15 research outputs found
Energy Efficiency Multi task Offloading and Resource Allocation in Mobile Edge Computing
On edge computing, mobile devices can offload some computing intensive tasks to the cloud so that the time delay and battery losses can be reduced. Different from cloud computing, an edge computing model is under the constraint of radio transmitting bandwidth, power and etc. With regard to most models in presence, each user is assigned to a single mission, transmitting power or local CPU frequency on mobile terminals is deemed to be a constant. Furthermore, energy consumption has a positive correlation with the above two parameters. In a context of multitask, such values could be increased or reduced according to workload to save energy. Additionally, the existing offloading methods are inappropriate if all the compute densities of multiple tasks are high. In this paper, a single-user multi-task with high computing density model is proposed and partial task is offloaded when use the different offload algorithm. Simulated annealing algorithm is the best method to select offloading tasks, which can enhance the offloading ratio and save energy consumption
A game-theoretic approach to computation offloading in mobile cloud computing
We consider a three-tier architecture for mobile and pervasive computing
scenarios, consisting of a local tier ofmobile nodes, a middle tier (cloudlets) of nearby
computing nodes, typically located at the mobile nodes access points but characterized by a limited amount of resources, and a remote tier of distant cloud servers, which have
practically infinite resources. This architecture has been proposed to get the benefits of
computation offloading from mobile nodes to external servers while limiting the use
of distant servers whose higher latency could negatively impact the user experience.
For this architecture, we consider a usage scenario where no central authority exists
and multiple non-cooperative mobile users share the limited computing resources of
a close-by cloudlet and can selfishly decide to send their computations to any of the
three tiers. We define a model to capture the users interaction and to investigate the
effects of computation offloading on the users’ perceived performance. We formulate
the problem as a generalized Nash equilibrium problem and show existence of an
equilibrium.We present a distributed algorithm for the computation of an equilibrium
which is tailored to the problem structure and is based on an in-depth analysis of
the underlying equilibrium problem. Through numerical examples, we illustrate its
behavior and the characteristics of the achieved equilibria