Loading Balancing and Resource Allocation for Edge Computing-enabled IoT
As response time is the key performance metric for many delay sensitive IoT applications, edge computing can move the computing resources close to IoT devices. Therefore, data flows of IoT devices can be offloaded to fog nodes in their proximity, instead of the remote cloud, for processing. However, owing to the spatial and temporal dynamics of IoT device distributions, the workload distribution among edge servers are unbalanced, which incurs network congestion. Meanwhile, as different application tasks have different service requirements and require various computation resources, the workload allocation and resource allocation remain critical issues that directly affect the response time of various IoT tasks. To solve this problem, we designed some innovative load balancing schemes and resource allocation schemes to minimize the latency of IoT tasks by jointly considering the computation and communications sectors in mobile edge computing. Through extensive simulations, we have compared the performance of the designed schemes with other schemes and verified their advantages in edge computing.
Date and Time
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- Date: 21 Apr 2021
- Time: 04:00 PM to 05:00 PM
- All times are (GMT-05:00) US/Eastern
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- 1000 River Road
- Teaneck , New Jersey
- United States 07666
- Building: Muscarelle Center, M105,
- Room Number: M105
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- Co-sponsored by North Jersey Section, Signal Processing Chapter,
- Starts 29 March 2021 04:34 PM
- Ends 21 April 2021 04:34 PM
- All times are (GMT-05:00) US/Eastern
- No Admission Charge
Speakers
Dr. Qiang Fan of Virginia Tech, VA
Loading Balancing and Resource Allocation for Edge Computing-enabled IoT
Biography:
Dr. Qiang Fan is currently a postdoctoral researcher in the Department of Electrical and Computer Engineering at Virginia Tech, Blacksburg, VA. He received his Ph.D. in electrical engineering from New Jersey Institute of Technology, 2019. His current research interests focus on mobile edge computing, machine learning for wireless networks and federated learning. He has published more than 30 papers in high impact journals and conferences including IEEE Transactions on Network Science and Engineering, Transactions on Vehicular Technology, Transactions on Green Communications and Networking, and submitted two patent applications. Dr. Fan was the publicity co-chair of EdgeComm workshop for SEC 2020, and he has served as TPC member for various conferences such as Cloud Computing, IEEE Wireless Africa Conference and Globecome 2020. He has reviewed over 130 submissions for various prestigious journals and conferences.
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Address:United States
Agenda
As response time is the key performance metric for many delay sensitive IoT applications, edge computing can move the computing resources close to IoT devices. Therefore, data flows of IoT devices can be offloaded to fog nodes in their proximity, instead of the remote cloud, for processing. However, owing to the spatial and temporal dynamics of IoT device distributions, the workload distribution among edge servers are unbalanced, which incurs network congestion. Meanwhile, as different application tasks have different service requirements and require various computation resources, the workload allocation and resource allocation remain critical issues that directly affect the response time of various IoT tasks. To solve this problem, we designed some innovative load balancing schemes and resource allocation schemes to minimize the latency of IoT tasks by jointly considering the computation and communications sectors in mobile edge computing. Through extensive simulations, we have compared the performance of the designed schemes with other schemes and verified their advantages in edge computing.