Maintaining and Monitoring AIOps Models Against Concept Drift

AIOps solutions enable faster discovery of failures in operational large-scale systems through machine learning models trained on operation data. These models become outdated during the occurrence of concept drift, a term used to describe shifts in data distributions. In operation data concept drift is inevitable and it impacts the...

Retrain AI Systems Responsibly! Use Sustainable Concept Drift Adaptation Techniques

Deployed machine learning systems often suffer from accuracy degradation over time generated by constant data shifts, also known as concept drift. Therefore, these systems require regular maintenance, in which the machine learning model needs to be adapted to concept drift. The literature presents plenty of model adaptation techniques. The...

Are Concept Drift Detectors Reliable Alarming Systems?: A Comparative Study

As machine learning models increasingly replace traditional business logic in the production system, their lifecycle management is becoming a significant concern. Once deployed into production, the machine learning models are constantly evaluated on new streaming data. Given the continuous data flow, shifting data, also known as concept drift,...

Roadmap for edge AI: A Dagstuhl Perspective

Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation,...

In-Memory Indexed Caching for Distributed Data Processing

Powerful abstractions such as dataframes are only as efficient as their underlying runtime system. The de-facto distributed data processing framework, Apache Spark, is poorly suited for the modern cloud-based data-science workloads due to its outdated assumptions: static datasets analyzed using coarse-grained transformations. In this paper, we...

A Fresh Look at the Architecture and Performance of Contemporary Isolation Platforms

With the ever-increasing pervasiveness of the cloud computing paradigm, strong isolation guarantees and low performance overhead from isolation platforms are paramount. An ideal isolation platform offers both: an impermeable isolation boundary while imposing a negligible performance overhead. In this paper, we examine various isolation platforms...

SGD_Tucker: A Novel Stochastic Optimization Strategy for Parallel Sparse Tucker Decomposition

Sparse Tucker Decomposition (STD) algorithms learn a core tensor and a group of factor matrices to obtain an optimal low-rank representation feature for the High-Order, High-Dimension, and Sparse Tensor (HOHDST). However, existing STD algorithms face the problem of intermediate variables explosion which results from the fact that the...

Systematic Mapping Study on the Machine Learning Lifecycle

The development of artificial intelligence (AI) has made various industries eager to explore the benefits of AI. There is an increasing amount of research surrounding AI, most of which is centred on the development of new AI algorithms and techniques. However, the advent of AI is bringing an increasing set of practical problems related to AI...

Is Big Data Performance Reproducible in Modern Cloud Networks?

Performance variability has been acknowledged as a problem for over a decade by cloud practitioners and performance engineers. Yet, our survey of top systems conferences reveals that the research community regularly disregards variability when running experiments in the cloud. Focusing on networks, we assess the impact of variability on cloud...

A Survey on Distributed Machine Learning

The demand for artificial intelligence has grown significantly over the past decade, and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, to increase the quality of predictions and render machine learning solutions feasible for more complex applications, a...

Self-adaptive Executors for Big Data Processing

The demand for additional performance due to the rapid increase in the size and importance of data-intensive applications has considerably elevated the complexity of computer architecture. In response, systems offer pre-determined behaviors based on heuristics and then expose a large number of configuration parameters for operators to adjust...

IoT resource-aware orchestration framework for edge computing

Existing edge computing solutions in the Internet of Things (IoT) domain operate with the control plane residing in the cloud and edge as a slave that executes the workload deployed by the cloud. The growing diversity in the IoT applications requires the edge to be able to run multiple distinct workloads corresponding to the dedicated inputs...

The coming age of pervasive data processing

Emerging Big Data analytics and machine learning applications require a significant amount of computational power. While there exists a plethora of large-scale data processing frameworks which thrive in handling the various complexities of data-intensive workloads, the ever-increasing demand of applications have made us reconsider the...

Increasing Memory Density through Dynamic Memory Extension with Memory1 through Flash

Container Density Improvements with Dynamic Memory Extension using NAND Flash

While containers efficiently implement the idea of operating-system-level application virtualization, they are often insufficient to increase the server utilization to a desirable level. The reason is that in practice many containerized applications experience a limited amount of load while there are few containers with a high load. In such a...