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A Multi-Objective Compounded Local Mobile Cloud Architecture Using Priority Queues to Process Multiple Jobs.

Xiaohui Wei1,2, Bingyi Sun1, Jiaxu Cui3

  • 1College of Computer Science and Technology, Jilin University, Changchun, China.

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|July 16, 2016
PubMed
Summary
This summary is machine-generated.

Mobile cloud computing faces latency issues due to distance. A new Compounded Local Mobile Cloud Architecture with Dynamic Priority Queues (LMCpri) reduces processing time and cost by prioritizing jobs effectively.

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Area of Science:

  • Computer Science
  • Mobile Computing
  • Cloud Computing

Background:

  • Increased mobile device usage reveals limitations of portable computing.
  • Mobile cloud computing offers solutions but faces challenges with distant cloud data centers.
  • Local mobile cloud architectures aim to mitigate latency issues.

Purpose of the Study:

  • To propose an improved local mobile cloud model, the Compounded Local Mobile Cloud Architecture with Dynamic Priority Queues (LMCpri).
  • To enhance job scheduling and execution efficiency in mobile cloud environments.
  • To reduce processing time and requester costs compared to existing methods.

Main Methods:

  • Development of the LMCpri architecture featuring dynamic priority queues based on auction processing.
  • Implementation of the NSGA-II scheduling algorithm within the LMCpri's Scheduling Module.
  • Comparative simulation experiments against PSO, sequential scheduling, and traditional cloud assisting architectures.

Main Results:

  • The NSGA-II algorithm with 30 iterations proved optimal for the LMCpri system.
  • LMCpri demonstrated superior performance over LMCque by accommodating prioritized job execution and reducing overall execution time.
  • LMCpri exhibited a significant performance advantage compared to standard cloud assisting architectures.

Conclusions:

  • The LMCpri architecture effectively addresses latency and efficiency challenges in mobile cloud computing.
  • Dynamic priority queuing and advanced scheduling algorithms like NSGA-II are crucial for optimizing local mobile cloud performance.
  • LMCpri offers a promising alternative to traditional cloud assisting models for mobile applications.