Device-Centric Energy Optimization for Edge Cloud Offloading

Tayade, Shreya; Rost, Peter; Maeder, Andreas; Schotten, Hans D.

doi:10.1109/glocom.2017.8254628

Cited by 19 publications

(18 citation statements)

References 13 publications

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“…Satisfying the network specifications of all scheduled tasks in IoT devices, edge servers and the cloud are checked by (17). Constraint (12) prevents offloading more tasks than is possible due to limited links.…”

Section: Service Chainsmentioning

confidence: 99%

Optimal Placement of Recurrent Service Chains on Distributed Edge-Cloud Infrastructures

Mahjoubi

Taheri

Grinnemo

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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By increasing the number of IoT-devices, cloudcomputing faces challenges for some computation and timesensitive applications. Edge-computing has emerged to enable IoT-devices offload their computation tasks. Offloading tasks is a complex and challenging issue. We propose a comprehensive model including user, edge and cloud layers for scheduling continuous offering of services. Furthermore, we modeled the tasks of service as recurrent (repetitive) with a given frequency. The serviceplacement problem is formulated as a Mixed-Integer Linear Programming problem that aims to minimize the total delay of all services. We solve the problem with CPLEX, and proposed four fast heuristics to find near-optimal solutions. We compared the results of our proposed heuristics with the result obtained with CPLEX, in terms of problem-solving speed and accuracy, as well as resource utilization of all nodes. The results show that two of our proposed heuristics produce near-optimal solutions in a fraction of the time taken by CPLEX.Index Terms-edge computing, service placement, IoT devices, cloud computing• We propose a comprehensive model that includes all layers (user, edge, and cloud). It models three main node parameters (CPU, RAM, and network capacities), as well as the link

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Section: Service Chainsmentioning

confidence: 99%

Optimal Placement of Recurrent Service Chains on Distributed Edge-Cloud Infrastructures

Mahjoubi

Taheri

Grinnemo

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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“…As the energy consumption is one of the challenges for IoT networks [8], recent works, such as [9,10], study the trade-off between local and cloud computing in terms of device energy consumption. The former proposes an analytical framework that minimises the energy consumption by optimising the offloading decision of multiple user devices.…”

Section: Related Workmentioning

confidence: 99%

Energy‐Aware Smart Connectivity for IoT Networks: Enabling Smart Ports

Öztürk

Jaber

Imran

2018

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) is spreading much faster than the speed at which the supporting technology is maturing. Today, there are tens of wireless technologies competing for IoT and a myriad of IoT devices with disparate capabilities and constraints. Moreover, each of many verticals employing IoT networks dictates distinctive and differential network qualities. In this work, we present a context-aware framework that jointly optimises the connectivity and computational speed of the IoT network to deliver the qualities required by each vertical. Based on a smart port application, we identify energy efficiency, security, and response time as essential quality features and consider a wireless realisation of IoT connectivity using short range and long-range technologies. We propose a reinforcement learning technique and demonstrate significant reduction in energy consumption while meeting the quality requirements of all related applications.

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“…where ǫ i is the average amount of energy consumed by the user device for a single computation cycle, and C u,i is the computation load generated in terms of computation cycles on the user device [12]. The computational load is given as…”

Section: Device-centric Energy Consumption Modelmentioning

confidence: 99%

Delay Constrained Energy Optimization for Edge Cloud Offloading

Tayade

Rost

Maeder

et al. 2018

2018 IEEE International Conference on Communications Workshops (ICC Workshops)

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Resource limited user-devices may offload computation to a cloud server, in order to reduce power consumption and lower the execution time. However, to communicate to the cloud server over a wireless channel, additional energy is consumed for transmitting the data. Also a delay is introduced for offloading the data and receiving the response. Therefore, an optimal decision needs to be made that would reduce the energy consumption, while simultaneously satisfying the delay constraint. In this paper, we obtain an optimal closed form solution for these decision variables in a multi-user scenario. Furthermore, we optimally allocate the cloud server resources to the user devices, and evaluate the minimum delay that the system can provide, for a given bandwidth and number of user devices.

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Device-Centric Energy Optimization for Edge Cloud Offloading

Cited by 19 publications

References 13 publications

Optimal Placement of Recurrent Service Chains on Distributed Edge-Cloud Infrastructures

Optimal Placement of Recurrent Service Chains on Distributed Edge-Cloud Infrastructures

Energy‐Aware Smart Connectivity for IoT Networks: Enabling Smart Ports

Delay Constrained Energy Optimization for Edge Cloud Offloading

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