拟态多执行体调度算法研究进展

doi:10.11959/j.issn.1000-436x.2021072

Abstract

Abstract:

Mimic defense is the new active defense technology based on the dynamic heterogeneous redundant architecture.With inherent uncertainty, heterogeneous, redundant and negative feedback features, it can significantly improve the robustness and security of system.Among them, the scheduling algorithm is the key to mimic defense technology, which advantages and disadvantages directly affect the ability of system to resist attacks based on known or unknown vulnerabilities.Based on this, the principle and goal of mimic scheduling algorithm were firstly introduced.Then the state-of-the-art of mimic scheduling algorithms were analyzed and summarized from three aspects, such as scheduling object, scheduling quantity and scheduling timing.Finally, the future research direction and trend of mimic scheduling algorithms were prospected.

Key words: mimic defense, active defense, mimic scheduling, dynamic heterogeneous redundant

CLC Number:

TP309

Zhengbin ZHU, Qinrang LIU, Dongpei LIU, Chong WANG. Research progress of mimic multi-execution scheduling algorithm[J]. Journal on Communications, 2021, 42(5): 179-190.

Figures/Tables 6

References 64

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出发点	算法	优缺点	应用场景
	基于历史信息负反馈算法	考虑历史信息和负反馈，缺乏对异构度的考量	SDN
	基于正态分布算法	动态性、可控性好，但复杂度高	—
基于软件异构度度量	动态自学习调度算法	考虑了历史信息和执行体当前安全性并自适应迭代，但计算复杂度高	SDN
	基于信誉度和相异度自适应调度算法	量化了信誉度及更新准则，缺乏细粒度的异构度量化公式	SDN
	随机种子最小相似度算法	动态性好，执行体异构度量化更细粒度，但缺乏对负反馈特性	—
	基于BSG博弈算法	结合BSG博弈模型数学量化执行体间异构度，但计算复杂度高	Web服务器
基于异构体组件度量	随机种子调度算法	细粒度量化执行体集异构性，结合具体系统服务质量，需进一步确定权重	Web服务器
	基于优先级和时间片调度算法	动态性高，从时间和空间2个维度考虑，缺乏对历史信息的考虑	—
基于MOSS度量	负载感知调度算法	考虑了执行体集安全性和负载，可扩展性好，但复杂度高	SDN

算法	优缺点	应用场景
最优调度时间算法	权衡执行开销和攻击损耗，调度代价低，但复杂度高	SDN
基于滑动窗口模型算法	综合考虑了调度门限和周期，动态性较高，复杂度低，但缺乏对多执行体调度的考虑	分布式存储

算法	优缺点	应用场景
基于反馈的动态感知调度算法	动态性好，失效概率低，但复杂度高	网络操作系统
基于效用的动态弹性调度算法	动态性好，但缺乏对上线执行体异构度考虑，复杂度高	SDN
基于判决反馈调度算法	动态性好，系统失效率和代价低，但缺乏对变换时间和变化条件的研究	SDN

调度算法	动态性	平均失效率	异构度	系统开销	服务质量
MD	★	1.141 9 × 10 ^-4	0.114 3	O (1)	—
OMD	★	3.598 9 × 10 ^-4	0.218 3	O (1)	0.450 3
随机调度	★★★★★	7.664 7 × 10 ^-4	0.272 3	O (1)	—
RSMS	★★	2.766 4 × 10 ^-4	0.155	O (n)	—
PSPT	★★★★★	—	0.249	O (1)	—
RSMHQ	★★	—	0.376 8	O (n)	0.519 3
RSMHQH	★★	—	—	O (1)	—
基于正态分布	★★★★	—	—	O (2ⁿ)	—
基于BSG	★★	—	MOSS	O (n)	—
基于反馈判决	★★★	—	MOSS	O (n)	—
基于自学习	★★★	—	—	O (2ⁿ)	—
基于安全策略	★★	—	—	O (n)	—
滑动窗口	★★★★	—	—	O(n)	—
注：★表示低，★★表示中低，★★★表示中，★★★★表示中高，★★★★★表示高；MOSS表示用MOSS方法衡量执行体间的异构度，—表示未考虑该指标。

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