Joint Job Partitioning and Collaborative Computation Offloading for Internet of Things

Mu, Siqi; Zhang, Zhong; Zhao, Dongmei; Ni, Minming

doi:10.1109/jiot.2018.2866945

Cited by 35 publications

(14 citation statements)

References 33 publications

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“…Several IoT applicationa are latency sensitive [1,25]. Due to computation power limitation of IoT devices, the IoT application owners may prefer to run their applications on the edge servers.…”

Section: Related Workmentioning

confidence: 99%

Latency minimization model towards high efficiency edge-IoT service provisioning in horizontal edge federation

Baghban

Huang

Hsu

2021

Multimed Tools Appl

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Edge computing plays a critical role in IoT as it potentially minimized the computation tasks response latency demanded by time-critical IoT applications. The growth of IoT users with high demanded computation power as well as ultra-low latency tasks may cause the performance degradation. One way to minimize the end-to-end (E2E) latency is to form horizontal edge federation (HEF) so that the computation resources can be shared with each participating edge node. Achieving ultra-low latency in HEF-IoT ecosystem involves setting two factor: resource allocation and task dispatching. This two factor interact with each other yet feasible solutions must provide satisfactory service level to meet latency constraints demanded by target applications. In this paper, we formulate it as E2E latency minimization problem and proposed a two-phase iterative (TPI) approach. The TPI method alternately determines optimal task dispatching and computation resource allocation. We exploit bin packing problem and, genetic algorithm (GA) to determine the edge nodes, and the required computation resources. The simulation results show that by using TPI approach, we can achieve more throughput, minimum E2E latency and optimum number of required edge nodes.

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Section: Related Workmentioning

confidence: 99%

Latency minimization model towards high efficiency edge-IoT service provisioning in horizontal edge federation

Baghban

Huang

Hsu

2021

Multimed Tools Appl

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“…Chen et al [17] considered the energy and the latency as the overhead and adopted a distributed gametheoretic approach to reduce the overall network overhead. Mu et al [18] jointly considered the application partitioning and collaborative computation offloading, so that UEs may help each other on task execution to meet the completion deadline of the applications while minimizing the overall energy consumption. Meskar et al [19] constructed a network where users share the communication channel to offload their computations as a competitive game, and obtained an optimum offloading decision for minimizing user energy consumption while satisfying the deadline of the applications.…”

Section: Related Workmentioning

confidence: 99%

A Fast Algorithm for Energy-Saving Offloading With Reliability and Latency Requirements in Multi-Access Edge Computing

et al. 2020

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Multi-Access Edge Computing (MEC) is a promising paradigm that providing cloud-like service for handling the high-complexity and latency-sensitive applications on user equipment (UE) via computation offloading. However, the execution reliability is rarely considered in current MEC studies, which is an important factor to guarantee the quality of service (QoS). For that, this paper considers an energy-saving offloading to satisfy the reliability and latency requirements of the application. Specifically, we formulate an optimization problem to minimize the UE's energy consumption with reliability and latency constraints. To tackle this NP-hard problem, we first divide the entire application into multiple directed-acyclic-graph-(DAG)-based subtasks, where the subtask can be executed on the UE locally or MEC server remotely. Then, we decompose the overall reliability and latency requirements into multiple constraints for each subtask. Finally, we propose a fast heuristic algorithm to find a solution satisfying the constraints. Simulation results demonstrate the proposed algorithm obtains lower energy consumption compared with the local execution and random assignment and costs less runtime compared with the greedy algorithm. INDEX TERMS Multi-access edge computing, computation offloading, energy consumption minimization, reliability guarantee.

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“…Computation latency was minimized by optimizing task assignment jointly with the time for task offloading and results downloading, subject to the individual energy constraints at the local user and the collaborators. In [9], we investigated the application partitioning and collaborator selection problem for multiple users when multiple idle collaborative devices are available. Both the centralized and decentralized algorithms were proposed to minimize the energy consumption of all the users.…”

Section: Related Workmentioning

confidence: 99%

“…Meanwhile, the traffic burden and computation load on the edge base station and servers are effectively alleviated. Most of the existing works [8][9][10][11] on peer-to-peer computing mainly consider that collaborators offer constant idle computation resources until completing the offloaded computation loads. However, due to its own tasks with high priority, the central processing unit (CPU) state of collaborators is opportunistically idle and available to the offloaded tasks [12,13], which is different from the dedicated resource provisioning of edge servers for IoT subscribers.…”

mentioning

confidence: 99%

Computation offloading to edge cloud and dynamically resource-sharing collaborators in Internet of Things

Zhang

2020

J Wireless Com Network

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With the diversity of the communication technology and the heterogeneity of the computation resources at network edge, both the edge cloud and peer devices (collaborators) can be scavenged to provide computation resources for the resource-limited Internet-of-Things (IoT) devices. In this paper, a novel cooperative computing paradigm is proposed, in which the computation resources of IoT device, opportunistically idle collaborators and dedicated edge cloud are fully exploited. Computation/offloading assistance is provided by collaborators at idle/busy states, respectively. Considering the channel randomness and opportunistic computation resource share of collaborators, we study the stochastic offloading control for an IoT device, regarding how much computation load is processed locally, offloaded to the edge cloud and a collaborator. The problem is formulated into a finite horizon Markov decision problem with the objective of minimizing the expected total energy consumption of the IoT device and the collaborator, subject to satisfying the hard computation deadline constraint. Optimal offloading policy is derived based on the stochastic optimization theory, which demonstrates that the energy consumption can be reduced by a proportional factor through the cooperative computing. More energy saving is achieved with better wireless channel condition or higher computation energy efficiency of collaborators. Simulation results validate the optimality of the proposed policy and the efficiency of the cooperative computing between end devices and edge cloud, compared to several other offloading schemes.

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Joint Job Partitioning and Collaborative Computation Offloading for Internet of Things

Cited by 35 publications

References 33 publications

Latency minimization model towards high efficiency edge-IoT service provisioning in horizontal edge federation

Latency minimization model towards high efficiency edge-IoT service provisioning in horizontal edge federation

A Fast Algorithm for Energy-Saving Offloading With Reliability and Latency Requirements in Multi-Access Edge Computing

Computation offloading to edge cloud and dynamically resource-sharing collaborators in Internet of Things

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