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Cloud computing has offered remarkable scalability and elasticity to distributed computing paradigm. It provides implicit fault tolerance through virtual machine (VM) migration. However, VM migration needs heavy replication and incurs storage overhead as well as loss of computation. In early cloud infrastructure, these factors were ignorable due to light load conditions; however, nowadays due to exploding task population, they trigger considerable performance degradation in cloud. Therefore, fault detection and recovery is gaining attention in cloud research community. The Failure Detectors (FDs) are modules employed at the nodes to perform fault detection. The paper proposes a failure detector to handle crash recoverable nodes and the system recovery is performed by a designated checkpoint in the event of failure. We use Machine Repairman model to estimate the recovery latency. The simulation experiments have been carried out using CloudSim plus.

Users worldwide can access utility-oriented computing services through cloud computing. It enables the pay-as-you-go model to host well-known consumer, educational, and business applications. However, cloud data centers encounter significant issues in effectively managing resources, ensuring optimal performance, and reducing energy usage as the demand for cloud computing services is increasing rapidly. A possible strategy to reduce energy consumption and operating expenses is the energy-efficient management of virtual machines (VMs) and server consolidation. In this paper, a VM migration and server consolidation algorithm is proposed, which is energy efficient and SLA aware. The proposed approach is evaluated using the CloudSim simulation kit and PlanetLab workload. The experiment result shows that the proposed approach has reduced energy consumption and SLA violation.