Evolvable Autonomic Management

TL;DR

This paper introduces GENESIS, an evolutionary algorithm for autonomic management under uncertainty, modeling the problem as an NP-hard multi-objective optimization task with applications in cancer treatment using sensor networks.

Contribution

It formulates the autonomic management problem as an NP-hard multi-objective optimization and develops GENESIS, a decomposition-based evolutionary algorithm to solve it.

Findings

GENESIS effectively manages uncertainty in autonomic systems.

The problem is proven NP-hard and requires multi-objective optimization.

Application demonstrated in cancer treatment with sensor networks.

Abstract

Autonomic management is aimed at adapting to uncertainty. Hence, it is devised as m-connected k-dominating set problem, resembled by dominator and dominate, such that dominators are resilient up to m-1 uncertainty among them and dominate are resilient up to k-1 uncertainty on their way to dominators. Therefore, an evolutionary algorithm GENESIS is proposed, which resolves uncertainty by evolving population of solutions, while considering uncertain constraints as sub-problems, started by initial populations by a greedy algorithm AVIDO. Theoretical analysis first justifies original problem as NP-hard problem. Eventually, the absence of polynomial time approximation scheme necessitates justification of original problem as multiobjective optimization problem. Furthermore, approximation to Pareto front is verified to be decomposed into scalar optimization sub-problems, which lays out the theoretical foundation for decomposition based evolutionary solution. Finally, case-study, feasibility analysis and exemplary implication are presented for evolvable autonomic management in combined cancer treatment with in-vivo sensor networks.