Abstract
Edge computing offers the possibility of deploying applications at the edge of the network. To take advantage of available devices’ distributed resources, applications often are structured as microservices, often having stringent requirements of low latency and high availability. However, a decentralized edge system that the application may be intended for is characterized by high volatility, due to devices making up the system being unreliable or leaving the network unexpectedly. This makes application deployment and assurance that it will continue to operate under volatility challenging. We propose an adaptive framework capable of deploying and efficiently maintaining a microservice-based application at runtime, by tackling two intertwined problems: (i) finding a microservice placement across device hosts and (ii) deriving invocation paths that serve it. Our objective is to maintain correct functionality by satisfying given requirements in terms of end-to-end latency and availability, in a volatile edge environment. We evaluate our solution quantitatively by considering performance and failure recovery.
- Danilo Ardagna and Li Zhang. 2010. Run-time Models for Self-Managing Systems and Applications. Springer Science & Business Media. Google Scholar
Digital Library
- Cosmin Avasalcai, Christos Tsigkanos, and Schahram Dustdar. 2019. Decentralized resource auctioning for latency-sensitive edge computing. In Proceedings of the IEEE International Conference on Edge Computing (EDGE’19).Google Scholar
Cross Ref
- Cosmin Avasalcai, Christos Tsigkanos, and Schahram Dustdar. 2021. Resource management for latency-sensitive IoT applications with satisfiability. IEEE Transactions on Services Computing. Early access, April 20, 2021.Google Scholar
Cross Ref
- Marios Avgeris, Dimitrios Dechouniotis, Nikolaos Athanasopoulos, and Symeon Papavassiliou. 2019. Adaptive resource allocation for computation offloading: A control-theoretic approach. ACM Transactions on Internet Technology 19, 2 (April 209), Article 23, 20 pages. Google Scholar
Digital Library
- Clark Barrett and Cesare Tinelli. 2018. Satisfiability Modulo Theories. Springer International Publishing, Cham, Switzerland, 305–343.Google Scholar
- Antonio Brogi and Stefano Forti. 2017. QoS-aware deployment of IoT applications through the fog. IEEE Internet of Things Journal 4, 5 (2017), 1185–1192.Google Scholar
Cross Ref
- J. Chen, S. Chen, Q. Wang, B. Cao, G. Feng, and J. Hu. 2019. iRAF: A deep reinforcement learning approach for collaborative mobile edge computing IoT networks. IEEE Internet of Things Journal 6, 4 (2019), 7011–7024.Google Scholar
Cross Ref
- Nader Daneshfar, Nikolaos Pappas, Valentin Polishchuk, and Vangelis Angelakis. 2018. Service allocation in a mobile fog infrastructure under availability and QoS constraints. In Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM’18). 1–6.Google Scholar
Cross Ref
- Leonardo De Moura and Nikolaj Bjørner. 2008. Z3: An efficient SMT solver. In Proceedings of the International Conference Tools and Algorithms for the Construction and Analysis of Systems. 337–340. Google Scholar
Digital Library
- Raphael Eidenbenz, Yvonne-Anne Pignolet, and Alain Ryser. 2020. Latency-aware industrial fog application orchestration with Kubernetes. In Proceedings of the 2020 5th International Conference on Fog and Mobile Edge Computing (FMEC’20). 164–171.Google Scholar
Cross Ref
- Diogo Goncalves, Karima Velasquez, Marilia Curado, Luiz Bittencourt, and Edmundo Madeira. 2018. Proactive virtual machine migration in fog environments. In Proceedings of the 2018 IEEE Symposium on Computers and Communications (ISCC’18). 00742–00745.Google Scholar
Cross Ref
- Keerthana Govindaraj, Jibin P. John, Alexander Artemenko, and Andreas Kirstaedter. 2019. Smart resource planning for live migration in edge computing for industrial scenario. In Proceedings of the 2019 7th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud’19). 30–37.Google Scholar
Cross Ref
- M. Huang, W. Liu, T. Wang, A. Liu, and S. Zhang. 2020. A cloud-MEC collaborative task offloading scheme with service orchestration. IEEE Internet of Things Journal 7, 7 (2020), 5792–5805.Google Scholar
Cross Ref
- Saadallah Kassir, Gustavo de Veciana, Nannan Wang, Xi Wang, and Paparao Palacharla. 2020. Service placement for real-time applications: Rate-adaptation and load-balancing at the network edge. In Proceedings of the 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud’20) and the 2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom’20). 207–215.Google Scholar
- Jeffrey O. Kephart and David M. Chess. 2003. The vision of autonomic computing. Computer 36, 1 (2003), 41–50. Google Scholar
Digital Library
- Isaac Lera, Carlos Guerrero, and Carlos Juiz. 2019. Availability-aware service placement policy in fog computing based on graph partitions. IEEE Internet of Things Journal 6, 2 (2019), 3641–3651.Google Scholar
Cross Ref
- C. Liu, M. Bennis, M. Debbah, and H. V. Poor. 2019. Dynamic task offloading and resource allocation for ultra-reliable low-latency edge computing. IEEE Transactions on Communications 67, 6 (2019), 4132–4150.Google Scholar
Cross Ref
- Redowan Mahmud, Kotagiri Ramamohanarao, and Rajkumar Buyya. 2018. Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology 19, 1 (Nov. 2018), Article 9, 21 pages. Google Scholar
Digital Library
- V. De Maio and I. Brandic. 2018. First hop mobile offloading of DAG computations. In Proceedings of the 18th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing. 83–92. Google Scholar
Digital Library
- Amina Mseddi, Wael Jaafar, Halima Elbiaze, and Wessam Ajib. 2019. Intelligent resource allocation in dynamic fog computing environments. In Proceedings of the 2019 IEEE 8th International Conference on Cloud Networking (CloudNet’19). 1–7.Google Scholar
Cross Ref
- Fabiana Rossi, Valeria Cardellini, and Francesco Lo Presti. 2020. Self-adaptive threshold-based policy for microservices elasticity. In Proceedings of the 2020 IEEE International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS’20).Google Scholar
- Deepa R. Sangolli, Nagthej M. Ravindrarao, Priyanka C. Patil, Thrishna Palissery, and Kaikai Liu. 2019. Enabling high availability edge computing platform. In Proceedings of the 2019 7th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud’19). 85–92.Google Scholar
Cross Ref
- Olena Skarlat, Matteo Nardelli, Stefan Schulte, Michael Borkowski, and Philipp Leitner. 2017. Optimized IoT service placement in the fog. Service Oriented Computing and Applications 11, 4 (Dec. 2017), 427–443. Google Scholar
Digital Library
- Klervie Toczé and Simin Nadjm-Tehrani. 2018. A taxonomy for management and optimization of multiple resources in edge computing. arXiv:1801.05610.Google Scholar
- Christos Tsigkanos, Cosmin Avasalcai, and Schahram Dustdar. 2019. Architectural considerations for privacy on the edge. IEEE Internet Computing 23, 4 (2019), 76–83.Google Scholar
Cross Ref
- Christos Tsigkanos, Marcello Bersani, Pantelis A. Frangoudis, and Schahram Dustdar. 2021. Edge-based runtime verification for the Internet of Things. IEEE Transactions on Services Computing1 (2021), 1.Google Scholar
- Christos Tsigkanos, Stefan Nastic, and Schahram Dustdar. 2019. Towards resilient Internet of Things: Vision, challenges, and research roadmap. In Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS’19).Google Scholar
Cross Ref
- Cecil Wobker, Andreas Seitz, Harald Mueller, and Bernd Bruegge. 2018. Fogernetes: Deployment and management of fog computing applications. In Proceedings of the 2018 IEEE/IFIP Network Operations and Management Symposium (NOMS’18). 1–7.Google Scholar
Cross Ref
- Kuang Yuejuan, Luo Zhuojun, and Ouyang Weihao. 2021. Task scheduling algorithm based on reliability perception in cloud computing. Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 14, 1 (2021), 52–58.Google Scholar
Cross Ref
- He Zhu and Changcheng Huang. 2018. EdgePlace: Availability-aware placement for chained mobile edge applications. Transactions on Emerging Telecommunications Technologies 29, 11 (2018), e3504. Google Scholar
Digital Library
Index Terms
Adaptive Management of Volatile Edge Systems at Runtime With Satisfiability
Recommendations
EdgeWorkflow: One click to test and deploy your workflow applications to the edge
AbstractIn recent years, edge computing has become the ideal computing paradigm for various smart systems, such as smart logistics, smart health and smart transportation. This is due to its advantages including fast response times, energy ...
Highlights- A novel model for workflow applications and computing resources in the edge computing environment.
The Requirements Problem for Adaptive Systems
Special Issue on Complexity of Systems Evolution: Requirements Engineering PerspectiveRequirements Engineering (RE) focuses on eliciting, modeling, and analyzing the requirements and environment of a system-to-be in order to design its specification. The design of the specification, known as the Requirements Problem (RP), is a complex ...
Monitoring self-adaptive applications within edge computing frameworks
Comprehensive survey of monitoring concepts within edge computing frameworks.Taxonomy of monitoring requirements needed to support self-adaptive applications in edge computing scenarios.Comparison of widely-used monitoring tools for edge computing ...






Comments