Scalable Declarative IT System Update Automation by A* Search with Critical-Element Heuristics

Kuwahara, Tomohiro; Kuroda, Takayuki; Nakanoya, Manabu; Yakuwa, Yutaka; Shimonishi, Hideyuki

doi:10.1109/cloudnet.2018.8549556

Cited by 2 publications

(1 citation statement)

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“…In our preliminary report, we have proposed a basic method for efficient planning of declarative system update [13]. In this paper, we further propose complete method which can omit recomputation of values of heuristic function to be more efficient and present mathematical proof of justification for this optimization.…”

Section: Introductionmentioning

confidence: 99%

Scalable State Space Search with Structural-Bottleneck Heuristics for Declarative IT System Update Automation

Kuwahara¹,

Kuroda²,

Nakanoya³

et al. 2019

IEICE Trans. Commun.

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Takuya KUWAHARA †a) , Takayuki KURODA †b) , Manabu NAKANOYA †c) , Yutaka YAKUWA †d) , Nonmembers, and Hideyuki SHIMONISHI †e) , Member SUMMARY As IT systems, including network systems using SDN/NFV technologies, become large-scaled and complicated, the cost of system management also increases rapidly. Network operators have to maintain their workflow in constructing and consistently updating such complex systems, and thus these management tasks in generating system update plan are desired to be automated. Declarative system update with state space search is a promising approach to enable this automation, however, the current methods is not enough scalable to practical systems. In this paper, we propose a novel heuristic approach to greatly reduce computation time to solve system update procedure for practical systems. Our heuristics accounts for structural bottleneck of the system update and advance search to resolve bottlenecks of current system states. This paper includes the following contributions: (1) formal definition of a novel heuristic function specialized to system update for A* search algorithm, (2) proofs that our heuristic function is consistent, i.e., A* algorithm with our heuristics returns a correct optimal solution and can omit repeatedly expansion of nodes in search spaces, and (3) results of performance evaluation of our heuristics. We evaluate the proposed algorithm in two cases; upgrading running hypervisor and rolling update of running VMs. The results show that computation time to solve system update plan for a system with 100 VMs does not exceed several minutes, whereas the conventional algorithm is only applicable for a very small system.

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Section: Introductionmentioning

confidence: 99%