EBGO: an optimal load balancing algorithm, a solution for existing tribulation to balance the load efficiently on cloud servers

A burning challenge is balancing the demand for servers in computer data centers. In the modern era, the bulk of computer systems are used for big data and cloud computing. The Internet of Things (IoT) is becoming a hyper-world of physical, cyber and social worlds with big data as a connection. In the latest systems architectures, managing the load in cloud applications is a major challenge. While several techniques and algorithms have been framed to achieve optimality in the load balancing definition, they are limited to the current problems of that time. However, due to a massive increase in data to be handled for the computing activities that are running on servers, exponential petabytes of data that are stored, the complexity of the problem increases day by day. By efficiently balancing the load and high utilization of the available resources from the computing pool, the solution to such problems can be achieved. When these are performed professionally, optimum values can be accomplished with the prominent use of available resources. Genetic Algorithms, Bacteria Foraging, optimizes the algorithm, Artificial Bee Colony, Optimized ABC are current trending algorithms used in load balancing concepts. All these trending algorithms, while strong, lack certain factors. To overcome them, we proposed an EBGO algorithm that has overcome the disadvantages of the trending load balancing algorithms in terms of certain significant parameters such as response time, energy consumption, weighted total cost, procession time, and many. This model aims to achieve overall optimal parameter values, thus efficiently balancing the demand on servers in data centers, resulting in optimal resource use. This model is often appropriate for health care systems, where large number of different types of systems and data are shared between them.