Multi-user Multi-task Computation Offloading in Green Mobile Edge Cloud Computing
Abstract
Mobile Edge Computing (MEC) is an emerging computing model that extends the network edge of the cloud and its services. Multi-user Multi-task Computation Offloading in Green Mobile Edge Cloud Computing Consider running emerging resource-intensive applications in the MEC network, computation offloading is a proven successful paradigm for enabling resource-intensive applications on mobile devices. In addition, when multiple users are in the same proximity, the offloaded tasks can be duplicated in view of the emerging mobile collaborative application (MCA). This motivates us to design a multi-user MEC network collaborative computation offloading scheme.
Multi-user Multi-task Computation Offloading in Green Mobile Edge Cloud Computing We first formulate the multi-user multi-task offloading problem for green MECC and use the Lyaponuv Optimization Approach to determine the energy harvesting policy: how much energy to harvest at each WD. And the task offloading schedule: the set of computation offloading requests to be admitted into the mobile edge cloud, the set of WDs assigned to each admitted download request, and how much workload to be processed on the assigned WDs. We then prove that the problem of offloading scheduling is NP hard, and introduce centralized and distributed Greedy Maximum Scheduling algorithms to solve the problem efficiently.
System Configuration
H/W System Configuration
Speed : 1.1 GHz
RAM : 256 MB(min)
Hard Disk : 20 GB
Floppy Drive : 1.44 MB
Key Board : Standard Windows Keyboard
Mouse : Two or Three Button Mouse
Monitor : SVGA
S/W System Configuration
Platform : cloud computing
Operating system : Windows Xp,7,
Server : WAMP/Apache
Working on : Browser Like Firefox, IE
Conclusion
In this thesis, we addressed the issue of multi-user computing offloading in Mobile Edge Computing (MEC). We considered the execution of emerging mobile collaborative applications through MEC offloading, D2D offloading and hybrid offloading. To this end, we split the applications into several loosely coupled software components and studied fine-grained computing offloading strategies for components in the MEC network.
The problems of offloading decision-making in hand require combinatorial optimization and are NP-hard. We have proposed several effective strategies to overcome the great complexity involved. We will summarize our main contributions hereafter.
