QoS Driven Task Offloading with Statistical Guarantee in Mobile Edge Computing


Popular mobile applications, such as augmented reality, typically offload the work they need to do on their devices to resource-rich edge servers when using mobile edge computing. When a large number of mobile users compete for limited Communication and computation resources, the user experience can be adversely affected to a significant degree. Guaranteeing the Quality of Service (QoS) for the applications being offloaded is the primary technical challenge associated with task offloading. The current work on task offloading focuses on deterministic QoS (delay) guarantee, which means that tasks have to be finished before the specified deadline with one hundred percent accuracy. In spite of this, it is impractical to impose a deterministic QoS guarantee for tasks when offloading to edge servers because of the high levels of variability that are present in the wireless environment. In this paper, we focus on task offloading with statistical quality of service guarantee (tasks are allowed to complete before a given deadline with a probability above the given threshold). This type of offloading can save even more energy because there is no longer a quality of service requirement. In particular, we begin by putting forward a statistical computation model as well as a statistical transmission model in order to quantify the correlation between the statistical quality of service guarantee and task offloading strategy. After that, we turn the problem of task offloading into a mixed-integer, non-linear programming issue with a statistical delay constraint. We begin by transforming the statistical delay constraint into the constraints on the number of CPU cycles, and then we move on to the delay exponent. With the help of convex optimization theory and the Gibbs sampling method, we propose an algorithm that will provide the statistical quality of service guarantee for tasks. The findings of the experiment indicate that the proposed algorithm performs better than the three baselines.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Towards Personalized Privacy-Preserving Incentive for Truth Discovery in Mobile Crowdsensing Systems ABSTRACT: It is essential to have incentive mechanisms in place in mobile crowdsensing (MCS) systems in order
PROJECT TITLE : Supremo Cloud-Assisted Low-Latency Super-Resolution in Mobile Devices ABSTRACT: We present Supremo, an image super-resolution (SR) system for low-latency use in mobile devices that is assisted by the cloud. Because
PROJECT TITLE : SchrodinText: Strong Protection of Sensitive Textual Content of Mobile Applications ABSTRACT: A large number of mobile applications deliver and display sensitive and private textual content to users. Examples of
PROJECT TITLE : Resource-aware Feature Extraction in Mobile Edge Computing ABSTRACT: Mobile image recognition services are revolutionizing our everyday lives by providing people with image recognition services that they can access
PROJECT TITLE : PRIME: An Optimal Pricing Scheme for Mobile Sensors-as-a-Service ABSTRACT: In this article, we propose a pricing scheme for provisioning mobile Sensors-as-a-Service (mSe-aaS) in the mobile sensor-cloud (MSC) architecture.

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry