logseq/pages/hls__PositionPaper+_1673092009371_0.md

file:: PositionPaper+_1673092009371_0.pdf file-path:: ../assets/PositionPaper+_1673092009371_0.pdf

• AI needs resources at the edge of the network. ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63b95bc6-c0fb-4112-8cd0-147470f87d71
• AI applications developmen ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63b95bdc-9a45-4dfc-a6e2-20fdb715a460
• ecure execution ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63b95be7-fa6d-407d-b5c8-cf17fff66383
• easy deploymen ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63b95be9-f2e7-4697-afc4-b36580d418d5
• runtime management and optimization ls-type:: annotation hl-page:: 1 hl-color:: green id:: 63b95bec-a6eb-474b-8316-cf3f1211b585
• AI-SPRINT, a recently funded by the European Commission under the Horizon 2020 framewor ls-type:: annotation hl-page:: 2 hl-color:: green id:: 63b95bff-660d-48d2-8999-d7a6e7d9adde
• eamlessly design and partition AI applications among the current plethora of cloud-based solutions and AI-based sensor devices ls-type:: annotation hl-page:: 2 hl-color:: green id:: 63b95e4f-3544-4b8c-aca2-6cd2699389af
• design and efficient execution of AI applications exploiting resources in the edge-to-cloud continuum such as flexibility, scalability, interoperability, security and privacy, ls-type:: annotation hl-page:: 2 hl-color:: green id:: 63b95e95-3364-4411-9f48-e4c6b5df725c
• Provide design and development tools for the implementation of AI applications ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 63b95eaa-4593-4cb4-a951-1b80bd3b22c0 hl-stamp:: 1673102447999
• hide the communications across components and to transparently implement the parallelization of the compute-intensive part of the application possibly exploiting specialized resources ls-type:: annotation hl-page:: 2 hl-color:: green id:: 63b95eb6-a419-4fe3-8789-570de920d134
• Deliver tools for secure execution and privacy preservation ls-type:: annotation hl-page:: 3 hl-color:: purple id:: 63b95ebb-fd4e-40ef-b2e5-14bd9d480d29 hl-stamp:: 1673102451024
• Develop a runtime environment for application execution and monitoring that implements policies orchestrating the applications’ execution across the computing continuum ls-type:: annotation hl-page:: 3 hl-color:: purple id:: 63b95ec6-2c7a-4a9f-ae3c-5d8f82244acd hl-stamp:: 1673102454084
• Provide advanced solutions for AI architecture enhancement allowing iterative refinement of AI application architecture design and deployment ls-type:: annotation hl-page:: 3 hl-color:: purple id:: 63b95edb-ec85-4385-a90b-08520ffd3b41 hl-stamp:: 1673102457572
• farming 4.0 ls-type:: annotation hl-page:: 3 hl-color:: green id:: 63b95ef5-df9e-476b-89ff-254a8d884788
• maintenance & inspection ls-type:: annotation hl-page:: 3 hl-color:: green id:: 63b95ef7-e4a4-4335-9d8b-73dea6393da1
• personalized healthcar ls-type:: annotation hl-page:: 3 hl-color:: green id:: 63b95efb-ea49-468a-9fe9-446ad3cb8a6b
• evelop multi-cloud systems including resources at the full computing continuum stack involving classic components and AI models ls-type:: annotation hl-page:: 3 hl-color:: green id:: 63b95f0d-4833-4799-8d26-5b088e203700
• wind farm ls-type:: annotation hl-page:: 3 hl-color:: green id:: 63b95f21-322b-44e0-90dc-af115981d69b
• C1 and C2 on the drone ls-type:: annotation hl-page:: 4 hl-color:: green id:: 63b95fae-7710-49b1-9154-bc9d21b1aff0
• C3 on the edge server ls-type:: annotation hl-page:: 4 hl-color:: green id:: 63b95fb1-eb30-4521-a3db-9e64898d44d0
• C4, C5 and C6 are executed on the cloud. ls-type:: annotation hl-page:: 4 hl-color:: green id:: 63b95fb4-c63c-4260-87c4-2ef2c82f5cb1
• ast developmen ls-type:: annotation hl-page:: 5 hl-color:: green id:: 63b95fed-8cfc-4ea8-a071-65172564b582
• optimization ls-type:: annotation hl-page:: 5 hl-color:: green id:: 63b95ff6-f62e-400a-a716-eefd0697a57b
• and managemen ls-type:: annotation hl-page:: 5 hl-color:: green id:: 63b95ffb-d224-40fa-9ff6-783e18cba2de
• AI applications ls-type:: annotation hl-page:: 5 hl-color:: green id:: 63b95fff-551d-4684-bc75-f494a06195d4
• unning across a cloud-edge computing continuum ls-type:: annotation hl-page:: 5 hl-color:: green id:: 63b96006-3be4-4f0d-b7b6-538b42f5bfc0
• (1) annotating AI applications with non-functional/QoS constraints and guide code parallelizatio ls-type:: annotation hl-page:: 5 hl-color:: purple id:: 63b96013-bb9a-4acc-b07e-5428e5638428 hl-stamp:: 1673101193167
• (2) evaluating a priori performance of AI applications both at training and at inference time ls-type:: annotation hl-page:: 5 hl-color:: purple id:: 63b96019-9267-4e26-9221-0446fa5d52ac hl-stamp:: 1673101195126
• (3) identifying and automating the search for the most accurate deep neural network starting from labeled datase ls-type:: annotation hl-page:: 5 hl-color:: purple id:: 63b96022-b2b1-4181-b542-232404cbbae8 hl-stamp:: 1673101197448
• (4) automatically exploring multiple candidate deployments for the application components maximizing resource efficiency and minimising the cloud usage cost. ls-type:: annotation hl-page:: 5 hl-color:: purple id:: 63b9602c-4343-48d1-adea-8e2afcc0c6ae hl-stamp:: 1673101200142
• i) intra- and intercomponents parallelism ls-type:: annotation hl-page:: 5 hl-color:: green id:: 63b96042-9a8b-453b-bdc9-57eafd6844d5
• ii) constraints on application performance, security, privacy, and underlying resources ls-type:: annotation hl-page:: 5 hl-color:: green id:: 63b96049-22a2-48f5-8ad0-4c446bcd8ea7
• iii) deployment alternatives that will be automatically explored by the envisioned solutions. ls-type:: annotation hl-page:: 5 hl-color:: green id:: 63b9604f-8305-4eca-8161-89d59f86c37d
• predict the execution time of inference or training tasks of AI models deployed across the computing continuum. ls-type:: annotation hl-page:: 7 hl-color:: green id:: 63b960bf-8ee8-4f27-994c-346f3556d6cc
• AI-SPRINT will extend the use of such models to consider AI-based sensors and deep networks partitioned and deployed across computing continua ls-type:: annotation hl-page:: 7 hl-color:: green id:: 63b960d1-9c7f-4399-ab05-906132063ee3
• limited ML expertise to train high-quality models specific to their needs also in terms of Quality of Service (QoS) requirements ls-type:: annotation hl-page:: 7 hl-color:: green id:: 63b960e0-d535-4f55-a848-6e3952d2ba29
• AI-SPRINT Running Example ls-type:: annotation hl-page:: 3 hl-color:: green id:: 63b9620c-5222-40f5-afb5-242b358cf5bd
• internal parallelism ls-type:: annotation hl-page:: 5 hl-color:: yellow id:: 63b97f7f-9f49-43d4-8145-0b61fb71e001 hl-stamp:: 1673101185237
• programming layer ls-type:: annotation hl-page:: 5 hl-color:: purple id:: 63b98006-1e1a-487c-b9a7-f94688ec2e71
• https://www.bsc.es/research-and-development/software-and-apps/software-list/compsuperscalar ls-type:: annotation hl-page:: 6 hl-color:: purple id:: 63b98016-d678-480b-886e-d661cd1b5815
• PyCOMPSs ls-type:: annotation hl-page:: 6 hl-color:: purple id:: 63b98073-ff31-4834-a588-d9208de2aceb
• dentifying the functions to be executed as asynchronous parallel task ls-type:: annotation hl-page:: 6 hl-color:: yellow id:: 63b98089-4e33-41e8-b227-3d7e6ab3c85e hl-stamp:: 1673101471686
• annotating them with standard Python decorators ls-type:: annotation hl-page:: 6 hl-color:: yellow id:: 63b9808d-dd15-4e61-95ca-bb104f429333 hl-stamp:: 1673101473254
• New annotations will be introduced to allow predicating on components performance and to specify constraints on the target deployment ls-type:: annotation hl-page:: 6 hl-color:: purple id:: 63b980a9-4c4b-4be8-95a4-62e73a1d94b6 hl-stamp:: 1673101482951
• as a parallel framework, PyCOMPSs exploits inter-node and intra-node parallelism and executes neural networks tasks (like Tensorflow or Keras) as external processes. ls-type:: annotation hl-page:: 6 hl-color:: purple id:: 63b98114-59de-4db0-9f78-c627bc04d79f
• the design still requires specifying the parameters of a typically large network architecture with several layers and units, and then solve a difficult non-convex optimization problem ls-type:: annotation hl-page:: 7 hl-color:: green id:: 63b98132-e0ac-4383-aaa1-be5569cbb83c hl-stamp:: 1673101620848
• AI-SPRINT will provide tools for developing learning as a service solution, that, starting from a training set with labelled training examples (images or temporal data series which are of interest for use cases) will automatically identify the most accurate deep neural network which provides execution time guarantees. ls-type:: annotation hl-page:: 7 hl-color:: purple id:: 63b98142-f9e9-4129-aa93-865aba5900b3 hl-stamp:: 1673101636290
• AI-SPRINT will explore solutions where the edge servers or the AI-enabled sensors run the initial layers of the neural network, and then send the output to the cloud to feed the remaining layers and produce the final result. ls-type:: annotation hl-page:: 7 hl-color:: purple id:: 63b98164-8180-4d6b-8cb6-8934465109a9
• AI-SPRINT runtime environment will include tools to: i) support the continuous deployment of AI applications; ls-type:: annotation hl-page:: 8 hl-color:: purple id:: 63b9818e-5338-488b-af75-a529b47b90d1
• i) support application components concurrent execution reacting to node failures and identifying their optimal placement and resource capacity ls-type:: annotation hl-page:: 8 hl-color:: purple id:: 63b98199-ac2e-4404-834d-66f3c52e918e
• iii) trigger automated model retraining, leveraging on solutions for training and retraining at the edge; ls-type:: annotation hl-page:: 8 hl-color:: purple id:: 63b9819f-57aa-44d5-af6e-72733e67c64e
• v) optimize the scheduling and assignment of accelerator devices among competing training jobs. ls-type:: annotation hl-page:: 8 hl-color:: purple id:: 63b981a5-20cd-4c71-879a-c2f85d14dc1e
• Continuous deployment is the process where development, testing, delivery and progressive deployment of an application is considered as a whole, reducing operation and development costs ls-type:: annotation hl-page:: 8 hl-color:: green id:: 63b98246-6754-4a3a-b835-6289f9b76bee hl-stamp:: 1673101896873
• This is especially important in a scenario where data is continuously retrieved, and AI models are retrained. ls-type:: annotation hl-page:: 8 hl-color:: purple id:: 63b98269-1951-422b-adaf-3371d4d59ca7 hl-stamp:: 1673101932159
• heterogeneity of the edge laye ls-type:: annotation hl-page:: 8 hl-color:: purple id:: 63b98271-e42a-4fe7-8e2f-d98c20ff0d30
• streamlined mechanisms to guide the process of configuring computing resources across the computing continuum ls-type:: annotation hl-page:: 8 hl-color:: purple id:: 63b9827a-24fa-4c96-bfe1-63281e57a0a6
• https://www.egi.eu/services/cloud-compute ls-type:: annotation hl-page:: 8 hl-color:: purple id:: 63b98296-0d83-4c54-b756-97c96e77f101
• ocker containers to automate deployment and provide the level of isolation needed to enforce performance constraints with minimal overhead. ls-type:: annotation hl-page:: 9 hl-color:: purple id:: 63b982dc-4503-4bb9-8cf0-85e82531af62
• OASIS TOSCA templates ls-type:: annotation hl-page:: 9 hl-color:: purple id:: 63b982e3-6c41-41d2-b7cc-73cc945054d9
• AI model architecture search ls-type:: annotation hl-page:: 13 hl-color:: purple id:: 63b98326-ca0e-4376-a724-af4aa045cfbd
• AI cloud-edge application design ls-type:: annotation hl-page:: 13 hl-color:: purple id:: 63b9832b-fbe6-468b-9ef7-a330b97080e5
• CNNs for image recognition ls-type:: annotation hl-page:: 13 hl-color:: purple id:: 63b9833b-7517-4a2f-8895-0a5f19e40e67
• multi-objective perspective trading-off accuracy with energy consumption ls-type:: annotation hl-page:: 13 hl-color:: purple id:: 63b98346-44d5-4240-8fa1-e2d8de446bce
• for edge device ls-type:: annotation hl-page:: 13 hl-color:: purple id:: 63b98348-8d81-475f-959b-a73bfac64f4c
• federated setting ls-type:: annotation hl-page:: 13 hl-color:: purple id:: 63b9834a-49fd-479a-8090-cfc0a768df53
• cloud orchestration runtime that deploys complex and customized virtual infrastructures on multiple back-ends ls-type:: annotation hl-page:: 13 hl-color:: purple id:: 63b98375-330a-4caa-bd89-cc7bcc89a831
• edge limited capacity ls-type:: annotation hl-page:: 13 hl-color:: purple id:: 63b98382-9165-4d37-88ad-1f7a9aa820e7
• several approaches have been proposed to tackle this issue ls-type:: annotation hl-page:: 13 hl-color:: purple id:: 63b98386-0238-43bc-ab62-32188dd57970
• AI-SPRINT Objectives ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 63b98487-4ce8-4ff0-8697-d3b08a66d8e2