Energy-Aware Cloud-Edge Collaborative Task Offloading with Adjustable Base Station Radii in Smart Cities

Abstract

In smart cities, the computing power and battery life of terminal devices (TDs) can be effectively enhanced by offloading tasks to nearby base stations (BSs) with richer resources. With the goal of TDs being fully served and achieving low-carbon energy savings for the system, this paper investigates task offloading in cloud-edge collaborative heterogeneous scenarios with multiple BSs and TDs. According to the proportional relationship between the energy and coverage radii of BSs, a complete coverage task offloading model with adjustable BS radii is proposed. The task offloading problem is formulated as an integer linear program with multidimensional resource constraints to minimize the sum of energy consumption of BS coverage, offloading tasks to BSs and the cloud data center (CC). Since this task offloading problem is NP-hard, two approximate algorithms with polynomial time complexity are designed based on the greedy strategy of seeking the most energy-effective disk and the primal–dual method of constructing primal feasible solutions according to dual feasible solutions. Experimental results show that both the greedy and primal–dual algorithms can achieve good approximation performance, but each of them has its own advantages due to different design principles. The former is superior in execution time and energy consumption, while the latter has advantages in balancing loads among BSs and alleviating core network bandwidth pressure.

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

“…The authors suggested a MINLP for local decision‐making and task scheduling among ECSs, effective heuristic rules, and a priority‐aware algorithm were also proposed. Su et al 82 designed an energy‐aware cloud‐edge collaborative TO model for smart cities. Authors developed greedy and primal‐dual algorithms to reduce the ECp while offloading tasks to BSs and on the cloud server.…”

“…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 166 .…”

“… ECp : It is hard to achieve the goal of minimizing the ECp of underloaded edge devices, EdSrs, which are of distributed heterogeneous computing capacities. When tasks are processed in a real‐time EC environment, MEC servers that require less ECp are needed to enhance the fairness of devices in terms of computing power, channel, and connection capacity 67,82,87 Security and privacy : are other significant issues that need to be explored in an EC environment while offloading tasks from end devices to EdSr or cloud.…”

A Hybrid Many-Objective Optimization Algorithm for Job Scheduling in Cloud Computing Based on Merge-and-Split Theory

“…In recent times, researchers have introduced swarm intelligence optimization algorithms, like ant colonies [10], bat algorithms [11], and sparrow search [12], to achieve workload balancing across virtual machines (VMs) by efficiently assigning tasks to appropriate VMs. However, some of these cuttingedge metaheuristic approaches encounter difficulties such as slow convergence [13][14][15][16]. Consequently, grey wolf and sunflower optimization algorithms have garnered increased interest from scholars due to their superior optimization performance compared to other swarm intelligence optimization algorithms.…”

“…14 end 15 else if the predicted utilization of v j (U ) matches the optimal utilization of S. ∨ v j (U j+1 ) = S(OP T(U j )) + 3% then 16 The current processing server is considered the chosen host for executing incoming requests. Input: A collection of tasks denoted as J = {j 1 , j 2 , .…”