Optimizing Mobile Cloud Computing: A Comparative Analysis and Innovative Cost-Efficient Partitioning Model
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| Publicat a: | SN Computer Science vol. 6, no. 1 (Jan 2025), p. 43 |
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| Publicat: |
Springer Nature B.V.
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 2662-995X | ||
| 022 | |a 2661-8907 | ||
| 024 | 7 | |a 10.1007/s42979-024-03570-1 |2 doi | |
| 035 | |a 3149316457 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 245 | 1 | |a Optimizing Mobile Cloud Computing: A Comparative Analysis and Innovative Cost-Efficient Partitioning Model | |
| 260 | |b Springer Nature B.V. |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Mobile cloud computing (MCC) integrates mobile devices with cloud computing to enhance performance and provide scalable services. It enables the partitioning of computationally intensive mobile applications, utilizing the cloud resources for remote execution. This process is vital for optimizing resource utilization and energy efficiency in distributed computing environments. Initially, static application partitioning was developed, dividing system resources into fixed-sized partitions. Though it is simple and reduces operational overhead, it lacks adaptability to dynamic computing environments due to mobility. Modern frameworks use runtime partitioning, dynamically allocating resources based on runtime profiling, which increases computational overhead and energy consumption. Despite various studies on MCC, there is a notable oversight in numerical analysis of application partitioning. This paper presents a comparative numerical study of existing partitioning techniques and proposes a Cost-Efficient Partitioning (CEP) model. The CEP model combines the static nature to reduce computational overhead with the dynamic nature to address runtime challenges. This research addresses the shortcomings of existing approaches, potentially transforming computational offloading into energy-saving solutions. Our results show that the execution time (ET) of a task in the CEP model is 0.826 s, which is longer than the ET of static partitioning however shorter than the 0.962 s of dynamic partitioning models. Similarly, the CEP's energy consumption in a test scenario is 0.609 J, slightly higher than the 0.587 J of static partitioning but lower than the 0.711 J of dynamic partitioning. | |
| 653 | |a Integer programming | ||
| 653 | |a Collaboration | ||
| 653 | |a Edge computing | ||
| 653 | |a Applications programs | ||
| 653 | |a Mathematical models | ||
| 653 | |a Costs | ||
| 653 | |a Mobile communications networks | ||
| 653 | |a Cloud computing | ||
| 653 | |a Numerical analysis | ||
| 653 | |a Optimization | ||
| 653 | |a Mobile computing | ||
| 653 | |a Computation offloading | ||
| 653 | |a Decomposition | ||
| 653 | |a Resource utilization | ||
| 653 | |a Energy consumption | ||
| 653 | |a Partitioning | ||
| 653 | |a Distributed processing | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Run time (computers) | ||
| 773 | 0 | |t SN Computer Science |g vol. 6, no. 1 (Jan 2025), p. 43 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3149316457/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3149316457/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3149316457/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |