Joint Information Freshness and Completion Time Optimization for Vehicular Networks

Abstract

The demand for real-time cloud applications has seen an unprecedented growth over the past decade. These applications require rapidly data transfer and fast computations. This paper considers a scenario where multiple IoT devices update information on the cloud, and request a computation from the cloud at certain times. The time required to complete the request for computation includes the time to wait for computation to start on busy virtual machines, performing the computation, waiting and service in the networking stage for delivering the output to the end user. In this context, the freshness of the information is an important concern and is different from the completion time. This paper proposes novel scheduling strategies for both computation and networking stages. Based on these strategies, the age-of-information (AoI) metric and the completion time are characterized. A convex combination of the two metrics is optimized over the scheduling parameters. The problem is shown to be convex and thus can be solved optimally. Moreover, based on the offline policy, an online algorithm for job scheduling is developed. Numerical results demonstrate significant improvement as compared to the considered baselines.

Trading Off Computation with Transmission in Status Update Systems

“…Evolution of AoI through multiple hops in networks have been characterized in [18]- [22] and [23]- [25] consider AoI minimization over multiple access and broadcast channels. [26] [27], [28] consider AoI analysis with tandem computing and communication queues.…”

Analysis on Computation-Intensive Status Update in Mobile Edge Computing

“…Ref. [30] put forward new scheduling schemes for computing and network phases in vehicular networks by combining the computation and information freshness. In [31], the authors investigated bidirectional timely data exchanging between a fog node and a mobile user in a fog computing system.…”

Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach

“…However, for an accurate estimation of future AoI, knowledge of the network dynamics, i.e., wireless channels and interference, is required. Recently, several works have studied the optimization of AoI in vehicular networks [3]- [5]. In [3] and [4], the network dynamics are assumed to be known and in [5], they are estimated in a centralized manner without any consideration of future AoI.…”

“…Recently, several works have studied the optimization of AoI in vehicular networks [3]- [5]. In [3] and [4], the network dynamics are assumed to be known and in [5], they are estimated in a centralized manner without any consideration of future AoI. Yet, in a URLLC setting [6], [7], reliably learning and estimating the network dynamics with minimum communication overhead is desirable.…”