The purpose of this thesis is to explore the edge computing paradigm for IoT services and applications in the 5G era. A user device with limited processing and storage capabilities offloads a complex task to computing edge nodes, and then the edge nodes transmit the results back to the user. In order to comprehensively explore the topic of edge computing for the Internet of Things, we developed two different types of schemes.
Specifically, selection schemes, in which the user device offloads the task to one edge node, and combinational schemes, in which the user device offloads the task to several edge nodes simultaneously. As for the selection-based schemes, edge computation with cache-assisted relaying and cacheless relaying are considered. In these schemes, the offloading nodes are selected based on the best computational capability, the channel gain between the user and the edge node, or the channel gain between the user equipment and the relay node.
Numerical results show that the cacheless relay edge computing model, where the best channel gain between the relay and the edge node is selected, outperforms other models in system performance. Thus, we can summarize that the edge computing model with the performance achievement scheme shows the highest results among the others in terms of reliability metrics.
Introduction 7
Although edge computing offers significant benefits for IoT applications, there are major challenges to be addressed. This research work aims to examine the edge computing paradigm, its characteristics, implementation methods, benefits and challenges. It is clear that edge computing with its innovative tools for resource allocation, computing offloading and service caching can ensure significant benefits for smart technologies.
However, the question of effectively deploying edge computing for IoT technology remains open, as new advanced practices and schemes, with their respective benefits and drawbacks, are rapidly developing in this field. In our research work, we implement different edge computing models and analyze how the computational offloading efficiency of edge computing can be improved in terms of reliability metrics. Selection and combination schemes are proposed for offloading a task in the edge computing system with a single user device - multiple edge nodes.
For a choice-based example, we consider cache-relay and non-cache-relay edge computing. In Chapter 1, a schematic description of edge computing in IoT is recommended to help illustrate the main goals of the proposed work.
Literature review 11
In many studies, reliability is used in the context of delay-outage probability. Software-defined networking (SDN) here enables the simultaneous operation of different edge computing entities. The simulation results revealed the advantages of the proposed strategy; therefore, the researchers planned to implement it in real conditions in the future.
Thus, the same performance measures can be implemented for the assessment of the developed strategies. To deal with the mobility issue of the user devices, the authors proposed offloading tasks to edge nodes located in small areas. The researchers therefore aimed to maximize the capacity of the system through appropriate resource distribution and partial computation offloading.
Moreover, similar to the authors of [30] and [31], the researchers used the Lyapunov optimization approach to divide this sophisticated question into small tasks, which were then solved directly or through advanced algorithms. To ease the complexity of the problem, the researchers divided it into small tasks by means of auxiliary variables.
Selection schemes for task offloading in edge computing 27
As for scheme I of the first model, the edge node with the best CPU is used, i.e. the largest ci, so the probability of outage delay is based on the large transmission time from the relay to the edge node and [40]. According to scheme II of the first model, the channel between the relay and the edge node with the best power gain is selected, i.e. to the biggest ri. For scheme I of the second model, the edge node with the best CPU is used, i.e. maximum ci, so the probability of outage delay can be expressed by a high transmission time and [40].
For scheme II of the second model, the channel between the relay and the edge node with the best power gain is selected, i.e. to the biggest ri. For scheme III of the second model, the channel between the user device and the edge node with the best power gain is selected, i.e. the biggest gi. Consequently, the outage delay probability can be determined from (3.25) using the CDF of gamma RVs s, ri,.
Ultimately, the probability of delay failure depends on the computing capacity of the chosen edge node and the number of edge nodes in the system. It can be seen that the system delay and failure probability curves decrease with higher values of transmit SNR and latency threshold in both cases. The outage probability curves of the first model, Scheme I, with B = 50 Mbits, W = 200 MHz and m = 2 versus SNR for different delay threshold values.
The outcome probability curves for the first model, Scheme II, with B = 50 Mbits, W = 200 MHz and m = 2 versus SNR for different delay threshold values and different number of edge nodes. However, system performance can be improved with higher values of the latency threshold in both cases. Thus, higher values of the latency threshold also cause the delay outcome probability curves to decrease in both cases.
The failure probability curves of the first model, Scheme II, with 𝑷/𝝈𝟐 = 20 dB, W = 200 MHz and m = 2 versus task size for different delay thresholds and different number of edge nodes. The outage probability curves of the first model, Scheme I, with 𝑷/𝝈𝟐 = 20 dB, B = 50 Mbits and m = 2 versus channel bandwidth for different delay threshold values. The outage probability curves of the first model, Scheme II, with 𝑷/𝝈𝟐 = 20 dB, B = 50 Mbits and m = 2 versus channel bandwidth for different delay threshold values and different numbers of edge nodes.
Dropout probability curves of the second model, scheme III, with B = 50 Mbits, W = 200 MHz and m = 2 versus SNR for different delay threshold values and different number of edge nodes. Dropout probability curves of the second model, scheme I, with 𝑷/𝝈𝟐=20 dB, W = 200 MHz and m = 2 versus task size for different delay threshold values.
Combining schemes for task offloading in edge computing 50
To determine the probability of failure with delay, the CDF must be found from the sum of the RV densities. For comparison, we use different parameters of the delay threshold and a different number of edge nodes in seconds, and the number of edge nodes is N. The curves of the system delay-outage probability decrease with higher values of the transmission power and delay threshold in both cases. . Dropout probability curves of the TDMA model, Rayleigh channels with fading, with B = 50 Mbits and W = 200 MHz versus transmit power for different delay threshold values and different number of edge nodes.
At the same time, system performance can be improved by using higher values of the latency threshold. Moreover, the latency threshold is tthr seconds and the number of edge nodes, i.e., N. Naturally, higher values of the latency threshold guarantee that the delay and dropout probability curves decrease. Furthermore, higher values of the latency threshold significantly improve system performance in both models.
With respect to Nakagami-m fading channels, the delay cutoff probability can be obtained from (4.18) through the CDF of gamma RV gi, where 𝑓|,!|"(𝑥) =SbHHMU(S)H14𝑒FHIL. Figure 4.9 and 4.10 shows the influence of the transmission power on the performance of the system with different delay threshold parameters and different number of edge nodes in Rayleigh and Nakagami-m fading channels, respectively. From figures 4.11 and 4.12, we can observe the dependence of system delay - the probability of interruption in the size of the task.
Clearly, the higher values of the latency threshold provide a significant improvement in system performance. This does not apply to the number of edge nodes, as a larger number of edge nodes involved in the process deteriorates the reliability of the system in Nakagami-m fading channels and Rayleigh fading channels. The transfer of the data from the user device to all edge nodes is organized simultaneously through the common bandwidth W [58].
In Figures 4.16 and 4.17, we can observe the influence of transmission power on system performance with different delay threshold parameters and different number of edge nodes in the Rayleigh and Nakagami fading channels. It can be seen that the reliability of the system improves as the number of edge nodes increases, especially for the higher delay threshold case, i.e. tthr seconds where more edge nodes result in improved reliability. In addition, the reliability of the system depends on the size of the task and the parameters of the channel bandwidth.
Specifically, larger values of transmit power and channel bandwidth improve system performance, while larger task size degrades it. Additionally, a larger number of edge nodes increases system reliability in Nakagami-m fading channels and Rayleigh fading channels.
Conclusions 75
As a result, we conclude that the task offloading delay from the user device to the edge servers can be sufficiently reduced due to proper architecture and favorable system and channel parameters. Chen, “Towards edge intelligence: multi-access edge computing for 5G and the Internet of Things,” IEEE Internet of Things J., vol. Taleb, “Edge computing for the Internet of Things: A case study,” IEEE Internet of Things J., vol.
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