A resource optimization allocation method suitable for edge computing environment
A resource optimization and allocation method technology, applied in the field of edge computing, can solve problems such as the impossibility of finding polynomial time, difficulty in adapting to and satisfying the reasonable scheduling and optimal allocation of resources in the edge computing environment, and achieve optimal overall system performance and utilization of system resources high rate effect
Active Publication Date: 2021-08-03
STATE GRID SICHUAN ELECTRIC POWER CORP ELECTRIC POWER RES INST
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However, this is obviously an NP problem. In practical applications, it is impossible to find a reasonable method for polynomial time optimization. Therefore, we can only retreat to find an effective suboptimal algorithm
However, many existing heuristic algorithms, such as LPT algorithm, MULTIFIT algorithm, LPT algorithm combined with MULTIFIT algorithm and BoundFit algorithm, mostly simplify the system, only focus on a certain type of resources, and the optimal allocation method does not make the system as a whole To achieve the optimum, it is difficult to adapt and meet the requirements of reasonable scheduling and optimal allocation of resources in the edge computing environment
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[0036] In order to maximize the utilization of system resources, the cloud computing center receives as many requested tasks as possible, selects appropriate tasks from the task set, and distributes these tasks to edge computing devices or processing nodes in the system in a balanced manner; Each processing node is load-balanced, and the remaining resources on each processing node are the least. Its realization principle is as follows:
[0037] Given c task processing nodes, each processing node has system resource R(r 1 , r 2 ,...,r m ): A total of m-dimensional resources, the set of tasks requested to be executed {T 1 ,T 2 ,...,T n}: A total of n requests, and each task requires r resources i ={r i，1 , r i，2 ,...,r i，m}, i ∈ {1, 2, ..., n}, then how to get the task set from n requests {T 1 ,T 2 ,...,T n} select c disjoint subsets {s 1 ,s 2 ,...,s c}, and assign them to c task processing nodes, so that the remaining resources of each processing node The small...
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The invention discloses a resource optimization allocation method suitable for an edge computing environment, comprising: obtaining the current resource utilization rate of each processing node; obtaining the current remaining resource amount of each processing node in a processing node set; selecting from the processing node set The processing node with the largest sum of remaining resources at present, and set the processing node as processing node o; select task k from the remaining task set to join processing node o, and recalculate the resource balance degree of processing node o after adding task k P ok , optimize resource allocation based on resource balance. Under the premise of fully considering the resource load constraints of each edge computing device or processing node, the present invention selects appropriate tasks from the requested tasks and assigns them to appropriate edge computing devices or nodes, so that the utilization rate of system resources is the highest and the overall performance of the system is improved. reach the optimum.
Description
technical field [0001] The present invention relates to the technical field of edge computing, in particular to a resource optimization allocation method suitable for an edge computing environment. Background technique [0002] With the concept of the ubiquitous power Internet of Things being proposed and deepening, the existing cloud computing-related technologies have been difficult to efficiently process the massive data generated by network edge devices. The development of ubiquitous power Internet of Things application requirements objectively promotes the rapid development of edge computing models, enabling them to increase task execution and data analysis capabilities on network edge devices, and migrate some or all of the computing tasks of the original cloud computing model to On the network edge device, thereby reducing the computing load of the cloud computing center, alleviating the pressure on the network bandwidth, and improving the data processing efficiency. ...
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IPC IPC(8): G06F9/50
CPCG06F9/5088
Inventor 李琪林程志炯
Owner STATE GRID SICHUAN ELECTRIC POWER CORP ELECTRIC POWER RES INST