29 Papers
32 Citations
Mian Guo is an academic researcher from South China University of Technology. The author has contributed to research in topics: Computer science & Enhanced Data Rates for GSM Evolution. The author has an hindex of 6, co-authored 15 publications.
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Papers
Resource Allocation and Task Offloading for Heterogeneous Real-Time Tasks With Uncertain Duration Time in a Fog Queueing System
TL;DR: This work considers a fog queueing system with limited infrastructure resources to accommodate real-time tasks with heterogeneities in task types and execution deadlines and proposes policies that can avoid task starvation and yields a tradeoff between high throughput and a high task completion ratio.
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Optimal Scheduling of VMs in Queueing Cloud Computing Systems With a Heterogeneous Workload
Mian Guo,Quansheng Guan,Wende Ke +2 more
TL;DR: The simulation results show that SJF-RL achieves its goal of delay-optimal scheduling of VMs by provisioning a low delay at various job arrival rates for various shapes of job length distributions, and the simulation results illustrate that although SJf-MMBF is sub-delay- optimal in a heavy-loaded and highly dynamic environment, it is efficient in throughput performance in terms of the average job hosting rate provisioning.
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Delay-Optimal Scheduling of VMs in a Queueing Cloud Computing System with Heterogeneous Workloads
TL;DR: Study of virtual machine scheduling in a queueing cloud computing system with stochastical arrivals of heterogeneous jobs by considering jobs’ delay requirements proposes a queue-length-based MaxWeight policy based on Lyapunov drift to minimize the queue lengths of VM jobs, which is called SJF-QMW.
Provisioning of QoS adaptability in wired-wireless integrated networks
TL;DR: A scheme to determine dynamic delay bounds is proposed, which is the key step to implement DQS to support QoS adaptability in wired-wireless integrated networks.
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Computation Offloading for Machine Learning in Industrial Environments
Mian Guo,Mithun Mukherjee,Gen Liang,Jinyou Zhang +3 more
- 18 Oct 2020
TL;DR: A machine learning-based offloading problem for edge computing based machine learning in an industrial environment is formulated with the goal of minimizing the training delay, and an energy-constrained delay-greedy algorithm is designed to solve the problem.
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