H2RUN: An efficient vendor lock‐in solution for multi‐cloud environment using horse herd Runge Kutta based data placement optimization

Abstract

Vendor lock‐in is a scenario where the cost of switching to another vendor is mainly high and for this reason, the consumer is mainly struck with a single cloud vendor. The vendor lock‐in mainly implies the situation of an organization where it is locked into a service or product of a single Cloud Service Providers (CSP) to prevent the disruption of the business operation or due to an insufficient workforce. Due to its high availability, portability, and other benefits, various researchers frequently adopt the multi‐cloud strategy to prevent vendor lock‐in. However, the multi‐cloud architecture also comes with major drawbacks such as cost, multiple CSP management, and the tradeoff between cost and availability. The organizations are mainly considerate about achieving the tradeoff between the cost and availability. To overcome this problem, we have proposed an H2RUN (Horse Herd Runge Kutta) optimization algorithm to achieve a tradeoff between the cost and availability using erasure coding. The proposed H2RUN optimization algorithm selects the optimal CSP and erasure coding parameters that enhance the availability of the CSP with minimal cost. The efficiency of the proposed methodology can be evaluated using the experiments conducted on different real‐time CSPs using various metrics. Based on the results achieved, the H2RUN optimizer minimizes the cost up to 48% and availability up to 58%.

An Effective Runge-Kutta Optimizer Based on Adaptive Population Size and Search Step Size

“…This search approach makes use of two effective exploitation and exploration stages to find attractive areas in the search space and progress to the optimal global solution [13]. Despite RUN being a recent algorithm, it has demonstrated excellent performance in solving complex real-world problems such as parameters estimation of photovoltaic models [15,16], power systems [17,18], lithium-ion batteries management [19], identification of the optimal operating parameters for the carbon dioxide capture process in industrial settings [20], water reservoir optimization problems [21], resource allocation in cloud computing [22], and machine learning models parameters tuning [23] to name a few. However, it was noticed that the original RUN consumes more time in solving optimization problems without finding the optimal solution, and in high-dimensional problems, the search capabilities and convergence speed of the original RUN deteriorate.…”