Predictive Resource Allocation for Tactile Internet Capable Passive Optical LANs

Wong, Elaine; Dias, Maluge Pubudini Imali; Ruan, Ling

doi:10.1109/jlt.2017.2654365

Cited by 103 publications

(47 citation statements)

References 23 publications

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“…End-toend latency for some applications can be limited to few milliseconds (e.g., 1 ms for tactile internet). Thus, the distance between the edge and computing resources must be limited to some tens of kilometers [148], and a decentralized service platform architecture based on Mobile Edge Computing (MEC) or Fog Computing (FC) is required. However, the integration of various computing paradigms (MEC, Fog and cloud) involves the development of integrated resource management, task allocation and failure handling techniques, to name just a few.…”

Section: Efficient Joint Operation Of Network and Computing Re-mentioning

confidence: 99%

“…AI is expected to play a key role to facilitate efficient joint operation of network and computing devices, performing tasks like virtual network function (VNF) distribution, task allocation, predictive caching and interpolation/extrapolation of human actions, and thus enhancing performance and providing better support for IoT and tactile Internet applications. For instance, along this line, a recent work by Wong et al [148] has proposed a novel tactile Internet capable PON and a dynamic wavelength and bandwidth allocation method, which incorporates a mechanism to predict the traffic load to vary the number of active wavelength channels in the network, and prioritize the transmission of tactile Internet traffic (vs. other traffic) to comply with delay requirements. Due to the huge expansion of IoT applications and services, we envision more advances to come along this line in the next years.…”

Section: Efficient Joint Operation Of Network and Computing Re-mentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Mata

Miguel

Durán

et al. 2018

Optical Switching and Networking

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174

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Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.

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Section: Efficient Joint Operation Of Network and Computing Re-mentioning

confidence: 99%

Section: Efficient Joint Operation Of Network and Computing Re-mentioning

confidence: 99%

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Mata

Miguel

Durán

et al. 2018

Optical Switching and Networking

336

174

View full text Add to dashboard Cite

show abstract

“…Other important challenges for the TI are [248][249]: resource management; task allocation and orchestration; mobility of robots; remote robot steering and control applications. In [80] the authors studied a solution to accommodate, within the same network, latency-sensitive TI and bandwidth-intensive traffic types.…”

Section: Other Scenariosmentioning

confidence: 99%

Game Theory for Multi-Access Edge Computing: Survey, Use Cases, and Future Trends

Moura

Hutchison

2019

IEEE Commun. Surv. Tutorials

182

108

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Game Theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming Multi-Access Edge Computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as Quality of Experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely: rational vs. evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse trade-offs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios.

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“…In [32], the authors studied the performance of centralized and decentralized bandwidth allocation algorithms in a long-reach optical access network to investigate the feasibility of computation offloading to edge-computing servers and develop an analytical framework to validate against simulated results. The authors of [1] presented the implementation of a cloudlet framework for human-machine interactive applications with control server at the CO of a fiber-based access network. The authors of [33] presented the idea of cloud and cloudlet empowered FiWi-heterogeneous network architecture for LTE-A and designed a cloudlet-aware resource management algorithm that aims to reduce the offload latency and prolong mobile-devices' battery life.…”

Section: Related Workmentioning

confidence: 99%

“…However, these applications are highly computation intensive, and this limited computing capability of portable mobile devices like iPhones, Android phones, and Google glasses are unable to support these applications. Although mobile cloud computing enables mobile devices to access a shared pool of configurable computational and storage resources, providing ubiquitous, convenient, and on demand services, large communication latency between mobile devices and remote clouds still presents a new challenge for low-latency applications that demand 1-100 ms end-to-end system latency [1]. To achieve this low-latency requirement, the authors of [2] proposed the idea (ii) We solve this modified constrained optimization problem by using KKT conditions on the Lagrangian functions and derive closed-form expression for the cloudlet deployment cost.…”

Section: Introductionmentioning

confidence: 99%

An Analytical Cost-Optimal Cloudlet Placement Framework over Fiber-Wireless Networks with Quasi-Convex Latency Constraint

2019

Self Cite

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To meet the low-latency constraints arising from future smart mobile devices, Internet-of-Things, and 5G applications, major interest is currently given to the integration of centralized cloud computing and distributed edge computing infrastructures to deliver higher performance and reliability to edge devices in accessing mobile cloud services. The three-tier network architecture arising from cloud, cloudlet, and edge-devices can handle miscellaneous latency requirements for both latency-sensitive and latency-tolerant applications more efficiently than conventional two-tier networks. In this paper, we primarily focus on the static cloudlet network planning problem and propose an analytical hybrid cost-optimization framework for optimal cloudlet placement. We formulate this problem as a convex optimization problem and solve by using Karush-Kuhn-Tucker (KKT) conditions, and show that this framework can be evaluated without any scalability issues observed with integer programming based frameworks for large datasets. Moreover, we derive user-friendly closed form expressions that provide a first-hand estimation of cloudlet deployment cost depending on a few important network parameters like split-ratio, population density, and network bandwidth. Finally, we also show that the optimal solution of this analytical framework can be considered as a tight lower bound of the optimal solutions of integer programming based frameworks and makes a better cloudlet installation cost estimation compared to other existing frameworks.Electronics 2019, 8, 404 2 of 18 of cloudlet computing similar to fog and mobile-edge computing, where cloudlets are considered as trusted, resource-rich computer or cluster of computers. Nonetheless, cloudlets are designed to be self-managing and function with low-context-awareness. Hence, they are most suitable for deployment in adversarial environments [3].The authors of [4] showed that the system performance in terms of expected latency for the requested jobs from a single-hop cloudlet server is always better than a distant cloud server for low-latency applications. Thus, the optimal placement of cloudlets over wireless access networks seemed to be an important research problem to enhance the system performance. Along with the wireless access networks, researchers started to focus on optical and fiber-wireless (FiWi) access networks for edge-computing solutions deployment due to their wide deployment coverage, low cost per bit, very high data transmission bandwidth, efficient network virtualization and network scalability [5]. The time-division multiplexed passive optical network (TDM-PON) has also been considered as a front/back-haul support for wireless access network, i.e., access technologies like Radio-over-Fiber (RoF), WiFi, LTE-A and millimeter-wave (mmWave) can be integrated with optical network units (ONUs) for a higher user coverage [6].A static cloudlet placement framework considers that the network is static in time, i.e., it does not consider user mobility and virtual machine (VM) mobil...

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Predictive Resource Allocation for Tactile Internet Capable Passive Optical LANs

Cited by 103 publications

References 23 publications

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Game Theory for Multi-Access Edge Computing: Survey, Use Cases, and Future Trends

An Analytical Cost-Optimal Cloudlet Placement Framework over Fiber-Wireless Networks with Quasi-Convex Latency Constraint

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