Computation Offloading Strategy Optimization with Multiple Heterogeneous Servers in Mobile Edge Computing

Li, Keqin

doi:10.1109/tsusc.2019.2904680

Cited by 54 publications

(18 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A similar problem were studied in References 24,25. In Reference 26, Li established an M/G/1 queueing model with infinite waiting queue capacity to characterize multiple heterogeneous MDS and edge servers, such that the performance and energy consumption of the MEC platform can be calculated analytically. He studied computation offloading strategy optimization in an MEC environment to balance power and performance.…”

Section: Related Researchmentioning

confidence: 99%

“…In addition, the dynamic power consumption of processor is related to its working model. According to some existing research, 26,41,42 processors generally have two working modes namely, the idle‐speed model and the constant‐speed model . When there is no task to execute, the processor speed is zero for the idle‐speed model, but is s for the constant‐speed model.…”

Section: Problem Definition and Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Server configuration optimization in mobile edge computing: A cost‐performance tradeoff perspective

et al. 2021

Softw Pract Exp

Self Cite

View full text Add to dashboard Cite

Before service providers build up an mobile edge computing (MEC) platform, an important issue that needs to be considered is the configuration of computing resources on edge servers. Since the computing resources on an edge server are limited compared with a cloud server and the service provider's deployment budget is limited, it would be unrealistic to equip all edge servers with abundant computing resources. In addition, the edge servers have different computation demands due to their different geographies. Therefore, this article investigates the problem of server configuration optimization in an MEC environment based on a given computation demand statistics of the selected deployment locations. Our strategy is to treat each edge server as an M/M/m queueing model, and then establish the performance and cost models for the system. Two optimization problems, including cost constrained performance optimization, and performance constrained cost optimization are formulated based on our models and solved by a series of fast numerical algorithms. We also conduct extensive numerical simulation examples to show the effectiveness of the proposed algorithms. MEC service providers can use our strategy to get the appropriate type of processor and obtain the optimal processor number for each edge server to achieve two different goals: (1) deliver the highest‐quality services with a given cost constraint; (2) minimize the investment cost with a service‐quality guarantee. Our research is of great significance for service providers to control the tradeoff between investment cost and service quality.

show abstract

Section: Related Researchmentioning

confidence: 99%

Section: Problem Definition and Modelsmentioning

confidence: 99%

Server configuration optimization in mobile edge computing: A cost‐performance tradeoff perspective

et al. 2021

Softw Pract Exp

Self Cite

View full text Add to dashboard Cite

show abstract

“…The main target of these studies is minimizing the average response time of tasks. Some studies focus on one type of task [2], [9], while others focus on multiple types of tasks [8], [11], [12], [13]. When a system performs multiple types of tasks, it needs to set priority for each type of tasks so as to distinct the importance from different types of tasks.…”

Section: Related Workmentioning

confidence: 99%

Bi-Directional Timing-Power Optimisation on Heterogeneous Multi-Core Architectures

Huang

Wei

et al. 2021

IEEE Trans. Sustain. Comput.

View full text Add to dashboard Cite

Optimisation of timing performance and power consumption on heterogeneous multi-core architectures is gaining increasing attention. Systems and devices may have varying demands on timing and power, which motivates more flexible optimisation. Along this line, we consider a heterogeneous computing architecture with multiple cores, where each core runs a mixed stream of general and dedicated tasks with a certain scheduling strategy. Employing the queuing model, we first propose a load balancing algorithm, which minimises the average response time of the general tasks whilst guaranteeing the timing requirements of the dedicated tasks. Built upon the above, we propose a bi-directional optimisation algorithm that is able to improve the timing performance under the constraint of power consumption, and reduces the power consumption for the given timing requirement.Extensive numerical experiments illustrate the significance of the proposed algorithms. Implementation on a real platform validates the consistency between the theoretical analysis and the practical results.

show abstract

“…Thus, the approach of offloading the localization technique to be processed on a remote server that is characterized by high processing capability and continuous power supply is a promising alternative to local processing [26], as it enhances the latency and the energy consumption of the system without affecting the accuracy. Computation offloading is the transfer of specific computing tasks to an external platform such as a cluster, a grid, or a cloud [36]. In this paper, offloading the indoor localization task to the cloud and Mobile Edge Computing (MEC) servers is examined.…”

Section: Introductionmentioning

confidence: 99%

A Minimized Latency Collaborative Computation Offloading Game Under Mobile Edge Computing for Indoor Localization

2021

View full text Add to dashboard Cite

Indoor localization has become one of the fundamental services that is required in a diverse set of applications these days, such as patient monitoring and smart parking. Highly accurate localization techniques impose high latency and high energy consumption on the underlying application system. Thus, for such indoor location-based application, offloading the computation of the localization process to a remote server with high resource capability has been recently introduced as an avenue to address such a challenge. In this paper, a computation offloading problem is formulated to find the optimal decision with regard to the operation of the localization process. This decision includes: a) Where to compute the localization task, either locally on the end device or on the edge server or on the cloud server, b) Which localization technique should be used, and finally, c) Which transmission technology is recommended to be chosen in combination with the localization technique. All these decisions are constrained by the device, and the servers resource capabilities load. They are also constrained by the fact that the localization algorithm has to satisfy a certain application QoS requirement. Within such context, three algorithms are proposed for task offload decision making. First, the Indoor Localization Latency Optimal Offloading algorithm, which finds the optimal offloading decision that minimizes the total latency of the system and is considered a benchmark for the other algorithms. Second, Indoor Localization Latency Centralized Offloading algorithm that finds a sub optimal solution with lower complexity. Third, Indoor Localization Latency Game-Theoretic Offloading decentralized algorithm that converges after finite improvement steps and achieves Nash equilibrium. Altogether, the paper finds the optimum localization strategy for all users with the minimum latency under mobile edge computing environment.

show abstract

Computation Offloading Strategy Optimization with Multiple Heterogeneous Servers in Mobile Edge Computing

Cited by 54 publications

References 21 publications

Server configuration optimization in mobile edge computing: A cost‐performance tradeoff perspective

Server configuration optimization in mobile edge computing: A cost‐performance tradeoff perspective

Bi-Directional Timing-Power Optimisation on Heterogeneous Multi-Core Architectures

A Minimized Latency Collaborative Computation Offloading Game Under Mobile Edge Computing for Indoor Localization

Contact Info

Product

Resources

About