Optimizing Information Freshness Through Computation-Transmission Tradeoff and Queue Management in Edge Computing

Edge computing applications typically require generated data to be preprocessed at the source and then transmitted to an edge server. In such cases, transmission time and preprocessing time are coupled, yielding a tradeoff between them to achieve the targeted objective. This paper presents analysis of such a system with the objective of optimizing freshness of received data at the edge server. We model this system as two queues in tandem whose service times are independent but the transmission service time is monotonically dependent on the computation service time in mean value. This dependence captures the natural decrease in transmission time due to lower offloaded computation. We analyze various queue management schemes in this tandem queue where the compute queue has a single server, Poisson packet arrivals, general independent service and no extra buffer to save incoming packets. The transmit queue has a single server receiving packets from the compute queue with memoryless service time. We consider the transmit queue in two forms: (i) No data buffer and (ii) One unit data buffer and last come first serve with discarding. We analyze various non-preemptive as well as preemptive cases. We perform stationary distribution analysis and obtain closed form expressions for average age of information (AoI) and average peak AoI. Our numerical results illustrate analytical findings on how computation and transmission times could be traded off to optimize AoI and reveal a consequent tradeoff between average AoI and average peak AoI.

Automated summary

This paper analyzes the tradeoff between transmission time and preprocessing time in edge computing applications, aiming to optimize the freshness of received data at the edge server. By modeling the system as two queues in tandem, various queue management schemes are studied to achieve the targeted objective. The analysis provides insights on how computation and transmission times can be traded off to optimize the average age of information (AoI) and reveals a consequent tradeoff between average AoI and average peak AoI.