ESCOVE: Energy-SLA-Aware Edge–Cloud Computation Offloading in Vehicular Networks

Abstract

The vehicular network is an emerging technology in the Intelligent Smart Transportation era. The network provides mechanisms for running different applications, such as accident prevention, publishing and consuming services, and traffic flow management. In such scenarios, edge and cloud computing come into the picture to offload computation from vehicles that have limited processing capabilities. Optimizing the energy consumption of the edge and cloud servers becomes crucial. However, existing research efforts focus on either vehicle or edge energy optimization, and do not account for vehicular applications’ quality of services. In this paper, we address this void by proposing a novel offloading algorithm, ESCOVE, which optimizes the energy of the edge–cloud computing platform. The proposed algorithm respects the Service level agreement (SLA) in terms of latency, processing and total execution times. The experimental results show that ESCOVE is a promising approach in energy savings while preserving SLAs compared to the state-of-the-art approach.

Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication

“…Researchers have used various ways to evaluate results using tools, as shown in Figure 13. A 31.25% of researchers have used the development environments such as Microsoft Visual Studio, 82,89 UBUNTU, 67,111 Anaconda(spyder), 81 MATLAB, 37,54,56,59,68,69,72,73,77,103,105,116,126,138,141,142,146,154,158,162 IBM Ilog Cplex optimization Studio. 71 A 20.00% of researchers used DL and ML based libraries such as TensorFlow, 46,47,62,63,65,66,110,144,151,161 Keras, 47 scipy, 45 sckit tool, 113 Pytorch, 111,123 TVM deep learning compiler, 120 LIBSVM.…”

“…By taking uncertainties into account and enabling effective decision‐making based on probabilistic considerations, various techniques were proposed by authors to improve the flexibility and dependability of offloading systems. In order to offload tasks for ships in the complex and dynamic marine environment, Ismail and Materwala 37 considered the various QoS needs of maritime applications. To overcome the problem of servers that were used for measuring ECp of computationally complex and time‐sensitive applications that consume more energy, they proposed the energy‐SLA‐aware edge–cloud CO in vehicular networks (ESCOVE) algorithm that schedules a vehicle request on either an edge or cloud server to optimize energy usage while adhering to the service level agreement (SLA) of the request.…”

MIA-NDN: Microservice-Centric Interest Aggregation in Named Data Networking

“…Let be the total PIT lifetime, including the communication time and computation time required to send an interest, performing the computation at the compute node, and receiving results (data packet) of the microservice interests [ 36 ]. The can be calculated by using the following equation: where is the time required to execute an interest at a compute node and a is the total time required to send an interest and receive a data packet (communication time).…”

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

“…Thus, an integrated edge-cloud computing system is often used to handle compute-intensive and/or time-critical applications [ 139 , 140 ]. However, the underlying 6G networks should consider the energy efficiency [ 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 ], optimal resource provisioning and scheduling [ 149 , 150 , 151 , 152 ], and contextual-aware application partitioning [ 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 ] requirements of this integrated system.…”