Timely-Throughput Optimal Scheduling with Prediction

Chen, Kun; Huang, Longbo

doi:10.1109/infocom.2018.8486337

Cited by 14 publications

(21 citation statements)

References 27 publications

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“…When the prediction window size is zero, i.e., when there is no prediction, the corresponding total average arrival queue backlog size of all EFNs is Dividing both sides by i∈N λ i and using Little's theorem, we obtain (27). Next, we prove (28). Taking the limit of W (W → ∞), we obtain…”

Section: Discussionmentioning

confidence: 98%

“…Dividing both sides by i∈N λ i and using Little's theorem, we obtain (27). Next, we prove (28). Taking…”

Section: Discussionmentioning

confidence: 99%

“…In practice, prediction errors are inevitable. Hence, we investigate the performance of PORA in the presence of prediction errors [28]. Particularly, we consider two kinds of prediction errors: false alarm and missed detection.…”

Section: Evaluation With Imperfect Predictionmentioning

confidence: 99%

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PORA: Predictive Offloading and Resource Allocation in Dynamic Fog Computing Systems

Gao

Huang

Bian

et al. 2020

IEEE Internet Things J.

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In multi-tiered fog computing systems, to accelerate the processing of computation-intensive tasks for real-time IoT applications, resource-limited IoT devices can offload part of their workloads to nearby fog nodes, whereafter such workloads may be offloaded to upper-tier fog nodes with greater computation capacities. Such hierarchical offloading, though promising to shorten processing latencies, may also induce excessive power consumptions and latencies for wireless transmissions. With the temporal variation of various system dynamics, such a tradeoff makes it rather challenging to conduct effective and online offloading decision making. Meanwhile, the fundamental benefits of predictive offloading to fog computing systems still remain unexplored. In this paper, we focus on the problem of dynamic offloading and resource allocation with traffic prediction in multitiered fog computing systems. By formulating the problem as a stochastic network optimization problem, we aim to minimize the time-average power consumptions with stability guarantee for all queues in the system. We exploit unique problem structures and propose PORA, an efficient and distributed predictive offloading and resource allocation scheme for multi-tiered fog computing systems. Our theoretical analysis and simulation results show that PORA incurs near-optimal power consumptions with queue stability guarantee. Furthermore, PORA requires only mild-value of predictive information to achieve a notable latency reduction, even with prediction errors.

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Section: Discussionmentioning

confidence: 98%

“…Dividing both sides by i∈N λ i and using Little's theorem, we obtain (27). Next, we prove (28). Taking…”

Section: Discussionmentioning

confidence: 99%

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PORA: Predictive Offloading and Resource Allocation in Dynamic Fog Computing Systems

Gao

Huang

Bian

et al. 2020

IEEE Internet Things J.

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“…However the above work consider wireless fading to be an i.i.d process. When channel state evolution has Markov properties, scheduling to minimize delay performance and maximize throughput have been studied in [27], [28], [34]. Scheduling policy based on value iteration is proposed in [28] and a Whittle-like index policy to achieve delay-power trade-off is studied in [27].…”

Section: Related Workmentioning

confidence: 99%

Minimizing Age of Information With Power Constraints: Multi-User Opportunistic Scheduling in Multi-State Time-Varying Channels

Tang

Wang

Song

et al. 2020

IEEE J. Select. Areas Commun.

139

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This work is motivated by the need of collecting fresh data from power-constrained sensors in the industrial Internet of Things (IIoT) network. A recently proposed metric, the Age of Information (AoI) is adopted to measure data freshness from the perspective of the central controller in the IIoT network. We wonder what is the minimum average AoI the network can achieve and how to design scheduling algorithms to approach it. To answer these questions when the channel states of the network are Markov time-varying and scheduling decisions are restricted to bandwidth constraint, we first decouple the multi-sensor scheduling problem into a single-sensor constrained Markov decision process (CMDP) by relaxing the hard bandwidth constraint. Next we exploit the threshold structure of the optimal policy for the decoupled single sensor CMDP and obtain the optimum solution through linear programming (LP). Finally, an asymptotically optimal truncated policy that can satisfy the hard bandwidth constraint is built upon the optimal solution to each of the decoupled single-sensor. Our investigation shows that to obtain a small AoI performance: (1) The scheduler exploits good channels to schedule sensors supported by limited power; (2) Sensors equipped with enough transmission power are updated in a timely manner such that the bandwidth constraint can be satisfied.

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“…Notice that the above work assumed the channel fading to be an i.i.d process. A more practical assumption to model the timevarying channel is to assume that channel states evolve as a Markov chain similar to [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Delay Optimal Cross-Layer Scheduling Over Markov Channels with Power Constraint

Zhan

Tang

Wang

2020

Preprint

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We consider a scenario where a power constrained transmitter delivers randomly arriving packets to the destination over Markov time-varying channel and adapts different transmission power to each channel state in order to guarantee successful transmission. To minimize the expected average transmission delay of each packet, we formulate the problem into a constrained Markov decision process (CMDP). We reveal the queue-length threshold structure of the optimal policy, i.e., the transmitter sends packets if and only if the queue length surpasses a threshold and obtain the optimal cross-layer scheduling strategy through linear programming (LP). Numerical results validate the performance of the proposed strategy and illustrate a delaypower tradeoff in such scenario.

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Timely-Throughput Optimal Scheduling with Prediction

Cited by 14 publications

References 27 publications

PORA: Predictive Offloading and Resource Allocation in Dynamic Fog Computing Systems

PORA: Predictive Offloading and Resource Allocation in Dynamic Fog Computing Systems

Minimizing Age of Information With Power Constraints: Multi-User Opportunistic Scheduling in Multi-State Time-Varying Channels

Delay Optimal Cross-Layer Scheduling Over Markov Channels with Power Constraint

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