Abstract
Nowadays, research in the field of task offloading not only pays attention to the computing performance of computing nodes, but also pays attention to the impact of energy consumption on computing performance. The current multi-core frequency conversion processors mainly use dynamic voltage frequency scaling (DVFS) technology to reduce processor frequency and energy consumption. The most representative is ARM’s big.LITTLE which is a heterogeneous processor architecture, with large cores having strong performance and small cores being more energy efficient. In this article, we aim to implement energy-saving task offloading based on the big.LITTLE processor. We regard multi-core and multi-edge clouds as multiple computing nodes with different computing capabilities and express the task offloading problem as a computing resource scheduling problem. We modeled the calculation time and energy consumption through a non-cooperative game and proved that the non-cooperative game is an accurate potential game. The theory proves that non-cooperative games have at least one pure strategy Nash equilibrium point (NEP). We designed an edge cloud multi-user computing resource scheduling algorithm based on big.LITTLE processor (MUCRS). The simulation experiment results show that the MUCRS algorithm can obtain a suitable task offloading scheme, and the time and energy cost are lower than several similar algorithms.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-021-04146-z/MediaObjects/11227_2021_4146_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-021-04146-z/MediaObjects/11227_2021_4146_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-021-04146-z/MediaObjects/11227_2021_4146_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-021-04146-z/MediaObjects/11227_2021_4146_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-021-04146-z/MediaObjects/11227_2021_4146_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-021-04146-z/MediaObjects/11227_2021_4146_Fig6_HTML.png)
Similar content being viewed by others
Data Availability Statement
Generate random numbers to check validity; see Sect. 6.1 for details. Therefore, no supporting datasets are available.
References
Zhao D, Mohamed M, Ludwig H (2020) Locality-aware scheduling for containers in cloud computing. IEEE Trans Cloud Comput 8:635–646
Haratian P, Safi-Esfahani F, Salimian L, Nabiollahi A (2019) An adaptive and fuzzy resource management approach in cloud computing. IEEE Trans Cloud Comput 7:907–920
Narkhede BE, Raut RD, Narwane VS, Gardas BB (2020) Cloud computing in healthcare—a vision, challenges and future directions. Int J Coop Inf Syst 34:1–39
Ferrer AJ, Marquès JM, Jorba J (2019) Towards the decentralised cloud: survey on approaches and challenges for mobile, ad hoc, and edge computing. ACM Comput Surv 51:111:1-111:36
Shirazi SN, Gouglidis A, Farshad A, Hutchison D (2017) The extended cloud: review and analysis of mobile edge computing and fog from a security and resilience perspective. IEEE J Sel Areas Commun 35:2586–2595
Tang H, Li D, Wan J, Imran M, Shoaib M (2020) A reconfigurable method for intelligent manufacturing based on industrial cloud and edge intelligence. IEEE Internet Things J 7:4248–4259
Song F, Zhu M, Zhou Y, You I, Zhang H (2020) Smart collaborative tracking for ubiquitous power iot in edge-cloud interplay domain. IEEE Internet Things J 7:6046–6055
Ebrahimzadeh A, Maier M (2020) Cooperative computation offloading in FiWi enhanced 4G hetnets using self-organizing MEC. IEEE Trans Wirel Commun 19:4480–4493
Amani N, Pedram H, Taheri H, Parsaeefard S (2019) Energy-efficient resource allocation in heterogeneous cloud radio access networks via BBU offloading. IEEE Trans Veh Technol 68:1365–1377
Cheng F, Zhang S, Li Z, Chen Y, Zhao N, Yu FR, Leung VCM (2018) UAV trajectory optimization for data offloading at the edge of multiple cells. IEEE Trans Veh Technol 67:6732–6736
Alharbi HA, Aldossary M (2021) Energy-efficient edge-fog-cloud architecture for iot-based smart agriculture environment. IEEE Access 9:110480–110492
Liu X, Liu J, Wu H (2021) Energy-efficient task allocation of heterogeneous resources in mobile edge computing. IEEE Access 9:119700–119711
Guo S, Liu J, Yang Y, Xiao B, Li Z (2019) Energy-efficient dynamic computation offloading and cooperative task scheduling in mobile cloud computing. IEEE Trans Mobile Comput 18:319–333
Jiang Y, Chen Y, Yang S, Wu C (2019) Energy-efficient task offloading for time-sensitive applications in fog computing. IEEE Syst J 13:2930–2941
Stepanovic S, Georgakarakos G, Holmbacka S, Lilius J (2020) An efficient model for quantifying the interaction between structural properties of software and hardware in the ARM big.little architecture. Concurr Comput Practice Exp 32
Panneerselvam S, Swift MM (2016) Rinnegan: efficient resource use in heterogeneous architectures. In: International Conference on Parallel Architectures and Compilation, Sept, pp 373–386
Geng Y, Yang Y, Cao G (2018) Energy-efficient computation offloading for multicore-based mobile devices. In: IEEE Conference on Computer Communications, April, pp 46–54
Zheng R, Liu K, Zhu J, Zhang M, Wu Q (2019) Stochastic resource scheduling via bilayer dynamic markov decision process in mobile cloud networks. Comput Commun 145:234–242
Liang Z, Liu Y, Lok T, Huang K (2019) Multiuser computation offloading and downloading for edge computing with virtualization. IEEE Trans Wirel Commun 18:4298–4311
Zeng J, Wang Q, Liu J, Chen J, Chen H (2019) A potential game approach to distributed operational optimization for microgrid energy management with renewable energy and demand response. IEEE Trans Indust Electron 66:4479–4489
Behera HS (2011) Experimental analysis of new fair-share scheduling algorithm with weighted time slice for real time systems. J Global Res Comput Sci 2
Rzadca K, Yong JTT, Datta A (2010) Multi-objective optimization of multicast overlays for collaborative applications. Comput Netw 54:1986–2006
Cao H, Cai J (2018) Distributed multiuser computation offloading for cloudlet-based mobile cloud computing: a game-theoretic machine learning approach. IEEE Trans Veh Technol 67:752–764
Chen M, Hao Y (2018) Task offloading for mobile edge computing in software defined ultra-dense network. IEEE J Sel Areas Commun 36:587–597
Cardellini V, Persone VDN, Valerio VD, Facchinei F, Grassi V, Presti FL, Piccialli V (2016) A game-theoretic approach to computation offloading in mobile cloud computing. Math Programm 157:421–449
Chen X (2015) Decentralized computation offloading game for mobile cloud computing. IEEE Trans Parallel Distrib Syst 26:974–983
Catalán S, Rodríguez-Sánchez R, Quintana-Ortí ES, Herrero JR (2017) Static versus dynamic task scheduling of the lu factorization on ARM big. LITTLE architectures. In: IEEE International Parallel and Distributed Processing Symposium Workshops, May–June, pp 733–742
Tang L, Yan J, Sun Z (2015) Towards high-performance packet processing on commodity multi-cores: current issues and future directions, Science China. Inf Sci 58:1–16
Qin Y, Zeng G, Kurachi R, Matsubara Y, Takada H (2019) Execution-variance-aware task allocation for energy minimization on the big.little architecture. Sustain Comput Inform Syst 22:155–166
Wolff W, Porter B (2020) Performance optimization on big.little architectures: a memory-latency aware approach. In: ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems, June, pp 51–61
Nam Y, Park M (2018) Energy-aware core switching for big.little multicore mobile platform. In: IEEE International Conference on Consumer Electronics, Jan, pp 1–2
Meng X, Wang W, Wang Y, Lau VKN, Zhang Z (2019) Closed-form delay-optimal computation offloading in mobile edge computing systems. IEEE Trans Wirel Commun 18:4653–4667
Li M, Wu Q, Zhu J, Zheng R, Zhang M (2018) A computing offloading game for mobile devices and edge cloud servers. Wirel Commun Mobile Comput 2179316(1–2179316):10
Wang Y, Tao X, Zhang X, Zhang P, Hou YT (2019) Cooperative task offloading in three-tier mobile computing networks: an ADMM framework. IEEE Trans Veh Technol 68:2763–2776
Li Y, Xu G, Ge J, Liu P, Fu X, Jin Z (2020) Jointly optimizing helpers selection and resource allocation in D2D mobile edge computing. In: IEEE Wireless Communications and Networking Conference, May 25–28, pp 1–6
Yoo S (2015) An empirical validation of power-performance scaling: DVFS vs. multi-core scaling in big.little processor. IEICE Electron Express 12:20150236
Acknowledgements
The authors thank the editors and anonymous reviewers for their valuable comments and suggestions.
Funding
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants No. 61976243 and No. 61602155, in part by the Scientific and Technological Innovation Team of Colleges and Universities in Henan Province under Grant No. 20IRTSTHN018, and in part by the basic research projects in the University of Henan Province under Grant No. 19zx010.
Author information
Authors and Affiliations
Contributions
JZ contributed to writing, theoretical analysis, review and editing, and funding acquisition. RZ helped in methodology, supervision, project administration and funding acquisition. XZ provided resources, validation and software. JX contributed to validation and software. QW helped in conceptualization and review.
Corresponding author
Ethics declarations
Conflict of interests
The authors declare that they have no conflict of interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhang, J., Zheng, R., Zhao, X. et al. A computational resources scheduling algorithm in edge cloud computing: from the energy efficiency of users’ perspective. J Supercomput 78, 9355–9376 (2022). https://doi.org/10.1007/s11227-021-04146-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-021-04146-z