Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things
The Internet of Things (IoT) requires a new processing paradigm that inherits the scalability of the cloud while minimizing network latency using resources closer to the network edge. On the one hand, building up such flexibility within the edge-to-cloud continuum consisting of a distributed network...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9418552/ |
| _version_ | 1818715366090604544 |
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| author | Zeinab Nezami Kamran Zamanifar Karim Djemame Evangelos Pournaras |
| author_facet | Zeinab Nezami Kamran Zamanifar Karim Djemame Evangelos Pournaras |
| author_sort | Zeinab Nezami |
| collection | DOAJ |
| description | The Internet of Things (IoT) requires a new processing paradigm that inherits the scalability of the cloud while minimizing network latency using resources closer to the network edge. On the one hand, building up such flexibility within the edge-to-cloud continuum consisting of a distributed networked ecosystem of heterogeneous computing resources is challenging. On the other hand, IoT traffic dynamics and the rising demand for low-latency services foster the need for minimizing the response time and a balanced service placement. Load-balancing for fog computing becomes a cornerstone for cost-effective system management and operations. This paper studies two optimization objectives and formulates a decentralized load-balancing problem for IoT service placement: (global) IoT workload balance and (local) quality of service (QoS), in terms of minimizing the cost of deadline violation, service deployment, and unhosted services. The proposed solution, EPOS Fog, introduces a decentralized multi-agent system for collective learning that utilizes edge-to-cloud nodes to jointly balance the input workload across the network and minimize the costs involved in service execution. The agents locally generate possible assignments of requests to resources and then cooperatively select an assignment such that their combination maximizes edge utilization while minimizes service execution cost. Extensive experimental evaluation with realistic Google cluster workloads on various networks demonstrates the superior performance of EPOS Fog in terms of workload balance and QoS, compared to approaches such as First Fit and exclusively Cloud-based. The results confirm that EPOS Fog reduces service execution delay up to 25% and the load-balance of network nodes up to 90%. The findings also demonstrate how distributed computational resources on the edge can be utilized more cost-effectively by harvesting collective intelligence. |
| first_indexed | 2024-12-17T19:02:13Z |
| format | Article |
| id | doaj.art-3aa7a5b17f174daea8a1c6360bc0ebae |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T19:02:13Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3aa7a5b17f174daea8a1c6360bc0ebae2022-12-21T21:36:05ZengIEEEIEEE Access2169-35362021-01-019649836500010.1109/ACCESS.2021.30749629418552Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of ThingsZeinab Nezami0https://orcid.org/0000-0002-5962-5908Kamran Zamanifar1https://orcid.org/0000-0001-5417-0177Karim Djemame2https://orcid.org/0000-0001-5811-5263Evangelos Pournaras3https://orcid.org/0000-0003-3900-2057Faculty of Computer Engineering, University of Isfahan, Isfahan, IranFaculty of Computer Engineering, University of Isfahan, Isfahan, IranSchool of Computing, University of Leeds, Leeds, U.K.School of Computing, University of Leeds, Leeds, U.K.The Internet of Things (IoT) requires a new processing paradigm that inherits the scalability of the cloud while minimizing network latency using resources closer to the network edge. On the one hand, building up such flexibility within the edge-to-cloud continuum consisting of a distributed networked ecosystem of heterogeneous computing resources is challenging. On the other hand, IoT traffic dynamics and the rising demand for low-latency services foster the need for minimizing the response time and a balanced service placement. Load-balancing for fog computing becomes a cornerstone for cost-effective system management and operations. This paper studies two optimization objectives and formulates a decentralized load-balancing problem for IoT service placement: (global) IoT workload balance and (local) quality of service (QoS), in terms of minimizing the cost of deadline violation, service deployment, and unhosted services. The proposed solution, EPOS Fog, introduces a decentralized multi-agent system for collective learning that utilizes edge-to-cloud nodes to jointly balance the input workload across the network and minimize the costs involved in service execution. The agents locally generate possible assignments of requests to resources and then cooperatively select an assignment such that their combination maximizes edge utilization while minimizes service execution cost. Extensive experimental evaluation with realistic Google cluster workloads on various networks demonstrates the superior performance of EPOS Fog in terms of workload balance and QoS, compared to approaches such as First Fit and exclusively Cloud-based. The results confirm that EPOS Fog reduces service execution delay up to 25% and the load-balance of network nodes up to 90%. The findings also demonstrate how distributed computational resources on the edge can be utilized more cost-effectively by harvesting collective intelligence.https://ieeexplore.ieee.org/document/9418552/Agentcloud computingcollective learningdistributed optimizationedge computingfog computing |
| spellingShingle | Zeinab Nezami Kamran Zamanifar Karim Djemame Evangelos Pournaras Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things IEEE Access Agent cloud computing collective learning distributed optimization edge computing fog computing |
| title | Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things |
| title_full | Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things |
| title_fullStr | Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things |
| title_full_unstemmed | Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things |
| title_short | Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things |
| title_sort | decentralized edge to cloud load balancing service placement for the internet of things |
| topic | Agent cloud computing collective learning distributed optimization edge computing fog computing |
| url | https://ieeexplore.ieee.org/document/9418552/ |
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