SEGUE: Quality of Service Aware Edge Cloud Service Migration

Zhang, Wuyang; Hu, Yi; Zhang, Yanyong; Raychaudhuri, Dipankar

doi:10.1109/cloudcom.2016.0061

Cited by 46 publications

(22 citation statements)

References 13 publications

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“…Migration. The authors of [232] study the service migration problem in edge clouds, in response to user movement and network performance. The solution is based on based on Markov Decision Process (MDP) that considers network state and server response time in making migration decisions.…”

Section: Servicementioning

confidence: 99%

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Yousefpour

Fung

Nguyen

et al. 2019

Journal of Systems Architecture

1,078

613

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With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of securitycritical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

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Section: Servicementioning

confidence: 99%

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Yousefpour

Fung

Nguyen

et al. 2019

Journal of Systems Architecture

1,078

613

View full text Add to dashboard Cite

show abstract

“…Some frameworks are conceptually similar to FOGPLAN but with goals that differ from the goal of meeting the ultralow latency requirements of IoT applications, that is the goal of FOGPLAN. Service migration in edge clouds in response to user movement [13], network workload performance [14], and for reducing file system transfer size [15]; and VM migration and handoff in edge clouds [16] [17] [18] [19] [20] are the most notable among these frameworks. Comparably, the studies [21] [22][23] [24] [25] propose deployment platforms and programming models for service provisioning in the fog.…”

Section: Related Workmentioning

confidence: 99%

FOGPLAN: A Lightweight QoS-Aware Dynamic Fog Service Provisioning Framework

Yousefpour

Patil

Ishigaki

et al. 2019

IEEE Internet Things J.

162

142

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Recent advances in the areas of Internet of Things (IoT), Big Data, and Machine Learning have contributed to the rise of a growing number of complex applications. These applications will be data-intensive, delay-sensitive, and real-time as smart devices prevail more in our daily life. Ensuring Quality of Service (QoS) for delay-sensitive applications is a must, and fog computing is seen as one of the primary enablers for satisfying such tight QoS requirements, as it puts compute, storage, and networking resources closer to the user.In this paper, we first introduce FOGPLAN, a framework for QoS-aware Dynamic Fog Service Provisioning (QDFSP). QDFSP concerns the dynamic deployment of application services on fog nodes, or the release of application services that have previously been deployed on fog nodes, in order to meet low latency and QoS requirements of applications while minimizing cost. FOGPLAN framework is practical and operates with no assumptions and minimal information about IoT nodes. Next, we present a possible formulation (as an optimization problem) and two efficient greedy algorithms for addressing the QDFSP at one instance of time. Finally, the FOGPLAN framework is evaluated using a simulation based on real-world traffic traces.

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“…In the traditional cloud migration decision model, the primary migration variable was the allocation of bandwidth resources [31] and the objective was to maximize the use of computing resources (such as CPU, memory, etc.). However, in the service migration process running in the edge cloud servers, user mobility is a key factor due to the limited coverage area of the edge server [32]. For that reason, the traditional cloud service migration decision model cannot directly be applied to the edge cloud server migration scenario.…”

Section: Service Migrationmentioning

confidence: 99%

A novel edge server selection method based on combined genetic algorithm and simulated annealing algorithm

Zhang

Peng

et al. 2020

Automatika

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Mobile edge computing is a new paradigm which provides cloud computing capabilities at the edge of pervasive radio access networks in close proximity to users. The problem of edge server selection in mobile edge environment in terms of user's overhead is investigated in this paper. Due to the limited resources of edge server, we firstly study the task completion probability of edge servers. Secondly, we formally model the problem of edge server selection in terms of time latency and energy consumption. More especially, the computation overhead method for completing the task in cases of both service migration and non-migration is investigated. Then, a new optimized edge server selection algorithm, called combined Genetic algorithm and simulated Annealing algorithm for edge Server Selection (GASS) is designed. Finally, a series of experiments on a real-word data-trace are conducted to evaluate the performance of GASS. The results show that GASS can effectively minimize the overhead of the user and outperform traditional heuristic algorithms.

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SEGUE: Quality of Service Aware Edge Cloud Service Migration

Cited by 46 publications

References 13 publications

All one needs to know about fog computing and related edge computing paradigms: A complete survey

All one needs to know about fog computing and related edge computing paradigms: A complete survey

FOGPLAN: A Lightweight QoS-Aware Dynamic Fog Service Provisioning Framework

A novel edge server selection method based on combined genetic algorithm and simulated annealing algorithm

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