logseq/pages/hls__Balouek-Thomert et al_2019_Towards a computing continuum.md

file:: [Balouek-Thomert et al_2019_Towards a computing continuum.pdf](file://C:\Users\david\Zotero/storage/LDNCVMY8/Balouek-Thomert et al_2019_Towards a computing continuum.pdf) file-path:: file://C:\Users\david\Zotero/storage/LDNCVMY8/Balouek-Thomert et al_2019_Towards a computing continuum.pdf

• fluid integration of resources at the edge, the core, and along the data path to support dynamic and data-driven application workflows, ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 63b99a39-dc30-49a5-b673-f05828ac2abd
• leverage a computing continuum ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 63b99a3e-2479-462b-b56b-9fa25293cdf4
• edge-tocloud integration to support data-driven workflows. ls-type:: annotation hl-page:: 1 hl-color:: blue id:: 63b99a46-34ab-412d-a922-6b6d1ca3108c hl-stamp:: 1673108040427
• The last decade has witnessed a dramatic change in the technology landscape marked by increasing scales and pervasiveness of compute and data. ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63b99b6c-3291-4925-b2ac-180ee7726b59 hl-stamp:: 1673108345071
• significant investment in edge computing to support timely processing close to the data sources ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63b99b86-4fc0-494b-944c-96e9800b7671
• performance ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63b99b8a-50bc-4b2a-a22a-095a0d06c766
• latency ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63b99b8b-ad88-453d-b307-10f269f18811
• interoperability ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63b99b8c-a8fd-4652-9242-66fca5de6fc4
• security ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63b99b8d-3c3d-44ab-b1b8-0623038da0cf
• privacy ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63b99b8e-65a1-475d-8647-59144b56de63
• Knowledge extraction in these applications combines various data sources, such as those coming from the IoT devices, statistical data about cities and its population and data from location-based social network ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63b99ce3-1e84-4379-8607-970245d74ae5
• A framework for enabling continuum computing ls-type:: annotation hl-page:: 3 hl-color:: blue id:: 63b99eb4-9990-4b0b-b2a7-ba5d945e5134
• [:span] ls-type:: annotation hl-page:: 4 hl-color:: blue id:: 63b99ec9-cbac-43a3-ab54-52fb5d2fff6e hl-type:: area hl-stamp:: 1673109193303
• The automotive industry is facing similar challenges as it is developing technologies for autonomous vehicles. To operate safely, these vehicles will need to gather and analyze vast amounts of data pertaining to their surroundings, directions, and weather conditions, not to mention communicating with other vehicles on the road ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63b9b7bf-d172-4098-a155-51305d01b140 hl-stamp:: 1673115584878
• send data back to manufacturers to track usage and maintenance alerts as well as interface with local municipal networks. ls-type:: annotation hl-page:: 2 hl-color:: green id:: 63b9b865-d423-4412-8d54-077440ea4dee
• Similar requirements and challenges are present in many other disciplines, including health care, finance, science and engineering, business analytics, and cybersecurity ls-type:: annotation hl-page:: 2 hl-color:: green id:: 63b9b874-62fd-4cc8-a41b-752767f3c3cb
• while these facilities provide access to the data and data products, they tend to be remote and/or distributed, and transforming these data and data products into insights requires combining it with complex models and access to powerful computing, storage, and networking resources. ls-type:: annotation hl-page:: 2 hl-color:: blue id:: 63b9b89a-c3ed-4acc-b755-4d65fc85c0e1 hl-stamp:: 1673115804162
• As these emerging classes of applications mature and their data and processing requirements grow, they cannot be sustained by solely using edge resources or by sending all the data to the cloud. ls-type:: annotation hl-page:: 2 hl-color:: blue id:: 63b9b8de-0d31-4b3a-80da-5e4697b690df
• luid integration of resources at the edge, the core, and along the data path to support dynamic and data-driven application workflows, that is, they need to leverage a computing continuum ls-type:: annotation hl-page:: 2 hl-color:: blue id:: 63b9b8f3-4553-453e-a9f8-c07dea30e0ad
• Continuum computing aims at realizing a fluid ecosystem where distributed resources and services are programmatically aggregated on demand to support emerging data-driven application workflows. ls-type:: annotation hl-page:: 2 hl-color:: blue id:: 63b9b906-62f2-464d-b9d1-2806380bd4e8
• Computing in the continuum: combining pervasive devices and services to support data-driven applications ls-type:: annotation hl-page:: 13 hl-color:: blue id:: 63b9b93d-c0f4-43c2-91bb-1480d6afe32d
• enabling edge-to-cloud integration to support datadriven workflow ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 63b9ba16-710a-471c-990e-0589430be280 hl-stamp:: 1673116185046
• ederating infrastructure, programming services, and composing dynamic workflows, which are capable of reacting in real time to unpredictable data sizes, availabilities, locations, and rates. ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 63b9ba22-14d5-4ab8-8feb-05eb1a46d1cf
• (1) how do we take into account what, where, and when data get collected and analyzed; ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 63b9ba2e-8cd7-4cf4-a12b-eafd7f57ef4c
• (2) how do we program services to respond to changes in application behavior or data variability; ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 63b9ba35-ba74-47d3-9ebe-20021e686dc5
• (3) how to react to changes and trigger rules associated to the content of the data; ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 63b9ba4b-4c3b-40f6-b2be-522afc0c4071
• (4) how to consider users constraints and quality of service to deliver data products. ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 63b9ba53-82ba-4a5b-b9d9-1d8e29ee7fc5
• programming approach for data-driven applications that allows the system to respond to dynamic data patterns. ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 63b9ba67-1fc6-46b2-8ac6-e138326d0921
• exploiting network resources to run workflows along the data path and deliver data products with respect to application constraints ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 63b9ba6f-7f19-4fa1-93d4-186ca0db7d53
• a conceptual framework that implements the vision of computing in the continuum to complement next-generation IoT systems and cyberinfrastructures ls-type:: annotation hl-page:: 3 hl-color:: purple id:: 63b9ba97-e1c8-47bd-a8da-6da0eb100181
• Infrastructure ls-type:: annotation hl-page:: 3 hl-color:: purple id:: 63b9baa5-7b34-4a2b-9bd0-3befd08c3163
• Federation ls-type:: annotation hl-page:: 3 hl-color:: purple id:: 63b9bab5-7366-4e8a-bc04-4bd802afeadf
• low overhe ls-type:: annotation hl-page:: 3 hl-color:: purple id:: 63b9bac1-32a7-4822-9893-71e6f5cc62e5
• distributed management ls-type:: annotation hl-page:: 3 hl-color:: purple id:: 63b9bac5-c5e0-471d-9172-420c5336c419
• The low runtime overhead is critical when deployed in performance-limited hardware platforms ls-type:: annotation hl-page:: 3 hl-color:: purple id:: 63b9bad1-5166-4bf3-aec3-90bee56a7ea2
• The Streaming layer consolidates the data from multiple sources, processing data, and providing data indexing and discover ls-type:: annotation hl-page:: 4 hl-color:: purple id:: 63b9bbfa-e358-41f9-a976-fe4ce9172c7a
• data ingestion, data analysis, data storage, and data query. ls-type:: annotation hl-page:: 4 hl-color:: purple id:: 63b9bc09-674a-491c-a14c-ade19b0cc912
• data processing layer: ls-type:: annotation hl-page:: 4 hl-color:: purple id:: 63b9bce9-8628-4ab2-9106-ac4ac9c3b523
• Distributed ls-type:: annotation hl-page:: 4 hl-color:: purple id:: 63b9bceb-0013-4f76-8d3e-759f1eda3213
• Scalable ls-type:: annotation hl-page:: 4 hl-color:: purple id:: 63b9bced-4a7a-4d93-9ce6-9fb9a92b5cdd
• Real-time ls-type:: annotation hl-page:: 4 hl-color:: purple id:: 63b9bcef-c225-45af-bcaa-0081d7f21a2e
• Application layer ls-type:: annotation hl-page:: 4 hl-color:: purple id:: 63b9bcf2-2559-460c-90a2-8fac65c63d6f
• driven programming model for analyzing requests from streaming-based workflows at runtime and deciding how to process the data. ls-type:: annotation hl-page:: 5 hl-color:: purple id:: 63b9bd5a-e567-4bcf-b1b4-88e860228259
• discovering and composing heterogeneous computational data pipelines by reacting to the content of the data ls-type:: annotation hl-page:: 5 hl-color:: purple id:: 63b9bd64-beee-46f1-8024-98eea8dc8384
• performing in-transit processing by utilizing resources at the edge, at the core, and along the data path, and exploiting network resource capabilities. ls-type:: annotation hl-page:: 5 hl-color:: purple id:: 63b9bd6f-bc29-409a-ad30-fa55718bbfe9