Load balancing is usually a technique used for enhancing the performance of cloud computing environment. It is the process of evenly distribution of work load over the network without disturbing the other nodes. To attain this, a Q-Learning algorithm has been designed for each small cell base station but it still contains certain limitations, like the use of soft computing algorithm is ignored in existing algorithm. Also it can be improved further using multivalent logic i.e. fuzzy logic. So, the proposed fuzzy logic algorithm that uses membership functions will overcome the above limitations. The comparison results show that the throughput can be improved than the existing approaches by using fuzzy logic. Also the bias value and the outage probability can be decreased.
Load Balancing, Cloud computing, Fuzzy Logic, Membership Functions, Throughput, Bias value, Outage probability
