Dynamic resource allocation of smart home workloads in the cloud
Cloud computing offers provision for elastic and scalable infrastructure resource allocation across the network that allows deployment of services for controlling home devices and appliances. Data generated from heterogeneous smart home devices are processed in different application services deployed in the cloud data center. The primary challenge of smart home service provider's is to optimize the cloud resource allocation while satisfying the Quality of Service(QoS) constraints of the application services. Service execution time is one of the most vital QoS parameters. In this paper, a queuing theoretic approach is proposed to model the smart home workload. First, M/M/c queue model is applied to find the response time of smart home tasks with light variation over the arrival rate. Then, Markovian Modulated Poisson Process (MMPP) is used to extend the model to a more advanced type of smart home processing workloads. Next, the optimal number of Virtual Machines(VMs) required deploying the application servers that can satisfy the execution time constraint of incoming workloads is calculated. Finally, total service time of a smart home application is calculated considering into account the possible level of concurrency and dependency among tasks of an application service. In the end, some numerical and simulation examples are provided to validate our findings.