Optimizing Resource Allocation in Cloud Computing Using Machine Learning

Abstract

Purpose: A key component of large-scale distributed computing is the allocation of resources, as computer networks cooperate to address complex optimization problems. To get the most out of computers in general, or throughput, is the goal of resource allocation in this case. When it comes to distributed computing, there are two main varieties: grid computing and cloud computing. In grid computing, many geographically dispersed clusters are linked and made available to the general public.

Materials and Methods: We looked at Monte Carlo Tree Search and Long-Short Term Memory and examined how efficient they were. By maintaining consistent traffic patterns, the simulation demonstrated that MCTS performance was improved. However, such a strategy is difficult to implement due to the potential speed with which traffic patterns may alter. A successful service level agreement (SLA) was achieved, and the issue was shown to be fixable using LSTM. We compare the suggested model to different load-balancing algorithms to find the one that best allocates resources.

Findings: The results show that compared to the state-of-the-art models, the suggested model achieves an accuracy rate that is 10-15% higher. The suggested model lowers the error percentage rate of the average request blocking likelihood of traffic load by around 9.5-10.2% when compared to the predictions of existing models. Therefore, the proposed method has the potential to enhance network utilization by reducing the amount of time required by memory and the central processing unit.

Implications to Theory, Practice and Policy:  One advantage of the new method is a more robust forecasting strategy in comparison to earlier models. Using firefly algorithms, future research will construct a cloud data center that employs a variety of heuristics and machine learning methodologies to load balance the energy cloud (Oshawa et al., 2022).