logseq/pages/hls__AbdelBaky et al_2017_Computing in the Continuum.md

file:: [AbdelBaky et al_2017_Computing in the Continuum.pdf](file://C:\Users\david\Zotero/storage/33BF2A43/AbdelBaky et al_2017_Computing in the Continuum.pdf) file-path:: file://C:\Users\david\Zotero/storage/33BF2A43/AbdelBaky et al_2017_Computing in the Continuum.pdf

• traditional approaches that rely on moving data to remote data centers for processing are no longer feasible. ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 63b9be65-9128-4332-8e28-283bf0e83329
• new approaches that effectively leverage distributed computational infrastructure and services are necessary. ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 63b9be6c-205f-4d40-a3e8-19c07bb067fa
• realizing ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 63b9be7c-f450-464c-9903-6ea2da46ec4d
• uid ecosystem where distributed resources and services are programmatically aggregated ondemand to support emerging data-driven application workflows. ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 63b9be83-c199-47bd-a9de-1147b21b7f50
• federating infrastructure, programming applications and 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:: 1 hl-color:: purple id:: 63b9be90-f885-43c1-a32d-8f688264a6ae
• computing in the continuum, i.e., 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:: 1 hl-color:: green id:: 63bbd10d-e68c-4ede-947f-524e8ec9ad98
• Data-driven Applications ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63bbd179-c3c0-4ecc-912e-9f83005da70e
• data-driven programming model for analyzing requests from streaming-based workflows at runtime and deciding how to process the data ls-type:: annotation hl-page:: 2 hl-color:: green id:: 63bbd1ce-9ad7-4d47-8ae1-ac8ec9db6fa5
• 1. discovering and composing heterogeneous computational services ls-type:: annotation hl-page:: 2 hl-color:: green id:: 63bbd1e0-3a21-4894-9a77-66bffb5aa4f2
• (2) 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:: 2 hl-color:: green id:: 63bbd1ee-a37a-4d3f-aa34-9e6468974e0c
• ynamically composes services, executes the workflow, and adapts the composition in real-time accordingly. ls-type:: annotation hl-page:: 2 hl-color:: green id:: 63bbd1fe-e8b2-4cc1-9c57-a88001b66e03
• data streams are evaluated at runtime to decide how and where to process their data ls-type:: annotation hl-page:: 3 hl-color:: green id:: 63bbd271-b29b-474f-a931-39cc1d0b0b5e
• geographically distributed resources based on user objectives, service availability, and data locality ls-type:: annotation hl-page:: 3 hl-color:: green id:: 63bbd285-eec5-442a-bd64-428132cd565a
• Data-driven Programming Models: ls-type:: annotation hl-page:: 8 hl-color:: blue id:: 63bbd2dd-e65e-4ee1-a150-e5ab67fdedd9 hl-stamp:: 1673253780396
• enhance user’s privacy and security and prevent exposing other users’ information ls-type:: annotation hl-page:: 8 hl-color:: green id:: 63bbd2eb-bda4-4369-827b-7697c91ad1c8
• we plan to explore the impact of in-transit staging, caching, approximation, and compression/decompression services on the workflow execution ls-type:: annotation hl-page:: 8 hl-color:: green id:: 63bbd308-5a57-4a81-8396-c2d09936d288
• opportunistically modify already created workflows over time to improve the utilization of the infrastructure or take advantage of recently discovered services. ls-type:: annotation hl-page:: 8 hl-color:: green id:: 63bbd319-c946-4078-bf57-2508925abfca
• In-transit Computing ls-type:: annotation hl-page:: 8 hl-color:: blue id:: 63bbd399-22c2-49c7-92ee-dc22497b9058
• Workflow Management ls-type:: annotation hl-page:: 8 hl-color:: blue id:: 63bbd39d-6aa6-428b-959c-0c8a9e0f7415
• Programmable Distributed Infrastructure ls-type:: annotation hl-page:: 8 hl-color:: blue id:: 63bbd3a2-d997-42b9-810b-4b340d1744bb
• quantifying these compositions and modeling their performance and behavior at any given tim ls-type:: annotation hl-page:: 8 hl-color:: green id:: 63bbd3b3-d8a7-4219-9eb9-20f53b05d051
• metrics ls-type:: annotation hl-page:: 8 hl-color:: green id:: 63bbd3bb-ec14-4a14-b3a4-0d0713298fd2