MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication Environments
Many novel IoT-based applications demand low latency, large compute resources, and high privacy. These requirements have motivated the emergence of fog and edge computing to complement the low-privacy and high-latency cloud. The intention behind Fog computing is to place computational servers closer...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9714351/ |
| _version_ | 1818286045297377280 |
|---|---|
| author | Hend Gedawy Ali Elgazar Khaled A. Harras |
| author_facet | Hend Gedawy Ali Elgazar Khaled A. Harras |
| author_sort | Hend Gedawy |
| collection | DOAJ |
| description | Many novel IoT-based applications demand low latency, large compute resources, and high privacy. These requirements have motivated the emergence of fog and edge computing to complement the low-privacy and high-latency cloud. The intention behind Fog computing is to place computational servers closer to the user, typically within the city’s vicinity, to reduce latency. However, because of the high deployment cost of these servers at scale, and unreliable network infrastructures in many countries or areas, edge computing was proposed. Edge computing advocates leveraging compute resources, typically 0-hops away, on distributed ensembles of colocated devices called FemtoClouds. In this paper, we propose MAESTRO, a system that enables users to offload computational jobs to multiple FemtoClouds in their immediate vicinity. For MAESTRO, we build an integrated architecture that includes two new scheduling algorithms for assigning computing workloads to FemtoClouds. Each of our scheduling algorithms is designed to allow the system to operate more efficiently given poor or strong network infrastructures. We implement a full prototype of our system to assess its performance on our experimental testbed. The results indicate that in communication-challenged environments, our specialized scheduler outperforms state-of-the-art schedulers by up to 55%, while in communication-friendly environments our other specialized scheduler outperforms state-of-the-art schedulers by up to 67%. |
| first_indexed | 2024-12-13T01:18:21Z |
| format | Article |
| id | doaj.art-6a6645b741f142b1b96fc5ccd60ae2c1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T01:18:21Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6a6645b741f142b1b96fc5ccd60ae2c12022-12-22T00:04:17ZengIEEEIEEE Access2169-35362022-01-0110270962711210.1109/ACCESS.2022.31520759714351MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication EnvironmentsHend Gedawy0https://orcid.org/0000-0003-0006-4701Ali Elgazar1Khaled A. Harras2Computer Science Department, Carnegie Mellon University Qatar, Doha, QatarComputer Science Department, Carnegie Mellon University Qatar, Doha, QatarComputer Science Department, Carnegie Mellon University Qatar, Doha, QatarMany novel IoT-based applications demand low latency, large compute resources, and high privacy. These requirements have motivated the emergence of fog and edge computing to complement the low-privacy and high-latency cloud. The intention behind Fog computing is to place computational servers closer to the user, typically within the city’s vicinity, to reduce latency. However, because of the high deployment cost of these servers at scale, and unreliable network infrastructures in many countries or areas, edge computing was proposed. Edge computing advocates leveraging compute resources, typically 0-hops away, on distributed ensembles of colocated devices called FemtoClouds. In this paper, we propose MAESTRO, a system that enables users to offload computational jobs to multiple FemtoClouds in their immediate vicinity. For MAESTRO, we build an integrated architecture that includes two new scheduling algorithms for assigning computing workloads to FemtoClouds. Each of our scheduling algorithms is designed to allow the system to operate more efficiently given poor or strong network infrastructures. We implement a full prototype of our system to assess its performance on our experimental testbed. The results indicate that in communication-challenged environments, our specialized scheduler outperforms state-of-the-art schedulers by up to 55%, while in communication-friendly environments our other specialized scheduler outperforms state-of-the-art schedulers by up to 67%.https://ieeexplore.ieee.org/document/9714351/Edge computingFemtoCloudsInternet of ThingsIoT cloudmobile computing |
| spellingShingle | Hend Gedawy Ali Elgazar Khaled A. Harras MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication Environments IEEE Access Edge computing FemtoClouds Internet of Things IoT cloud mobile computing |
| title | MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication Environments |
| title_full | MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication Environments |
| title_fullStr | MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication Environments |
| title_full_unstemmed | MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication Environments |
| title_short | MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication Environments |
| title_sort | maestro orchestrating computational offloading to multiple femtoclouds in various communication environments |
| topic | Edge computing FemtoClouds Internet of Things IoT cloud mobile computing |
| url | https://ieeexplore.ieee.org/document/9714351/ |
| work_keys_str_mv | AT hendgedawy maestroorchestratingcomputationaloffloadingtomultiplefemtocloudsinvariouscommunicationenvironments AT alielgazar maestroorchestratingcomputationaloffloadingtomultiplefemtocloudsinvariouscommunicationenvironments AT khaledaharras maestroorchestratingcomputationaloffloadingtomultiplefemtocloudsinvariouscommunicationenvironments |