Instance Data Protection on Cloud Environment using Multi-Layered Approach based on Fog Computing

Recent growth in technology has proven the power of cloud computing. The majority of applications are associated with the cloud to execute their instances remotely due to various reasons. With demand in the execution of these instances with low latency, efficient cloud scheduling algorithms get more attention and need for better resource scheduling algorithms. At the same time when instances are executed on remote servers, the security of these instances data is high in priority. In other words, consumers lose their control on instance data when hosted and executed on remote machines. Though the traditional methods provide privacy using encryption technology, these methods do not provide guaranty for the attacks raised within the server. To address this problem, we have designed the Network Attentive Payload Allocation [NAPA] algorithm to split the instance (payload) into multiple sub payloads and execute on multiple servers parallelly. Then we have scheduled these instances to execute parallelly on three different locations, few on local machine, cloud server and fog server to sure the instance data and execute effectively. Using efficient hash function the distribution of instances are done. Through the theoretical analysis and simulation, we have validated our methods and found the proposed methods are fairly protecting the instance data.