ABSTRACT This paper proposes a neural network‐based adaptive sliding mode controller combined with a nonlinear disturbance observer to enhance the stability and precision of the upper limb rehabilitation robot in uncertain environments. The upper limb movement intention is initially captured using an optical motion capture system and a surface electromyography acquisition system. An adaptive sliding mode control method, powered by a neural network, dynamically adjusts the controller's parameters to effectively address system uncertainties and external disturbances. The nonlinear disturbance observer in the controller helps identify and mitigate disturbances from the external environment, including Fourier‐type, power‐type, and mixed disturbances. Furthermore, the stability of the human‐machine interaction controller is rigorously verified using the Lyapunov theorem. Simulation results demonstrate that the proposed neural network‐based adaptive sliding mode control method significantly improves the performance and robustness of the upper limb rehabilitation robot.

Computing power network (CPN) has been proposed to allocate and schedule computing power resources among cloud, network, and edge according to the needs of computing services. CPN can improve the utilization rate of various computing resource pools. However, it brings other challenges that how to transfer data packets based on computing resource information. Since the size of routing table will be too large to store and search with lots of computing information. To solve this problem, we define three computing service types firstly. Then propose a hierarchical routing mechanism for computing services in CPN. Based on this mechanism, CPN can improve data forwarding efficiency and user experience. In the future, we will research standard of computing resource identification to provide more intelligent service for various application.

Computing power network (CPN) has been proposed to allocate and schedule computing power resources among cloud, network, and edge according to the needs of computing services. CPN can improve the utilization rate of various computing resource pools. However, it brings another challenge that how to ensure the security of multi-tenant information and the resource information which they rent. To solve this problem, we propose an isolation architecture in CPN, add a tenant mapping management module in network control layer firstly. Then we design the security mapping process between the tenant and the computing routing node based on this architecture. At last, we propose a mapping method between tenants and computing routing nodes based on hash ring which can avoid the problem of data migration caused by increasing the number of computing routing nodes. In the future, we will study the mapping algorithm to improve the efficiency of CPN.

Cloud computing is a unified management and scheduling model of computing resources. To satisfy multiple resource requirements for various application, edge computing has been proposed. One challenge of edge computing is cross-domain data security sharing problem. Ciphertext policy attribute-based encryption (CP-ABE) is an effective way to ensure data security sharing. However, many existing schemes focus on could computing, and do not consider the features of edge computing. In order to address this issue, we propose a cross-domain data security sharing approach for edge computing based on CP-ABE. Besides data user attributes, we also consider access control from edge nodes to user data. Our scheme first calculates public-secret key peer of each edge node based on its attributes, and then uses it to encrypt secret key of data ciphertext to ensure data security. In addition, our scheme can add non-user access control attributes such as time, location, frequency according to the different demands. In this paper we take time as example. Finally, the simulation experiments and analysis exhibit the feasibility and effectiveness of our approach.

Edge computing has been proposed to satisfy delay requirement for various applications. It brings another challenge that the collaboration problem among cloud computing, edge computing and network resources. Diversification of computing power nodes makes the optimization of resource utilization more complicated. To solve this problem, we first describe a general computing power network (CPN) framework and analyze different roles and their focuses on computing power resource. Then we propose a computing power resource modeling approach from the operator's perspective. In this paper, we quantify the computing resource, storage resource and transmission time of wireless and wired. At last, we present completion time of a calculation task to measure computing power for CPN.

Software-defined networking (SDN) uses a centralized control plane to manage the whole network. Distributed SDN architecture has been proposed to resolve scalability and reliability problem. One challenge of multiple controllers is load balancing problem, that uneven load distribution among controllers. Dynamic switches migration is an effective way to solve this problem. However, the existing schemes focus on effective migration algorithms and different metrics to realize the load balancing of control plane in SDN, and do not consider the communication cost between controllers and computation cost of migration scheme. In order to address this issue, we propose a load balancing approach based on sharing data store. Sharing data store has the information of all controllers, and the calculator module in it can compute switches migration scheme based saved information. It can also reduce the computation and communication pressure of controllers. Simulation experiments exhibit the feasibility and effectiveness of our approach.

In network virtualization environment, multiple virtual networks share the same resource of a physical network. Since the physical resources of a substrate network is limited, it is necessary to improve the utilization of physical resources. Considering the resource requirement of a virtual network may change over its lifetime, we propose a prediction-based resource management mechanism. To increase the utilization of the substrate network, we can adjust the resource allocated to the virtual network based on the result of prediction. Additionally, in order to avoid the result of prediction deviates from the real requirement, we compare our prediction result with the collection of the resource utilization at real time to ensure the correctness of our result. The simulation results show that our approach can increase the utilization of the physical resource and improve the virtual network acceptance ratio while ensuring the requirement of the virtual networks.

Software-defined networking (SDN) uses a centralized control plane to manage the whole network. If the scale of the network is large, it is necessary to divide it into multiple domains. Since the network scale becomes larger, the probability of failure occurrences is higher. Therefore, it is important to guarantee the control plane resilience in multi-domain SDN. However, the existing approaches cannot store the network state in real time, and do not consider the backup controllers placement problem in multi-domain SDN. In order to ensure the resilience of the control plane in multi-domain SDN, we propose a sharing data store and backup controllers based approach. Sharing data store is used to ensure that each master controller has a view of the whole network and data store can save the network state during the failure time. The sharing backup controllers are used to guarantee the resilience of control plane with minimum cost. Simulations show that our approach can use as less backup controllers as possible to ensure the resilience of control plane.

Hadoop has become a popular platform for the management of big data. To provide a healthy Hadoop platform for big data application, an HMM-based approach for performance diagnosis in Hadoop clusters is proposed. We use metrics which are collected under the normal situation to train HMM (Hidden Markov Model), then use this model to detect anomaly based on the probability, which is more accurate than other methods. Through evaluation in a controlled environment running Hadoop clusters, we find our approach can find out the real cause of performance problems in an average 84% precision and 83% recall, which is better than the method based on ARIMA and KNN (k-Nearest Neighbor).

Software defined networking (SDN) is currently regarded as one of the most promising paradigms of future Internet. Although the availability and scalability that a single and centralized controller suffers from could be alleviated by using multiple controllers, there lacks a flexible mechanism to balance load among controllers. This paper proposes a load balancing mechanism based on a load informing strategy for multiple distributed controllers. With the mechanism, a controller can make load balancing decision locally as rapidly as possible. Experiments based on floodlight show that our mechanism can balance the load of each controller dynamically and reduce the time of load balancing.