Contact Project Developer Ashish D. Tiwari []
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Prioritization of Overflow Tasks to Improve Performance of Mobile Cloud

Prioritization of Overflow Tasks to Improve Performance of Mobile Cloud

Prioritization of Overflow Tasks to Improve Performance of Mobile Cloud


Mobile devices may offload their applications to avirtual machine running on a cloud host. This application mayfork new tasks which require virtual machines of their own onthe same physical machine. Achieving satisfactory performancelevel in such a scenario requires flexible resource allocationmechanisms in the cloud data center. In this paper we presenttwo such mechanisms which use prioritization:one in which forked tasks are given full priority over newlyarrived tasks, and another in which a threshold is establishedto control the priority so that full priority is given to the forkedtasks if their number exceeds a predefined threshold.We analyze the performance of both mechanisms using a Markovianmultiserver queueing system with two priority levels tomodel the resource allocation process, and a multi-dimensionalMarkov system based on a Birth-Death queueing system withfinite population, to model virtual machine provisioning. Ourperformance results indicate that the threshold-based priorityscheme not only performs better, but can also be tuned to achievethe desired performance level.



Proposed solutions to address the issues of computational power. Personal use is permitted, but republication/redistribution requires  mobile devices by offloading computing tasks has been an approach for extending the concept of VM-based clone cloud offloading from LAN surrogates to cloud servers.

Proposed a polynomial time approximation scheme (FPTAS) algorithm to solve thelatency problem. The model proposed in is based on the wireless network cloud concept and a multi-objective linear optimization approach using an event-based finite state model and dynamic constraint programming method has been used to determine the appropriate transmission power, cloud offloading and optimum QoS profiles.

Proposed a randomized auction mechanism based on an application of smoothed analysis and randomized reduction, for dynamic distributed cloud data centers


o Heuristic algorithm.

o Polynomial time approximation  algorithm

o Decision algorithm

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