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Age of information has been proposed recently to measure information freshness, especially for a class of real-time video applications. These applications often demand timely updates with edge cloud computing to guarantee the user experience. However, the edge cloud is usually equipped with limited computation and network resources and therefore, resource contention among different video streams can contribute to making the updates stale. Aiming to minimize a penalty function of the weighted sum of the average age over multiple end users, this paper presents a greedy traffic scheduling policy for the processor to choose the next processing request with the maximum immediate penalty reduction. In this work, we formulate the service process when requests from multiple users arrive at edge cloud servers asynchronously and show that the proposed greedy scheduling algorithm is the optimal work- conserving policy for a class of age penalty functions.
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Real-time Multi-resource Allocation via Structured Policy Table
Mobile Edge Cloud endures limited computational resources as compared to back-end cloud. Semi-Markov Decision Process (SMDP) basedMulti-Resource Allocation (MRA) work [6] introduces optimal resource allocation for mobile requests in the resource constrained edge cloud environments. In this study, we scale existing SMDPMRA work for real-world scenarios. First, we structure the policy tables in a two dimensional matrix such that columns represent states of the system and rows for the actions. Second, we propose an index based search technique over structured policy tables. Simulation results demonstrate that our approach outperforms the legacy method and retrieves an optimal action from the policy tables in the order of microseconds, which meets the delay criteria of real-time applications in edge cloud based systems.
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- Award ID(s):
- 1818884
- PAR ID:
- 10165173
- Date Published:
- Journal Name:
- The 11-th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2019)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Age of information has been proposed recently to measure information freshness, especially for a class of real-time video applications. These applications often demand timely updates with edge cloud computing to guarantee the user experience. However, the edge cloud is usually equipped with limited computation and network resources and therefore, resource contention among different video streams can contribute to making the updates stale. Aiming to minimize a penalty function of the weighted sum of the average age over multiple end users, this paper presents a greedy traffic scheduling policy for the processor to choose the next processing request with the maximum immediate penalty reduction. In this work, we formulate the service process when requests from multiple users arrive at edge cloud servers asynchronously and show that the proposed greedy scheduling algorithm is the optimal work-conserving policy for a class of age penalty functions.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-29035-1_36
