Latency Minimization in a Fuzzy-Based Mobile Edge Orchestrator for IoT Applications

Nguyen, VanDung; Khanh, Tran Trong; Oo, Thant Zin; Tran, Nguyen H.; Huh, Eui‐Nam; Hong, Choong Seon

doi:10.1109/lcomm.2020.3024957

Cited by 13 publications

(11 citation statements)

References 16 publications

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“…With the goal of reducing the delay of handling tasks execution and tasks failure of data partitioned based applications, Nguyen et al [45] proposed a fuzzy based logic mobile edge orchestrator to segment tasks from UEs and associate them to the appropriate edge servers. The proposed framework gets as input the network and resources information such as bandwidth, size of the task being processed, the characteristic of the edge server's virtual machine being used, and the latency sensitivity associated with each task.…”

Section: Energy Consumption and Latency Minimization During Data Of Loadingmentioning

confidence: 99%

Seamless communication techniques in mobile cloud computing: A survey

Constant¹,

Lee²,

Suo³

et al. 2021

ITU-J FET

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Achieving seamless communication and smooth service provision between the cloud and end user's mobile device is one of the main challenges existing in mobile cloud environments. Mobile Cloud Computing (MCC) allows cloud environments to mitigate resource limitation problems for mobile devices. The most popular mobile devices such as smartphones, autonomous vehicles, drones, and other smart electronic equipment are in constant motion and frequently change their point of connection (base station or edge) to mobile computing networks. In these situations of mobility, the data being transmitted, and the services being provided to the device should not be interrupted as the proper function of the device depends on these services. Applications that rely heavily on data and services stored in the cloud environment should be available even when the device has moved from one pole to another. Various existing generic surveys emphasize important solutions to some of the challenges faced in MCC. Different solutions were proposed to achieve seamless communication in MCC, presenting the taxonomy of the interworking and mobility techniques and their possibilities. However, they have not provided a clear evaluation of MCC techniques for achieving seamless communication and service provision, and have not taken into consideration current technological advances such as 5G, femtocell, etc. In this paper, we provide a survey of the different solutions proposed to achieve seamless communication in MCC by taking current technological advances into account. Furthermore, some shortcomings associated with the presented methods are outlined, along with the current issues and research challenges faced in MCC. However, for the purposes of data protection and security, previously proposed schemes already achieve the goal of protecting users' attribute privacy and they have the same access policy; some can even achieve full security, but they are just limited in decryption efficiency.

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Section: Energy Consumption and Latency Minimization During Data Of Loadingmentioning

confidence: 99%

Seamless communication techniques in mobile cloud computing: A survey

Constant¹,

Lee²,

Suo³

et al. 2021

ITU-J FET

View full text Add to dashboard Cite

show abstract

“…Harris et al [35] defined the problems of virtual network function placement and distribution and provided algorithms with guaranteed performance to realize the placement of delaysensitive services in appropriate network locations according to the specific needs and related requirements of each service. In response to the need for Mobile edge orchestrator (MEO) to expand capacity on many devices, Nguyen et al [36] proposed a fuzzy-logic based MEO that separates tasks from mobile devices and maps them to the cloud servers and edge servers, reducing the delay of task processing. Specially, the fuzzy-based MEO was employed to make multi-criteria decision-making which selects the appropriate host to perform tasks by considering multiple parameters in the same framework and find the optimal task segmentation strategy.…”

Section: Related Workmentioning

confidence: 99%

Latency-Aware Computation Offloading for 5G Networks in Edge Computing

2021

Security and Communication Networks

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With the development of Internet of Things, massive computation-intensive tasks are generated by mobile devices whose limited computing and storage capacity lead to poor quality of services. Edge computing, as an effective computing paradigm, was proposed for efficient and real-time data processing by providing computing resources at the edge of the network. The deployment of 5G promises to speed up data transmission but also further increases the tasks to be offloaded. However, how to transfer the data or tasks to the edge servers in 5G for processing with high response efficiency remains a challenge. In this paper, a latency-aware computation offloading method in 5G networks is proposed. Firstly, the latency and energy consumption models of edge computation offloading in 5G are defined. Then the fine-grained computation offloading method is employed to reduce the overall completion time of the tasks. The approach is further extended to solve the multiuser computation offloading problem. To verify the effectiveness of the proposed method, extensive simulation experiments are conducted. The results show that the proposed offloading method can effectively reduce the execution latency of the tasks.

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“…Edge computing networks, such as fog computing and cloudlet computing in general, and the MEC network in particular, are types of swiftly changing uncertain networks. Fuzzy logic is appropriate to cope with changes of parameters, for instance, central unit processing (CPU) utilization on a VM, which regularly changes based on the number of tasks being executed or the bandwidth fluctuations that frequently occur when the number of users increases [16,17]. The reasons for this are briefly described as follows.…”

Section: Introductionmentioning

confidence: 99%

“…First, because the fuzzy-logic-based approach has a lower computational complexity than other decisionmaking algorithms, it is effective for solving online and real-time problems without the need for detailed mathematical models [18]. Second, to support the heterogeneity of devices and the unpredictability of environments, fuzzy logic sets the rules, which are based on well-understood principles and the use of imprecise information provided in a high-level human-understandable format [16], and takes multiple network parameters of the network (e.g., task size, network latency, and server computational resources) into consideration [19]. Third, fuzzy logic considers multi-criteria decision analysis to determine the suitable servers at which IoT devices should offload the tasks [20].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Assisted Mobile Edge Orchestrator and SARSA Learning for Flexible Offloading in Heterogeneous IoT Environment

Khanh

Hai

Hossain

et al. 2022

Sensors

Self Cite

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In the era of heterogeneous 5G networks, Internet of Things (IoT) devices have significantly altered our daily life by providing innovative applications and services. However, these devices process large amounts of data traffic and their application requires an extremely fast response time and a massive amount of computational resources, leading to a high failure rate for task offloading and considerable latency due to congestion. To improve the quality of services (QoS) and performance due to the dynamic flow of requests from devices, numerous task offloading strategies in the area of multi-access edge computing (MEC) have been proposed in previous studies. Nevertheless, the neighboring edge servers, where computational resources are in excess, have not been considered, leading to unbalanced loads among edge servers in the same network tier. Therefore, in this paper, we propose a collaboration algorithm between a fuzzy-logic-based mobile edge orchestrator (MEO) and state-action-reward-state-action (SARSA) reinforcement learning, which we call the Fu-SARSA algorithm. We aim to minimize the failure rate and service time of tasks and decide on the optimal resource allocation for offloading, such as a local edge server, cloud server, or the best neighboring edge server in the MEC network. Four typical application types, healthcare, AR, infotainment, and compute-intensive applications, were used for the simulation. The performance results demonstrate that our proposed Fu-SARSA framework outperformed other algorithms in terms of service time and the task failure rate, especially when the system was overloaded.

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Latency Minimization in a Fuzzy-Based Mobile Edge Orchestrator for IoT Applications

Cited by 13 publications

References 16 publications

Seamless communication techniques in mobile cloud computing: A survey

Seamless communication techniques in mobile cloud computing: A survey

Latency-Aware Computation Offloading for 5G Networks in Edge Computing

Fuzzy-Assisted Mobile Edge Orchestrator and SARSA Learning for Flexible Offloading in Heterogeneous IoT Environment

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