Keqin Li
Abstract
The investigation in this article makes the following important contributions to combinatorial optimization of computation offloading in fog computing. First, we rigorously define the two problems of optimal computation offloading with energy constraint and optimal computation offloading with time constraint. We do this in such a way that between execution time and energy consumption, we can fix one and minimize the other. We prove that our optimization problems are NP-hard, even for very special cases. Second, we develop a unique and effective approach for solving the proposed combinatorial optimization problems, namely, a two-stage method. In the first stage, we generate a computation offloading strategy. In the second stage, we decide the computation speed and the communication speeds. This method is applicable to both optimization problems. Third, we use a simple yet efficient greedy method to produce a computation offloading strategy by taking all aspects into consideration, including the properties of the communication channels, the power consumption models of computation and communication, the tasks already assigned and allocated, and the characteristics of the current task being considered. Fourth, we experimentally evaluate the performance of our heuristic algorithms. We observe that while various heuristics do exhibit noticeably different performance, there can be a single and simple heuristic that can perform very well. Furthermore, the method of compound algorithm can be applied to obtain slightly improved performance. Fifth, we emphasize that our problems and algorithms can be easily extended to study combined performance and cost optimization (such as cost–performance ratio and weighted cost-performance sum optimization) and to accommodate more realistic and complicated fog computing environments (such as preloaded mobile edge servers and multiple users) with little extra effort. To the best of our knowledge, there has been no similar study in the existing fog computing literature.
- A. Bhattacharya and P. De. 2017. A survey of adaptation techniques in computation offloading. J. Netw. Comput. Appl. 78 (2017), 97--115.Google Scholar
Digital Library
- R. L. Burden, J. D. Faires, and A. C. Reynolds. 1981. Numerical Analysis (2nd ed.). Prindle, Weber 8 Schmidt, Boston, MA.Google Scholar
- J. Cao, K. Li, and I. Stojmenovic. 2014. Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. IEEE Trans. Comput. 63, 1 (2014), 45--58.Google ScholarDigital Library
- J. Chen, K. Li, Q. Deng, S. Yu, K. Li, and P. S. Yu. 2020. QoE-aware computation offloading game algorithm for 5G mobile edge computing. (unpublished).Google Scholar
- W. Chen, D. Wang, and K. Li. 2019. Multi-user multi-task computation offloading in green mobile edge cloud computing. IEEE Trans. Serv. Comput. 12, 5 (2019), 726--738.Google ScholarCross Ref
- M. R. Garey and D. S. Johnson. 1979. Computers and Intractability —A Guide to the Theory of NP-Completeness, W. H. Freeman and Company, New York.Google Scholar
- Y. He, F. R. Yu, N. Zhao, V. C. M. Leung, and H. Yin. 2017. Software-defined networks with mobile edge computing and caching for smart cities: A big data deep reinforcement learning approach. IEEE Comm. Mag. 55, 12 (2017), 31--37.Google ScholarDigital Library
- J. Hu, K. Li, C. Liu, A. T. Chronopoulos, and K. Li. 2020. Game-based task offloading of multi-MD with QoS in MEC systems of limited computation capacity. ACM Trans. Embed. Comput. Syst. 19, 4 (2020).Google ScholarDigital Library
- Y.-H. Kao, B. Krishnamachari, M.-R. Ra, and F. Bai. 2017. Hermes: Latency optimal task assignment for resource-constrained mobile computing. IEEE Trans. Mobile Comput. 16, 11 (2017), 3056--3069.Google ScholarDigital Library
- M. A. Khan. 2015. A survey of computation offloading strategies for performance improvement of applications running on mobile devices. J. Netw. Comput. Appl. 56 (2015), 28--40.Google ScholarDigital Library
- K. Kumar, J. Liu, Y.-H. Lu, and B. Bhargava. 2013. A survey of computation offloading for mobile systems. Mobile Netw. Appl. 18, 1 (2013), 129--140.Google ScholarDigital Library
- K. Li. 2012. Scheduling precedence constrained tasks with reduced processor energy on multiprocessor computers. IEEE Trans. Comput. 61, 12 (2012), 1668--1681.Google ScholarDigital Library
- K. Li. 2018. A game theoretic approach to computation offloading strategy optimization for non-cooperative users in mobile edge computing. IEEE Trans. Sust. Comput. (September 2018). DOI:10.1109/TSUSC.2018.2868655Google ScholarCross Ref
- K. Li. 2019a. Computation offloading strategy optimization with multiple heterogeneous servers in mobile edge computing. IEEE Trans. Sust. Comput. (March 2019). DOI:10.1109/TSUSC.2019.2904680Google Scholar
- K. Li. 2019b. How to stabilize a competitive mobile edge computing environment: A game theoretic approach. IEEE Access 7, 1 (2019), 69960--69985.Google ScholarCross Ref
- W. Lin, F. Shi, W. Wu, G. Wu, A.-A. Mohammed, and K. Li. 2020. A taxonomy and survey of power models and power modeling for cloud servers. ACM Computing Surveys 53, 5, Article 100 (October 2020), 1--41.Google Scholar
- C. Liu, K. Li, J. Liang, and K. Li. 2019a. COOPER-SCHED: A cooperative scheduling framework for mobile edge computing with expected deadline guarantee. IEEE Trans. Parallel Distrib. Syst. (June 2019). DOI:10.1109/TPDS.2019.2921761Google Scholar
- C. Liu, K. Li, J. Liang, and K. Li. 2019b. COOPER-MATCH: Job offloading with a cooperative game for guaranteeing strict deadlines in MEC. IEEE Trans. Mobile Comput. (June 2019). DOI:10.1109/TMC.2019.2921713Google Scholar
- P. Mach and Z. Becvar. 2017. Mobile edge computing: A survey on architecture and computation offloading. IEEE Commun. Surv. Tutor. 19, 3 (2017), 1628--1656.Google ScholarDigital Library
- Y. Mao, J. Zhang, and K. B. Letaief. 2016a. Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE J. Select. Areas Commun. 34, 12 (2016), 3590--3604.Google ScholarDigital Library
- Y. Mao, J. Zhang, and K. B. Letaief. 2017. Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems. Proceedings of the IEEE Wireless Communications and Networking Conference.Google Scholar
- Y. Mao, J. Zhang, S. H. Song, and K. B. Letaief. 2016b. Power-delay tradeoff in multi-user mobile-edge computing systems. In IEEE Global Communications Conference.Google Scholar
- G. Mitsis, P. A. Apostolopoulos, E. E. Tsiropoulou, and S. Papavassiliou. 2019. Intelligent dynamic data offloading in a competitive mobile edge computing market. Fut. Internet 11, 5 (2019), 118.Google ScholarCross Ref
- H. Shah-Mansouri, V. W. S. Wong, and R. Schober. 2017. Joint optimal pricing and task scheduling in mobile cloud computing systems. IEEE Trans. Wireless Commun. 16, 8 (2017), 5218--5232.Google ScholarDigital Library
- M. Shiraz, M. Sookhak, A. Gani, S. A. A. Shah. 2015. A study on the critical analysis of computational offloading frameworks for mobile cloud computing. J. Netw. Comput. Appl. 47 (2015), 47--60.Google ScholarDigital Library
- C. Singhal and S. De, eds. 2017. Resource Allocation in Next-Generation Broadband Wireless Access Networks. IGI Global, Hershey, United States.Google Scholar
- J. Stewart. 1991. Multivariable Calculus (2nd ed.). Brooks/Cole, Pacific Grove, CA.Google Scholar
- H. Tan, Z. Han, X.-Y. Li, and F. C. M. Lau. 2017. Online job dispatching and scheduling in edge-clouds. In Proceedings of the 36th IEEE Conference on Computer Communications.Google ScholarCross Ref
- L. Tong, Y. Li, and W. Gao. 2016. A hierarchical edge cloud architecture for mobile computing. In Proceedings of the 35th Annual IEEE International Conference on Computer Communications.Google Scholar
- T. X. Tran and D. Pompili. 2017. Joint task offloading and resource allocation for multi-server mobile-edge computing networks. arXiv:1705.00704v1. https://arxiv.org/abs/1705.00704.Google Scholar
- G. Xie, G. Zeng, R. Li, and K. Li. 2019. Scheduling Parallel Applications on Heterogeneous Distributed Systems. Springer Nature Singapore Pte Ltd.Google Scholar
- Y. Xu, K. Li, L. He, L. Zhang, and K. Li. 2015. A hybrid chemical reaction optimization scheme for task scheduling on heterogeneous computing systems. IEEE Trans. Parallel Distrib. Syst. 26, 12 (2015), 3208--3222.Google ScholarDigital Library
- K. Zhang, Y. Mao, S. Leng, Q. Zhao, L. Li, X. Peng, L. Pan, S. Maharjan, and Y. Zhang. 2016. Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks. IEEE Access 4 (2016), 5896--5907.Google ScholarCross Ref
Index Terms
- Heuristic Computation Offloading Algorithms for Mobile Users in Fog Computing
Recommendations
A Heuristic Algorithm for Multi-site Computation Offloading in Mobile Cloud Computing
Due to limitation of mobile device in terms of battery life and processing power, Mobile Cloud Computing (MCC) has become an attractive choice to leverage this shortcoming as the mobile computation could be offloaded to the cloud, which is so-called ...
Cost‐efficient computation offloading in UAV‐enabled edge computing
With the popularity of computationally intensive applications, more and more computing resources are required. Mobile edge computing (MEC) is widely applied as an effective method to meet the increasing computing demands. In a relatively stable state, MEC ...
A survey on computation offloading and service placement in fog computing-based IoT
AbstractIn recent years, fog computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited Internet of Things (IoT) devices. The main feature of fog computing is to push ...
Comments