Adaptive Partial Task Offloading and Virtual Resource Placement in SDN/NFV-Based Network Softwarization

Abstract

Edge intelligence brings the deployment of applied deep learning (DL) models in edge computing systems to alleviate the core backbone network congestions. The setup of programmable software-defined networking (SDN) control and elastic virtual computing resources within network functions virtualization (NFV) are cooperative for enhancing the applicability of intelligent edge softwarization. To offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization, this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows, link delays, and allocatable bandwidth capacities. Adaptive partial task offloading policy considered the DL-based recommendation to modify efficient virtual resource placement for minimizing the completion time and termination drop ratio. The optimization problem of resource placement is tackled by a deep reinforcement learning (DRL)-based policy following the Markov decision process (MDP). The agent observes the state spaces and applies value-maximized action of available computation resources and adjustable resource allocation steps. The reward formulation primarily considers taskrequired computing resources and action-applied allocation properties. With defined policies of resource determination, the orchestration procedure is configured within each virtual network function (VNF) descriptor using topology and orchestration specification for cloud applications (TOSCA) by specifying the allocated properties. The simulation for the control rule installation is conducted using Mininet and Ryu SDN controller. Average delay and task delivery/drop ratios are used as the key performance metrics.

Offloading Decision and Resource Allocation in Mobile Edge Computing for Cost and Latency Efficiencies in Real-Time IoT

“…Task offloading on the edge servers refers to the process of transferring computational tasks from the user device to more powerful servers located at the edge of the network. The transmission time of task i from user device is given in (5), where D i is the data size of computation task i to be distributed to the edge server for computation and t o f f is the offloading time of task i from mobile device to the edge server.…”

Large-Scale Service Function Chaining Management and Orchestration in Smart City

“…After the aggregated message m i is obtained, the algorithm proceeds the combination with the current feature x i using update function U, as expressed in (9), to get the hidden x i . The update function can be a neural network layer or a sequence of layers as expressed in (10) and (11). ReLU and σ represent the activation functions, while W is the learnable weight matrix and b is bias.…”

“…The functions (SFs) is defined in SFCO associated to VNF forwarding graph (VNFFG). The SFCO, VNFM, and SDN controller continually monitor and optimize the SFC for efficient performance [11,12]. Finally, the SFCO manages the termination of the SFC when the service is no longer required, then resources are released for further requests.…”