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Cooperative unloading method based on Markov decision process in mobile cloud computing system

A computing system and mobile cloud technology, applied in computing, energy-saving computing, program control design, etc., can solve the problems of transmission delay and unloading delay reduction, high cloud server load, unbalanced utilization of cloud server resources, etc., to achieve Make full use of it, minimize delay and energy consumption, and achieve the effect of load balancing

Active Publication Date: 2019-01-04
SOUTHEAST UNIV
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Problems solved by technology

In a multi-layer cloud environment, offloading tasks to one cloud server may cause the cloud server to be overloaded, while other cloud servers are idle, so the resources of the cloud servers are not used in a balanced manner, resulting in transmission delay and unloading delay. failed to reduce further

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  • Cooperative unloading method based on Markov decision process in mobile cloud computing system
  • Cooperative unloading method based on Markov decision process in mobile cloud computing system
  • Cooperative unloading method based on Markov decision process in mobile cloud computing system

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Embodiment Construction

[0042] The method of the present invention comprehensively considers the state of the multi-layer cloud as the state space of the Markov decision-making process, and uses task adaptive segmentation to reduce the delay and the load of a single cloud server, and calculates the optimal unloading scheme through the state transition probability matrix and the reward function , the goal is to minimize delay and energy consumption.

[0043] Based on the Markov decision-making process, the present invention makes full use of the limited computing resources in the system, while aiming at minimizing the unloading delay and energy consumption of all terminal tasks, it ensures the load balance of each cloud server and satisfies the needs of each mobile terminal. Task offloading requirements.

[0044] The implementation method of the present invention will be further described below in conjunction with the accompanying drawings.

[0045] Such as figure 1 As shown, consider the offloading...

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Abstract

The invention discloses a cooperative unloading method based on a Markov decision process in a mobile cloud computing system, which comprises the following steps: (1) combining a task queue state, anedge cloud state, a mobile self-organizing cloud state and a center cloud state into a state space of the Markov decision process, and calculating a state transition probability matrix; (2) defining action space; (3) defining the immediate return function of Markov decision process by time delay and energy consumption; (4) taking a series of input tasks as statistical samples to calculate task segmentation threshold; (5) adaptive task segmentation being realized by task segmentation algorithm according to the obtained threshold; (6) according to the size of the subtask, the state transition probability matrix and the immediate return function, the unloading decision result being obtained by the value iteration algorithm. The invention is based on a Markov decision process and meets the requirements of minimizing time delay and energy consumption; the adaptive task segmentation algorithm is used to achieve the full utilization of cloud computing resources and load balancing.

Description

technical field [0001] The invention relates to a collaborative unloading method based on a Markov decision process in mobile cloud computing technology (Mobile Cloud Computing, MCC). Background technique [0002] In the research of key technologies of mobile cloud computing, how to achieve task offloading with low latency and low energy consumption is one of the key points, and using Markov decision process for analysis and modeling can reduce the delay and energy consumption of task offloading. [0003] Most of the existing offloading algorithms offload a task to a cloud server in order to reduce the offloading delay and energy consumption. In a multi-layer cloud environment, offloading tasks to one cloud server may lead to excessive load on the cloud server, while other cloud servers are idle, so the resources of the cloud servers are not balancedly utilized, resulting in transmission delays and offloading delays could not be reduced further. Therefore, a new offload me...

Claims

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Application Information

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IPC IPC(8): G06F9/50
CPCG06F9/5083G06F2209/5017G06F2209/509Y02D10/00
Inventor 夏玮玮吴思运燕锋兰卓睿崔文清钱潮沈连丰
Owner SOUTHEAST UNIV
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