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 benchmark 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, Ant Colony, Cross Entropy, Neural Networks, 2opt and Simulated Annealing. The proposed algorithm can be used by applications to deploy services across large cluster of nodes by making better decision in the route design. The findings in this paper unifies critical areas in Computer Science, Mathematics and Statistics that many researchers have not explored and provided a new interpretation that advances the understanding of the role of entropy in decision problems encoded in Turing Machines.
Highlights
Distributed Information Systems (DS) are growing in popularity across the software industry as it provides more computational and data transmission capacity for applications and become an essential infrastructure that is needed to address the increase in demand for data processing.DS are used as a cost-efficient way to obtain higher levels of performance by using a cluster of low-capacity machines instead of a unique–single point of failure—large node
We have provided a sample to demonstrate the results between Quantitative Algorithm (QA) and Simulated Annealing (SA) from the 180 trials (N = 60 for each sample)
The data shows the first initial tour and the optimal solution found for each algorithm
Summary
A DS is more tolerant to individual machine failures and provides more reliability than a monolithic system. Parallel computation such as Cloud Computing and High-Performance Computing (HPC) are applications of distributed computing [1]. As of 2020 there are 3 major companies: Amazon AWS, Microsoft Azure and Google Cloud Platform. Companies can reduce their IT costs by orchestrating efficiently their workloads across different data centers by their respective weight impact, defined as a utility function with the Euclidian distance between nodes and its respective influence on network latency or the financial utilization time-rate cost for a given set of machines.
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