Application Provisioning in FOG Computing-enabled Internet-of-Things: A Network Perspective

Yu, Ruozhou; Xue, Guoliang; Zhang, Xiang

doi:10.1109/infocom.2018.8486269

Cited by 98 publications

(72 citation statements)

References 21 publications

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“…Relevant to our work, previous research works have investigated task allocation in fog/edge computing [17][18][19][20][21][22][23][24]. Li et al [11] minimized service response time and energy consumption by jointly optimizing the offloading strategy and the QoR for all edge nodes.…”

Section: Related Workmentioning

confidence: 87%

Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

Zhu¹,

Jin²,

Pastor³

et al. 2019

IEEE Internet Things J.

169

121

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With the emerging vehicular applications such as real-time situational awareness and cooperative lane change, there exist huge demands for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Folo, a novel solution for latency and quality optimized task allocation in Vehicular Fog Computing (VFC). Folo is designed to support the mobility of vehicles, including vehicles that generate tasks and the others that serve as fog nodes. Considering constraints on service latency, quality loss, and fog capacity, the process of task allocation across stationary and mobile fog nodes is formulated into a joint optimization problem. This task allocation in VFC is known as a non-deterministic polynomial-time hard (NP-hard) problem. In this paper, we present the task allocation to fog nodes as a bi-objective minimization problem, where a trade-off is maintained between the service latency and quality loss. Specifically, we propose an event-triggered dynamic task allocation (DTA) framework using Linear Programming based Optimization (LBO) and Binary Particle Swarm Optimization (BPSO). To assess the effectiveness of Folo, we simulated the mobility of fog nodes at different times of a day based on real-world taxi traces and implemented two representative tasks, including video streaming and real-time object recognition. Simulation results show that the task allocation provided by Folo can be adjusted according to actual requirements of the service latency and quality, and achieves higher performance compared with naive and random fog node selection. To be more specific, Folo shortens the average service latency by up to 27% while reducing the quality loss by up to 56%.

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Section: Related Workmentioning

confidence: 87%

Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

Zhu¹,

Jin²,

Pastor³

et al. 2019

IEEE Internet Things J.

169

121

View full text Add to dashboard Cite

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“…• Generic edge clouds [15], [16], [19], [21], [29], [30] • Hierarchical clouds [13], [14], [20], [22]- [24], [26] • Fog clouds [18], [25], [27], [28] • Other [17] Decision variables…”

Section: Target Environmentmentioning

confidence: 99%

QoS-Aware Workload Distribution in Hierarchical Edge Clouds: A Reinforcement Learning Approach

et al. 2020

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Recently, edge computing is getting attention as a new computing paradigm that is expected to achieve short-delay and high-throughput task offloading for large scale Internet-of-Things (IoT) applications. In edge computing, workload distribution is one of the most critical issues that largely influences the delay and throughput performance of edge clouds, especially in distributed Function-as-a-Service (FaaS) over networked edge nodes. In this paper, we propose the Resource Allocation Control Engine with Reinforcement learning (RACER), which provides an efficient workload distribution strategy to reduce the task response slowdown with per-task response time Quality-of-Service (QoS). First, we present a novel problem formulation with the per-task QoS constraint derived from the well-known token bucket mechanism. Second, we employ a problem relaxation to reduce the overall computation complexity by compromising just a bit of optimality. Lastly, we take the deep reinforcement learning approach as an alternative solution to the workload distribution problem to cope with the uncertainty and dynamicity of underlying environments. Evaluation results show that RACER achieves a significant improvement in terms of per-task QoS violation ratio, average slowdown, and control efficiency, compared to AREA, a state-ofthe-art workload distribution method. INDEX TERMS deep reinforcement learning, edge computing, resource allocation, workload distribution

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“…In [6], application provisioning is studied from the perspective of the network infrastructure. A fully polynomial- time approximation scheme is derived for single and multiple application deployment, showing large QoS performance improvement with respect to applications' bandwidth and delay figures; computational requirements are not accounted for.…”

Section: Related Workmentioning

confidence: 99%

“…The tradeoff in this context is represented by edge resource occupation: compared to standard cloud technologies -based Fondazione Bruno Kessler, via Sommarive, 18 I-38123 Povo, Trento, Italy on overprovisioned datacenters -the business of edge infrastructure owners will not be able to rely on overprovisioning. Rather, they need to trade off premier service provision based on localized data processing and low round-trip time for storage, memory and processing capabilities of edge units [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Cutting Throughput with the Edge: App-Aware Placement in Fog Computing

Faticanti

Pellegrini

Siracusa

et al. 2019

2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference

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Fog computing extends cloud computing technology to the edge of the infrastructure to let IoT applications access objects' data with reduced latency, location awareness and dynamic computation. By displacing workloads from the central cloud to the edge devices, fog computing overcomes communication bottlenecks avoiding raw data transfer to the central cloud, thus paving the way for the next generation IoT-based applications.In this paper we study scheduling and placement of applications in fog computing, which is key to ensure profitability for the involved stakeholders. We consider a scenario where the emerging microservice architecture allows for the design of applications as cascades of coupled microservice modules. It results into a mixed integer non linear problem involving constraints on both application data flows and computation placement. Due to the complexity of the original problem, we resort to a simplified version, which is further solved using a greedy algorithm. This algorithm is the core placement logic of the FogAtlas platform, a fog computing platform based on existing virtualization technologies.Extensive numerical results validate the model and the scalability of the proposed solution, showing it attains performance close to the optimal solution and, in our real implementation, it scales well with respect to the number of served applications.

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Application Provisioning in FOG Computing-enabled Internet-of-Things: A Network Perspective

Cited by 98 publications

References 21 publications

Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

QoS-Aware Workload Distribution in Hierarchical Edge Clouds: A Reinforcement Learning Approach

Cutting Throughput with the Edge: App-Aware Placement in Fog Computing

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