For dynamic application scenarios of Mobile Edge Computing (MEC), an Energy-efficient Multiuser and Multitask Computation Offloading (EMMCO) optimization method is proposed. Under the consideration of multiuser and multitask computation offloading, first, the EMMCO method takes into account the existence of dependencies among different tasks within an implementation, abstracts these dependencies as a Directed Acyclic Graph (DAG), and models the computation offloading problem as a Markov decision process. Subsequently, the task embedding sequence in the DAG is fed to the RNN encoder-decoder neural network with combination of the attention mechanism, the long-term dependencies among different tasks are successfully captured by this scheme. Finally, the Improved Policy Loss Clip-based PPO2 (IPLC-PPO2) algorithm is developed, and the RNN encoder-decoder neural network is trained by the developed algorithm. The loss function in the IPLC-PPO2 algorithm is utilized as a preference for the training process, and the neural network parameters are continuously updated to select the optimal offloading scheduling decisions. Simulation results demonstrate that the proposed EMMCO method can achieve lower latency, reduce energy consumption, and obtain a significant improvement in the Quality of Service (QoS) than the compared algorithms under different situations of mobile edge network.

The unbalanced resource utilization of physical machines (PMs) in cloud data centers could cause resource wasting, workload imbalance and even negatively impact quality of service (QoS). To address this problem, this paper proposes a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration. It uses Gaussian model to adaptively estimate the probability that the running PMs are in the multi-resource utilization balance status. Given the estimated probability of the multi-resource utilization balance state, we propose effective selection algorithms for live VM migration between the source hosts and destination hosts, including adaptive Gaussian model-based VMs placement (AGM-VMP) algorithm and VMs consolidation (AGM-VMC) method. Experimental results show that the AGM-VMC method can effectively achieve load balance and significantly improve resource utilization, reduce data center energy consumption while guaranteeing QoS.

Background: Lung cancer is one of the most common malignant tumors in the world. Exportins are closely associated with the cellular activity and disease progression in a variety of different tumors. However, the expression level, genetic variation, immune infiltration, and biological function of different exportins in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), as well as their relationship with the prognosis of patients with LUAD and LUSC have not been fully clarified. Methods: To analyze the differential expression, prognostic value, genetic variation, biological function, and immune cell infiltration of exportins in patients with LUAD and LUSC, the ONCOMINE; UALCAN; Human Protein Atlas (HPA); Kaplan-Meier plotter; cBioPortal; Search Tool for the Retrieval of Interacting Genes/Proteins (STRING); Database for Annotation, Visualization, and Integrated Discovery (DAVID);Tumor Immune Estimation Resource (TIMER); and LinkedOmics databases were used in this study. Results:The transcriptional and protein expression levels of CSE1L and XPO1/5/6/7 were increased in patients with LUAD and LUSC, and the increased transcriptional levels of CSE1L and XPO5/6/7 were related to worse prognosis. An increased transcriptional level of XPO1 was associated with a better prognosis.These results indicated that CSE1L and XPO1/5/6/7 may be potential prognostic biomarkers for the survival of patients with LUAD and LUSC. Moreover, the high mutation rate of exportins in non-small cell lung cancer was 50.48%, and the largest proportion of mutations included high messenger RNA expression.The expression of exportins was significantly correlated with the infiltration of various immune cells.Differentially expressed exportins could regulate the occurrence and development of LUAD and LUSC by involving a variety of microRNAs and transcription factor E2F1.Conclusions: Our study provides novel insights into the selection of prognostic biomarkers of exportins in LUAD and LUSC.

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Background Lung cancer is one of the most common malignant tumors in the world. Exportins are closely associated with the cellular activity and disease progression in a variety of different tumors. However, the expression level, genetic variation, immune infiltration and biological function of different exportins in LUAD and LUSC and their relationship with the prognosis of LUAD and LUSC patients have not been fully clarified. Methods In this study, ONCOMINE, UALCAN, HPA, Kaplan-Meier plotter, cBioPortal, STRING, DAVID, TIMER and LinkedOmics databases were used to analyze the differential expression, prognostic value, genetic variation, biological function and immune cell infiltration of exportins in patients with LUAD and LUSC. Results The transcriptional and protein expression levels of CSE1L and XPO1 / 5 / 6 / 7 were increased in LUAD and LUSC patients, and the increased transcriptional levels of CSE1L and XPO5 / 6 / 7 were related to worse prognosis. The increased transcriptional level of XPO1 suggested a better prognosis. These results indicated that CSE1L and XPO1 / 5 / 6 / 7 may be potential prognostic biomarkers for the survival of patients with LUAD and LUSC. Besides, the high mutation rate of exportins in NSCLC was 50.48%, and the largest proportion of mutations was high mRNA expression. The expression of exportins was significantly correlated with the infiltration of various immune cells. Differentially expressed exportins could regulate the occurrence and development of LUAD and LUSC by involving a variety of miRNAs and transcription factor E2F1. Conclusions Our study could provide novel insights for the selection of prognostic biomarkers of exportins in LUAD and LUSC.