Energy and cost trade-off for computational tasks offloading in mobile multi-tenant clouds

Patel, Yashwant Singh; Reddy, M. Babu; Misra, Rajiv

doi:10.1007/s10586-020-03226-8

Cited by 12 publications

(7 citation statements)

References 58 publications

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“…Further, the study continued for authentication literature in 5G. Patel et al [ 31 ] presented an energy and cost trade-off for computational tasks offloading in mobile multi-tenant clouds. Various policies proposed that 5G enabled cloud can employ to solve the general case.…”

Section: Literature Surveymentioning

confidence: 93%

Cloud to cloud data migration using self sovereign identity for 5G and beyond

Aruna¹,

Hasan

Islam

et al. 2021

Cluster Comput

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Section: Literature Surveymentioning

confidence: 93%

Cloud to cloud data migration using self sovereign identity for 5G and beyond

Aruna¹,

Hasan

Islam

et al. 2021

Cluster Comput

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“…The Kuhn-Munkras (KM) algorithm is used to find the maximum stable matching for the considered scenario. The work in [23] considers a mobile edge computing scenario intending to minimize energy consumption and monetary cost. The joint optimization problem is converted into a graph theory problem for which a distributed matching algorithm is proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Stable Matching Assisted Resource Allocation in Fog Computing Based IoT Networks

Alfakeeh,

Javed

2023

Mathematics

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Future Internet of Things (IoT) will be a connected network of sensors enabling applications such as industrial automation and autonomous driving. To manage such a large number of applications, efficient computing techniques using fog nodes will be required. A major challenge in such IoT networks is to manage the resource allocation of fog computing nodes considering security and system efficiency. A secure selection of fog nodes will be needed for forwarding the tasks without interception by the eavesdropper and minimizing the task delay. However, challenges such as the secure selection of fog nodes for forwarding the tasks without interception by the eavesdropper and minimizing the task delay are critical in IoT-based fog computing. In this paper, an efficient technique is proposed that solves the formulated problem of allocation of the tasks to the fog node resources using a stable matching algorithm. The proposed technique develops preference profiles for both IoT and fog nodes based on factors such as delay and secrecy rate. Finally, Gale–Shapley matching is used for task offloading. Detailed simulation results show that the performance of the proposed technique is significantly higher than the recent techniques in the literature.

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“…The master node in the server‐side determines whether a job should be offloaded or not and which VM should perform the work in order to minimize the process's overall execution time and energy usage. Patel et al 31 had developed a “MinEMC” optimization problem to reduce the financial and energy costs of mobile devices. It is established that the “MinEMC” problem is non‐deterministic polynomial‐time hard (NP‐hard).…”

Section: Related Workmentioning

confidence: 99%

“…An intelligent task offloading model 19 Task offloading Logistics regression Latency, bandwidth, and response time MinEMC, off-mat 31 To balance the trade-off between monetary cost and energy consumption of the mobile devices Heuristic approach Energy as well as the monetary cost F I G U R E 2 Data size-aware offloading framework…”

Section: Lyapunov Optimization Reduces Energy Consumptionmentioning

confidence: 99%

A novel data size‐aware offloading technique for resource provisioning in mobile cloud computing

Manukumar

Muthuswamy

2022

Int J Communication

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Mobile device users are involved in social networking, gaming, learning, and even some office work, so the end users expect mobile devices with highresponse computing capacities, storage, and high battery power consumption. The data-intensive applications, such as text search, online gaming, and face recognition usage, have tremendously increased. With such high complex applications, there are many issues in mobile devices, namely, fast battery draining, limited power, low storage capacity, and increased energy consumption. The novelty of this work is to strike a balance between time and energy consumption of mobile devices while using data-intensive applications by finding the optimal offloading decisions. This paper proposes a novel efficient Data Size-Aware Offloading Model (DSAOM) for data-intensive applications and to predict the appropriate resource provider for dynamic resource allocation in mobile cloud computing. Based on the data size, the tasks are separated and gradually allocated to the appropriate resource providers for execution. The task is placed into the appropriate resource provider by considering the availability services in the fog nodes or the cloud. The tasks are split into smaller portions for execution in the neighbor fog nodes. To execute the task in the remote side, the offloading decision is made by using the min-cut algorithm by considering the monetary cost of the mobile device. This proposed system achieves low-latency time 13.2% and low response time 14.1% and minimizes 24% of the energy consumption over the existing model. Finally, according to experimental findings, this framework efficiently lowers energy use and improves performance for data-intensive demanding application activities, and the task offloading strategy is effective for intensive offloading requests.

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Energy and cost trade-off for computational tasks offloading in mobile multi-tenant clouds

Cited by 12 publications

References 58 publications

Cloud to cloud data migration using self sovereign identity for 5G and beyond

Cloud to cloud data migration using self sovereign identity for 5G and beyond

Stable Matching Assisted Resource Allocation in Fog Computing Based IoT Networks

A novel data size‐aware offloading technique for resource provisioning in mobile cloud computing

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