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Network function virtualization (NFV) is a key technology to decouple hardware device and software function. Several virtual network functions (VNFs) combine into a function sequence in a certain order, that is defined as service function chain (SFC). A significant challenge is guaranteeing reliability. First, deployment server is selected to place VNF, then, backup server is determined to place the VNF as a backup which is running when deployment server is failed. Moreover, how to determine the accurate locations dynamically with machine learning is challenging. This paper focuses on resource requirements of SFC to measure its priority meanwhile calculates node priority by current resource capacity and node degree, then, a novel priority-awareness deep reinforcement learning (PA-DRL) algorithm is proposed to implement reliable SFC dynamically. PA-DRL determines the backup scheme of each VNF, then, the model jointly utilizes delay, load balancing of network as feedback factors to optimize the quality of service. In the experimental results, resource efficient utilization, survival rate, and load balancing of PA-DRL were improved by 36.7%, 35.1%, and 78.9% on average compared with benchmark algorithm respectively, average delay was reduced by 14.9%. Therefore, PA-DRL can effectively improve reliability and optimization targets compared with other benchmark methods.

Surging multi-service heterogeneous data offloading in new type distribution grid places new demands on latency. Cloud-edge collaboration enables low-latency data offloading through sufficient information sharing and resource allocation. However, there exist transmission and computing uncertainty on data offloading such as emergent task and burst interference in distribution grid. The conflicts of resource competition bring another challenge to multi-service data offloading with different priorities. In this paper, we address the cloud-edge multi-service data offloading problem which aims to minimize the weighted total data offloading delay based on the priorities. Then, the optimization problem is transformed as two-dimensional matching problem. We propose the uncertainty and priority awareness based low-latency multi-service data offloading optimization algorithm which achieves uncertainty and priority awareness and solves the resource competition among devices through price rising matching. Simulation results indicate that the proposed algorithm outperforms existing algorithms in total data offloading delay.

This survey reviews how electronically aided health monitor ingand observing systems with wireless body area networks (WBANs) support patient's data analyzing and processing. These technologies in communications domain integrated into conventional medical system and assists as a capable methodology to boost the efficiency of health-care. Wireless body area networks contain dissimilar biosensors are fitted in a patient for analyzing the condition of health continuously. The collected parameters of signals are initially given at a gateway for the accretion of data and then furthered to out-of-the-way medical centers. Patient's smart devices which has internet connectivity act as the gateways in internet of things-based WBANs, each among them generally stands for single patient. A huge volume of medical data congestion will be occurred during the widespread use of IoT-based WBANs, which is leading to an imperative prerequisite with high utilization based efficiency system for radio resource management. Henceforth, this survey examines methodically the management of transmissions of wireless body area network (WBAN) for internet of things-based a network is sculpted as a multi-server priority queueing system with multi-class delay-constrained with. Based on the result of queueing data packet, a mechanism for the arrangement of data packet transmissions for medical purposes with delay constraints is deliberated.

Although HIV prevalence in South Asia is relatively low, the epidemic is growing among marginalized groups, including sex workers, injection drug users, men who have sex with men, and transgender communities. Despite prevention and other efforts to reduce high-risk behaviors such as unprotected sex, buying and selling of sex, and injecting drug use, HIV vulnerability and risk remain high. This problem is partly due to a widespread failure to respond adequately to key social drivers of HIV: stigma and discrimination. Stigmatizing attitudes in the general population and discriminatory treatment by actors ranging from health providers to local policy makers intensify the marginalization of vulnerable groups at highest risk, driving them further from the reach of health services and much-needed prevention, treatment, care, and support. Daily harassment and abuse also cause health problems and adversely affect mental health, thereby leading to depression, social isolation, and an array of adverse socioeconomic outcomes related to HIV and AIDS. The South Asia Region Development Marketplace1 (SARDM) took an innovative and unique approach to addressing these gaps and needs through its 2008 development marketplace, \"tackling HIV and AIDS stigma and discrimination.\" Part one of this reports describes key findings and lessons learned that emerged across the 26 implementers. Part two contains case studies for six of the implementers, offering a more in-depth look at the lessons and challenges of intervening against stigma and discrimination. Part three provides summaries of all 26 projects.