Cooperative Service Placement and Scheduling in Edge Clouds: A Deadline-Driven Approach

Li, Yuqing; Dai, Wenkuan; Gan, Xiaoying; Jin, Haiming; Fu, Luoyi; Ma, Huadóng; Wang, Xinbing

doi:10.1109/tmc.2021.3061602

Cited by 28 publications

(17 citation statements)

References 40 publications

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“…In addition, this model closely considers the scaling of VM's (e.g., number and type: small, medium and large) during the deployment period at every time step. In the same fashion, Li et al [24] proposed a heuristic approach to resolve the cooperative service placement and scheduling framework for cost optimization under varying user demands by considering the deadline constraint. Similarly, Yu et al [25] derived a novel strategy that used a centralized approach to address the joint task unloading and resource scheduling problem in the Mobile Edge Cloud paradigm.…”

Section: Scheduling Problems In the Edge Cloudmentioning

confidence: 99%

“…We have segregated the heuristic algorithms based on multiple factors by analyzing the keywords from the article title and abstract. As a result, the categories in our taxonomy includes Adaptive and Dynamic Approach [14,16,22,27,28,[32][33][34]36], Greedy Approach [37,41], QoS Parameters Based Approach [15,[23][24][25]42,44,[46][47][48][49], Machine Learning Based Approach [18,29,[53][54][55][56][57][58], Distributed Approach [17,30,43,45,50,51,60,61], Incentive Based Approach [62], Prediction Based Approach [19,35] and Others [20,21,26,38,39,59]. Review of different heuristic scheduling techniques together with dominance and drawbacks depicted in Table 4,5 and 6.…”

Section: Heuristic Algorithmsmentioning

confidence: 99%

“…Finally, the simulation shows the proposed algorithm has better efficiency and stability compared with the three heuristic algorithms FirstFit, MinCompletion and RoundRobin. Li et al [24] discussed a joint co-operative and placement strategy by considering the resource cost and diversities in the workload. By taking the leverage of user deadline tolerance a Lyapunov based joint cooperative mechanism has been adopted for cost reduction and user satisfaction.…”

Section: Qos Parameters Based Approachmentioning

confidence: 99%

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A Survey on Scheduling Techniques in the Edge Cloud: Issues, Challenges and Future Directions

Asghar

Jadoon

2022

Preprint

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After the advent of the Internet of Things and 5G networks, edge computing became the 1 center of attraction. The tasks demanding high computation are generally offloaded to the cloud since 2 the edge is resource-limited. The Edge Cloud is a promising platform where the devices can offload 3 delay-sensitive workloads. In this regard, scheduling holds great importance in offloading decisions 4 in the Edge Cloud collaboration. The ultimate objectives of scheduling are the quality of experience, 5 minimizing latency, and increasing performance. An abundance of efforts on scheduling has been 6 done in the past. In this paper, we have surveyed proposed scheduling strategies in the context of 7 edge cloud computing in various aspects such as advantages and demerits, QoS parameters, and fault 8 tolerance. We have also surveyed such scheduling approaches to evaluate which one is feasible under 9 what circumstances. We first classify all the algorithms into heuristic algorithms and meta-heuristics, 10 and we subcategorize algorithms in each class further based on extracted attributes of algorithms. 11 We hope that this survey will be very thoughtful in the development of new scheduling techniques. 12 Issues, challenges, and future directions have also been examined.

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Section: Scheduling Problems In the Edge Cloudmentioning

confidence: 99%

Section: Heuristic Algorithmsmentioning

confidence: 99%

Section: Qos Parameters Based Approachmentioning

confidence: 99%

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A Survey on Scheduling Techniques in the Edge Cloud: Issues, Challenges and Future Directions

Asghar

Jadoon

2022

Preprint

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“…In addition, this model closely considers the scaling of VM's (e.g., number and type: small, medium and large) during the deployment period at every time step. In the same fashion, Li et al [23] proposed a heuristic approach to resolve the cooperative service placement and scheduling framework for cost optimization under varying user demands by considering the deadline constraint. Similarly, Yu et al [24] derived a novel strategy that used a centralized approach to address the joint task unloading and resource scheduling problem in the Mobile Edge Cloud paradigm.…”

Section: Scheduling Problems In the Edge Cloudmentioning

confidence: 99%

A Survey on Scheduling Techniques in the Edge Cloud: Issues, Challenges and Future Directions

Asghar¹,

Jung²

2022

Preprint

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After the advent of the Internet of Things and 5G networks, edge computing became the center of attraction. The tasks demanding high computation are generally offloaded to the cloud since the edge is resource-limited. The Edge Cloud is a promising platform where the devices can offload delay-sensitive workloads. In this regard, scheduling holds great importance in offloading decisions in the Edge Cloud collaboration. The ultimate objectives of scheduling are the quality of experience, minimizing latency, and increasing performance. An abundance of efforts on scheduling has been done in the past. In this paper, we have surveyed proposed scheduling strategies in the context of edge cloud computing in various aspects such as advantages and demerits, QoS parameters, and fault tolerance. We have also surveyed such scheduling approaches to evaluate which one is feasible under what circumstances. We first classify all the algorithms into heuristic algorithms and meta-heuristics, and we subcategorize algorithms in each class further based on extracted attributes of algorithms. We hope that this survey will be very thoughtful in the development of new scheduling techniques. Issues, challenges, and future directions have also been examined.

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“…Consequently, finding valid deployment options becomes exponentially harder with each added component and is infeasible for larger deployments. Tong et al 38 and, to some extent Heintz et al 36 take a similar approach to FogTorch, while 4‐7,35,39‐55 employ a more efficient heuristics approach to solve the formalized optimization problem. Naas et al 56 offer a heuristics‐based solution as well, but place data replicas rather than services, a challenge that is also addressed in References 57,58.…”

Section: Related Workmentioning

confidence: 99%

From zero to fog: Efficient engineering of fog‐based Internet of Things applications

2021

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In Internet of Things (IoT) data processing, cloud computing alone does not suffice due to latency constraints, bandwidth limitations, and privacy concerns. By introducing intermediary nodes closer to the edge of the network that offer compute services in proximity to IoT devices, fog computing can reduce network strain and high access latency to application services. While this is the only viable approach to enable efficient IoT applications, the issue of component placement among cloud and intermediary nodes in the fog adds a new dimension to system design. State‐of‐the‐art solutions to this issue rely on simulation or solving a formalized assignment problem through heuristics only, which both have their drawbacks. In this article, we present a five‐step process for designing practical fog‐based IoT applications that combines best practices, simulation, and testbed analysis to converge towards an efficient system architecture. We then apply this process in a smart factory case study. By deploying filtered options to a physical testbed, we show that each step of our process converges towards more efficient application designs.

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Cooperative Service Placement and Scheduling in Edge Clouds: A Deadline-Driven Approach

Cited by 28 publications

References 40 publications

A Survey on Scheduling Techniques in the Edge Cloud: Issues, Challenges and Future Directions

A Survey on Scheduling Techniques in the Edge Cloud: Issues, Challenges and Future Directions

A Survey on Scheduling Techniques in the Edge Cloud: Issues, Challenges and Future Directions

From zero to fog: Efficient engineering of fog‐based Internet of Things applications

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