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1st Conference on High Performance Serverless computing (HiPS)

作者： Baughman, Matt Foster, Ian Kumar, Rohan Chard, Kyle Univ Chicago Dept Comp Sci Chicago IL 60637 USA Univ Chicago DSL Div Chicago IL 60637 USA Argonne Natl Lab Chicago IL USA

ISBN: (纸本)9781450383882

Recent advances in networking technology and serverless architectures have enabled automated distribution of compute workloads at the function level. As heterogeneity and physical distribution of computing resources increase, so too does the need to effectively use those resources. This is especially true when leveraging multiple compute resources in the form of local, distributed, and cloud resources. Adding to the complexity of the problem is different notions of "cost" when it comes to using these resources. Tradeoffs exist due to the inherent difference between costs of computation for the end user. For example, deploying a workload on the cloud could be much faster than using local resources but using the cloud incurs a financial cost. Here, the end user is presented with the tradeoff between time and money. We describe preliminary work towards Delta+, a framework that integrates multidimensional cost objectives, cost tradeoffs, and optimization under constraints.

关键词： computing continuum serverless heterogeneous computing

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Enabling microservices management for Deep Learning applications across the Edge-Cloud continuum 33

Enabling microservices management for Deep Learning applicat...

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33rd IEEE International Symposium on Computer Architecture and High Performance computing (SBAC-PAD)

作者： Houmani, Zeina Balouek-Thomert, Daniel Caron, Eddy Parashar, Manish Univ Lyon Inria Avalon Team LIP Lab UMR CNRS ENS Lyon Lyon France Univ Utah Sci Comp Imaging SCI Inst Salt Lake City UT 84112 USA

ISBN: (纸本)9781665443012

Deep Learning has shifted the focus of traditional batch workflows to data-driven feature engineering on streaming data. In particular, the execution of Deep Learning workflows presents expectations of near-real-time results with user-defined acceptable accuracy. Meeting the objectives of such applications across heterogeneous resources located at the edge of the network, the core, and in-between requires managing trade-offs between the accuracy and the urgency of the results. However, current data analysis rarely manages the entire Deep Learning pipeline along the data path, making it complex for developers to implement strategies in real-world deployments. Driven by an object detection use case, this paper presents an architecture for time-critical Deep Learning workflows by providing a data-driven scheduling approach to distribute the pipeline across Edge to Cloud resources. Furthermore, it adopts a data management strategy that reduces the resolution of incoming data when potential trade-off optimizations are available. We illustrate the system's viability through a performance evaluation of the object detection use case on the Grid'5000 testbed. We demonstrate that in a multi-user scenario, with a standard frame rate of 25 frames per second, the system speed-up data analysis up to 54.4% compared to a Cloud-only-based scenario with an analysis accuracy higher than a fixed threshold.

关键词： Cloud computing Edge computing Microservices Task allocation Real-time processing computing continuum Deep Learning

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Evaluating policy-driven adaptation on the Edge-to-Cloud continuum 3

Evaluating policy-driven adaptation on the Edge-to-Cloud Con...

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3rd International Workshop on HPC for Urgent Decision Making (UrgentHPC)

作者： Balouek-Thomert, Daniel Rodero, Ivan Parashar, Manish Univ Utah SCI Inst Salt Lake City UT 84112 USA

ISBN: (纸本)9781665411301

Developing data-driven applications requires developers and service providers to orchestrate data-to-discovery pipelines across distributed data sources and computing units. Realizing such pipelines poses two major challenges: programming analytics that reacts at runtime to unforeseen events, and adaptation of the resources and computing paths between the edge and the cloud. While these concerns are interdependent, they must be separated during the design process of the application and the deployment operations of the infrastructure. This work proposes a system stack for the adaptation of distributed analytics across the computing continuum. We implemented this software stack to evaluate its ability to continually balance the computation or data movement's cost with the value of operations to the application objectives. Using a disaster response application, we observe that the system can select appropriate configurations while managing trade-offs between user-defined constraints, quality of results, and resource utilization. The evaluation shows that our model is able to adapt to variations in the data input size, bandwidth, and CPU capacities with minimal deadline violations (close to 10%). This constitutes encouraging results to benefit and facilitate the creation of ad-hoc computing paths for urgent science and time-critical decision-making.

关键词： Cloud computing Edge computing computing continuum Decision Model

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Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud continuum

Reproducible Performance Optimization of Complex Application...

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IEEE International Conference on Cluster computing (Cluster)

作者： Rosendo, Daniel Costan, Alexandru Antoniu, Gabriel Simonin, Matthieu Lombardo, Jean-Christophe Joly, Alexis Valduriez, Patrick Univ Rennes IRISA Rennes CNRS Inria Rennes France Univ Montpellier LIRMM Montpellier CNRS INRIA Montpellier France

ISBN: (纸本)9781728196664

In more and more application areas, we are witnessing the emergence of complex workflows that combine computing, analytics and learning. They often require a hybrid execution infrastructure with IoT devices interconnected to cloud/HPC systems (aka computing continuum). Such workflows are subject to complex constraints and requirements in terms of performance, resource usage, energy consumption and financial costs. This makes it challenging to optimize their configuration and deployment. We propose a methodology to support the optimization of real-life applications on the Edge-to-Cloud continuum. We implement it as an extension of E2Clab, a previously proposed framework supporting the complete experimental cycle across the Edge-to-Cloud continuum. Our approach relies on a rigorous analysis of possible configurations in a controlled testbed environment to understand their behaviour and related performance tradeoffs. We illustrate our methodology by optimizing Pl@ntNet, a world-wide plant identification application. Our methodology can be generalized to other applications in the Edge-to-Cloud continuum.

关键词： Reproducibility Methodology computing continuum Optimization

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A Two-Sided Matching Model for Data Stream Processing in the Cloud - Fog continuum 21

A Two-Sided Matching Model for Data Stream Processing in the...

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21st IEEE/ACM International Symposium on Cluster, Cloud and Internet computing (CCGrid)

作者： Mehran, Narges Kimovski, Dragi Prodan, Radu Alpen Adria Univ Klagenfurt Inst Informat Technol Klagenfurt Austria

ISBN: (纸本)9781728195865

Latency-sensitive and bandwidth-intensive stream processing applications are dominant traffic generators over the Internet network. A stream consists of a continuous sequence of data elements, which require processing in nearly real-time. To improve communication latency and reduce the network congestion, Fog computing complements the Cloud services by moving the computation towards the edge of the network. Unfortunately, the heterogeneity of the new Cloud - Fog continuum raises important challenges related to deploying and executing data stream applications. We explore in this work a two-sided stable matching model called Cloud - Fog to data stream application matching (CODA) for deploying a distributed application represented as a workflow of stream processing microservices on heterogeneous computing continuum resources. In CODA, the application microservices rank the continuum resources based on their microservice stream processing time, while resources rank the stream processing microservices based on their residual bandwidth. A stable many-to-one matching algorithm assigns microservices to resources based on their mutual preferences, aiming to optimize the complete stream processing time on the application side, and the total streaming traffic on the resource side. We evaluate the CODA algorithm using simulated and real-world Cloud - Fog experimental scenarios. We achieved 11-45% lower stream processing time and 1.3-20% lower streaming traffic compared to related state-of-the-art approaches.

关键词： Cloud - Fog computing computing continuum matching game algorithm microservice data stream processing

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ICPE'23 Fast continuum Workshop Chairs' Welcome

ICPE'23 Fast Continuum Workshop Chairs' Welcome

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Companion of the 2023 ACM/SPEC International Conference on Performance Engineering

作者： Danilo Ardagna Elisabetta Di Nitto Lorenzo Blasi Francesc Lordan Politecnico di Milano Milan Italy Hewlett-Packard Enterprise Milan Italy Barcelona Supercomputing Center Barcelona Spain

ISBN: (纸本)9798400700729

It is our great pleasure to welcome you to the first Fastcontinuum Workshop held on April 16th 2023. The goal of the workshop is to foster discussion and collaboration among researchers from cloud/edge/fog computing and performance analysis communities, to share the relevant topics and results of the current approaches proposed by industry and academia. Fastcontinuum solicited full papers as well as demo and short papers including reports about research activities not mature enough for a full paper as well as new ideas and vision *** final program includes four full papers and three short ones. They cover some of the most interesting areas of computing continua, from FaaS development and acceleration to the management of heterogeneous datasets to the development of Infrastructure as Code and the automation of deployment through the computing continuum. DevSecOps is also brought to the attendees' attention as one of the crucial ingredients for proper management of the *** workshop keynote, given by Samuel Kunev, investigates further the area of serverless computing properly positioning the multiple aspects and approaches developed in this area and highlighting the main challenges related to the performance of these approaches. The keynote is held in collaboration with the eleventh International Workshop on Load Testing and Benchmarking of Software Systems (LTB 2023).

关键词： computing continuum edge fog computing cloud

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MDSC: modelling distributed stream processing across the edge-to-cloud continuum 21

MDSC: modelling distributed stream processing across the edg...

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Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud computing Companion

作者： Daniel Balouek-Thomert Pedro Silva Kevin Fauvel Alexandru Costan Gabriel Antoniu Manish Parashar University of Utah Uni. Potsdam Univ Rennes

ISBN: (纸本)9781450391634

The growth of the Internet of Things is resulting in an explosion of data volumes at the Edge of the Internet. To reduce costs incurred due to data movement and centralized cloud-based processing, it is becoming increasingly important to process and analyze such data closer to the data sources. Exploiting Edge computing capabilities for stream-based processing is however challenging. It requires addressing the complex characteristics and constraints imposed by all the resources along the data path, as well as the large set of heterogeneous data processing and management frameworks. Consequently, the community needs tools that can facilitate the modeling of this complexity and can integrate the various components involved. In this work, we introduce MDSC, a hierarchical approach for modeling distributed stream-based applications on Edge-to-Cloud continuum infrastructures. We demonstrate how MDSC can be applied to a concrete real-life ML-based application - early earthquake warning - to help answer questions such as: when is it worth decentralizing the classification load from the Cloud to the Edge and how?

关键词： modelling computing continuum stream processing

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A Unified Model for the Mobile-Edge-Cloud continuum

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ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2019年第2期19卷 29-29页

作者： Baresi, L. Mendonca, D. F. Garriga, M. Guinea, S. Quattrocchi, G. Politecn Milan Dipartimento Elettron Informaz & Bioingn Bldg 22Floor 3 I-20133 Milan MI Italy

Technologies such as mobile, edge, and cloud computing have the potential to form a computing continuum for new, disruptive applications. At runtime, applications can choose to execute parts of their logic on different infrastructures that constitute the continuum, with the goal of minimizing latency and battery consumption and maximizing availability. In this article, we propose A3-E, a unified model for managing the life cycle of continuum applications. In particular, A3-E exploits the Functions-as-a-Service model to bring computation to the continuum in the form of microservices. Furthermore, A3-E selects where to execute a certain function based on the specific context and user requirements. The article also presents a prototype framework that implements the concepts behind A3-E. Results show that A3-E is capable of dynamically deploying microservices and routing the application's requests, reducing latency by up to 90% when using edge instead of cloud resources, and battery consumption by 74% when computation has been offloaded.

关键词： computing continuum edge computing fog computing mobile computing real-time systems ops automation Functions-as-a-Service

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