Search for a command to run...
Authors propose a cloud-based memory capacity management system having distinct layers to access frameworks through multiple entry points such as, user module, administrative module and controller module which have been operated from different geographical locations using distributed approach. Several benefits of industry 4.0 are being achieved using proposed framework. End user has achieved flexible and less time-consuming environment to perform their necessary activities due to usage of cloud services. An efficient memory allocation and data storage are being presented in proposed framework for reducing reallocation and replication transparency. User requirements are being submitted for receiving a suitable resource. Administrative authorities collect memory information from actively interconnected systems and store into server side using AI based automated mechanism. In controller, cellular automata-based mapping is applied on systems’ collected memory information for measuring system capacity over the cloud. User requirements should be authorized and analyzed into controller module for mapping an efficient resource within minimum time. Reliability factor, linear time complexity and minimum server load are being measured for realizing system accuracy using cellular automata based proposed algorithms respectively. Maximum system capacity has been measured for each particular user based on number of user inputs. Memory capacity factor is calculated using required dataset for realizing the system efficiency. Comparative analysis is presented for establishing system competency using experimental results. Overall linearity is observed in each particular layer of proposed framework.