Main Conference
Track 2 – MAC and Cross Layer Design
Session T2-S7
Scheduling
Conference
10:40 AM
—
12:10 PM KST
Local
May 27 Wed, 6:40 PM
—
8:10 PM PDT
Co-Optimizing Performance and Fairness Using Weighted PF Scheduling and IAB-aware Flow Control
Yingjie Zhang (University of California, Davis, USA); Vishwanath Ramamurthi (Verizon Wireless, USA); Zhiyi Huang and Dipak Ghosal (University of California, Davis, USA)
0
In 5G networks, wide-band mmWave can be used to provide extreme data rates to user equipments (UEs). However, since mmWave is coverage limited, it necessitates dense placement of base stations, which in turn can significantly increase the fiber deployment cost. One solution being considered is to replace fibers with Integrated Access and Backhaul (IAB) network, where a part of the wireless spectrum is used for connecting base stations. In an asymmetric IAB network, standard proportional fair scheduling algorithm (PF) fails to distribute resources among UEs fairly. We propose a new weighted proportional fair (WPF) scheduling algorithm to improve the fairness of UE's achieved throughput in an IAB network. Also to mitigate congestion in IAB nodes and improve system throughput, we propose an IAB- aware end-to-end flow control (I-EEFC) algorithm. Through detailed analyses for both symmetric and asymmetric network topologies, we show that our proposed combined scheduling and flow control algorithm (WPF + I-EEFC) improves both fairness and system throughput.
Scheduling Stochastic Real-Time Jobs in Unreliable Workers
Yu-Pin Hsu (National Taipei University, Taiwan); Yu-Chih Huang (National Chiao Tung University, Taiwan); Shin-Lin Shieh (National Taipei University, Taiwan)
0
We consider a distributed computing network consisting of a master and multiple workers processing tasks of different types. The master is running multiple applications. Each application stochastically generates real-time jobs with a strict job deadline, where each job is a collection of tasks of some types specified by the application. A real-time job is completed only when all its tasks are completed by the corresponding workers within the deadline. Moreover, we consider unreliable workers, whose processing speeds are uncertain. Because of the limited processing abilities of the workers, an algorithm for scheduling the jobs in the workers is needed to maximize the average number of completed jobs for each application. The scheduling problem is not only critical but also practical in distributed computing networks. In this paper, we develop two scheduling algorithms, namely, a feasibility-optimal scheduling algorithm and an approximate scheduling algorithm. The feasibility-optimal scheduling algorithm can fulfill the largest region of applications' requirements for the average number of completed jobs. However, the feasibility-optimal scheduling algorithm suffers from high computational complexity when the number of applications is large. To address the issue, the approximate scheduling algorithm is proposed with a guaranteed approximation ratio in the worst- case scenario. The approximate scheduling algorithm is also validated in the average-case scenario via computer simulations.
Exception of Dominant Interfering Beam: Low Complex Beam Scheduling in mmWave Networks
Eunkyung Kim (Electronics and Telecommunications Research Institute (ETRI), Korea (South)); Jeongho Kwak (DGIST, Korea (South)); Song Chong (KAIST, Korea (South))
2
We begin this paper by asking a simple question: All beams can be simultaneously activated thanks to the ignorable inter-beam interference and sharp beam shape in mmWave networks? This paper provides a counter-intuitive observation that interference between one-hop adjacent beams still significantly affects the network performance in mmWave networks. Leveraging this observation, we revisit an optimization of inter- beam scheduling problem in a network-wide mmWave system on top of a physical layer precoding technique and suggest practical and low-complex beam on/off scheduling and corresponding user scheduling algorithms. Finally, via simulations in a real mmWave network environment, we reveal that the proposed algorithm attains close to the performance of an optimal policy which has much higher computational complexity.
Average Age of Changed Information in the Internet of Things
Wenrui Lin (Sun Yat-Sen University, China); Xijun Wang (Sun Yat-sen University, China); Chao Xu (Northwest A&F University, China); Xinghua Sun and Xiang Chen (Sun Yat-sen University, China)
0
The freshness of status updates is imperative in mission-critical Internet of things (IoT) applications. Recently, Age of Information (AoI) has been proposed to measure the freshness of updates at the receiver. However, AoI only characterizes the freshness over time, but ignores the freshness in the content. In this paper, we introduce a new performance metric, Age of Changed Information (AoCI), which captures both the passage of time and the change of information content. Also, we examine the AoCI in a time-slotted status update system, where a sensor samples the physical process and transmits the update packets with a cost. We formulate a Markov Decision Process (MDP) to find the optimal updating policy that minimizes the weighted sum of the AoCI and the update cost. Particularly, in a special case that the physical process is modeled by a two-state discrete time Markov chain with equal transition probability, we show that the optimal policy is of threshold type with respect to the AoCI and derive the closed-form of the threshold. Finally, simulations are conducted to exhibit the performance of the threshold policy and its superiority over the zero-wait baseline policy.
Age of Information in Scheduled Wireless Relay Networks
Masoumeh Moradian (IPM, Iran); Aresh Dadlani (Nazarbayev University, Kazakhstan)
0
In this paper, we use the concept of Markovian jump linear systems in order to analyze the age of information (AoI) in discrete-time Markovian systems. This approach is in fact, the discrete-time counterpart of the stochastic hybrid system (SHS) model reported for analyzing AoI in continuous- time Markovian systems and thus, is referred to as the discrete-time SHS (DT-SHS) model. We then apply the DT-SHS model in two wireless relay network settings to analyze and optimize the AoI associated with the static link scheduling policies. The first relay network comprises of one relay and a direct link between source and destination, whereas the second setting has two relays and no direct link. Moreover, a static link scheduling policy schedules the links without any knowledge about the state of the network. Using results obtained through numerical simulations, we validate our analytical approach and show the effect of relays in AoI improvement.
Session Chair
Changhee Joo (Korea University, Korea)