Online Orchestration of Cross-Edge Service Function Chaining for Cost-Efficient Edge Computing

Zhou, Zhi; Wu, Qiong; Chen, Xu

doi:10.1109/jsac.2019.2927070

Cited by 105 publications

(32 citation statements)

References 32 publications

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“…Mobile elements can be vehicles or robots [60]- [64]. Such mobile elements may also act as mobile edge nodes [65]- [69], which can be used to greatly diminish the calculation and processing tasks for the cloud in the IoT ecosystem. The following is the typical ACO-based transmission protocols for WSNs with mobile sinks.…”

Section: B Aco-based Transmission Methods For Mobile Wsnsmentioning

confidence: 99%

Artificial Intelligence-Empowered Path Selection: A Survey of Ant Colony Optimization for Static and Mobile Sensor Networks

Chen

Wang

et al. 2020

IEEE Access

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Artificial intelligence-empowered path selection plays an important role in wireless sensor networks (WSNs), because it can exceed the cognitive performance of humans and determine multiple aspects of the network performance. Ant colony optimization (ACO) is an effective intelligence algorithm which succeeds in addressing several issues of WSNs, including data transmission, node deployment, etc. There exist several ACO-based transmission strategies for WSNs, but the summary and comparison of such works are very limited. This paper provides a comprehensive overview of ACO-based transmission strategies for static and mobile WSNs. First, we provide a classification of existing ACO-based transmission methods, which distinguishes itself from other works in network types. Second, the highly typical ACObased transmission strategies for WSNs are illustrated and discussed. Finally, we summarize the paper and present several open issues concerning the design of such networks. This survey contributes to system design guidance and network performance improvement. INDEX TERMS Wireless sensor networks, transmission protocol, ant colony optimization.

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Section: B Aco-based Transmission Methods For Mobile Wsnsmentioning

confidence: 99%

Artificial Intelligence-Empowered Path Selection: A Survey of Ant Colony Optimization for Static and Mobile Sensor Networks

Chen

Wang

et al. 2020

IEEE Access

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“…These network functions may include classical functions, such as firewalls, deep packet inspection, the elements of the Evolved Packet Core (EPC), which is a framework to provide converged voice and data on LTE networks, but also innovative functions, including network coding, data aggregation, or computation as a service. An intuitive extension of the NFV concept combines single virtual network functions in a sequence to modularize complex functionalities in so-called Service Function Chains (SFCs) [101]- [104].…”

Section: ) Network Function Virtualization (Nfv)mentioning

confidence: 99%

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

et al. 2019

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Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute-and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions. INDEX TERMS Caching, computation offloading, device-to-device (D2D) communication, mobile edge computing (MEC). I. INTRODUCTION A. MOTIVATION

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“…This method can effectively mine training samples and avoid time-consuming initialization. An online orchestration framework that can be used for cross-edge service function chains is proposed by Zhou et al [12], the framework can dynamically optimize the flow routing and resource allocation jointly to improve the overall cost efficiency as much as possible. In order to sample and improve network decisions in flow-aware software-defined networks, Wang et al [13] proposed a space-time cooperative sampling (STCS) framework, and the experimental results prove the effectiveness of its sampling.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Edge Computing and Caching With Deep Reinforcement Learning Decision Agents

et al. 2020

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Large amounts of data will be generated due to the rapid development of the Internet of Things (IoT) technologies and 5th generation mobile networks (5G), the processing and analysis requirements of big data will challenge existing networks and processing platforms. As the most promising technology in 5G networks, edge computing will greatly ease the pressure on network and data processing analysis on the edge. In this paper, we considered the coordination between compute and cache resources between multi-level edge computing nodes (ENs), users under this system can offload computing tasks to ENs to improve quality of service (QoS). We aimed to maximize the long-term profit on the edge, while satisfying the low-latency computing of the users, and jointly optimize the edge-side node offloading strategy and resource allocation. However, it is challenging to obtain an optimal strategy in such a dynamic and complex system. To solve the complex resource allocation problem on the edge and make edge have certain adaptation and cooperation, we used double deep Q-learning (DDQN) to make decisions, ability to maximize long-term gains while making quick decisions. The simulation results prove the effectiveness of DDQN in maximizing revenue when allocation resources on the edge.

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Online Orchestration of Cross-Edge Service Function Chaining for Cost-Efficient Edge Computing

Cited by 105 publications

References 32 publications

Artificial Intelligence-Empowered Path Selection: A Survey of Ant Colony Optimization for Static and Mobile Sensor Networks

Artificial Intelligence-Empowered Path Selection: A Survey of Ant Colony Optimization for Static and Mobile Sensor Networks

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Collaborative Edge Computing and Caching With Deep Reinforcement Learning Decision Agents

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