Introduction
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Both MD and ES have been taken into consideration in this study. More specifically, the time consumption and energy consumption of MDs, as well as resource utilization and load balancing of ES cluster are regarded as the optimization objectives.
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A multi-objective optimization model is built to represent the optimization problem, deadline constraint is considered to ensure the safety of delay-sensitive applications.
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We implement a new optimization method on the basis of many-objective metaheuristic based on the R2 Indicator to obtain the reasonable candidate strategies. And then simple additive weighting (SAW) and multi-criteria decision-making (MCDM) are utilized to obtain the optimal computation offloading strategy.
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We conduct a large number of experimental evaluations to verify the effectiveness and superiority of our proposed method in comparison with other methods in different scenarios.
Related work
System model and problem formulation
System model
Time consumption model
Executing time
Waiting time
Propagation time
Transmission time
Total time consumption
Energy consumption model
Executing energy consumption
Waiting energy consumption
Propagation energy consumption
Transmission energy consumption
Total energy consumption
Resource utilization model
Load balancing model
Problem formulation
Algorithm design
Initialization
Crossover and mutation
R2 ranking and reference points
Selection
Optimal selection
Method overview
Comparison and analysis of experimental results
Experimental setting
Parameter | Value |
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The active power of MD | 0.6 W |
The idle power of MD | 0.01 W |
The transmitted power of MD | 0.5 W |
The propagation time of LAN | 2 ms |
The propagation time of WAN | 20 ms |
The bandwidth of LAN | 200 kps |
The bandwidth of WAN | 150 kps |
The computing frequency of MD | 500 Mhz |
The computing frequency of the remote cloud | 2000 MHz |
The computing frequency of the ESs | 800 MHz |
The number of ESs in multi-user experiment | 5 |
The number of ESs in multi-application experiment | 5 |
The number of ESs in large-scale application experiment | 20 |
The value of the time constraint for each application | 4.8 |
The maximum number of VMs in ES | 25 |
The minimum number of VMs in ES | 15 |
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Benchmark All applications are executed locally, or are offloaded to ES cluster, or cloud randomly. Especially, the first task and last task of each MU are executed locally by MD.
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First Come First Service (FCFS) All applications are executed sequentially, namely, the first one is executed locally, the second one is executed in ES1, and so on, the N+1−th one is executed in cloud. Similarly to Benchmark, the last task of each MU are executed locally.