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How to Optimize UIs in Unity: Slow Performance Causes and Solutions

ঐক্যে UIs কিভাবে অপ্টিমাইজ করা যায়: ধীর কর্মক্ষমতা কারণ এবং সমাধান

So optimieren Sie Benutzeroberflächen in Unity: Ursachen und Lösungen für langsame Leistung

Cómo optimizar las interfaces de usuario en Unity: causas y soluciones del bajo rendimiento

Comment optimiser les interfaces utilisateur dans Unity : causes et solutions des performances lentes

यूनिटी में UI को कैसे अनुकूलित करें: धीमी प्रदर्शन के कारण और समाधान

Unity で UI を最適化する方法: パフォーマンス低下の原因と解決策

Unity에서 UI를 최적화하는 방법: 성능 저하 원인 및 해결책

Como otimizar UIs no Unity: causas e soluções de desempenho lento

Как оптимизировать пользовательский интерфейс в Unity: причины низкой производительности и решения

Unity'de Kullanıcı Arayüzleri Nasıl Optimize Edilir: Yavaş Performans Nedenleri ve Çözümleri

Cách tối ưu hóa giao diện người dùng trong Unity: Nguyên nhân và giải pháp hiệu suất chậm

如何优化 Unity 中的 UI：性能缓慢的原因及解决方案

Temporary Lifetime Extension: Mistakes and Solutions

Authors:
(1) Vinod S. Khandkar and Manjesh K. Hanawal, Industrial Engineering and Operations Research Indian Institute of Technology Bombay, Mumbai, India and {vinod.khandkar, mhanawal}@iitb.ac.in. Table of Links Abstract & Introduction Related Work and Background Challenges in TD Detection Measurement Setup Development Case Study : Wehe - TD Detection Tool for Mobile Environment Shortcoming of Wehe on HTTPS Traffic TD Detection of HTTPS Traffic Conclusion & References VII. CONCLUSION Net neutrality violation detection is a need of an hour. As many of the ISPs are also content providers these days, they compete with each other, which can lead to one deliberately discriminating the services of the other to gain market share. However, users should have the freedom to choose services as per their wishes. Our work considered various challenges in the detection of traffic discrimination in HTTPS traffic. As a case study, we validated Wehe, one of the latest tools available to detect traffic differentiation. The described challenges helped us divide the entire tool into multiple interdependent components and validate them independently. Our validation using commercial traffic shaper revealed that traffic in Wehe setup may not mimic the characteristics of HTTPS traffic accessed from the original servers. Hence, middle-boxes may not subject them to intended discrimination. Thus, Wehe may not detect discrimination of HTTPS traffic. Our new method that uses the appropriate SNI parameter value in the initial TLS handshake message overcomes this shortcoming. Hence our work provided a mechanism to detect a wide range of possible discriminations on the Internet. REFERENCES [1] T. Garrett, L. E. Setenareski, L. M. Peres, L. C. E. Bona, and E. P. Duarte, “Monitoring network neutrality: A survey on traffic differentiation detection,” IEEE Communications Surveys Tutorials, vol. 20, no. 3, pp. 2486–2517, 2018. [2] V. Nguyen, D. Mohammed, M. Omar, and P. Dean, “Net neutrality around the globe: A survey,” in 2020 3rd International Conference on Information and Computer Technologies (ICICT), 2020, pp. 480–488. [3] Ramneek, P. Hosein, W. Choi, and W. Seok, “Detecting network neutrality violations through packet loss statistics,” in 2015 17th AsiaPacific Network Operations and Management Symposium (APNOMS), 2015, pp. 404–407. [4] D. Li, F. Tian, M. Zhu, L. Wang, and L. Sun, “A novel framework for analysis of global network neutrality based on packet loss rate,” in 2015 International Conference on Cloud Computing and Big Data (CCBD), 2015, pp. 297–304. [5] M. Dischinger, A. Mislove, A. Haeberlen, and K. P. Gummadi, “Detecting bittorrent blocking,” in Proceedings of the 8th ACM SIGCOMM Conference on Internet Measurement, Oct. 2008, pp. 3–8. [6] M. B. Tariq, M. Motiwala, N. Feamster, and M. Ammar, “Detecting network neutrality violations with causal inference,” in Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies, ser. CoNEXT ’09. New York, NY, USA: Association for Computing Machinery, 2009, p. 289–300. [Online]. Available: https://doi.org/10.1145/1658939.1658972 [7] R. Ravaioli, G. Urvoy-Keller, and C. Barakat, “Towards a general solution for detecting traffic differentiation at the internet access,” in 2015 27th International Teletraffic Congress, Sep. 2015, pp. 1–9. [8] Y. Zhang, Z. M. Mao, and M. Zhang, “Detecting traffic differentiation in backbone isps with netpolice,” in Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, ser. IMC ’09. New York, NY, USA: ACM, 2009, pp. 103–115. [Online]. Available: http://doi.acm.org/10.1145/1644893.1644905 [9] P. Kanuparthy and C. Dovrolis, “Shaperprobe: End-to-end detection of isp traffic shaping using active methods,” in Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, Nov. 2011, pp. 473–482. [10] U. Weinsberg, A. Soule, and L. Massoulie, “Inferring traffic shaping and policy parameters using end host measurements,” in Proceedings of IEEE INFOCOM, 2011. [11] M. Dischinger, M. Marcon, S. Guha, K. Gummadi, R. Mahajan, and S. Saroiu, “Glasnost: Enabling end users to detect traffic differentiation,” in 7th USENIX Conf. Networked Systems Design and Implementation, Apr. 2010, pp. 27–27. [12] V. Bashko, N. Melnikov, A. Sehgal, and J. Schonw ¨ alder, “Bonafide: A ¨ traffic shaping detection tool for mobile networks,” in 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), 2013, pp. 328–335. [13] A. Molavi Kakhki, A. Razaghpanah, A. Li, H. Koo, R. Golani, D. Choffnes, P. Gill, and A. Mislove, “Identifying traffic differentiation in mobile networks,” in Proceedings of the 2015 Internet Measurement Conference, Oct. 2015, pp. 239–251. [14] R. Narisetty and D. Gurkan, “Identification of network measurement challenges in openflow-based service chaining,” in 39th Annual IEEE Conference on Local Computer Networks Workshops, 2014, pp. 663– 670. [15] E. Balestrieri, L. De Vito, F. Lamonaca, F. Picariello, S. Rapuano, and I. Tudosa, “Research challenges in measurement for internet of things systems,” ACTA IMEKO, vol. 7, p. 82, 01 2019. [16] E. Karoly. (2000, Feb.) Qos performance validation in real life scenarios. [Online]. Available: http://archive.opengroup.org/tech/qos/ conference/q101/karoly.pdf [17] S. Molnar, P. Megyesi, and G. Szab ´ o, “How to validate traffic gen- ´ erators?” in 2013 IEEE International Conference on Communications Workshops (ICC), 2013, pp. 1340–1344. [18] G. Lu, Y. Chen, S. Birrer, F. E. Bustamante, C. Y. Cheung, and X. Li, “End-to-end inference of router packet forwarding priority,” in IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications, 2007, pp. 1784–1792. [19] R. Mahajan, M. Zhang, L. Poole, and V. Pai, “Uncovering performance differences among backbone isps with netdiff,” in Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, 2008, pp. 205–218. [20] (2008, Jun.) Ookla, measuring and understanding broadband : Speed, quality and application. [Online]. Available: https://www.ookla.com/ docs/UnderstandingBroadbandMeasurement.pdf [21] V. S. Khandkar and M. K. Hanawal, “Detection of traffic discrimination in the internet,” in 2020 International Conference on COMmunication Systems NETworkS (COMSNETS), 2020, pp. 677–679. [22] Tcpreplay: Pcap editing and replay tools for *nix. [Online]. Available: https://github.com/appneta/tcpreplay [23] W. Cui, V. Paxson, N. Weaver, and R. Katz, “Protocol-independent adaptive replay of application dialog,” in 13th Annual Network and Distributed System Security Symposium (NDSS), Feb. 2006, pp. 27–27. [24] R. T. El-Maghraby, N. M. Abd Elazim, and A. M. Bahaa-Eldin, “A survey on deep packet inspection,” in 2017 12th International Conference on Computer Engineering and Systems (ICCES), 2017, pp. 188–197. [25] M. AlSabah, K. Bauer, and I. Goldberg, “Enhancing tor’s performance using real-time traffic classification,” in Proceedings of the 2012 ACM Conference on Computer and Communications Security, ser. CCS ’12. New York, NY, USA: Association for Computing Machinery, 2012, p. 73–84. [Online]. Available: https://doi.org/10.1145/2382196.2382208 [26] Z. Hu, L. Zhu, J. Heidemann, A. Mankin, D. Wessels, and P. Hoffman, “The Transport Layer Security (TLS) Protocol Version 1.3,” Internet Requests for Comments, IETF, RFC 7858, August 2018. [Online]. Available: https://tools.ietf.org/pdf/rfc8446.pdf [27] C. Zhou, C. Lin, and Z. Guo, “mdash: A markov decision-based rate adaptation approach for dynamic http streaming,” IEEE Transactions on Multimedia, vol. 18, no. 4, pp. 738–751, Apr. 2016. [28] T. Joe, L. Eliot, M. Allison, K. Markku, O. Kumiko, S. Martin, E. Lars, M. Alexey, E. Wes, Z. Alexander, T. Brian, I. Jana, M. Allison, T. Michael, K. Eddie, and N. Yoshifumi. (2020, Sept) Service name and transport protocol port number registry. [Online]. Available: https://www.iana.org/assignments/ service-names-port-numbers/service-names-port-numbers.xhtml [29] G. Keinan. (2018, January) Optimizing enterprise networks through sd-avc (software define application visibility and control). [Online]. Available: https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2018/ pdf/BRKCRS-2502.pdf [30] A. Rao, J. Sherry, A. Legout, A. Krishnamurthy, W. Dabbous, and D. Choffnes, “Meddle: middleboxes for increased transparency and control of mobile traffic,” in CoNEXT Student Workshop, ACM, 12 2012, pp. 65–66. [31] R. Liu and X. Yu, “A survey on encrypted traffic identification,” in Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies, ser. CIAT 2020. New York, NY, USA: Association for Computing Machinery, 2020, p. 159–163. [Online]. Available: https://doi.org/10.1145/3444370.3444564 [32] F. Li, A. A. Niaki, D. Choffnes, P. Gill, and A. Mislove, “A large-scale analysis of deployed traffic differentiation practices,” in Proceedings of the ACM Special Interest Group on Data Communication, ser. SIGCOMM ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 130–144. [Online]. Available: https://doi.org/10.1145/3341302.3342092 [33] P. Srisuresh and K. Egevang, “Traditional IP Network Address Translator (Traditional NAT),” Internet Requests for Comments, IETF, RFC 3022, Jan 2001. [Online]. Available: https://tools.ietf.org/pdf/rfc3022.pdf This paper is available on arxiv under CC 4.0 license. Authors: (1) Vinod S. Khandkar and Manjesh K. Hanawal, Industrial Engineering and Operations Research Indian Institute of Technology Bombay, Mumbai, India and {vinod.khandkar, mhanawal}@iitb.ac.in. Authors: Authors: (1) Vinod S. Khandkar and Manjesh K. Hanawal, Industrial Engineering and Operations Research Indian Institute of Technology Bombay, Mumbai, India and {vinod.khandkar, mhanawal}@iitb.ac.in. Table of Links Abstract & Introduction Abstract & Introduction Related Work and Background Related Work and Background Challenges in TD Detection Measurement Setup Development Challenges in TD Detection Measurement Setup Development Case Study : Wehe - TD Detection Tool for Mobile Environment Case Study : Wehe - TD Detection Tool for Mobile Environment Shortcoming of Wehe on HTTPS Traffic Shortcoming of Wehe on HTTPS Traffic TD Detection of HTTPS Traffic TD Detection of HTTPS Traffic Conclusion & References Conclusion & References VII. CONCLUSION Net neutrality violation detection is a need of an hour. As many of the ISPs are also content providers these days, they compete with each other, which can lead to one deliberately discriminating the services of the other to gain market share. However, users should have the freedom to choose services as per their wishes. Our work considered various challenges in the detection of traffic discrimination in HTTPS traffic. As a case study, we validated Wehe, one of the latest tools available to detect traffic differentiation. The described challenges helped us divide the entire tool into multiple interdependent components and validate them independently. Our validation using commercial traffic shaper revealed that traffic in Wehe setup may not mimic the characteristics of HTTPS traffic accessed from the original servers. Hence, middle-boxes may not subject them to intended discrimination. Thus, Wehe may not detect discrimination of HTTPS traffic. Our new method that uses the appropriate SNI parameter value in the initial TLS handshake message overcomes this shortcoming. Hence our work provided a mechanism to detect a wide range of possible discriminations on the Internet. REFERENCES [1] T. Garrett, L. E. Setenareski, L. M. Peres, L. C. E. Bona, and E. P. Duarte, “Monitoring network neutrality: A survey on traffic differentiation detection,” IEEE Communications Surveys Tutorials, vol. 20, no. 3, pp. 2486–2517, 2018. [2] V. Nguyen, D. Mohammed, M. Omar, and P. Dean, “Net neutrality around the globe: A survey,” in 2020 3rd International Conference on Information and Computer Technologies (ICICT), 2020, pp. 480–488. [3] Ramneek, P. Hosein, W. Choi, and W. Seok, “Detecting network neutrality violations through packet loss statistics,” in 2015 17th AsiaPacific Network Operations and Management Symposium (APNOMS), 2015, pp. 404–407. [4] D. Li, F. Tian, M. Zhu, L. Wang, and L. Sun, “A novel framework for analysis of global network neutrality based on packet loss rate,” in 2015 International Conference on Cloud Computing and Big Data (CCBD), 2015, pp. 297–304. [5] M. Dischinger, A. Mislove, A. Haeberlen, and K. P. Gummadi, “Detecting bittorrent blocking,” in Proceedings of the 8th ACM SIGCOMM Conference on Internet Measurement, Oct. 2008, pp. 3–8. [6] M. B. Tariq, M. Motiwala, N. Feamster, and M. Ammar, “Detecting network neutrality violations with causal inference,” in Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies, ser. CoNEXT ’09. New York, NY, USA: Association for Computing Machinery, 2009, p. 289–300. [Online]. Available: https://doi.org/10.1145/1658939.1658972 [7] R. Ravaioli, G. Urvoy-Keller, and C. Barakat, “Towards a general solution for detecting traffic differentiation at the internet access,” in 2015 27th International Teletraffic Congress, Sep. 2015, pp. 1–9. [8] Y. Zhang, Z. M. Mao, and M. Zhang, “Detecting traffic differentiation in backbone isps with netpolice,” in Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, ser. IMC ’09. New York, NY, USA: ACM, 2009, pp. 103–115. [Online]. Available: http://doi.acm.org/10.1145/1644893.1644905 [9] P. Kanuparthy and C. Dovrolis, “Shaperprobe: End-to-end detection of isp traffic shaping using active methods,” in Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, Nov. 2011, pp. 473–482. [10] U. Weinsberg, A. Soule, and L. Massoulie, “Inferring traffic shaping and policy parameters using end host measurements,” in Proceedings of IEEE INFOCOM, 2011. [11] M. Dischinger, M. Marcon, S. Guha, K. Gummadi, R. Mahajan, and S. Saroiu, “Glasnost: Enabling end users to detect traffic differentiation,” in 7th USENIX Conf. Networked Systems Design and Implementation, Apr. 2010, pp. 27–27. [12] V. Bashko, N. Melnikov, A. Sehgal, and J. Schonw ¨ alder, “Bonafide: A ¨ traffic shaping detection tool for mobile networks,” in 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), 2013, pp. 328–335. [13] A. Molavi Kakhki, A. Razaghpanah, A. Li, H. Koo, R. Golani, D. Choffnes, P. Gill, and A. Mislove, “Identifying traffic differentiation in mobile networks,” in Proceedings of the 2015 Internet Measurement Conference, Oct. 2015, pp. 239–251. [14] R. Narisetty and D. Gurkan, “Identification of network measurement challenges in openflow-based service chaining,” in 39th Annual IEEE Conference on Local Computer Networks Workshops, 2014, pp. 663– 670. [15] E. Balestrieri, L. De Vito, F. Lamonaca, F. Picariello, S. Rapuano, and I. Tudosa, “Research challenges in measurement for internet of things systems,” ACTA IMEKO, vol. 7, p. 82, 01 2019. [16] E. Karoly. (2000, Feb.) Qos performance validation in real life scenarios. [Online]. Available: http://archive.opengroup.org/tech/qos/ conference/q101/karoly.pdf [17] S. Molnar, P. Megyesi, and G. Szab ´ o, “How to validate traffic gen- ´ erators?” in 2013 IEEE International Conference on Communications Workshops (ICC), 2013, pp. 1340–1344. [18] G. Lu, Y. Chen, S. Birrer, F. E. Bustamante, C. Y. Cheung, and X. Li, “End-to-end inference of router packet forwarding priority,” in IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications, 2007, pp. 1784–1792. [19] R. Mahajan, M. Zhang, L. Poole, and V. Pai, “Uncovering performance differences among backbone isps with netdiff,” in Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, 2008, pp. 205–218. [20] (2008, Jun.) Ookla, measuring and understanding broadband : Speed, quality and application. [Online]. Available: https://www.ookla.com/ docs/UnderstandingBroadbandMeasurement.pdf [21] V. S. Khandkar and M. K. Hanawal, “Detection of traffic discrimination in the internet,” in 2020 International Conference on COMmunication Systems NETworkS (COMSNETS), 2020, pp. 677–679. [22] Tcpreplay: Pcap editing and replay tools for *nix. [Online]. Available: https://github.com/appneta/tcpreplay [23] W. Cui, V. Paxson, N. Weaver, and R. Katz, “Protocol-independent adaptive replay of application dialog,” in 13th Annual Network and Distributed System Security Symposium (NDSS), Feb. 2006, pp. 27–27. [24] R. T. El-Maghraby, N. M. Abd Elazim, and A. M. Bahaa-Eldin, “A survey on deep packet inspection,” in 2017 12th International Conference on Computer Engineering and Systems (ICCES), 2017, pp. 188–197. [25] M. AlSabah, K. Bauer, and I. Goldberg, “Enhancing tor’s performance using real-time traffic classification,” in Proceedings of the 2012 ACM Conference on Computer and Communications Security, ser. CCS ’12. New York, NY, USA: Association for Computing Machinery, 2012, p. 73–84. [Online]. Available: https://doi.org/10.1145/2382196.2382208 [26] Z. Hu, L. Zhu, J. Heidemann, A. Mankin, D. Wessels, and P. Hoffman, “The Transport Layer Security (TLS) Protocol Version 1.3,” Internet Requests for Comments, IETF, RFC 7858, August 2018. [Online]. Available: https://tools.ietf.org/pdf/rfc8446.pdf [27] C. Zhou, C. Lin, and Z. Guo, “mdash: A markov decision-based rate adaptation approach for dynamic http streaming,” IEEE Transactions on Multimedia, vol. 18, no. 4, pp. 738–751, Apr. 2016. [28] T. Joe, L. Eliot, M. Allison, K. Markku, O. Kumiko, S. Martin, E. Lars, M. Alexey, E. Wes, Z. Alexander, T. Brian, I. Jana, M. Allison, T. Michael, K. Eddie, and N. Yoshifumi. (2020, Sept) Service name and transport protocol port number registry. [Online]. Available: https://www.iana.org/assignments/ service-names-port-numbers/service-names-port-numbers.xhtml [29] G. Keinan. (2018, January) Optimizing enterprise networks through sd-avc (software define application visibility and control). [Online]. Available: https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2018/ pdf/BRKCRS-2502.pdf [30] A. Rao, J. Sherry, A. Legout, A. Krishnamurthy, W. Dabbous, and D. Choffnes, “Meddle: middleboxes for increased transparency and control of mobile traffic,” in CoNEXT Student Workshop, ACM, 12 2012, pp. 65–66. [31] R. Liu and X. Yu, “A survey on encrypted traffic identification,” in Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies, ser. CIAT 2020. New York, NY, USA: Association for Computing Machinery, 2020, p. 159–163. [Online]. Available: https://doi.org/10.1145/3444370.3444564 [32] F. Li, A. A. Niaki, D. Choffnes, P. Gill, and A. Mislove, “A large-scale analysis of deployed traffic differentiation practices,” in Proceedings of the ACM Special Interest Group on Data Communication, ser. SIGCOMM ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 130–144. [Online]. Available: https://doi.org/10.1145/3341302.3342092 [33] P. Srisuresh and K. Egevang, “Traditional IP Network Address Translator (Traditional NAT),” Internet Requests for Comments, IETF, RFC 3022, Jan 2001. [Online]. Available: https://tools.ietf.org/pdf/rfc3022.pdf This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Ensuring Fair Internet Access: Challenges and Solutions in Traffic Discrimination Detection

Authors: (1) LI LI, Beihang University, China; (2) XIANG GAO, Beihang University, China; (3) HAILONG SUN, Beihang University, China; (4) CHUNMING HU, Beihang University, China; (5) XIAOYU SUN, The Australian National University, Australia; (6) HAOYU WANG, Huazhong University of Science and Technology, China; (7) HAIPENG CAI, Washington State University, Pullman, USA; (8) TING SU, East China Normal University, China; (9) XIAPU LUO, The Hong Kong Polytechnic University, China; (10) TEGAWENDÉ F. BISSYANDÉ, University of Luxembourg, Luxembourg; (11) JACQUES KLEIN, University of Luxembourg, Luxembourg; (12) JOHN GRUNDY, Monash University, Australia; (13) TAO XIE, Peking University, China; (14) HAIBO CHEN, Shanghai Jiao Tong University, China; (15) HUAIMIN WANG, National University of Defense Technology, China. Table of Links Introduction Background of OpenHarmony The State Of OpenHarmony Ecosystem Overview Of Mobile Software Engineering The Research Roadmap Discussion Related Work Conclusion & References 6 DISCUSSION OpenHarmony, as an emerging mobile platform, is still in its early stage, and so is OpenHarmonyfocused software engineering research. As summarized previously, although there are plenty of opportunities for our fellow researchers to explore in this field, there are still various challenges that need to be addressed. In this section, we highlight some of the representative ones. 6.1 Challenges in App/Library Development. In this work, we have highlighted the gaps that require to be filled in order to catch up with the popular mobile platforms (i.e., Android and iOS). Towards filling the gaps we argue that there are still a number of challenges that need to be addressed. The rise of large language models has been demonstrated to be promising for automated code generation, automated test case generation, library API recommendation, etc. However, it is not yet possible to directly achieve that for OpenHarmony as there is generally no data available for training (or fine-tuning). Even with a set of OpenHarmonyrelated software data (e.g., ArkTS code and its comments), there is also a requirement to further distill high-quality ones in order to achieve a highly precise large language model, as the performance of large language models is known to be highly correlated with the quality of the training dataset. Lacking Data for (AI-based ) Learning. At the moment, there are only a limited number of libraries (in ArkTS) available for supporting the implementation of OpenHarmony apps. The lack of third-party libraries makes it difficult for developers to implement OpenHarmony apps as many of the functions need to be developed from scratch. To fill this gap, the OpenHarmony community is currently encouraging practitioners and researchers to translate popular libraries in other languages to ArkTS. However, this simple translation campaign will introduce another challenge, which is to keep updating the library following the updates of the original version. To that end, we argue that dedicated efforts are required to ensure the maintainability of these libraries. Lacking Third-party Libraries. 6.2 Challenges in App/Library Analysis After app (or library) development, there is a strong need to ensure that the app/library satisfies the requirements and is of high quality. The relevant challenges include the newly designed system architecture of OpenHarmony, the comprehensive GUI interactions, the newly introduced app programming language, etc. We now summarize the representative ones. The Android system has introduced various challenges to the software engineering community in order to develop automated approaches to analyze Android apps. First, Android takes components to construct apps, for which the components by themselves are independently developed. The components will not be directly connected at the code side and the actual invocation (via the so-called Inter-Component Communication (ICC) mechanism) will be done over the system. This ICC mechanism could also be leveraged to implement inter-app communications, making it a challenge to perform inter-app analyses. Second, the components in Android are designed to be run over a set of pre-defined methods (known as lifecycle methods) that will be triggered by the system following a certain order. These lifecycle methods are not connected at the code site as well, making it also a challenge for static app analysis (from the analyzer’s point of view, there is no relationship between two lifecycle methods, despite they may be continuously called by the system). Third, similar to that of lifecycle methods, there are callback methods that are not directly connected to the app code as well. These callback methods are directly invoked by the system when certain events (either system events such as receiving an SMS or UI events such as clicking a button) are triggered. OpenHarmony generally shares the same challenges as that of Android. System-related Challenges. The GUI part has been known to be a challenge for precisely analyzing Android apps. First of all, a given GUI page often contains a comprehensive view tree that includes various widgets with different types positioned via different layout strategies. The widgets in the GUI page are further associated with interactive actions (e.g., a button is associated with a click event). Furthermore, a given GUI page may contain different groups of widgets that will only be rendered if a certain condition is satisfied. In OpenHarmony, the analysis of GUI pages is even more challenging as its design principle encourages to use a single component (i.e., Ability) to implement multiple visual pages, which would be implemented via multiple components (i.e., Activities, one page per Activity) in Android. GUI-related Challenges. The language used to implement mobile apps per se may introduce challenges to the software engineering community. For example, in the Android world, the reflection mechanism (inherited from Java) has been known to be a challenge for static analysis. OpenHarmony takes a new language called ArkTS for developers to implement OpenHarmony apps and the ArkTS language per se may introduce various challenges to the software engineering community as well. Indeed, ArkTS allows defining functions with optional parameters and default parameters, which may cause inconsistency between the function signature and its usage in practice. Language-induced Challenges. This paper is under CC 4.0 license. available on arxiv

Addressing OpenHarmony Development Challenges: Insights and Solutions

The rapidly increasing popularity of cryptocurrency has sparked discussions not only about its potential but also about its environmental impact. While the promise of decentralized currencies and blockchain technology is enticing, rising concerns regarding the carbon footprint of cryptocurrency have surfaced as a significant point of contention. The Impact of Crypto on the Planet While many point to cryptocurrency as the future, the concerns regarding its impact on our planet are more than warranted. The carbon footprint of cryptocurrency mining is substantial. A Cambridge University study estimated that Bitcoin mining alone consumes more electricity than entire countries like Argentina or the Netherlands. This energy-intensive process primarily relies on fossil fuels, contributing to greenhouse gas emissions and exacerbating climate change concerns. In another study out of Cambridge University, it was discovered that since its inception, cryptocurrency has accounted for over 200 million tons of Carbon Dioxide pollution, with 92% of those emissions coming in the past five years. Cryptocurrency mining, the process by which new coins are created and transactions are verified, predominantly relies on energy-intensive computational power. The most well-known cryptocurrencies like Bitcoin and Ethereum utilize a consensus mechanism known as Proof of Work (PoW). PoW requires miners to solve complex mathematical problems, consuming substantial amounts of electricity in the process. This energy consumption has led to mounting worries about the environmental sustainability of these digital currencies. Addressing the environmental challenges posed by cryptocurrency requires multifaceted solutions. One avenue involves transitioning toward alternative consensus mechanisms. How to Address the Environmental Challenges Proof of Stake (PoS), utilized by some cryptocurrencies like Cardano and Ethereum 2.0 (in the process of transitioning), requires far less energy by allowing validators to create new blocks based on the number of coins they hold, rather than computational power. This shift can significantly reduce the carbon footprint associated with mining. Furthermore, the utilization of renewable energy sources presents a viable solution. Initiatives to power mining operations with renewable energy, such as hydroelectric, solar, or wind power, have gained traction within the crypto community. Some mining farms have already made commitments to operate entirely on renewable energy, aiming to mitigate the environmental impact of their operations, with roughly 52% of crypto farms turning to the environmentally sound practice. This number is only expected to grow in the further years along with other practices being introduced to the crypto space. The most popular renewable energy source for mining cryptocurrency has been hydropower which powers roughly a quarter of all cryptomining operations. This was pioneered by HydroCryp, a Canadian-based company that utilizes water from the Parana River to generate hydroelectric energy for their power plants. This practice has helped HydroCryp achieve one of the lowest Kw/hr prices globally at just 0.045 Kw/hr. In addition to hydropower being used to help mitigate the environmental effects of cryptocurrency mining, companies have utilized wind power via turbines to account for roughly 15% of mining operations, with solar and nuclear-powered farms accounting for a similar percentage. In a recent study done by Cornell University in the United States, it was found that in periods of excess power generation, the revenue made from these mining operations could be substantial, allowing profits to be reinvested into other eco-friendly mining practices. Texas is currently leading the way in eco-friendly mining practices with 32 projects currently planned that are projected to make roughly 50 million dollars combined, providing an economic rise for both the State of Texas and crypto as a whole. Moreover, encouraging and incentivizing eco-friendly practices within the crypto sphere, such as carbon offset programs have become increasingly popular in recent years. These programs aim to counteract carbon emissions produced by various activities, including cryptocurrency operations, by investing in projects that reduce or offset an equivalent amount of carbon elsewhere. Participants calculate their carbon footprint and then invest in verified projects such as reforestation or renewable energy to mitigate their environmental impact. These programs promote environmental responsibility by supporting initiatives that reduce greenhouse gases which in turn contributes to a more sustainable future. Additionally, improving the efficiency of mining hardware and exploring innovative cooling mechanisms can contribute to reducing energy consumption in cryptocurrency mining. Advancements in technology might offer more energy-efficient solutions, making mining operations greener and more sustainable. In conclusion, acknowledging and addressing the carbon footprint of cryptocurrency mining is crucial for the long-term viability and acceptance of digital currencies. Embracing alternative consensus mechanisms, integrating renewable energy sources, and fostering eco-conscious practices within the crypto ecosystem are pivotal steps toward minimizing the environmental impact of this transformative technology. Balancing innovation with sustainability will be instrumental in shaping a more environmentally responsible future for cryptocurrencies.

The Carbon Footprint of Cryptocurrency: Challenges and Solutions

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The TechBeat: Linux Process Management in Containers: Challenges and Solutions (10/31/2023)

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The TechBeat: Linux Process Management in Containers: Challenges and Solutions (10/29/2023)

In recent years, containerisation has emerged as a transformative technology that has refashioned the way applications are developed, deployed, and managed in the world of IT. This technological advacement, driven by a rich history has significantly shaped the modern computing landscape. Containerisation has become synonymous with agility, scalability, and efficiency, and it makes it an indispensable tool for organisations striving to stay competitive in the digital age, like Google with their internal cloud manager, , which has been leading the way since the early 2000s. Borg Linux operating system lies at the core of this container revolution. It plays a pivotal role in container orchestration. Linux, with its potent capabilities and open-source nature, has become the de facto standard for containerisation, powering platforms like and that have gained widespread adoption. Docker Kubernetes The purpose of this article is to illuminate the intricacies of containerisation, with a specific focus on the complexities of process management within containers. I aim to unpack the challenges faced in orchestrating processes in containerised environments and overview innovative solutions to address these issues. By the end of this article, readers will gain a comprehensive understanding of the pivotal role Linux plays in container orchestration and will be equipped with valuable acumen for optimising process management within containerised applications. The Basics of Containerisation Containers are lightweight, stand-alone executable packages that encapsulate an application along with all its dependencies, libraries, and configuration files. They isolate these components from the host system and other containers — it ensures consistency and portability across different environments. Containers have become essential because they facilitate developers to package applications once and run them anywhere. It streamlines the development-to-deployment pipeline and advances a consistent, reproducible environment. Docker, Kubernetes, and Other Containerisation Platforms While containers as a concept existed long before, their explosion in popularity can be attributed to platforms like Docker and Kubernetes. Docker simplified container creation and management, making them accessible to a wider audience. Kubernetes, on the other hand, brought a kind of revolution to container orchestration and scalability. Notably, even default Linux daemons nowadays are run in containers, emphasising the pervasive influence of containerisation. The Role of Linux Linux, with its capabilities and open-source nature, plays a pivotal role in the . It emerged as the mainstay of containerisation due to its flexibility, scalability, and community-driven development. It provided the ideal environment for it to flourish, thanks to its rich feature set and robust security model. containerisation ecosystem The secret bit behind Linux's containerisation mastery lies in and . Kernel namespaces enable the isolation of resources, such as file systems, network interfaces, and process IDs, allowing multiple containers to coexist on the same host without interference. kernel namespaces cgroups , short for control groups, regulate resource allocation, ensuring that containers receive their fair share of CPU, memory, and other resources. These Linux features are fundamental to the operation of containers and underpin their ability to provide a secure, isolated environment for applications. cgroups Process Isolation in Containers The foundation of containerisation is in its ability to provide robust process isolation. Understanding why this isolation is crucial is fundamental to grasping the value of containers. Security Concerns in Shared Computing Environments In shared computing environments, where multiple applications coexist on the same host, there is no way of compromising security. Without proper isolation, one misbehaving or compromised application could potentially impact others, leading to data ruptures, service disruptions, and compromised system integrity. Containers address these security concerns by creating a barrier between processes, ensuring that each operates within its own isolated environment. Resource Allocation and Predictability Resource management is another critical aspect of process isolation. Containers offer a predictable and controlled environment where resource allocation can be finely tuned. This predictability is needed for maintaining consistent performance and establishing that applications do not contend for resources, guaranteeing a stable and efficient computing experience. Kernel Namespaces Kernel namespaces are a fundamental building block of process isolation within containers. These namespaces provide a means to create separate and distinct environments for processes, which effectively shield them from each other. PID, NET, IPC, UTS, Mount Namespaces, and User Namespaces for Security Kernel namespaces come in different shapes, each serving a specific purpose. Process ID (PID) namespaces isolate process IDs, ensuring that processes within a container have their own unique view of the system's process tree. Network (NET) namespaces control network-related resources via creating isolated network stacks for each container. IPC, UTS, and Mount namespaces isolate interprocess communication, system identification, and file system mounts, respectively. Moreover, User namespaces provide additional security by mapping container-level user identities to distinct host-level user identities. Namespaces achieve isolation by providing each container with its private namespace for each resource type by employing special system calls (mainly ) or providing specific flags to syscall. This partition means that processes inside a container believe they are operating within their dedicated environment because they receive a copy of one or several and seize sharing it with other processes.. This isolation ensures that processes cannot interfere with each other, which in turn enhances security and resource predictability. unshare(2) clone(2) namespaces Challenges in Process Management Process management in a complex computing environment comes with its fair share of difficulties. As organisations scale up their operations, they often encounter a myriad of challenges that demand solutions. As the number of concurrent processes increases, resource contention becomes a troublesome issue: processes vying for CPU time, memory, and I/O resources can lead to barriers, slowing down overall system performance. becomes an art in this situation. Ensuring that CPU, memory, and I/O constraints are met without starving or overwhelming processes is a continuous struggle. Striking the right balance is the key. Resource management Furthermore, keeping track of an expanding array of processes bears resemblance to the intricate task of managing disparate entities. The orchestration of process scheduling, prioritisation, and the imperative of optimising their individual efficiency compounds in complexity as the volume of processes proliferates. Fairness, in this regard, is the cornerstone of effective resource management. Allocating resources to different processes, regardless of their size or priority, is a constant endeavour. Solutions to the Challenges We have thoroughly dissected the challenges inherent in process management, and this would be pointless without pointing out designed to mitigate these challenges effectively. Here is a short summary of the most outstanding: solutions and methodologies Container orchestration platforms like Kubernetes and Docker Swarm offer powerful tools for process management at scale. Orchestrators streamline process management by automating deployment, scaling, and resource allocation. Robust monitoring tools such as , , and provide critical insights into process performance. They ensire proactive monitoring and efficient resource utilisation. cAdvisor Prometheus Grafana Furthermore, establishing resource quotas and limits is essential to prevent resource overutilisation and ensure fair distribution. There are also a myriad of practices for efficient process management, including prioritising scheduling, proactively monitoring performance, and adopting auto-scaling mechanisms. These strategies, along with maintaining documentation, backup plans, security protocols, and performance tuning, collectively help organisations secure a stable and resilient computing environment and seamless operations while overcoming process management challenges. The Summary: Trends to Watch The future promises to bring more . We anticipate innovations in orchestration platforms that will streamline process management further. As containers continue to gain momentum, we foresee process management evolving to address specific challenges. Technologies like , bridging the gap between traditional virtual machines (VMs) and containers, will play a pivotal role in enhancing security and isolation. automation, self-healing, and adaptability gVisor With the increasing importance of hardware-level optimisations, technologies like are believed to recast process management. Addressing memory bandwidth and CPU cache limits at the hardware level will be instrumental in optimising performance. Intel Resource Director Undoubtedly, the future of Linux process management within containers is shaped by the relentless advance of technology. As the Linux kernel evolves, containerisation matures, and innovative technologies like and Intel Resource Director come to the fore, we anticipate a landscape where process management is both efficient and finely tuned to the demands of each workload. eBPF

Linux Process Management in Containers: Challenges and Solutions

This article focuses on pitfalls, solutions, and my findings with regard to scheduling Python scripts. : periodically, you have to execute updating s using ORM models from your codebase with intermediate processing. For example, you want to detect inactive users by side factors like actions they've taken or not taken within a certain time frame once per day and mark them as inactive. This scenario often occurs in data-driven applications where you need to maintain the integrity and accuracy of your data. Imagine the following case SQL every Monday at 10 AM your script must send internal reports on to every manager about their employees' non-compliant Key Performance Indicators. This scenario is a common requirement in organizations striving to monitor and improve performance across various teams and departments. Or, a different case: Slack What could go wrong with scheduling Python scripts? Async code If your codebase contains asynchronous parts that you'd like to reuse, you need to consider async support. I wouldn't recommend relying solely on the standard approach because you'll forfeit the advantages of asynchrony, and your code will operate like regular synchronous code. asyncio.run If you have more than one job, you'll have to deal with parallel job execution. However, a common issue that arises is time shifting, as the scheduler may need to wait until one job finishes before it can move on to the next. This can potentially lead to delays in executions, especially when jobs have varying runtimes or when you have many jobs. Concurrent execution There are different ways to start jobs, like using separate Python threads, individual processes, or event loops. The choice of initiation method can have a significant impact on CPU and memory utilization when you have a whole lot of jobs. Resource leakage Inability to be resistant to reboots without affecting job intervals. In other words, after reboot, you get a time shift because of interrupting clocking. For example, you have a job whose interval is 3 hours. After 2 hours of clocking the CI/CD pipeline rebuilt the image and recreated the container to update the code because you pushed new commits. If the scheduler starts clocking right from the start, you'll get 2 hours of clocking before the reboot, and 3 after. So, in this case, the interval between executions is 5 hours and you have a significant time shift. Sometimes it might be critical. Restart intolerance If the execution time of a job is more than an interval between executions or it varies, you may easily get a situation when the scheduler starts new executions of the job before the previous execution ends. It leads to job multiplication, resource leakage, and even a critical system shutdown due to overload. This case must be considered if you have short intervals or long-running jobs. Duplicate parallel executions If you have a need to execute jobs at different times by different time zones on different days, it might be tricky to configure. For example, one job must be executed at 11 AM by New York on Monday only, a different one at 5 PM by Berlin on weekends, and the last one every 3 hours starting by Tokio on Friday only. Localization So, a lot could go wrong:) Solutions Let's look at possible ways to overcome pitfalls in order of their effectiveness. Schedule Python lib is the most basic scheduler for Python and the first one that you'll find if start googling. It looks interesting, but the main disadvantage of this lib is that it's not really designed to be used somewhere. Schedule According to the official documentation: You should probably look somewhere else if you need: Job persistence (remember schedule between restarts) Exact timing (sub-second precision execution) Concurrent execution (multiple threads) Localization (workdays or holidays) Schedule does not account for the time it takes for the job function to execute. Anyway, let's look at an example, pros and cons for further comparison. import schedule
import time

def job():
    print("I'm working...")

schedule.every().day.at("10:30").do(job)

while True:
    schedule.run_pending()
    time.sleep(1) Pros Simple Cons No async code support No concurrent execution Restart intolerance No localization Cron Unix utility is the most popular general-purpose scheduler in the world. This scheduler is universal and starts jobs as a shell command. Take note that Cron starts a separate process for every job and this may take a lot of system resources. Cron Here you need a file: crontab # m h dom mon dow user  command
*/30 * * * *      /usr/local/bin/python /path/to/the/script.py >> /var/log/cron.log 2>&1 In turn, the script can contain whatever your heart desires: if __name__ == "__main__":
    print("Whatever your heart desires") Pros Concurrent execution Versatility Cons No async code support Resource leakage Restart intolerance Duplicate parallel executions No localization Regta Python utility is a scheduling tool designed with these pitfalls in mind especially for . The key advantage is that it has async, multithreading, and multiprocessing support just like restart tolerance. Regta Python from regta import async_job, Period

@async_job(Period().on.sunday.at("18:35").by("Asia/Almaty"))
async def my_async_job():
    pass  # Do some stuff here To run it use command. regta run Pros Async code support Concurrent execution Restart tolerance Localization Cons No strong community Summary When it comes to scheduling Python programs, the first option, Schedule, may not be the most suitable choice for solving real-world problems. Instead, consider the following points: For internal automation tasks that involve various programming languages and CLI tools, stands as a robust choice. Cron If your automation needs are focused on Python exclusively, emerges as a compelling option, offering a wealth of Python-specific optimizations. Give it a try, and feel free to share your thoughts in the comments 🙌 Regta Also published here.

Mastering Python Script Scheduling: Pitfalls and Solutions

offers a convenient environment for developers to focus on building applications without worrying about the underlying infrastructure. However, as applications become more complex and interconnected, integration challenges can arise. Integrating various components, services, and data sources within a PaaS environment requires careful consideration to ensure seamless communication and functionality. Platform as a Service (PaaS) Integration Challenges in PaaS PaaS brings forth a treasure trove of services and components that developers can cherry-pick to craft their applications. However, this abundant selection can inadvertently lead to the creation of application and data silos. Each service operates in its bubble, isolated from the rest, which can hamper communication and collaboration between components. This isolation often breeds data inconsistency and redundancy – a veritable headache for developers seeking to maintain a cohesive and streamlined application. Heterogeneous Environments PaaS ecosystems are a melting pot of diverse technologies and frameworks. While this diversity empowers developers to choose tools that best suit their needs, it also introduces the challenge of ensuring compatibility. Integrating components that speak different technological languages can lead to puzzling compatibility issues, stalling development, and frustrating developers. Scalability and Performance The allure of PaaS lies in its promise of scalability – the ability to effortlessly accommodate surging demands. However, the reality is not always seamless. Load balancing in a dynamic PaaS setting poses a challenge, as the distribution of workloads across various services must be orchestrated carefully to prevent bottlenecks and optimize performance. Moreover, the speed at which data traverses between services can introduce latency issues, leading to sluggish application responses. Data Integration Solutions The conductor's baton starts with . Imagine as the language of digital communication, and RESTful APIs as its mellifluous dialect. RESTful APIs, akin to linguistic messengers, facilitate smooth conversations between diverse services. JSON and XML data formats are the exquisite parchments that carry the messages – simple, yet rich, ensuring that information is both understood and retained by each participant in this symphonic dialogue. API-Based Integration APIs Introducing Data Middleware – the Mystical Bridge linking diverse data realms. Envision it as a cosmic portal, powered by message queues and publish-subscribe systems, seamlessly uniting PaaS domains. An ethereal Event-Driven Architecture conducts data like celestial choreography, assuring no missed steps or unheard notes. The final movement brings us to Data Transformation and ETL – Extract, Transform, and Load. Imagine data as raw rivers from diverse sources. ETL processes refine and shape this like skilled artisans, aided by data mapping tools. Precision sculpting ensures data fits required patterns. In this symphony, APIs, Data Middleware, and Data Transformation unite. RESTful APIs conduct seamless conversations. Data Middleware bridges the world with queues and event-driven magic. Data Transformation crafts raw data into a polished masterpiece. Application Integration Solutions Imagine digital Lego blocks, each self-contained and ready to snap into place. , led by powerhouses like Docker and Kubernetes, is akin to this concept. Docker encapsulates applications and their dependencies, creating portable units known as containers. Kubernetes, the orchestrator, coordinates these containers, making management a breeze. Containerization Microservices, on the other hand, embody a divide-and-conquer approach. Applications are broken into smaller, self-sufficient modules, each serving a specific function. This modular design enhances flexibility, and scalability, and allows independent deployment. It's like constructing a grand symphony by orchestrating individual musical notes. Event-Driven Architecture In the world of software, events are the messengers that relay important occurrences. Event-Driven Architecture (EDA) capitalizes on this by establishing a communication model where systems respond to and emit events. Event sourcing takes this further by storing every change as an event, forming a detailed history log. Imagine rewinding a tape to any point – that's the power of event sourcing. Asynchronous communication, a core EDA principle, untangles dependencies. Imagine sending a letter; you continue with your tasks as the post office delivers it. Similarly, systems communicate without waiting for an immediate response. This decouples components, ensuring one's hiccups don't disturb the harmony of others. API Management Platforms APIs, akin to bridges between software islands, require careful governance. API Management Platforms act as traffic controllers, directing the flow efficiently. API gateways, the guardians of the entrance, manage traffic, enforce security protocols, and offer rate limiting to prevent overwhelming the system. These platforms ensure that only authorized parties access the APIs, safeguarding sensitive data like knights guarding a treasure chest. Think of it as a bouncer ensuring only VIPs enter a party. Rate limiting, on the other hand, prevents overuse, much like a bartender ensuring a patron doesn't drink too much too quickly. Security and Governance Considerations : In the realm of PaaS, data is the heart of every application. Encryption acts as a secure cloak, ensuring that even if data falls into the wrong hands, it remains unintelligible. By encrypting data both at rest and in transit, we create a protective shield that only authorized users can penetrate. This simple yet powerful technique fortifies our applications against potential breaches. Encryption and Data Protection Access Controls and Authentication: Think of access controls like having guards at the entrance of a castle. You decide who enters and who doesn't. Multi-factor authentication is like an extra layer of verification – a secret passphrase known only to you. It ensures that only authorized personnel can pass through the castle gates and access the valuable treasures within. Compliance and Auditing: Navigating the Legal Terrain Tracking and Monitoring Data Flows: Just as a diligent detective traces clues, tracking and monitoring data flows allow you to follow the footprints of your data. You can see who accessed it, when, and from where. It's like having a detailed logbook that records every movement of your treasure. This ensures that any suspicious activity is promptly identified and addressed. Ensuring Regulatory Compliance: Regulations are the rules of the land, and compliance is your adherence to these rules. Just as you follow laws in your country, your data must follow the rules of the digital realm. It's like obtaining a permit to carry your precious gem. Ensuring regulatory compliance means you're playing by the book, avoiding penalties, and keeping your treasure safe from legal troubles. Conclusion While Platform as a Service ( ) brings advantages to app development, it also brings integration hurdles due to its diverse services. Yet, solutions exist. Through strategic API design, standard protocols, and middleware, devs can conquer PaaS integration issues. Open communication and teamwork between devs and ops are vital for early issue detection. PaaS In the end, smart architecture, modern tools, and collaboration ensure smooth PaaS integration. This unlocks platform potential, delivering seamless apps and user satisfaction.

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