Nowadays, Cloud Computing (CC) has emerged as a new paradigm for hosting and delivering services over the Internet. However, the wider deployment of Cloud and the rapid increase in the capacity, as well as the size of data centers, induces a tremendous rise in electricity consumption, escalating data center ownership costs and increasing carbon footprints. This expanding scale of data centers has made energy consumption an imperative issue. Besides, users’ requirements regarding execution time, deadline, QoS have become more sophisticated and demanding. These requirements often conflict with the objectives of cloud providers, especially in a high-stress environment in which the tasks have very critical deadlines. To address these issues, this paper proposes an efficient Energy-Aware Tasks Scheduling with Deadline-constrained in Cloud Computing (EATSD). The main goal of the proposed solution is to reduce the energy consumption of the cloud resources, consider different users’ priorities and optimize the makespan under the deadlines constraints. Further, the proposed algorithm has been simulated using the CloudSim simulator. The experimental results validate that the proposed approach can effectively achieve good performance by minimizing the makespan, reducing energy consumption and improving resource utilization while meeting deadline constraints.

Due to the proliferation of cloud computing and Internet of Things (IoT) systems in healthcare industry, remote real-time monitoring and tracking of patient conditions have become the norm. In a typical deployment, Medical IoT (MIoT) devices transmit patient data to a medical datacenters where the data get stored, analyzed, and made available to concerned parties or individuals. Healthcare providers, physicians and care takers can access remotely these medical data located in these medical datacenters which are typically deployed as a private cloud or public cloud. In such cloud deployment, the performance services to access the data in a timely manner is critical. One of the key performances metrics is the response time. It is important to deploy the medical datacenters in a cloud environment in a way to minimize the response time. Moreover, Fog/Edge computing layer is also used to perform localized processing and storage to further reduce response time. This paper studies, models, and shows how to reduce computing resources cost while guaranteeing health requests performance constraints-particularly response time of accessing medical data stored in a fog-cloud environment. To address this issue, this paper proposes a queuing model to predict the minimum required number of computing resources (both fog and cloud nodes) to meet the Service Level Agreement (SLA) for response time. We verify and cross validate our analytical model through discrete event simulation. Obtained results from our analytical models show that the proposed model is able to correctly and effectively predict the number of computing resources needed for health data services in order to achieve the required response time under different workload conditions.

Task scheduling is one of the most challenging aspects in cloud computing nowadays, which plays an important role to improve the overall performance and services of the cloud such as response time, cost, makespan, throughput etc. Mostly a non-optimal task scheduling algorithm can be a key tool in over utilization or under utilization of cloud resources. In order to solve these problems, this paper proposes a novel architecture to schedule the tasks in cloud computing on the basis of a new Dynamic Dispatch Queues Algorithm (DDQA) and Particle Swarm Optimization (PSO) algorithm. The proposed algorithm DDQA-PSO gives full consideration to the dynamic characteristics of the cloud computing environment. The experimental results based on CloudSim simulator show that the proposed architecture can effectively achieve good performance, load balancing, and improve the resource utilization.