FLINT: A Platform for Federated Learning Integration

Cross-device federated learning (FL) has been well-studied from algorithmic, system scalability, and training speed perspectives. Nonetheless, moving from centralized training to cross-device FL for millions or billions of devices presents many risks, including performance loss, developer inertia, poor user experience, and unexpected application failures. In addition, the corresponding infrastructure, development costs, and return on investment are difficult to estimate. In this paper, we present a device-cloud collaborative FL platform that integrates with an existing machine learning platform, providing tools to measure real-world constraints, assess infrastructure capabilities, evaluate model training performance, and estimate system resource requirements to responsibly bring FL into production. We also present a decision workflow that leverages the FL-integrated platform to comprehensively evaluate the trade-offs of cross-device FL and share our empirical evaluations of business-critical machine learning applications that impact hundreds of millions of users.Comment: Preprint for MLSys 202

MACHINE LEARNING ALGORITHMS FOR DETECTION OF CYBER THREATS USING LOGISTIC REGRESSION

The threat of cyberattacks is expanding globally; thus, businesses are developing intelligent artificial intelligence systems that can analyze security and other infrastructure logs from their systems department and quickly and automatically identify cyberattacks. Security analytics based on machine learning the next big thing in cybersecurity is machine data, which aims to mine security data to show the high maintenance costs of static relationship rules and methods. But, choosing the appropriate machine learning technique for log analytics using ML continues to be a significant barrier to AI success in cyber security due to the possibility of a substantial number of false-positive detections in large-scale or global Security Operations Centre (SOC) settings, selecting the proper machine learning technique for security log analytics remains a substantial obstacle to AI success in cyber security. A machine learning technique for a cyber threat exposure system that can minimize false positives is required. Today\u27s machine learning methods for identifying threats frequently use logistic regression. Logistic regression is the first of three machine learning subcategories—supervised, unsupervised, and reinforcement learning. Any machine learning enthusiast will encounter this supervised machine learning algorithm at the beginning of their machine learning career. It\u27s an essential and often applied classification algorithm

Atlas: Hybrid Cloud Migration Advisor for Interactive Microservices

Hybrid cloud provides an attractive solution to microservices for better resource elasticity. A subset of application components can be offloaded from the on-premises cluster to the cloud, where they can readily access additional resources. However, the selection of this subset is challenging because of the large number of possible combinations. A poor choice degrades the application performance, disrupts the critical services, and increases the cost to the extent of making the use of hybrid cloud unviable. This paper presents Atlas, a hybrid cloud migration advisor. Atlas uses a data-driven approach to learn how each user-facing API utilizes different components and their network footprints to drive the migration decision. It learns to accelerate the discovery of high-quality migration plans from millions and offers recommendations with customizable trade-offs among three quality indicators: end-to-end latency of user-facing APIs representing application performance, service availability, and cloud hosting costs. Atlas continuously monitors the application even after the migration for proactive recommendations. Our evaluation shows that Atlas can achieve 21% better API performance (latency) and 11% cheaper cost with less service disruption than widely used solutions.Comment: To appear at EuroSys 202

Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence

The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose the workflows but also from the type of computations they perform. While traditional HPC workflows target simulations and modelling of physical phenomena, current needs require in addition data analytics (DA) and artificial intelligence (AI) tasks. However, the development of these workflows is hampered by the lack of proper programming models and environments that support the integration of HPC, DA, and AI, as well as the lack of tools to easily deploy and execute the workflows in HPC systems. To progress in this direction, this paper presents use cases where complex workflows are required and investigates the main issues to be addressed for the HPC/DA/AI convergence. Based on this study, the paper identifies the challenges of a new workflow platform to manage complex workflows. Finally, it proposes a development approach for such a workflow platform addressing these challenges in two directions: first, by defining a software stack that provides the functionalities to manage these complex workflows; and second, by proposing the HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software stack to facilitate the reusability of complex workflows in federated HPC infrastructures. Proposals presented in this work are subject to study and development as part of the EuroHPC eFlows4HPC project.This work has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 955558. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Germany, France, Italy, Poland, Switzerland and Norway. In Spain, it has received complementary funding from MCIN/AEI/10.13039/501100011033, Spain and the European Union NextGenerationEU/PRTR (contracts PCI2021-121957, PCI2021-121931, PCI2021-121944, and PCI2021-121927). In Germany, it has received complementary funding from the German Federal Ministry of Education and Research (contracts 16HPC016K, 6GPC016K, 16HPC017 and 16HPC018). In France, it has received financial support from Caisse des dépôts et consignations (CDC) under the action PIA ADEIP (project Calculateurs). In Italy, it has been preliminary approved for complimentary funding by Ministero dello Sviluppo Economico (MiSE) (ref. project prop. 2659). In Norway, it has received complementary funding from the Norwegian Research Council, Norway under project number 323825. In Switzerland, it has been preliminary approved for complimentary funding by the State Secretariat for Education, Research, and Innovation (SERI), Norway. In Poland, it is partially supported by the National Centre for Research and Development under decision DWM/EuroHPCJU/4/2021. The authors also acknowledge financial support by MCIN/AEI /10.13039/501100011033, Spain through the “Severo Ochoa Programme for Centres of Excellence in R&D” under Grant CEX2018-000797-S, the Spanish Government, Spain (contract PID2019-107255 GB) and by Generalitat de Catalunya, Spain (contract 2017-SGR-01414). Anna Queralt is a Serra Húnter Fellow.With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2018-000797-S)

Trust based Privacy Policy Enforcement in Cloud Computing

Cloud computing offers opportunities for organizations to reduce IT costs by using the computation and storage of a remote provider. Despite the benefits offered by cloud computing paradigm, organizations are still wary of delegating their computation and storage to a cloud service provider due to trust concerns. The trust issues with the cloud can be addressed by a combination of regulatory frameworks and supporting technologies. Privacy Enhancing Technologies (PET) and remote attestation provide the technologies for addressing the trust concerns. PET provides proactive measures through cryptography and selective dissemination of data to the client. Remote attestation mechanisms provides reactive measures by enabling the client to remotely verify if a provider is compromised. The contributions of this work are three fold. This thesis explores the PET landscape by studying in detail the implications of using PET in cloud architectures. The practicality of remote attestation in Software as a Service (SaaS) and Infrastructure as a Service (IaaS) scenarios is also analyzed and improvements have been proposed to the state of the art. This thesis also propose a fresh look at trust relationships in cloud computing, where a single provider changes its configuration for each client based on the subjective and dynamic trust assessments of clients. We conclude by proposing a plan for expanding on the completed work

Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data

Metabolomics data are complex with a high degree of multicollinearity. As such, multivariate linear projection methods, such as partial least squares discriminant analysis (PLS-DA) have become standard. Non-linear projections methods, typified by Artificial Neural Networks (ANNs) may be more appropriate to model potential nonlinear latent covariance; however, they are not widely used due to difficulty in deriving statistical inference, and thus biological interpretation. Herein, we illustrate the utility of ANNs for clinical metabolomics using publicly available data sets and develop an open framework for deriving and visualising statistical inference from ANNs equivalent to standard PLS-DA methods

BigDL: A Distributed Deep Learning Framework for Big Data

This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning applications on production big data platforms. It allows deep learning applications to run on the Apache Hadoop/Spark cluster so as to directly process the production data, and as a part of the end-to-end data analysis pipeline for deployment and management. Unlike existing deep learning frameworks, BigDL implements distributed, data parallel training directly on top of the functional compute model (with copy-on-write and coarse-grained operations) of Spark. We also share real-world experience and "war stories" of users that have adopted BigDL to address their challenges(i.e., how to easily build end-to-end data analysis and deep learning pipelines for their production data).Comment: In ACM Symposium of Cloud Computing conference (SoCC) 201

AI-Driven Decision Support Systems in Management: Enhancing Strategic Planning and Execution

Artificial intelligence (AI) is transforming strategic decision-making processes across various industries. Organizations increasingly rely on AI-driven decision support systems that leverage massive amounts of data and real-time analytics to enable more informed planning and predictive capabilities. However, less focused research has explored the integration and impact of such tools specifically within managerial strategy and execution contexts. This study conducts qualitative and quantitative analysis on the deployment of machine learning-based recommendation systems aimed at enhancing the strategic capabilities of management teams. Results indicate that AI decision tools led to improved analytic capacities, competitive response times, and reimagined vision planning, yet also posed transparency and trust challenges around advanced automation techniques. Findings provide novel implications into AI’s emerging role in augmenting and extending higher-level organizational strategy design and enactment by key decision-makers and leaders. Future directions are discussed related to addressing responsible development issues as adoption continues accelerating