FAILURE NODES DETECTION AND RECOVERY SYSTEM IN CLOUD COMPUTING TO IMPROVE RESOURCES RELIABILITY

FAILURE NODES DETECTION AND RECOVERY SYSTEM IN CLOUD COMPUTING TO IMPROVE RESOURCES RELIABILITY Intelligently connected machines such as servers, virtual machines and load balancer provides different computing resources to users in cloud computing. The Cloud Computing responds and provides resources upon user request. The cloud is not able to respond to requests because of the heavy load on the cloud nodes when several requests are received by users or due to the nodes failure. The major challenge of the Cloud Computing is to schedule user tasks as fast as users require by detecting and recovering failure nodes, while retaining a high degree of service quality (QoS) and Resources Reliability. Because cloud computing relies on a large number of nodes to execute high performance applications, it is critical to distribute resources throughout the network's nodes and creating a system and the method for failure nodes detection and recovery in the event of a node failure. The present invention disclosed herein is Failure Nodes Detection and Recovery System in Cloud Computing to Improve Resources Reliability comprising of User Tasks (201), Scheduling Policies (202), Task Scheduler (203), Task Assignment System (204), Node Data (205), LSTM-RF (206), Vector Matrix (207), Greedy Search Firefly Optimization (208), and Failure Node Detection and Recovery (209); provides an efficient method and the system for detecting and recovering the failure nodes to improve the Resources Reliable Routing, performance and Quality of Services (QoS) in Cloud Computing. The present invention uses combined network of Long Short-Term Memory and Random Forest (LSTM-RF) to analyze the Node Data, Greedy Search Firefly Optimization (GSFO) to provide global optimal ranking of nodes and facilitate the failure nodes detection and recovery. The performance parameters of the present invention are validated experimentally by considering the 100 dead nodes on CloudSim, out of 100 dead nodes only 4 nodes remains as dead nodes, yield node recovery rate of 96% with reduced energy consumption of 0.45 Joules for dead nodes. The Resources Reliable Routing through active nodes improves the task scheduling and execution of 100 tasks in 2.109 Seconds. FAILURE NODES DETECTION AND RECOVERY SYSTEM IN CLOUD COMPUTING TO IMPROVE RESOURCES RELIABILITY r, TASKS 103 TASK MANAGER TASKSCHEDULER TASKS ASSIGN Figure 1: Task Level Scheduling in Cloud Computing. SCHEDULING POLICIES 201 204 205 USER TASKS TASKSCHEDULER