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Server consolidation in a virtualized cloud environment improves resource utilization by hosting many virtual machines (VMs) on a single machine. The host server failure induces a serious risk of the interruption of services provided through VMs hosted on the failed server. Ensuring high reliability and availability from the unpredicted host server failures is a challenging task, and this assurance guarantee is the quality of service to the users. Executing latency-sensitive workloads within a time frame, multireplica strategy-based reliability assurance is used in general. In this article, we propose two reliability-ensured scheduling approaches by strategically allocating the jobs to machines to satisfy their reliability and deadline requirements. The proposed approaches “Replication with equal failure rate” and “Efficient reliability replication method” are used to schedule the jobs to machines with equal and different failure rates, respectively, while satisfying both the deadline and reliability requirements of the jobs. Both the scheduling approaches dynamically select the machines for the jobs to minimize the number of replicas based on job characteristics and failure rate of machines. Also, we adopt an overlapping mechanism to further minimize the resource utilization. The extensive simulation results on randomly generated and real-world data at different scales show that the proposed approaches perform better than other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged to be a promising computing paradigm of the recent time. As the high energy consumption in the cloud system creates several problems, the cloud service providers need to focus on the energy consumption along with providing the required service to their users. Cloud system needs to efficiently execute various real-time applications and designing energy-efficient scheduling algorithms for these applications has gained the research momentum. In this paper, we consider scheduling of real-time tasks for a virtualized cloud system which provides VMs with discrete compute capacities. Depending on the characteristics of the tasks, we divide the problem into four subproblems and propose solution for each subproblem. For the subproblem with arbitrary execution time and deadline of tasks, we use four different methods to cluster the tasks depending on their deadline values. Experiment is performed in CloudSim tool to make a comparison among the clustering methods and results show that the clustering method can be chosen based on the specification of the cloud system. We also made a comparison of our approach with standard energy-efficient scheduling technique both for the synthetic data sets and for the real world trace and we observed an average energy reduction of around 17 % and 15 % for the synthetic data sets and for the real world trace respectively (as compared to the baseline policy). [ABSTRACT FROM AUTHOR]