In recent years,the Internet of Things technology has developed rapidly,and smart Internet of Things devices have also been widely popularized.A large amount of data is generated every moment.Now we are in the era of ...In recent years,the Internet of Things technology has developed rapidly,and smart Internet of Things devices have also been widely popularized.A large amount of data is generated every moment.Now we are in the era of big data in the Internet of Things.The rapid growth of massive data has brought great challenges to storage technology,which cannot be well coped with by traditional storage technology.The demand for massive data storage has given birth to cloud storage technology.Load balancing technology plays an important role in improving the performance and resource utilization of cloud storage systems.Therefore,it is of great practical significance to study how to improve the performance and resource utilization of cloud storage systems through load balancing technology.On the basis of studying the read strategy of Swift,this article proposes a reread strategy based on load balancing of storage resources to solve the problem of unbalanced read load between interruptions caused by random data copying in Swift.The storage asynchronously tracks the I/O conversion to select the storage with the smallest load for asynchronous reading.The experimental results indicate that the proposed strategy can achieve a better load balancing state in terms of storage I/O utilization and CPU utilization than the random read strategy index of Swift.展开更多
Despite intensive efforts,there are still enormous challenges in provision of healthcare services to the increasing aging population.Recent observations have raised concerns regarding the soaring costs of healthcare,t...Despite intensive efforts,there are still enormous challenges in provision of healthcare services to the increasing aging population.Recent observations have raised concerns regarding the soaring costs of healthcare,the imbalance of medical resources,inefficient healthcare system administration,and inconvenient medical experiences.However,cutting-edge technologies are being developed to meet these challenges,including,but not limited to,Internet of Things(IoT),big data,artificial intelligence,and 5G wireless transmission technology to improve the patient experience and healthcare service quality,while cutting the total cost attributable to healthcare.This is not an unrealistic fantasy,as these emerging technologies are beginning to impact and reconstruct healthcare in subtleways.Although the technologies mentioned above are integrated,in this review we take a brief look at cases focusing on the application of 5G wireless transmission technology in healthcare.We also highlight the potential pitfalls to availability of 5G technologies.展开更多
This paper studies a dynamic multi-user wireless network,where users have no knowledge of the arrival rate and size of data block and suffer from a constraint on long-term average power consumption.Considering such a ...This paper studies a dynamic multi-user wireless network,where users have no knowledge of the arrival rate and size of data block and suffer from a constraint on long-term average power consumption.Considering such a network,we address the problem of dynamically optimizing channel/power allocation,so as to minimize the long-term average data backlog.The design problem is shown to be a constrained Markov decision process.In order to solve the problem without knowledge on dynamics of the system,we introduce post-decision states and propose a resource allocation algorithm based on reinforcement learning.Since the channel/power allocation problem is coupled,the multiuser decision problem suffers from curses of dimensions(of state/action/outcome space).This makes centralized decision-making and optimization on channel/power allocation suffer from a long convergence time.As a countermeasure,a partially distributed resource allocation framework is proposed.The multiuser power allocation problem is decoupled into single-user decision problems,while channel allocation optimization is performed in a centralized manner.In order to further reduce computational complexity,we propose a low-complexity reinforcement learning method.Simulation results reveal that the proposed algorithm outperforms the state-of-the-art myopic optimizations in terms of energy efficiency and the backlog performance.展开更多
基金This work is supported by the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201714)Weihai Science and Technology Development Program(2016DXGJMS15)+1 种基金Key Research and Development Program in Shandong Provincial(2017GGX90103)Weihai Scientific Research and Innovation Fund(2020).
文摘In recent years,the Internet of Things technology has developed rapidly,and smart Internet of Things devices have also been widely popularized.A large amount of data is generated every moment.Now we are in the era of big data in the Internet of Things.The rapid growth of massive data has brought great challenges to storage technology,which cannot be well coped with by traditional storage technology.The demand for massive data storage has given birth to cloud storage technology.Load balancing technology plays an important role in improving the performance and resource utilization of cloud storage systems.Therefore,it is of great practical significance to study how to improve the performance and resource utilization of cloud storage systems through load balancing technology.On the basis of studying the read strategy of Swift,this article proposes a reread strategy based on load balancing of storage resources to solve the problem of unbalanced read load between interruptions caused by random data copying in Swift.The storage asynchronously tracks the I/O conversion to select the storage with the smallest load for asynchronous reading.The experimental results indicate that the proposed strategy can achieve a better load balancing state in terms of storage I/O utilization and CPU utilization than the random read strategy index of Swift.
基金Thisworkwas supported by National Institutes of Health(Grant No.UL1TR001881).
文摘Despite intensive efforts,there are still enormous challenges in provision of healthcare services to the increasing aging population.Recent observations have raised concerns regarding the soaring costs of healthcare,the imbalance of medical resources,inefficient healthcare system administration,and inconvenient medical experiences.However,cutting-edge technologies are being developed to meet these challenges,including,but not limited to,Internet of Things(IoT),big data,artificial intelligence,and 5G wireless transmission technology to improve the patient experience and healthcare service quality,while cutting the total cost attributable to healthcare.This is not an unrealistic fantasy,as these emerging technologies are beginning to impact and reconstruct healthcare in subtleways.Although the technologies mentioned above are integrated,in this review we take a brief look at cases focusing on the application of 5G wireless transmission technology in healthcare.We also highlight the potential pitfalls to availability of 5G technologies.
基金This work was supported in part by National Natural Science Foundation of China under Grant 61901216,61631020 and 61827801Natural Science Foundation of Jiangsu Province under Grant BK20190400+1 种基金Open Research Fund of National Mobile Communications Research Laboratory,Southeast University(No.2020D08)Foundation of Graduate Innovation Center in NUAA under Grant kfjj20190408。
文摘This paper studies a dynamic multi-user wireless network,where users have no knowledge of the arrival rate and size of data block and suffer from a constraint on long-term average power consumption.Considering such a network,we address the problem of dynamically optimizing channel/power allocation,so as to minimize the long-term average data backlog.The design problem is shown to be a constrained Markov decision process.In order to solve the problem without knowledge on dynamics of the system,we introduce post-decision states and propose a resource allocation algorithm based on reinforcement learning.Since the channel/power allocation problem is coupled,the multiuser decision problem suffers from curses of dimensions(of state/action/outcome space).This makes centralized decision-making and optimization on channel/power allocation suffer from a long convergence time.As a countermeasure,a partially distributed resource allocation framework is proposed.The multiuser power allocation problem is decoupled into single-user decision problems,while channel allocation optimization is performed in a centralized manner.In order to further reduce computational complexity,we propose a low-complexity reinforcement learning method.Simulation results reveal that the proposed algorithm outperforms the state-of-the-art myopic optimizations in terms of energy efficiency and the backlog performance.