Delay-optimal computation task scheduling for mobile-edge computing systems

Liu, Juan; Mao, Yuyi; Zhang, Jun; Letaief, Khaled B.

doi:10.1109/isit.2016.7541539

Cited by 699 publications

(379 citation statements)

References 11 publications

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“…It aims at balancing the energy consumption and delay between the device and remote server according to the current network condition and task queue backlogs. Liu et al [8] formulate the delay minimization problem under power-constrained using Markov chain. An efficient one-dimensional search algorithm is proposed to find the optimal task offloading policy.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

Wang

Ren

2018

Future Internet

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Abstract:Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical machine and the delay for offloaded tasks are taken into account simultaneously by introducing a weighted reward. The high dimensionality of the state space and action space might affect the speed of convergence. Therefore, a novel reinforcement learning algorithm with a two-layered structure is presented to address this problem. First, k clusters of the physical machines are generated based on the k-nearest neighbors algorithm (k-NN). The first layer of TLRL is implemented by a deep reinforcement learning to determine the cluster to be assigned for the offloaded tasks. On this basis, the second layer intends to further specify a physical machine for task execution. Finally, simulation examples are carried out to verify that the proposed TLRL algorithm is able to speed up the optimal policy learning and can deal with the tradeoff between physical machine utilization rate and delay.

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Section: Related Workmentioning

confidence: 99%

“…Papers [6][7][8]10] only consider the energy and delay as optimization objects that ignore the management of cloud resources. Moreover, some works [11,12,14] focus on resource management for task offloading, which do not consider utilization rate of cloud resources.…”

Section: Related Workmentioning

confidence: 99%

A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

Wang

Ren

2018

Future Internet

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“…In the literature, many works investigated computation offloading in MEC systems. Minimizing the execution delay is studied in [16][17][18][19]. Under the constraint of execution delay, [20][21][22][23] minimized the energy consumption and [24] maximized the system scalability.…”

Section: Introductionmentioning

confidence: 99%

Analysis on Computation-Intensive Status Update in Mobile Edge Computing

Kuang

Gong

Chen

et al. 2020

IEEE Trans. Veh. Technol.

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In status update scenarios, the freshness of information is measured in terms of age-of-information (AoI), which essentially reflects the timeliness for real-time applications to transmit status update messages to a remote controller. For some applications, computational expensive and time consuming data processing is inevitable for status information of messages to be displayed. Mobile edge servers are equipped with adequate computation resources and they are placed close to users. Thus, mobile edge computing (MEC) can be a promising technology to reduce AoI for computation-intensive messages. In this paper, we study the AoI for computation-intensive messages with MEC, and consider three computing schemes: local computing, remote computing at the MEC server, and partial computing, i.e., some part of computing tasks are performed locally, and the rest is executed at the MEC server. Zero-wait policy is adopted in all three schemes. Specifically, in local computing, a new message is generated immediately after the previous one is revealed by computing. While in remote computing and partial computing, a new message is generated once the previous one is received by the remote MEC server. With infinite queue size and exponentially distributed transmission time, closed-form average AoI for exponentially distributed computing time is derived for the three computing schemes. For deterministic computing time, the average AoI is analyzed numerically. Simulation results show that by carefully partitioning the computing tasks, the average AoI in partial computing is the smallest compared to local computing and remote computing. The results also indicate numerically the conditions on which remote computing attains smaller average AoI compared with local computing.

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“…The multiple tasks were scheduled based on a queuing state in order to adapt channel variations. Alternatively, in [3], the latency was minimized with scheduling the MEC offloading, while the energy consumption was considered as an individual constraint in MDs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Cross Entropy Approach for Offloading Learning in Mobile Edge Computing

Zhu

Fan

et al. 2020

IEEE Wireless Commun. Lett.

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In this paper, we propose a novel offloading learning approach to compromise energy consumption and latency in a multi-tier network with mobile edge computing. In order to solve this integer programming problem, instead of using conventional optimization tools, we apply a cross entropy approach with iterative learning of the probability of elite solution samples. Compared to existing methods, the proposed one in this network permits a parallel computing architecture and is verified to be computationally very efficient. Specifically, it achieves performance close to the optimal and performs well with different choices of the values of hyperparameters in the proposed learning approach.

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Delay-optimal computation task scheduling for mobile-edge computing systems

Cited by 699 publications

References 11 publications

A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

Analysis on Computation-Intensive Status Update in Mobile Edge Computing

A Novel Cross Entropy Approach for Offloading Learning in Mobile Edge Computing

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