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Fault tolerance in cloud computing is considered as one of the most vital issues to deliver reliable services. Checkpoint/restart is one of the methods used to enhance the reliability of the cloud services. However, many existing methods do not focus on virtual machine (VM) failure that occurs due to the higher response time of a node, byzantine fault, and performance fault, and existing methods also ignore the optimization during the recovery phase. This paper proposes a checkpoint/restart mechanism to enhance reliability of cloud services. Our work is threefold: (1) we design an algorithm to identify virtual machine failure due to several faults; (2) an algorithm to optimize the checkpoint interval time is designed; (3) lastly, the asynchronous checkpoint/restart with log-based recovery mechanism is used to restart the failed tasks. The valuation results obtained using a real-time dataset shows that the proposed model reduces power consumption and improves the performance with a better fault tolerance solution compared to the nonoptimization method.

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Presently, the Internet of Things (IoT) is playing an important role in data gathering and submitting information to different data analysis engines for most of the real-world applications. However, IoT applications have a danger of information theft or manipulation by malicious attacks which lead to a wrong conclusion or result. Therefore, malicious attacks are to be taken care of by using some means like prediction dynamics of malicious objects in IoT. In this manuscript, the behavior of malicious objects in the IoT network is studied with the help of two deterministic models. These models are working like the pre-predator model in IoT networks where prey consists of infected and uninfected nodes, whereas, the predator consists of malicious objects. Besides, the time delay is not much real in the spread of infection in networks due to the chaotic nature of the malicious object's outbursts, and therefore, these models are explored with delay differential equation modeling. Stochastic behavior of malicious objects in real dynamics of transmission of malicious objects makes things worse. Therefore, the study of proposed models in absence of anti-malicious software as well as in presence of anti-malicious software is carried out. Threshold conditions are characterized by the reproductive number and the system is identified as in an asymptotically stable state to help the fast recovery from malicious objects which helps to model the behavior of malicious objects spread in the real environment like IoT.