Version 1
: Received: 28 July 2020 / Approved: 30 July 2020 / Online: 30 July 2020 (09:08:04 CEST)
How to cite:
Santana Lima, M. Information Theory Optimization Algorithm for Efficient Service Orchestration in Distributed Systems. Preprints2020, 2020070710. https://doi.org/10.20944/preprints202007.0710.v1
Santana Lima, M. Information Theory Optimization Algorithm for Efficient Service Orchestration in Distributed Systems. Preprints 2020, 2020070710. https://doi.org/10.20944/preprints202007.0710.v1
Santana Lima, M. Information Theory Optimization Algorithm for Efficient Service Orchestration in Distributed Systems. Preprints2020, 2020070710. https://doi.org/10.20944/preprints202007.0710.v1
APA Style
Santana Lima, M. (2020). Information Theory Optimization Algorithm for Efficient Service Orchestration in Distributed Systems. Preprints. https://doi.org/10.20944/preprints202007.0710.v1
Chicago/Turabian Style
Santana Lima, M. 2020 "Information Theory Optimization Algorithm for Efficient Service Orchestration in Distributed Systems" Preprints. https://doi.org/10.20944/preprints202007.0710.v1
Abstract
Distributed Systems architectures are becoming the standard computational model for processing and transportation of information, especially for Cloud Computing environments. The increase in demand for application processing and data management from enterprise and end-user workloads continues to move from a single-node client-server architecture to a distributed multitier design where data processing and transmission are segregated. Software development must considerer the orchestration required to provision its core components in order to deploy the services efficiently in many independent, loosely coupled - physically and virtually interconnected - data centers spread geographically, across the globe. This network routing challenge can be modeled as a variation of the Travelling Salesman Problem (TSP). This paper proposes a new optimization algorithm for optimum route selection using Algorithmic Information Theory. The Kelly criterion for a Shannon-Bernoulli process is used to generate a reliable quantitative algorithm to find a near optimal solution tour. The algorithm is then verified by comparing the results with heuristic solutions in 3 test cases. A statistical analysis is designed to measure the significance of the results between the algorithms and the entropy function can be derived from the distribution. The tested results shown an improvement in the solution quality by producing routes with smaller length and time requirements. The quality of the results proves the flexibility of the proposed algorithm for problems with different complexities without relying in nature-inspired models such as Genetic Algorithms and Simulated Annealing. This algorithm can be used by orchestration applications to deploy services across large cluster of nodes by making better decision in the route design.
Keywords
traveling salesman problem; information theory; artificial intelligence; computational complex theory; kolmogorov-complexity; kelly criterion and logarithmic utility
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
