Abstract
The network buffer is a key factor affecting the communication efficiency of a multi-core embedded real-time system protocol stack. Therefore, it requires that the buffer structure should be simple, effective, stable and easy to manage. The buffer mBlk in the Vxworks system is a more complex buffer designed based on the buffer pool. Its advantage is that the data is shared by reference, so as to achieve “zero copy” and improve efficiency. However, the disadvantage is that its structure is too complicated and trouble to use. Therefore, according to the actual project requirements, this paper first improves its complex structure and designs a network buffer named sbuf. Secondly, according to the problem of insufficient use of the designed buffer management mechanism in multithreading, a network buffer allocation algorithm is designed to solve this problem. Finally, the designed network buffer is tested under different multi-core scheduling algorithms on litmus RT multi-core platform. The results show that the performance of the improved buffer has been significantly improved in three aspects: response time, preemption and execution time.













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References
Ahmed S, Anderson JH (2020) A soft-real-time-optimal semi-clustered scheduler with a constant tardiness bound. https://doi.org/10.1109/RTCSA50079.2020.9203605
Asheralieva A, Miyanaga Y (2016) QoS-oriented mode, spectrum, and power allocation for D2D communication underlaying LTE-a network. IEEE Trans Veh Technol 65(12). https://doi.org/10.1109/TVT.2016.2531290
Chen H, Zhu X, Liu G (2018) Uncertainty-aware online scheduling for real-time workflows in cloud service environment. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2018.2866421
Chwa HS, Lee J, Lee J et al (2017) Global EDF schedulability analysis for parallel tasks on multi-core platforms. IEEE Trans Parallel Distrib Syst 28(5):1331–1344
Díaz NL, Luna AC, Vasquez JC (2017) Centralized control architecture for coordination of distributed renewable generation and energy storage in islanded AC microgrids. IEEE Trans Power Electron 32(7). https://doi.org/10.1109/TPEL.2016.2606653
Elwalid A, Jin C, Low S (2001) MATE: MPLS adaptive traffic engineering. In Proceedings - IEEE INFOCOM 3. https://doi.org/10.1109/infcom.2001.916625
Feng WC, Shin KG, Kandlur DD, Saha D (2002) The Blue active queue management algorithms. IEEE/ACM Trans Netw 10(4). https://doi.org/10.1109/TNET.2002.801399
Gang H, Hai-bo Z, Natale MD et al (2014) Experimental evaluation and selection of data consistency mechanisms for hard real-time applications on multicore platforms. IEEE Trans Industr Inform 10(2): 903–918
Greenstein B, Estrin D, Govindan R et al (2003) DIFS: a distributed index for features in sensor networks. ∥1st IEEE International Workshop on Sensor Network Protocols and Applications Anchorage: IEEE, 333–349
Haque MS, Easwaran A (2018) Predictability and performance aware replacement strategy PVISAM for unified shared caches in real-time multicores. IEEE Trans Comput Aided Design Integrated Circ Syst 37(11):2720–2731
Katevenis M, Sidiropoulos S, Courcoubetis C (1991) Weighted round-robin cell multiplexing in a general-purpose ATM switch chip. IEEE J Sel Areas Commun 9(8). https://doi.org/10.1109/49.105173
Kedar G, Mendelson A, Cidon I (2017) SPACE: semi-partitioned cache for energy efficient, hard real-time systems. IEEE Trans Comput 66(4):717–730
Lai CF, Chang YC, Chao HC (2017) A buffer-aware QoS streaming approach for SDN-enabled 5G vehicular networks. IEEE Commun Mag 55(8). https://doi.org/10.1109/MCOM.2017.1601142
Li X, Wan J, Dai HN (2019) A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing. IEEE Trans Ind Inform 15(7). https://doi.org/10.1109/TII.2019.2899679
Liu J, Mao Y, Zhang J (2016) Delay-optimal computation task scheduling for mobile-edge computing systems. in IEEE International Symposium on Information Theory - Proceedings, vol. 2016-August. https://doi.org/10.1109/ISIT.2016.7541539
Melani A, Bertogna M, Bonifaci V et al (2017) Schedulability analysis of conditional parallel task graphs in multicore systems. IEEE Trans Comput 66(2):339–353
Merl R, Graham P (2016) A low-cost, radiation-hardened single-board computer for command and data handling. in IEEE Aerospace Conference Proceedings vol. 2016-June. https://doi.org/10.1109/AERO.2016.7500849
Merl R, Graham P (2018) Radiation-hardened SpaceVPX system controller. in IEEE Aerospace Conference Proceedings, vol. 2018-March. https://doi.org/10.1109/AERO.2018.8396380
Pathan R, Voudouris P, Stenstrom P (2018) Scheduling parallel real-time recurrent tasks on multicore platforms. IEEE Trans Parallel Distrib Syst 29(4):915–928
Phan H, Andreotti F, Cooray N (2019) Joint classification and prediction CNN framework for automatic sleep stage classification. IEEE Trans Biomed Eng 66(5). https://doi.org/10.1109/TBME.2018.2872652
Sharma V, Mukherji U, Joseph V (2010) Optimal energy management policies for energy harvesting sensor nodes. IEEE Trans Wirel Commun 9(4). https://doi.org/10.1109/TWC.2010.04.080749
Supratak A, Dong H, Wu C (2017) DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans Neural Syst Rehabil Eng 25(11). https://doi.org/10.1109/TNSRE.2017.2721116
Takeuchi H, Kage E, Sawata M (2001) Identification of a novel gene, Mblk-1 , that encodes a putative transcription factor expressed preferentially in the large-type Kenyon cells of the honeybee brain. Insect Mol Biol 10(5)
Wind River Systems (2011) High-performance multi-core networking software design options[R]. White Paper Intel
Xiang L, Ng DWK, Islam T (2017) Cross-layer optimization of fast video delivery in cache- and buffer-enabled relaying networks. in IEEE Transactions on Vehicular Technology 66(12). https://doi.org/10.1109/TVT.2017.2720481
Xiong Y, Vandenhoute M, Cankaya HC (2000) Control architecture in optical burst-switched WDM networks. IEEE J Sel Areas Commun 18(10). https://doi.org/10.1109/49.887906
Yuehong Z (2019) Construction of network protocol vulnerability analysis and testing platform. Softw Eng 22(11):35–38
Zhang D et al (2018) Resource allocation for green cloud radio access networks with hybrid energy supplies. IEEE Trans Veh Technol 67(2). https://doi.org/10.1109/TVT.2017.2754273
Zhao N, Schofield N, Niu W (2016) Energy storage system for a port crane hybrid power-train. IEEE Trans Transp Electrif 2(4). https://doi.org/10.1109/TTE.2016.2562360
Zhao Y, Sanan D, Zhang F (2019) Refinement-based specification and security analysis of separation kernels. IEEE Trans Dependable Secur Comput 16(1). https://doi.org/10.1109/TDSC.2017.2672983
Funding
This work was supported by the Special project of Shaanxi Provincial Department of Education under grant No.17JK0388. This work was supported by the General Project of Key Research and Development Plan of Shaanxi Province, China (No.2022GY-119).
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Conceptualization: Pingping Liu; Methodology: Jiaxing Lu; Formal analysis and investigation: Pingping Liu; Writing - original draft preparation: Pingping Liu, Jiaxing Lu; Writing - review and editing: Ping Lu; Funding acquisition: Jianguo Wang; Resources: Ping Lu; Supervision: Shujuan Huang.
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Liu, P., Lu, J., Huang, S. et al. Real-time performance analysis of network buffer under multi-core scheduling platform. Multimed Tools Appl 82, 34653–34677 (2023). https://doi.org/10.1007/s11042-023-14820-4
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DOI: https://doi.org/10.1007/s11042-023-14820-4