Enhancing Users' Quality of Experienced with Minimum Energy Consumption by Fog Nodes Cooperation in Internet of Things

Task Allocation for Energy Optimization in Fog Computing Networks with Latency Constraints

“…For [18], they propose a heuristic, population-based algorithm, i.e., cuckoo evolution algorithm. Results of [17] show a clear trade-off between energy consumption and delay in the network. The method proposed in [18] decreases both delay and energy consumption compared with a competing algorithm (proposed in [19]), while both works highlight that cooperation between nodes leads to lowered costs.…”

“…Similarly to [16], delay related to queuing and computing is calculated as an average response time of queuing model (M/M/1 for workload processed in FNs) with a constant value added for transmission delay between FNs. While both works use similar objective functions (weighted sum of energy consumption and delay), [18] includes fairness coefficients that depend on resources available to FNs while [17] does not. The authors conclude that the problem in [17] is convex.…”

“…While both works use similar objective functions (weighted sum of energy consumption and delay), [18] includes fairness coefficients that depend on resources available to FNs while [17] does not. The authors conclude that the problem in [17] is convex. For [18], they propose a heuristic, population-based algorithm, i.e., cuckoo evolution algorithm.…”

“…Vakilian et al [17], [18] do not consider adjusting CPU frequencies of nodes. Furthermore, offloaded traffic is not divided into a number of requests, packets, or instances in [17], [18].…”

“…Vakilian et al [17], [18] do not consider adjusting CPU frequencies of nodes. Furthermore, offloaded traffic is not divided into a number of requests, packets, or instances in [17], [18]. It is only parameterized with a single number (like in [16]) Hence, [17], [18] differ from our work in aspect (iii)).…”