.png){kind=icon}
返回
Data-driven mixed-Integer linear programming-based optimisation for efficient failure detection in large-scale distributed systems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2022.02.006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Failure detectors (FDs) are fundamental building blocks for distributed systems. An FD detects whether a process has crashed or not based on the reception of heartbeats' messages sent by this process over a communication channel. A key challenge of FDs is to tune their parameters to achieve optimal performance which satisfies the desired system requirements. This is challenging due to the complexities of large-scale networks. Existing FDs ignore such optimisation and adopt ad-hoc parameters. In this paper, we propose a new Mixed Integer Linear Programming (MILP) optimisation-based FD algorithm. We obtain the MILP formulation via piecewise linearisation relaxations. The MILP involves obtaining optimal FD parameters that meet the optimal trade-off between its performance metrics requirements, network conditions and system parameters. The MILP maximises our FD's accuracy under bounded failure detection time while considering network and system conditions as constraints. The MILP's solution represents optimised FD parameters that maximise FD's expected performance. To adapt to real-time network changes, our proposed MILP-based FD fits the probability distribution of heartbeats' inter-arrivals. To address our FD scalability challenge in large-scale systems where the MILP model needs to compute approximate optimal solutions quickly, we also propose a heuristic algorithm. To test our proposed approach, we adopt Amazon Cloud as a realistic testing environment and develop a simulator for robustness tests. Our results show consistent improvement of overall FD performance and scalability. To the best of our knowledge, this is the first attempt to combine the MILP-based optimisation modelling with FD to achieve performance guarantees.
Keyword:
Nonlinear programming
Mixed integer linear programming
Distributed systems
Failure detection
Heartbeats
期刊
IF:
6
论文数:
2.2W
被引数:
6.4W
机构
引用论文
.png){kind=icon}
Improving reliability in resource management through adaptive reinforcement learning for distributed systems通过分布式系统的自适应强化学习提高资源管理的可靠性
PREAI
分享收藏
查看更多内容