surface water monitoring and evaluation scheme for the 14th Five-Year plan" issued by the Ministry of ecological environment of the people's Republic of China, this study constructs a back propagation (BP) neural network model ...

This study presented the development of a web-based system that visualizes real-time traffic by deploying lightweight and mobile monitoring devices at roadside intersections in the vicinity of Butuan City to assist commuters and drivers in making ...

Abstract: Fruit counting is an integral part of achieving precision orchard management. Accurate counting of the number of fruits on a tree can provide critical information for yield estimation, thus promoting precision agriculture. However, today fruit ...

Abstract: Haze rendering aims to generate realistic nighttime haze images from clear images. The results can be applied to various practical applications, such as nighttime image dehazing algorithms, game scene rendering, shooting filters, etc. We ...