Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems...Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.展开更多
To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony sy...To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony system （ACS） based algorithm is proposed. First, how to map the resource assignment and task scheduling （RATS） problem into the optimization selection problem of task resource assignment graph （TRAG） and to add the semaphore mechanism in the optimal TRAG to solve deadlocks are explained. Secondly, how to utilize the grid pheromone system model to realize the algorithm based on ACS is explicated. This refers to the construction of TRAG by the random selection of appropriate resources for each task by the user agent and the optimization of TRAG through the positive feedback and distributed parallel computing mechanism of the ACS. Simulation results show that the proposed algorithm is effective and efficient in solving the deadlock problem.展开更多
Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,...Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.展开更多
With the rapid development and popularization of 5G and the Internetof Things, a number of new applications have emerged, such as driverless cars.Most of these applications are time-delay sensitive, and some deficienc...With the rapid development and popularization of 5G and the Internetof Things, a number of new applications have emerged, such as driverless cars.Most of these applications are time-delay sensitive, and some deficiencies werefound during data processing through the cloud centric architecture. The data generated by terminals at the edge of the network is an urgent problem to be solved atpresent. In 5 g environments, edge computing can better meet the needs of lowdelay and wide connection applications, and support the fast request of terminalusers. However, edge computing only has the edge layer computing advantage,and it is difficult to achieve global resource scheduling and configuration, whichmay lead to the problems of low resource utilization rate, long task processingdelay and unbalanced system load, so as to lead to affect the service quality ofusers. To solve this problem, this paper studies task scheduling and resource collaboration based on a Cloud-Edge-Terminal collaborative architecture, proposes agenetic simulated annealing fusion algorithm, called GSA-EDGE, to achieve taskscheduling and resource allocation, and designs a series of experiments to verifythe effectiveness of the GSA-EDGE algorithm. The experimental results showthat the proposed method can reduce the time delay of task processing comparedwith the local task processing method and the task average allocation method.展开更多
AIM： To identify the current roles of eye and health care workers in eye care delivery and investigate their potential roles in screening and detection for management of diabetic retinopathy（DR） through task sharin...AIM： To identify the current roles of eye and health care workers in eye care delivery and investigate their potential roles in screening and detection for management of diabetic retinopathy（DR） through task sharing.METHODS： Purposive sampling of 24 participants including health administrators, members from non-government organizations and all available eye care workers in Takeo province were recruited. This cross sectional mixed method study comprised a survey and in-depth interviews. Data were collected from medical records at Caritas Takeo Eye Hospital（CTEH） and Kiri Vong District Referral Hospital Vision Centre, and a survey and interviews with participants were done to explore the potential roles for task sharing in DR management. Qualitative data were transcribed into a text program and then entered into N-Vivo（version 10） software for data management and analysis. RESULTS： From 2009 to 2012, a total of 105 178 patients were examined and 14 030 eye surgeries were performed in CTEH by three ophthalmologists supported by ophthalmic nurses in operating and eye examination for patients. Between January 2011 and September 2012, 151 patients（72 males） with retinal pathology including 125（83%） with DR visited CTEH. In addition 170 patients with diabetes were referred to CTEH for eye examinations from Mo Po Tsyo screening programs for people with diabetes. Factors favouring task sharing included high demand for eye care services and scarcity of ophthalmologists.CONCLUSION： Task sharing and team work for eye care services is functional. Participants favor the potential role of ophthalmic nurses in screening for DR through task sharing.展开更多
基金supported by the National Natural Science Foundation of China(61571149,62001139)the Initiation Fund for Postdoctoral Research in Heilongjiang Province(LBH-Q19098)the Natural Science Foundation of Heilongjiang Province(LH2020F0178).
文摘Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks.Nevertheless,the fog computing Internet-of-Things(IoT)systems are susceptible to malicious eavesdropping attacks during the information transmission,and this issue has not been adequately addressed.In this paper,we propose a physical-layer secure fog computing IoT system model,which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers.The secrecy rate of the proposed model is analyzed,and the quantum galaxy–based search algorithm(QGSA)is proposed to solve the hybrid task scheduling and resource management problem of the network.The computational complexity and convergence of the proposed algorithm are analyzed.Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks.Moreover,the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.
文摘To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony system （ACS） based algorithm is proposed. First, how to map the resource assignment and task scheduling （RATS） problem into the optimization selection problem of task resource assignment graph （TRAG） and to add the semaphore mechanism in the optimal TRAG to solve deadlocks are explained. Secondly, how to utilize the grid pheromone system model to realize the algorithm based on ACS is explicated. This refers to the construction of TRAG by the random selection of appropriate resources for each task by the user agent and the optimization of TRAG through the positive feedback and distributed parallel computing mechanism of the ACS. Simulation results show that the proposed algorithm is effective and efficient in solving the deadlock problem.
基金ACKNOWLEDGEMENTS The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped improve the quality of this paper. The research has been partly supported by National Natural Science Foundation of China No. 61272528 and No. 61034005, and the Central University Fund （ID-ZYGX2013J073）.
文摘Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.
基金supported by the Social Science Foundation of Hebei Province(No.HB19JL007)the Education technology Foundation of the Ministry of Education(No.2017A01020)the Natural Science Foundation of Hebei Province(F2021207005).
文摘With the rapid development and popularization of 5G and the Internetof Things, a number of new applications have emerged, such as driverless cars.Most of these applications are time-delay sensitive, and some deficiencies werefound during data processing through the cloud centric architecture. The data generated by terminals at the edge of the network is an urgent problem to be solved atpresent. In 5 g environments, edge computing can better meet the needs of lowdelay and wide connection applications, and support the fast request of terminalusers. However, edge computing only has the edge layer computing advantage,and it is difficult to achieve global resource scheduling and configuration, whichmay lead to the problems of low resource utilization rate, long task processingdelay and unbalanced system load, so as to lead to affect the service quality ofusers. To solve this problem, this paper studies task scheduling and resource collaboration based on a Cloud-Edge-Terminal collaborative architecture, proposes agenetic simulated annealing fusion algorithm, called GSA-EDGE, to achieve taskscheduling and resource allocation, and designs a series of experiments to verifythe effectiveness of the GSA-EDGE algorithm. The experimental results showthat the proposed method can reduce the time delay of task processing comparedwith the local task processing method and the task average allocation method.
基金Supported by the Centre for Eye Research Austral(CERA),Royal Victorian Eye&Ear Hospital,MelbourneCERA receives Operational Infrastructure Support from the Victorian Government
文摘AIM： To identify the current roles of eye and health care workers in eye care delivery and investigate their potential roles in screening and detection for management of diabetic retinopathy（DR） through task sharing.METHODS： Purposive sampling of 24 participants including health administrators, members from non-government organizations and all available eye care workers in Takeo province were recruited. This cross sectional mixed method study comprised a survey and in-depth interviews. Data were collected from medical records at Caritas Takeo Eye Hospital（CTEH） and Kiri Vong District Referral Hospital Vision Centre, and a survey and interviews with participants were done to explore the potential roles for task sharing in DR management. Qualitative data were transcribed into a text program and then entered into N-Vivo（version 10） software for data management and analysis. RESULTS： From 2009 to 2012, a total of 105 178 patients were examined and 14 030 eye surgeries were performed in CTEH by three ophthalmologists supported by ophthalmic nurses in operating and eye examination for patients. Between January 2011 and September 2012, 151 patients（72 males） with retinal pathology including 125（83%） with DR visited CTEH. In addition 170 patients with diabetes were referred to CTEH for eye examinations from Mo Po Tsyo screening programs for people with diabetes. Factors favouring task sharing included high demand for eye care services and scarcity of ophthalmologists.CONCLUSION： Task sharing and team work for eye care services is functional. Participants favor the potential role of ophthalmic nurses in screening for DR through task sharing.
