Abstract
As a representative of lightweight virtualization, container technology has been widely used in cloud services and edge computing applications. However, in the resource pool scenario composed by multiple intelligent terminal devices, considering the limited resources and poor stability of these devices, it is necessary to split the overall service into multiple microservices and deploy the backups of them in respective containers. Traditional container scheduling policies tend to be less effective in solving such problems. Therefore, the article used a meta-heuristic algorithm to solve this kind of problems. Based on a newly proposed salp swarm algorithm (SSA), the paper presented a comprehensive improved SSA (CISSA). CISSA improved the performance of the original SSA by 4 steps. To verify the performance of CISSA in different kinds of test functions, the algorithm was compared with 7 commonly-used meta-heuristic algorithms in 29 benchmark functions provided by the author of SSA. In addition, the article constructed three container cluster models of different sizes, all these algorithms were used to solve these redundant container deployment problems, the experimental results indicate that the CISSA is superior to other algorithms in such problems of different dimensions.
Highlights
With the popularization of intelligent terminal equipment, there are a large number of intelligence terminal devices with idle resources, These devices are closer to users, in order to make reasonable use of these idle resources, it can be considered to integrate these devices into a resource pool to provide overall service, each device is responsible for some microservices
COMPARISON OF EXPERIMENTAL RESULTS In this work, in order to better compare the performance differences with the original SSA, we chose the same test functions used by the author of SSA.The improved algorithm comprehensive improved SSA (CISSA) was benchmarked on 29 test functions with 7 metaheuristic algorithms, including the original salp swarm algorithm (SSA) [4], whale optimization algorithm (WOA) [18], grasshopper optimization algorithm (GOA) [19], dragonfly algorithm (DA) [20], sine cosine algorithm (SCA) [21], ant lion optimizer (ALO) [22] and a classical heuristic algorithm particle swarm optimization (PSO) [23]
WORKS The article proposed a comprehensive improved salp swarm algorithm, which was modified in 4 mechanisms on the basis of the original SSA
Summary
With the popularization of intelligent terminal equipment, there are a large number of intelligence terminal devices with idle resources, These devices are closer to users, in order to make reasonable use of these idle resources, it can be considered to integrate these devices into a resource pool to provide overall service, each device is responsible for some microservices. Based on the background above, the paper proposed a scheme to deploy multiple containers in several terminal devices, on the premise that the total resources of containers deployed in one device should be less than the total resources of this device, the fitness of redundant container deployment scheme was evaluated by the robustness of the system and total completion time of service. It can be seen that the redundant container deployment problem in terminal device is a NPC problem, and there are multiple solving solutions. When the dimensions of the solution sets become larger as the number of container increases, the optimization problem will be much more complex For solving these single-objective problems with multidimensions, several meta-heuristic algorithms have been proposed in recent years. The proposed algorithm would be used to solve redundant container deployment problem.
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