Cloud computing has recently attracted both academics and industrialists in the field of research. Virtualization allows cloud service providers (CSPs) with their own data centers to supply infrastructures, resources, and services to users by converting real servers into virtual machines (VMs). Profit‐driven CSPs charge users for VM leasing and service access while reducing energy consumption to increase profits. But CSPs even face challenges like minimizing the energy cost for the data center. Several different algorithms were introduced for minimizing the energy cost by using task scheduling (TS) and/or resource provisioning. However, scalability issues were encountered, or TS with task dependencies were not considered, which is a critical factor in assuring exact parallel execution of tasks in parallel. This article introduces a novel artificial algorithm, called deep reinforcement Q‐learning for resource scheduling which integrates the features of the Q‐learning and reinforcement learning approaches. The objective of this new approach is to provide a solution to the problem of handling energy consumption in a cloud computing environment. Based on advancements in WorkflowSim, experiments are carried out comparatively by considering the variance of make‐span, time, cost analysis, deadline overflow, and load balance in resource scheduling. The proposed method tends to be effective in terms of cost, energy consumption, resource utilization, and response time. The resource reuse capability of the proposed methodology is 63% higher when compared to the modified particle swarm optimization and modified cat swarm optimization technique. The task approval rate of the proposed methodology is 54% higher than the crow search‐based load balancing algorithm and 50% higher than the task duplication‐based scheduling algorithm.

This article describes how physical therapy rehabilitation promotes functional ability of the disabled people to improve quality of life using Range of Motion exercises. The conventional rehabilitation seems to be effective; however, the efficiency of the treatment sessions is not guaranteed resulting in longer recovery period. Thus, there is a need of self-motivating and engaging training solution to support rehabilitation and enhance continuous assessment of disabled patients. The proposed framework is “AR-NUI-REHAB-MDSS,” augmented reality (AR) using natural user interface (NUI) based physical therapy rehabilitation with personalized exercise rendering and monitoring system for patients and mobile decision support system (MDSS) for therapists leading to a global solution for remote assistance. NUI allows human computer interaction intuitively through human body gestures. AR provides an entertaining environment for treatments with less-assistance. MDSS enables therapist to customize treatment plans at dynamic environments. Upper limb is considered as its functional recovery is significant and more challenging.

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This article describes how physical therapy rehabilitation promotes functional ability of the disabled people to improve quality of life using Range of Motion exercises. The conventional rehabilitation seems to be effective; however, the efficiency of the treatment sessions is not guaranteed resulting in longer recovery period. Thus, there is a need of self-motivating and engaging training solution to support rehabilitation and enhance continuous assessment of disabled patients. The proposed framework is “AR-NUI-REHAB-MDSS,” augmented reality (AR) using natural user interface (NUI) based physical therapy rehabilitation with personalized exercise rendering and monitoring system for patients and mobile decision support system (MDSS) for therapists leading to a global solution for remote assistance. NUI allows human computer interaction intuitively through human body gestures. AR provides an entertaining environment for treatments with less-assistance. MDSS enables therapist to customize treatment plans at dynamic environments. Upper limb is considered as its functional recovery is significant and more challenging.

No abstract