MobiQoR: Pushing the Envelope of Mobile Edge Computing Via Quality-of-Result Optimization

Li, Yongbo; Chen, Yurong; Lan, Tian; Venkataramani, Guru

doi:10.1109/icdcs.2017.54

Cited by 57 publications

(35 citation statements)

References 45 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. Sardellitti et al [25] jointly optimized radio and computational resources of multiple cells in edge computing.…”

Section: Related Workmentioning

confidence: 93%

“…Learn from [11], two linear approximate trade-off functions, P (q k ) = a t q k + b t , and R(q k ) = a r q k + b r are considered. In addition, a new variable t with an additional constraint t ≥ max…”

Section: Problem Linearizationmentioning

confidence: 99%

“…According to [10,11], by relaxing the tolerance of quality loss, service latency could be reduced to a certain extent. We explore a novel dimension, Quality Loss of Results (QLR) to represent the user acquired service with lower or lessthan-optimal quality compared with the perfect result.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Zhu¹,

Jin²,

Pastor³

et al. 2019

IEEE Internet Things J.

177

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: 93%

Section: Problem Linearizationmentioning

confidence: 99%

See 1 more Smart Citation

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

Zhu¹,

Jin²,

Pastor³

et al. 2019

IEEE Internet Things J.

177

121

View full text Add to dashboard Cite

show abstract

“…Osmotic Computing [11] relied on the deployment of lightweight microservices on resource-constrained IoT platforms at the network edge, coupled with more complex microservices running on large-scale datacenters. MobiQoR [12] introduced a new metric, Quality of Results, to validate the quality of edge resource deployment. Nevertheless, none of these approaches attempted to estimate the IoT workload, which in turn could significantly enhance the corresponding deployments.…”

Section: Iot Workload Profilementioning

confidence: 99%

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

Dechouniotis

Αθανασόπουλος

Leivadeas

et al. 2020

Sensors

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The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities.

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“…Inspired by [10], we explore a novel dimension, Quality Loss of Results (QLR) to represent the user acquired service with lower or less-than-optimal quality compared with the perfect result. For the vehicular applications designed for improving driving safety and efficiency, they are supposed to satisfy certain constraints on service latency and quality loss.…”

Section: Introductionmentioning

confidence: 99%

Fog Following Me: Latency and Quality Balanced Task Allocation in Vehicular Fog Computing

Zhu

Pastor

Xiao

et al. 2018

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

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Emerging vehicular applications, such as real-time situational awareness and cooperative lane change, demand for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Fog Following Me (Folo), a novel solution for latency and quality balanced task allocation in vehicular fog computing. Folo is designed to support the mobility of vehicles, including ones generating tasks and the others serving as fog nodes. We formulate the process of task allocation across stationary and mobile fog nodes into a joint optimization problem, with constraints on service latency, quality loss, and fog capacity. As it is a NP-hard problem, we linearize it and solve it using Mixed Integer Linear Programming. To evaluate the effectiveness of Folo, we simulate the mobility of fog nodes at different times of day based on real-world taxi traces, and implement two representative tasks, including video streaming and real-time object recognition. Compared with naive and random fog node selection, the latency and quality balanced task allocation provided by Folo achieves higher performance. More specifically, Folo shortens the average service latency by up to 41% while reducing the quality loss by up to 60%.

show abstract

MobiQoR: Pushing the Envelope of Mobile Edge Computing Via Quality-of-Result Optimization

Cited by 57 publications

References 45 publications

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

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

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

Fog Following Me: Latency and Quality Balanced Task Allocation in Vehicular Fog Computing

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