Query: edge computing sdn schedule

title: Smart and Resilient EV Charging in SDN Enhanced Vehicular Edge Computing Networks
abstract: Smart grid delivers power with two way flows of electricity and information with the support of information and communication technologies. Electric vehicles (EVs) with rechargeable batteries can be powered by external sources of electricity from the grid, and thus charging scheduling that guides low battery EVs to charging services is significant for service quality improvement of EV drivers. The revolution of communications and data analytics driven by massive data in smart grid brings many challenges as well as chances for EV charging scheduling, and how to schedule EV charging in a smart and resilient way has inevitably become a crucial problem. Toward this end, we in this paper leverage the techniques of software defined networking and vehicular edge computing to investigate a joint problem of fast charging station selection and EV route planning. Our objective is to minimize the total overhead from users' perspective, including time and charging fares in the whole process, considering charging availability and electricity price fluctuation. A deep reinforcement learning (DRL) based solution is proposed to determine an optimal charging scheduling policy for low battery EVs. Besides, in response to dynamic EV charging, we further develop a resilient EV charging strategy based on incremental update, with EV drivers' user experience being well considered. Extensive simulations demonstrate that our proposed DRL based solution obtains near optimal EV charging overhead with good adaptivity, and the solution with incremental update achieves much higher computation efficiency than conventional game theoretical method in dynamic EV charging.

title: An Offloading Mechanism Based on Software Defined Network and Mobile Edge Computing in Vehicular Networks
abstract: In the emerging vehicular networks, the task of the car terminal requesting offloading has more stringent requirements for network bandwidth and offload delay, and the proposed Mobile Edge Computing (MEC) in the new communication network research solves better this challenge. This paper focuses on matching the offloaded objects when the car terminal performs the task offloading. By introducing the Software Defined in Vehicle Network (SDN V) to schedule uniformly global variables, which realizes resource control management, device information collection and task information analysis. Based on the differentiated nature of user tasks, a model of importance is defined. On this basis, task priority is divided by designing the task to offload the priority mechanism. For the multi objective optimization model, the non convex optimization model is solved by the multiplier method. The simulation results show that compared with other offloading strategies, the proposed offloading mechanism has obvious effects on delay and energy consumption optimization, which can guarantee the benefit of users to the greatest extent.

title: A Novel Service Composition Approach for Offloading in Mobile Edge Computing
abstract: Computational offloading in Mobile Edge Computing (MEC) environment can help reduce energy consumption and response time in network, thus improve users' experience. However, the distributed nature of the mobile users and the complex applications make it challenging to schedule the tasks reasonably among multiple devices. In this paper, we propose to leverage the idea of Software Defined Networking (SDN) and Service Composition (SC) for such problem. We propose a software defined service composition model and formulate the low latency service composition as a Constraint Satisfaction Problem (CSP) to make it a user centric approach. We also define the QoS model which provides the composition rule that forms the best possible service composition at the time of need. The experimental results demonstrate that our approach can obtain better performance than existing methods.

title: Software Defined Computational Offloading for Mobile Edge Computing
abstract: Computational offloading advances the deployment of Mobile Edge Computing (MEC) in the next generation communication networks. However, the distributed nature of the mobile users and the complex applications make it challenging to schedule the tasks reasonably among multiple devices. Therefore, by leveraging the idea of Software Defined Networking (SDN) and Service Composition (SC) we propose a Software Defined Service Composition model (SDSC) In this model, the SDSC controller is deployed at the edge of the network and composes service in a centralized manner to reduce the latency of the task execution and the traffic on the access links by satisfying the user specific requirement. We formulate the low latency service composition as a Constraint Satisfaction Problem (CSP) to make it a user centric approach. With the advent of the SDN, the global view and the control of the entire network are made available to the network controller which is further leveraged by our SDSC approach. Furthermore, the service discovery and the offloading of tasks are designed for MEC environment so that the users can have a complex and robust system. Moreover, this approach performs the task execution in a distributed manner. We also define the QoS model which provides the composition rule that forms the best possible service composition at the time of need. Moreover, we have extended our SDSC model to involve the constant mobility of the mobile devices. To solve the mobility issue, we propose a mobility model and a mobilityaware QoS approach enabled in the SDSC model. The experimental simulation results demonstrate that our approach can obtain better performance than the energy saving greedy algorithm and the random offloading approach in a mobile environment.

title: Preemptive SDN Load Balancing With Machine Learning for Delay Sensitive Applications
abstract: SDN is a key enabler to achieve scalability in 5G and Multi access Edge Computing networks. To balance the load between distributed SDN controllers, the migration of the data plane components has been proposed. Different from most previous works which use reactive mechanisms, we propose to preemptively balance the load in the SDN control plane to support network flows that require low latency communications. First, we forecast the load of SDN controllers to prevent load imbalances and schedule data plane migrations in advance. Second, we optimize the migration operations to achieve better load balancing under delay constraints. Specifically, in the first step, we construct two prediction models based on Auto Regressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM) approaches to forecast SDN controllers load. Then, we conduct a comparative study between these two models and calculate their accuracies and forecast errors. The results show that, in long term predictions, the accuracy of LSTM model outperforms that of ARIMA by 55% in terms of prediction errors. In the second step, to select which data plane components to migrate and where the migration should happen under delay constraints, we formulate the problem as a non linear binary program, prove its NP completeness and propose a reinforcement learning algorithm to solve it. The simulations show that the proposed algorithm performs close to optimal and outperforms recent benchmark algorithms from the literature.

title: DASH based video caching in MEC assisted heterogeneous networks
abstract: Recently, the demand for video applications has become more and more pressing. To save backhaul bandwidth and reduce the network delay, Mobile Edge Computing (MEC) has arisen as a promising technique in future mobile networks. It can provide the storage, computing and networking resources in close proximity to the mobile users through deploying multiple MEC servers at mobile edge. However, how to effectively utilize the limited storage capacity of these MEC servers is still the major challenge, especially when Dynamic Adaptive Streaming over HTTP (DASH) is used for video transmission. Recent studies have shown that layered encoding is very suitable for streaming service using DASH standard. Therefore, in our paper, combining with the feature of DASH based scalable video transmission, we first design a novel multi server cooperative scheme with software defined networking (SDN) architecture for future MEC assisted heterogeneous networks, to effectively manage and schedule the available resources, thus guaranteeing the requested video delivered to the clients with higher quality and lower delay. And then, based on the above scheme, the end end delay is carefully analyzed for each real video streaming. With a full consideration of the averag end to end delay of video streaming and the provided video quality of MEC servers, we finally propose a Dynamic Programming based Adaptive Caching Algorithm (DPACA) to obtain an optimized caching strategy under the constraint of cache storage capacity at each MEC sever. Simulation results show that, our algorithm can improve the cache hit ratio while significantly reducing the end to end delay. Moreover, our algorithm has higher flexibility and adaptability.

title: Interaction of Edge Cloud Computing Based on SDN and NFV for Next Generation IoT
abstract: In order to improve the stability of mobile network system for application of the next generation of Internet of Things (IoT) balance the network load and guarantee the quality of user service experience, this article first introduces the computing migration framework for the network of the next generation, and summarizes the concept and content of mobile edge computing (MEC) using software defined network (SDN) and network function virtualization (NFV) And then, this article proceeds to introduce the MEC strategy based on SDN and NFV technology as well as multiattribute decision making, computing migration, multiattribute decision, the MEC decision model based on SDN and NFV technology and the solving process of the MEC decision model based on SDN and NFV. Finally, the three sets of simulation experiments based on MATLAB are designed to validate the multiattribute decision of MEC migration strategy based on SDN and NFV. The results show that the multiattribute decision making based on SDN and NFV can select the appropriate MEC center, further reduce the server response time and improve the quality of user service experience. This article is of great significance to the application of IoT terminal in the next generation of network environment.

title: SDN Powered Humanoid With Edge Computing for Assisting Paralyzed Patients
abstract: The number of people afflicted with paralysis is increasing worldwide due to stroke, spinal cord injury, polio, and other related diseases. Exoskeletons have emerged as one of the promising technologies to provide assistance and rehabilitation for the paralyzed people. But most of the exoskeletons are limited by its bulkiness, lack of flexibility and stability, instant control and adaptability. To overcome these issues, this article proposes a novel and efficient software defined network (SDN) powered humanoid assistive and rehabilitation system. In the proposed system, the signals acquired by the human sensor module are processed with multiple node MCUs and transmitted via the SDN incorporated with universal software radio peripheral (USRP) Using edge computing, the signal from the USRP is sent to the receiver node MCU and is used for controlling the movements of the humanoid that provides assistance to the paralyzed patients. The experimental setup is done for controlling a humanoid hand, and the results show high quality of service (QoS) for hand roll up and roll down posture. QoS is also evaluated for different electroencephalogram (EEG) signals, and the results show that the SDN enabled assistive humanoid system is an efficient method for providing instant control in rehabilitation of the paralyzed patients.

title: SDN Enhanced Multi Access Edge Computing (MEC) for E2E Mobility and QoS Management
abstract: Multi access Edge Computing (MEC) is a key enabler of the fifth generation (5G) mobile cellular networks. MEC enables Ultra reliable and Low latency Communications (URLLC) by bringing the data and computational resources closer to the mobile users. As 5G deployments commence in earnest, researchers have turned their attention to various aspects of edge computing in an effort to leverage the new capabilities offered by 5G. In this paper, we propose the integration of Software Defined Networking (SDN) and cloud native virtualization techniques, such as containers, with the MEC architecture, to facilitate the orchestration and management of Mobile Edge Hosts (MEH) The proposed architecture focuses on the end to end mobility support required to maintain service continuity when mobile users relocate from one MEH to another. SDN is proposed as a reliable, programmatic paradigm to provide mobile edge orchestration and dynamic configuration of the underlying network for improved service continuity and quality of experience. The proposed architecture is validated through vehicle to everything simulations that highlight the advantage of the centralized network intelligence and the modularity and portability offered by SDN and containers.

title: Joint resource allocation and computation offloading in mobile edge computing for SDN based wireless networks
abstract: The rapid growth of the internet usage and the distributed computing resources of edge devices create a necessity to have a reasonable controller to ensure efficient utilization of distributed computing resources in mobile edge computing (MEC) We envision the future MEC services, where quality of experience (QoE) of the services is further enhanced by software defined networks (SDNs) capabilities to reduce the application level response time without service disruptions. SDN, which is not proposed specifically for edge computing, can in fact serve as an enabler to lower the complexity barriers involved and let the real potential of edge computing be achieved. In this paper, we investigate the task offloading and resource allocation problem in wireless MEC aiming to minimize the delay while saving the battery power of user device simultaneously. However, it is challenging to obtain an optimal policy in such a dynamic task offloading system. Learning from experience plays a vital role in time variant dynamic systems where reinforcement learning (RL) takes a long term goal into consideration besides immediate reward, which is very important for a dynamic environment. A novel software defined edge cloudlet (SDEC) based RL optimization framework is proposed to tackle the task offloading and resource allocation in wireless MEC. Specifically, Q learning and cooperative Q learning based reinforcement learning schemes are proposed for the intractable problem. Simulation results show that the proposed scheme achieves 31.39% and 62.10% reduction on the sum delay compared to other benchmark methods such as traditional Q learning with a random algorithm and Q learning with epsilon greedy.