QoE-Aware Computation Offloading Scheduling to Capture Energy-Latency Tradeoff in Mobile Clouds

Hong, Sung-Tae; Kim, Hyoil

doi:10.1109/sahcn.2016.7733009

Cited by 45 publications

(24 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They first obtain an optimal solution for the energy consumption minimization problem and then a locally optimal solution for the latency minimization problem. Hong et al [26] consider both energy consumption and latency by formulating an aggregate objective function. Jiang et al [27] propose a Lyapunov optimization approach for cloud offloading scheduling, where multiple applications are running on multi-core CPU mobile devices.…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments

Bozorgchenani

Mashhadi

Tarchi

et al. 2021

IEEE Trans. on Mobile Comput.

123

View full text Add to dashboard Cite

In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments

Bozorgchenani

Mashhadi

Tarchi

et al. 2021

IEEE Trans. on Mobile Comput.

123

View full text Add to dashboard Cite

show abstract

“…As an emerging paradigm, MEC has attracted considerable attention in the literature [9,34]. Some works considering computation offloading for MEC have been done, which can be divided into three categories: (i) latency based computation offloading [35][36][37][38], (ii) energy based computation offloading [39,40] and (iii) energy and latency based computation offloading [41][42][43][44][45][46].…”

Section: Related Workmentioning

confidence: 99%

“…In [43], an algorithmic is designed and implemented using graph theory. In [44], a semi-mobile devices platform framework is proposed to minimize the energy consumption and execution time. In [45], a Lyapunov optimization-based algorithm is introduced to optimize the execution energy and latency.…”

Section: Related Workmentioning

confidence: 99%

Security modeling and efficient computation offloading for service workflow in mobile edge computing

Huang

Tang

et al. 2019

Future Generation Computer Systems

View full text Add to dashboard Cite

It is a big challenge for resource-limited mobile devices (MDs) to execute various complex and energy-consumed mobile applications. Fortunately, as a novel computing paradigm, edge computing (MEC) can provide abundant computing resources to execute all or parts of the tasks of MDs and thereby can greatly reduce the energy of MD and improve the QoS of applications. However, offloading workflow tasks to the MEC servers are liable to external security threats (e.g., snooping, alteration). In this paper, we propose a security and energy efficient computation offloading (SEECO) strategy for service workflows in MEC environment, the goal of which is to optimize the energy consumption under the risk probability and deadline constraints. First, we build a security overhead model to measure the execution time of security services. Then, we formulate the computation offloading problem by incorporating the security, energy consumption and execution time of workflow application. Finally, based on the genetic algorithm (GA), the corresponding coding strategies of SEECO are devised by considering tasks execution order and location and security services selection.Extensive experiments with the variety of workflow parameters demonstrate that SEECO strategy can achieve the security and energy efficiency for the mobile applications.

show abstract

“…Dynamic radio resource allocation method for computation tasks is described in [6] and [7]. In [8], a user experience based method is proposed to optimize mobile power consumption and computation delay. In MEC systems, most works are also interesting in reducing energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Performance Guaranteed Computation Offloading for Mobile-Edge Cloud Computing

Tao

Ota

Dong

et al. 2017

IEEE Wireless Commun. Lett.

205

103

View full text Add to dashboard Cite

In this paper, we investigate an energy efficiency with performance guaranteed problem in mobile-edge computing. The mobile users desire low energy consumption and performance guaranteed, we propose an energy minimizing optimization problem for mobile-edge cloud computing, that we apply KKT conditions to solve it, and we also present a request offloading scheme for this issue. In particular, the offloading scheme is determined by energy consumption and bandwidth capacity at each time slot. Numerical results demonstrate that our proposed offloading scheme outperforms local computing and entirely offloading method on energy consumption and performance on delay.

show abstract

QoE-Aware Computation Offloading Scheduling to Capture Energy-Latency Tradeoff in Mobile Clouds

Cited by 45 publications

References 13 publications

Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments

Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments

Security modeling and efficient computation offloading for service workflow in mobile edge computing

Performance Guaranteed Computation Offloading for Mobile-Edge Cloud Computing

Contact Info

Product

Resources

About