[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
research-article

To Migrate or Not to Migrate: An Analysis of Operator Migration in Distributed Stream Processing

Published: 07 November 2023 Publication History

Abstract

One of the most important issues in distributed data stream processing systems is using operator migration to handle highly variable workloads cost-efficiently and adapt to the needs at any given time on demand. Operator migration is a complex process involving changes in the state and stream management of a running query, typically without any data loss, and with as little disruption to the execution as possible. This tutorial aims to introduce operator migration, explain the core elements of operator migration, and provide the reader with a good understanding of the design alternatives used in existing solutions. We developed a conceptual model to explain the fundamentals of operator migration and introduce a unified terminology, leading to a taxonomy of existing solutions. The conceptual model separates mechanisms, i.e., how to migrate, and policy, i.e., when to migrate. This separation is further applied to structure the description of existing solutions, offering the reader an algorithmic perspective on various design alternatives. To enhance our understanding of the impact of various design alternatives on migration mechanisms, we also conducted an empirical study that provides quantitative insights. The operator downtime for the naïve migration approach is almost 20 times longer than when applying an incremental checkpoint-based approach.

References

[1]
E. Mehmood and T. Anees, “Challenges and solutions for processing real-time big data stream: A systematic literature review,” IEEE Access, vol. 8, pp. 119123–119143, 2020.
[2]
A. Gharaibehet al., “Smart cities: A survey on data management, security, and enabling technologies,” IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2456–2501, 4th Quart., 2017.
[3]
M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, “Deep learning for IoT big data and streaming analytics: A survey,” IEEE Commun. Surveys Tuts., vol. 20, no. 4, pp. 2923–2960, 4th Quart., 2018.
[4]
R. Sahal, J. G. Breslin, and M. I. Ali, “Big data and stream processing platforms for industry 4.0 requirements mapping for a predictive maintenance use case,” J. Manuf. Syst., vol. 54, pp. 138–151, Jan. 2020.
[5]
P. Carbone, A. Katsifodimos, S. Ewen, V. Markl, S. Haridi, and K. Tzoumas, “Apache fLink: Stream and batch processing in a single engine,” Bull. IEEE Comput. Soc. Techn. Committee Data Eng., vol. 36, no. 4, p. 10, 2015.
[6]
fLink.” Accessed: Feb. 27, 2023. [Online]. Available: https://flink.apache.org/powered-by
[7]
M. Fragkoulis, P. Carbone, V. Kalavri, and A. Katsifodimos. “A survey on the evolution of stream processing systems.” 2020. [Online]. Available: https://arxiv.org/abs/2008.00842
[8]
J. Manyikaet al., Unlocking the Potential of the Internet of Things. McKinsey Global Inst., New York, NY, USA 2015.
[9]
S. Suhothayan, K. Gajasinghe, I. L. Narangoda, S. Chaturanga, S. Perera, and V. Nanayakkara, “SIDDHI: A second look at complex event processing architectures,” in Proc. ACM Workshop Gateway Comput. Environ., 2011, pp. 43–50.
[10]
P. Tucker, K. Tufte, V. Papadimos, and D. Maier, “NexMark—A benchmark for queries over data streams draft,” OGI School Sci. Eng., OHSU, Portland, OR, USA, Sep. 2008.
[11]
T. Heinze, L. Aniello, L. Querzoni, and Z. Jerzak, “Cloud-based data stream processing,” in Proc. 8th ACM Int. Conf. Distrib. Event Based Syst., 2014, pp. 238–245.
[12]
G. T. Lakshmanan, Y. Li, and R. Strom, “Placement strategies for Internet-scale data stream systems,” IEEE Internet Comput., vol. 12, no. 6, pp. 50–60, Nov./Dec. 2008.
[13]
W. Hummer, B. Satzger, and S. Dustdar, “Elastic stream processing in the cloud,” Wiley Interdiscipl. Rev. Data Min. Knowl. Disc., vol. 3, no. 5, pp. 333–345, 2013.
[14]
M. Hirzel, R. Soulé, S. Schneider, B. Gedik, and R. Grimm, “A catalog of stream processing optimizations,” ACM Comput. Surveys, vol. 46, no. 4, pp. 1–34, 2014.
[15]
M. D. de Assunção, A. da Silva Veith, and R. Buyya, “Distributed data stream processing and edge computing: A survey on resource elasticity and future directions,” J. Netw. Comput. Appl., vol. 103, pp. 1–17, Feb. 2018.
[16]
Q.-C. To, J. Soto, and V. Markl, “A survey of state management in big data processing systems,” VLDB J., vol. 27, no. 6, pp. 847–872, 2018.
[17]
H. Röger and R. Mayer, “A comprehensive survey on parallelization and elasticity in stream processing,” ACM Comput. Surveys, vol. 52, no. 2, pp. 1–37, 2019.
[18]
C. Qin, H. Eichelberger, and K. Schmid, “Enactment of adaptation in data stream processing with latency implications—A systematic literature review,” Inf. Softw. Technol., vol. 111, pp. 1–21, Jul. 2019.
[19]
X. Liu and R. Buyya, “Resource management and scheduling in distributed stream processing systems: A taxonomy, review, and future directions,” ACM Comput. Surveys, vol. 53, no. 3, pp. 1–41, 2020.
[20]
M. Bergui, S. Najah, and N. S. Nikolov, “A survey on bandwidth-aware geo-distributed frameworks for big-data analytics,” J. Big Data, vol. 8, no. 1, pp. 1–26, 2021.
[21]
V. Cardellini, F. L. Presti, M. Nardelli, and G. R. Russo, “Run-time adaptation of data stream processing systems: The state of the art,” ACM Comput. Surveys, vol. 54, no. 11, pp. 1–36, 2022.
[22]
A. Vogel, D. Griebler, M. Danelutto, and L. G. Fernandes, “Selfadaptation on parallel stream processing: A systematic review,” Concurrency Comput. Pract. Exp., vol. 34, no. 6, 2022, pp. 1–36, 2022.
[23]
T. G. Rodrigues, K. Suto, H. Nishiyama, and N. Kato, “Hybrid method for minimizing service delay in edge cloud computing through VM migration and transmission power control,” IEEE Trans. Comput., vol. 66, no. 5, pp. 810–819, Nov. 2017.
[24]
O. Osanaiye, S. Chen, Z. Yan, R. Lu, K. R. Choo, and M. Dlodlo, “From cloud to fog computing: A review and a conceptual live VM migration framework,” IEEE Access, vol. 5, pp. 8284–8300, 2017.
[25]
J. Hu, G. Wang, X. Xu, and Y. Lu, “Study on dynamic service migration strategy with energy optimization in mobile edge computing,” Mobile Inf. Syst., vol. 2019, Oct. 2019, Art. no.
[26]
S. K. Pande, S. K. Panda, and S. Das, “Dynamic service migration and resource management for vehicular clouds,” J. Ambient Intell. Humanized Comput., vol. 12, pp. 1227–1247, Jan. 2021.
[27]
Z. Zenget al., “Efficient edge service migration in mobile edge computing,” in Proc. IEEE 26th Int. Conf. Parallel Distrib. Syst. (ICPADS), 2020, pp. 691–696.
[28]
R. Urgaonkar, S. Wang, T. He, M. Zaxfer, K. Chan, and K. K. Leung, “Dynamic service migration and workload scheduling in edge-cloud,” Perform. Eval., vol. 91, pp. 205–228, Jan. 2015.
[29]
A. Machen, S. Wang, K. K. Leung, B. J. Ko, and T. Salonidis, “Live service migration in mobile edge clouds,” IEEE Wireless Commun., vol. 25, no. 1, pp. 140–147, Feb. 2018.
[30]
L. Ma, S. Yi, and Q. Li, “Efficient service handoff across edge servers via docker container migration,” in Proc. 2nd ACM/IEEE Symp. Edge Comput. (SEC), 2017, pp. 1–13.
[31]
S. Wang, R. Urgaonkar, M. Zafer, T. He, K. Chan, and K. K. Leung, “Dynamic service migration in mobile edge-clouds,” in Proc. IFIP Netw. Conf. (IFIP Netw.), 2015, pp. 1–9.
[32]
M. Chen, W. Li, G. Fortino, Y. Hao, L. Hu, and I. Humar, “A dynamic service migration mechanism in edge cognitive computing,” ACM Trans. Internet Technol., vol. 19, no. 2, pp. 1–15, Apr. 2019.
[33]
S. Wang, R. Urgaonkar, T. He, M. Zafer, K. Chan, and K. K. Leung, “Mobility-induced service migration in mobile micro-clouds,” in Proc. IEEE Mil. Commun. Conf., 2014, pp. 835–840.
[34]
H. Wang, Y. Li, A. Zhou, Y. Guo, and S. Wang, “Service migration in mobile edge computing: A deep reinforcement learning approach,” Int. J. Commun. Syst., vol. 19, no. 2, 2020, Art. no.
[35]
C. Zhang and Z. Zheng, “Task migration for mobile edge computing using deep reinforcement learning,” Future Gener. Comput. Syst., vol. 96, pp. 111–118, Jul. 2019.
[36]
Z. Gao, Q. Jiao, K. Xiao, Q. Wang, Z. Mo, and Y. Yang, “Deep reinforcement learning based service migration strategy for edge computing,” in Proc. IEEE Int. Conf. Service Oriented Syst. Eng. (SOSE), 2019, pp. 116–1165.
[37]
L. Ma, S. Yi, N. Carter, and Q. Li, “Efficient live migration of edge services leveraging container layered storage,” IEEE Trans. Mobile Comput., vol. 18, no. 9, pp. 2020–2033, Sep. 2019.
[38]
C. Dupont, R. Giaffreda, and L. Capra, “Edge computing in IoT context: Horizontal and vertical Linux container migration,” in Proc. IEEE Global Internet Things Summit (GIoTS), 2017, pp. 1–4.
[39]
U. Mandal, M. F. Habib, S. Zhang, B. Mukherjee, and M. Tornatore, “Greening the cloud using renewable-energy-aware service migration,” IEEE Netw., vol. 27, no. 6, pp. 36–43, Nov./Dec. 2013.
[40]
P. Mach and Z. Becvar, “Mobile edge computing: A survey on architecture and computation offloading,” IEEE Commun. Surveys Tuts., vol. 19, no. 3, pp. 1628–1656, 3rd Quart., 2017.
[41]
S. Sakr, A. Liu, D. M. Batista, and M. Alomari, “A survey of large scale data management approaches in cloud environments,” IEEE Commun. Surveys Tuts., vol. 13, no. 3, pp. 311–336, 3rd Quart., 2011.
[42]
Q. Luo, S. Hu, C. Li, G. Li, and W. Shi, “Resource scheduling in edge computing: A survey,” IEEE Commun. Surveys Tuts., vol. 23, no. 4, pp. 2131–2165, 1st Quart., 2021.
[43]
X. Wang, Y. Han, V. C. M. Leung, D. Niyato, X. Yan, and X. Chen, “Convergence of edge computing and deep learning: A comprehensive survey,” IEEE Commun. Surveys Tuts., vol. 22, no. 2, pp. 869–904, 2nd Quart., 2020.
[44]
S. Yi, C. Li, and Q. Li, “A survey of fog computing: concepts, applications and issues,” in Proc. Workshop Mobile Big Data, 2015, pp. 37–42.
[45]
M. Mukherjee, L. Shu, and D. Wang, “Survey of fog computing: Fundamental, network applications, and research challenges,” IEEE Commun. Surveys Tuts., vol. 20, no. 3, pp. 1826–1857, 3rd Quart., 2018.
[46]
M. A. Shah, J. M. Hellerstein, S. Chandrasekaran, and M. J. Franklin, “FLUX: An adaptive partitioning operator for continuous query systems,” in Proc. 19th Int. Conf. Data Eng., 2003, pp. 25–36.
[47]
Y. Xing, S. Zdonik, and J.-H. Hwang, “Dynamic load distribution in the borealis stream processor,” in Proc. IEEE 21st Int. Conf. Data Eng. (ICDE), 2005, pp. 791–802.
[48]
J.-H. Hwang, Y. Xing, U. Cetintemel, and S. Zdonik, “A cooperative, self-configuring high-availability solution for stream processing,” in Proc. IEEE 23rd Int. Conf. Data Eng., 2007, pp. 176–185.
[49]
Y. Zhou, K. Aberer, and K.-L. Tan, “Toward massive query optimization in large-scale distributed stream systems,” in Proc. ACM/IFIP/USENIX Int. Conf. Distrib. Syst. Platforms Open Distrib. Process., 2008, pp. 326–345.
[50]
W. Hummer, P. Leitner, B. Satzger, and S. Dustdar, “Dynamic migration of processing elements for optimized query execution in event-based systems,” in Proc. OTM Confeder. Int. Conf. Move Meaningful Internet Syst., 2011, pp. 451–468.
[51]
R. C. Fernandez, M. Migliavacca, E. Kalyvianaki, and P. Pietzuch, “Integrating scale out and fault tolerance in stream processing using operator state management,” in Proc. ACM SIGMOD Int. Conf. Manag. Data (SIGMOD), ‘ 2013, pp. 725–736.
[52]
C. Lei and E. A. Rundensteiner, “Robust distributed query processing for streaming data,” ACM Trans. Database Syst., vol. 39, no. 2, pp. 1–45, 2014.
[53]
A. Martin, A. Brito, and C. Fetzer, “Scalable and elastic realtime click stream analysis using Streammine3G,” in Proc. 8th ACM Int. Conf. Distrib. Event Based Syst., 2014, pp. 198–205.
[54]
T. Heinze, V. Pappalardo, Z. Jerzak, and C. Fetzer, “Auto-scaling techniques for elastic data stream processing,” in Proc. IEEE 30th Int. Conf. Data Eng. Workshops, 2014, pp. 296–302.
[55]
T. Heinze, Z. Jerzak, G. Hackenbroich, and C. Fetzer, “Latencyaware elastic scaling for distributed data stream processing systems,” in Proc. 8th ACM Int. Conf. Distrib. Event Based Syst., 2014, pp. 13–22.
[56]
E. A. Rundensteiner, L. Ding, T. Sutherland, Y. Zhu, B. Pielech, and N. Mehta, “CAPE: Continuous query engine with heterogeneousgrained adaptivity,” in Proc. 13th Int. Conf. Very Large Data Bases, vol. 30, 2004, pp. 1353–1356.
[57]
B. Gedik, “Partitioning functions for stateful data parallelism in stream processing,” VLDB J., vol. 23, no. 4, pp. 517–539, 2014.
[58]
K. G. S. Madsen and Y. Zhou, “Dynamic resource management in a massively parallel stream processing engine,” in Proc. 24th ACM Int. Conf. Inf. Knowl. Manag., 2015, pp. 13–22.
[59]
A. Martin, T. Smaneoto, T. Dietze, A. Brito, and C. Fetzer, “Userconstraint and self-adaptive fault tolerance for event stream processing systems,” in Proc. 45th Annu. IEEE/IFIP Int. Conf. Depend. Syst. Netw., 2015, pp. 462–473.
[60]
N. Zacheilas, V. Kalogeraki, N. Zygouras, N. Panagiotou, and D. Gunopulos, “Elastic complex event processing exploiting prediction,” in Proc. IEEE Int. Conf. Big Data (Big Data), 2015, pp. 213–222.
[61]
V. Cardellini, M. Nardelli, and D. Luzi, “Elastic stateful stream processing in storm,” in Proc. Int. Conf. High Perform. Comput. Simulat. (HPCS), 2016, pp. 583–590.
[62]
K. G. S. Madsen, Y. Zhou, and L. Su, “ENORM: Efficient window-based computation in large-scale distributed stream processing systems,” in Proc. 10th ACM Int. Conf. Distrib. Event Syst., 2016, pp. 37–48.
[63]
J. Li, C. Pu, Y. Chen, D. Gmach, and D. Milojicic, “Enabling elastic stream processing in shared clusters,” in Proc. IEEE 9th Int. Conf. Cloud Comput. (CLOUD), 2016, pp. 108–115.
[64]
Y. Liu, X. Shi, and H. Jin, “Runtime-aware adaptive scheduling in stream processing,” Concurrency Comput. Pract. Exp., vol. 28, no. 14, pp. 3830–3843, 2016.
[65]
C. Hochreiner, M. Vögler, S. Schulte, and S. Dustdar, “Elastic stream processing for the Internet of Things,” in Proc. IEEE 9th Int. Conf. Cloud Comput. (CLOUD), 2016, pp. 100–107.
[66]
T. Buddhika, R. Stern, K. Lindburg, K. Ericson, and S. Pallickara, “Online scheduling and interference alleviation for low-latency, highthroughput processing of data streams,” IEEE Trans. Parallel Distrib. Syst., vol. 28, no. 12, pp. 3553–3569, May 2017.
[67]
K. G. S. Madsen, Y. Zhou, and J. Cao, “Integrative dynamic reconfiguration in a parallel stream processing engine,” in Proc. IEEE 33rd Int. Conf. Data Eng. (ICDE), 2017, pp. 227–230.
[68]
F. Lombardi, L. Aniello, S. Bonomi, and L. Querzoni, “Elastic symbiotic scaling of operators and resources in stream processing systems,” IEEE Trans. Parallel Distrib. Syst., vol. 29, no. 3, pp. 572–585, Mar. 2018.
[69]
C. Wang, X. Meng, Q. Guo, Z. Weng, and C. Yang, “Automating characterization deployment in distributed data stream management systems,” IEEE Trans. Knowl. Data Eng., vol. 29, no. 12, pp. 2669–2681, Dec. 2017.
[70]
J. Fang, R. Zhang, T. Z. J. Fu, Z. Zhang, A. Zhou, and J. Zhu, “Parallel stream processing against workload skewness and variance,” in Proc. 26th Int. Symp. High Perform. Parallel Distrib. Comput., 2017, pp. 15–26.
[71]
L. Maiet al., “CHI: A scalable and programmable control plane for distributed stream processing systems,” Proc. VLDB Endow., vol. 11, no. 10, pp. 1303–1316, 2018.
[72]
V. Cardellini, F. L. Presti, M. Nardelli, and G. R. Russo, “Optimal operator deployment and replication for elastic distributed data stream processing,” Concurrency Comput. Pract. Exp., vol. 30, no. 9, 2018, Art. no.
[73]
J. Fang, R. Zhang, T. Z. J. Fu, Z. Zhang, A. Zhou, and X. Zhou, “Distributed stream rebalance for stateful operator under workload variance,” IEEE Trans. Parallel Distrib. Syst., vol. 29, no. 10, pp. 2223–2240, Oct. 2018.
[74]
S. Liu, J. Weng, J. H. Wang, C. An, Y. Zhou, and J. Wang, “An adaptive online scheme for scheduling and resource enforcement in storm,” IEEE/ACM Trans. Netw., vol. 27, no. 4, pp. 1373–1386, 2019.
[75]
M. Hoffmann, A. Lattuada, F. McSherry, V. Kalavri, J. Liagouris, and T. Roscoe, “Megaphone: Latency-conscious state migration for distributed streaming dataflows,” Proc. VLDB Endow., vol. 12, no. 9, pp. 1002–1015, 2019.
[76]
L. Wang, T. Z. J. Fu, R. T. B. Ma, M. Winslett, and Z. Zhang, “Elasticutor: Rapid elasticity for realtime stateful stream processing,” in Proc. Int. Conf. Manag. Data, 2019, pp. 573–588.
[77]
D. Sun, S. Gao, X. Liu, X. You, and R. Buyya, “Dynamic redirection of real-time data streams for elastic stream computing,” Future Gener. Comput. Syst., vol. 112, pp. 193–208, Jun. 2020.
[78]
B. D. Monte, S. Zeuch, T. Rabl, and V. Markl, “Rhino: Efficient management of very large distributed state for stream processing engines,” in Proc. ACM SIGMOD Int. Conf. Manag. Data, 2020, pp. 2471–2486.
[79]
L. Zhang, W. Zheng, C. Li, Y. Shen, and M. Guo, “AutraScale: An automated and transfer learning solution for streaming system autoscaling,” in Proc. IEEE Int. Parallel Distrib. Process. Symp. (IPDPS), 2021, pp. 912–921.
[80]
R. Gu, H. Yin, W. Zhong, C. Yuan, and Y. Huang, “MECES: Latencyefficient rescaling via prioritized state migration for stateful distributed stream processing systems,” in Proc. USENIX Annu. Techn. Conf. (USENIX ATC), 2022, pp. 539–556.
[81]
M. K. Geldenhuys, D. Scheinert, O. Kao, and L. Thamsen, “PHOEBE: QoS-aware distributed stream processing through anticipating dynamic workloads,” in Proc. IEEE Int. Conf. Web Services (ICWS), 2022, pp. 198–207.
[82]
Y. Liu, H. Xu, and W. C. Lau, “Online resource optimization for elastic stream processing with regret guarantee,” in Proc. 51st Int. Conf. Parallel Process., 2022, pp. 1–11.
[83]
Y. Ahmad, U. Cetintemel, J. Jannotti, A. Zgolinski, and S. B. Zdonik, “Network awareness in Internet-scale stream processing,” IEEE Data Eng. Bull., vol. 28, no. 1, pp. 63–69, 2005.
[84]
O. Papaemmanouil, U. Cetintemel, and J. Jannotti, “Supporting generic cost models for wide-area stream processing,” in Proc. IEEE 25th Int. Conf. Data Eng., 2009, pp. 1084–1095.
[85]
T. Repantis and V. Kalogeraki, “Alleviating hot-spots in peer-to-peer stream processing environments,” in Proc. 5th Int. Workshop Databases Inf. Syst. Peer-to-Peer Comput. DBISP2P, 2007, p. 13.
[86]
T. Repantis and V. Kalogeraki, “Hot-spot prediction and alleviation in distributed stream processing applications,” in Proc. IEEE Int. Conf. Depend. Syst. Netw. FTCS DCC (DSN), 2008, pp. 346–355.
[87]
W. Wang, M. A. Sharaf, S. Guo, and M. Tamer Özsu, “Potential-driven load distribution for distributed data stream processing,” in Proc. 2nd Int. Workshop Scalable Stream Process. Syst., 2008, pp. 13–22.
[88]
S. Rizou, F. Dürr, and K. Rothermel, “Solving the multi-operator placement problem in large-scale operator networks,” in Proc. 19th Int. Conf. Comput. Commun. Netw., 2010, pp. 1–6.
[89]
V. Cardellini, F. L. Presti, M. Nardelli, and G. R. Russo, “Decentralized self-adaptation for elastic data stream processing,” Future Gener. Comput. Syst., vol. 87, pp. 171–185, Oct. 2018.
[90]
T. Hiessl, V. Karagiannis, C. Hochreiner, S. Schulte, and M. Nardelli, “Optimal placement of stream processing operators in the fog,” in Proc. IEEE 3rd Int. Conf. Fog Edge Comput. (ICFEC), 2019, pp. 1–10.
[91]
H. Röger, S. Bhowmik, and K. Rothermel, “Combining it all: Cost minimal and low-latency stream processing across distributed heterogeneous infrastructures,” in Proc. 20th Int. Middleware Conf., 2019, pp. 255–267.
[92]
A. Jonathan, A. Chandra, and J. Weissman, “WASP: Wide-area adaptive stream processing,” in Proc. 21st Int. Middleware Conf., 2020, pp. 221–235.
[93]
Y. Zhou, B. C. Ooi, K.-L. Tan, and J. Wu, “Efficient dynamic operator placement in a locally distributed continuous query system,” in Proc. OTM Conf. Int. Meaningful Internet Syst., 2006, pp. 54–71.
[94]
G. Brettlecker and H. Schuldt, “Reliable distributed data stream management in mobile environments,” Inf. Syst., vol. 36, no. 3, pp. 618–643, 2011.
[95]
V. Kakkad, A. E. Santosa, and B. Scholz, “Migrating operator placement for compositional stream graphs,” in Proc. 15th ACM Int. Conf. Model. Anal. Simulat. Wireless Mobile Syst., 2012, pp. 125–134.
[96]
B. Ottenwälder, B. Koldehofe, K. Rothermel, and U. Ramachandran, “MigCEP: Operator migration for mobility driven distributed complex event processing,” in Proc. 7th ACM Int. Conf. Distrib. Event Syst., 2013, pp. 183–194.
[97]
G. Chatzimilioudis, A. Cuzzocrea, D. Gunopulos, and N. Mamoulis, “A novel distributed framework for optimizing query routing trees in wireless sensor networks via optimal operator placement,” J. Comput. Syst. Sci., vol. 79, no. 3, pp. 349–368, 2013.
[98]
B. Ottenwälder, B. Koldehofe, K. Rothermel, K. Hong, D. Lillethun, and U. Ramachandran, “MCEP: A mobility-aware complex event processing system,” ACM Trans. Internet Technol., vol. 14, no. 1, pp. 1–24, 2014.
[99]
M. Luthra, B. Koldehofe, P. Weisenburger, G. Salvaneschi, and R. Arif, “TCEP: Adapting to dynamic user environments by enabling transitions between operator placement mechanisms,” in Proc. 12th ACM Int. Conf. Distrib. Event Syst., 2018, pp. 136–147.
[100]
J. Xu and B. Palanisamy, “Model-based reinforcement learning for elastic stream processing in edge computing,” in Proc. IEEE 28th Int. Conf. High Perform. Comput. Data Anal. (HiPC), 2021, pp. 292–301.
[101]
P. Liu, D. D. Silva, and L. Hu, “DART: A scalable and adaptive edge stream processing engine,” in Proc. USENIX Annu. Tech. Conf., 2021, pp. 239–252.
[102]
E. Oliveira, A. R. da Rocha, M. Mattoso, and F. C. Delicato, “Latency and energy-awareness in data stream processing for edge based IoT systems,” J. Grid Comput., vol. 20, no. 3, p. 27, 2022.
[103]
V. Gulisano, R. Jimenez-Peris, M. Patino-Martinez, C. Soriente, and P. Valduriez, “StreamCloud: An elastic and scalable data streaming system,” IEEE Trans. Parallel Distrib. Syst., vol. 23, no. 12, pp. 2351–2365, Dec. 2012.
[104]
B. Lohrmann, P. Janacik, and O. Kao, “Elastic stream processing with latency guarantees,” in Proc. IEEE 35th Int. Conf. Distrib. Comput. Syst., 2015, pp. 399–410.
[105]
T. De Matteis and G. Mencagli, “Keep calm and react with foresight: Strategies for low-latency and energy-efficient elastic data stream processing,” ACM SIGPLAN Notices, vol. 51, no. 8, pp. 1–12, 2016.
[106]
N. Tziritas, T. Loukopoulos, S. U. Khan, C.-Z. Xu, and A. Y. Zomaya, “On improving constrained single and group operator placement using evictions in big data environments,” IEEE Trans. Services Comput., vol. 9, no. 5, pp. 818–831, Sep./Oct. 2016.
[107]
T. De Matteis and G. Mencagli, “Proactive elasticity and energy awareness in data stream processing,” J. Syst. Softw., vol. 127, pp. 302–319, May 2017.
[108]
K. Ma, B. Yang, and Z. Yu, “Optimization of stream-based live data migration strategy in the cloud,” Concurrency Comput. Pract. Exp., vol. 30, no. 12, 2018, Art. no.
[109]
D. Dedousis, N. Zacheilas, and V. Kalogeraki, “On the fly load balancing to address hot topics in topic-based pub/sub systems,” in Proc. IEEE 38th Int. Conf. Distrib. Comput. Syst. (ICDCS), 2018, pp. 76–86.
[110]
B. Li, Z. Zhang, T. Zheng, Q. Zhong, Q. Huang, and X. Cheng, “Marabunta: Continuous distributed processing of skewed streams,” in Proc. 20th IEEE/ACM Int. Symp. Cluster Cloud Internet Comput. (CCGRID), 2020, pp. 252–261.
[111]
V. Gulisano, H. Najdataei, Y. Nikolakopoulos, A. V. Papadopoulos, M. Papatriantafilou, and P. Tsigas, “Stretch: Virtual shared-nothing parallelism for scalable and elastic stream processing,” IEEE Trans. Parallel Distrib. Syst., vol. 33, no. 12, pp. 4221–4238, Dec. 2022.
[112]
T. Heinzeet al., “FUGU: Elastic data stream processing with latency constraints,” IEEE Data Eng. Bull., vol. 38, no. 4, pp. 73–81, Jan. 2015.
[113]
Y. Wu and K.-L. Tan, “ChronoStream: Elastic stateful stream computation in the cloud,” in Proc. IEEE 31st Int. Conf. Data Eng., 2015, pp. 723–734.
[114]
L. Xu, B. Peng, and I. Gupta, “STELA: Enabling stream processing systems to scale-in and scale-out on-demand,” in Proc. IEEE Int. Conf. Cloud Eng. (IC2E), 2016, pp. 22–31.
[115]
X. Ni, S. Schneider, R. Pavuluri, J. Kaus, and K.-L. Wu, “Automating multi-level performance elastic components for IBM streams,” in Proc. 20th Int. Middleware Conf., 2019, pp. 163–175.
[116]
D. Sun, S. Gao, X. Liu, and R. Buyya, “A multi-level collaborative framework for elastic stream computing systems,” Future Gener. Comput. Syst., vol. 128, pp. 117–131, Mar. 2022.
[117]
P. Pietzuch, J. Ledlie, J. Shneidman, M. Roussopoulos, M. Welsh, and M. Seltzer, “Network-aware operator placement for stream-processing systems,” in Proc. IEEE 22nd Int. Conf. Data Eng. (ICDE), 2006, pp. 49–49.
[118]
F. Liu, Z. Jin, W. Mu, W. Zhu, Y. Zhang, and W. Wang, “DROAllocator: A dynamic resource-aware operator allocation framework in distributed streaming processing,” in Proc. Netw. Parallel Comput. 17th IFIP WG 10.3 Int. Conf. (NPC), Sep. 2021, pp. 349–360.
[119]
Apache.” Accessed: Feb. 26, 2023. [Online]. Available: https://storm.apache.org
[120]
Espertech.” Accessed: Feb. 26, 2023. [Online]. Available: https://www.espertech.com/esper
[121]
H. Isah, T. Abughofa, S. Mahfuz, D. Ajerla, F. Zulkernine, and S. Khan, “A survey of distributed data stream processing frameworks,” IEEE Access, vol. 7, pp. 154300–154316, 2019.
[122]
Apache Beam.” Accessed: Feb. 26, 2023. [Online]. Available: https://beam.apache.org
[123]
E. Volnes, T. Plagemann, V. Goebel, and S. Kristiansen, “EXPOSE: Experimental performance evaluation of stream processing engines made easy,” in Proc. Technol. Conf. Perform. Eval. Benchmarking, 2020, pp. 18–34.
[124]
F. Starks, T. P. Plagemann, and S. Kristiansen, “DCEP-SIM: An open simulation framework for distributed CEP,” in Proc. 11th ACM Int. Conf. Distrib. Event Syst., 2017, pp. 180–190.
[125]
G. Amarasinghe, M. D. de Assuncao, A. Harwood, and S. Karunasekera, “ECSNet: A simulator for distributed stream processing on edge and cloud environments,” Future Gener. Comput. Syst., vol. 111, pp. 401–418, Oct. 2020.
[126]
T. Goyal, A. Singh, and A. Agrawal, “CloudSim: Simulator for cloud computing infrastructure and modeling,” Procedia Eng., vol. 38, pp. 3566–3572, 2012. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1877705812023259
[127]
H. Gupta, A. V. Dastjerdi, S. K. Ghosh, and R. Buyya, “iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, edge and fog computing environments,” Softw. Pract. Exp., vol. 47, no. 9, pp. 1275–1296, 2017.
[128]
R. Mahmud, S. Pallewatta, M. Goudarzi, and R. Buyya, “iFogSim2: An extended iFogSim simulator for mobility, clustering, and microservice management in edge and fog computing environments,” J. Syst. Softw., vol. 190, Aug. 2022, Art. no.
[129]
C. Sonmez, A. Ozgovde, and C. Ersoy, “EdgeCloudSim: An environment for performance evaluation of edge computing systems,” Trans. Emerg. Telecommun. Technol., vol. 29, no. 11, 2018, Art. no.
[130]
T. Qayyum, A. W. Malik, M. A. K. Khattak, O. Khalid, and S. U. Khan, “FogNetSim: A toolkit for modeling and simulation of distributed fog environment,” IEEE Access, vol. 6, pp. 63570–63583, 2018.
[131]
D. N. Jhaet al., “IoTSim-edge: A simulation framework for modeling the behavior of Internet of Things and edge computing environments,” Softw. Pract. Exp., vol. 50, no. 6, pp. 844–867, 2020.
[132]
C. Puliafitoet al., “MobFogSim: Simulation of mobility and migration for fog computing,” Simulat. Model. Pract. Theory, vol. 101, May 2020, Art. no.
[133]
I. Lera, C. Guerrero, and C. Juiz, “YAFs: A simulator for IoT scenarios in fog computing,” IEEE Access, vol. 7, pp. 91745–91758, 2019.
[134]
C. Mechalikh, H. Taktak, and F. Moussa, “PureEdgeSim: A simulation toolkit for performance evaluation of cloud, fog, and pure edge computing environments,” in Proc. Int. Conf. High Perform. Comput. Simulat. (HPCS), 2019, pp. 700–707.
[135]
M. Salama, Y. Elkhatib, and G. Blair, “IoTNetSim: A modelling and simulation platform for end-to-end IoT services and networking,” in Proc. 12th IEEE/ACM Int. Conf. Utility Cloud Comput., 2019, pp. 251–261.
[136]
J. Wei, S. Cao, S. Pan, J. Han, L. Yan, and L. Zhang, “SatEdgeSim: A toolkit for modeling and simulation of performance evaluation in satellite edge computing environments,” in Proc. 12th Int. Conf. Commun. Softw. Netw. (ICCSN), 2020, pp. 307–313.
[137]
K. Alwaselet al., “IoTSim-OSMOSIS: A framework for modeling and simulating IoT applications over an edge-cloud continuum,” J. Syst. Architect., vol. 116, Jun. 2021, Art. no.
[138]
V. Cardellini, V. Grassi, F. L. Presti, and M. Nardelli, “Optimal operator replication and placement for distributed stream processing systems,” ACM SIGMETRICS Perform. Eval. Rev., vol. 44, no. 4, pp. 11–22, 2017.
[139]
B. Koldehofe, R. Mayer, U. Ramachandran, K. Rothermel, and M. Völz, “Rollback-recovery without checkpoints in distributed event processing systems,” in Proc. 7th ACM Int. Conf. Distrib. Event Syst., 2013, pp. 27–38.
[140]
Kafka.” Accessed: Jul. 5, 2021. [Online]. Available: https://kafka.apache.org
[141]
Y. Zhu, E. A. Rundensteiner, and G. T. Heineman, “Dynamic plan migration for continuous queries over data streams,” in Proc. ACM SIGMOD Int. Conf. Manag. Data, 2004, pp. 431–442.
[142]
B. Gedik, S. Schneider, M. Hirzel, and K.-L. Wu, “Elastic scaling for data stream processing,” IEEE Trans. Parallel Distrib. Syst., vol. 25, no. 6, pp. 1447–1463, Jun. 2014.
[143]
E. Volnes, T. Plagemann, B. Koldehofe, and V. Goebel, “Travel light: State shedding for efficient operator migration,” in Proc. 16th ACM Int. Conf. Distrib. Event Syst., 2022, pp. 79–84.
[144]
T. N. Pham, N. R. Katsipoulakis, P. K. Chrysanthis, and A. Labrinidis, “Uninterruptible migration of continuous queries without operator state migration,” ACM SIGMOD Rec., vol. 46, no. 3, pp. 17–22, 2017.
[145]
F. Starks and T. P. Plagemann, “Operator placement for efficient distributed complex event processing in MANETs,” in Proc. IEEE 11th Int. Conf. Wireless Mobile Comput. Netw. Commun. (WiMob), 2015, pp. 83–90.
[146]
U. Srivastava, K. Munagala, and J. Widom, “Operator placement for in-network stream query processing,” in Proc. 24th ACM SIGMODSIGACT- SIGART Symp. Principles Database Syst., 2005, pp. 250–258.
[147]
D. Abadiet al., “The beckman report on database research,” Commun. ACM, vol. 59, no. 2, pp. 92–99, 2016.
[148]
Gurobi.” Accessed: Jan. 23, 2023. [Online]. Available: https://www.gurobi.com
[149]
IBM.” Accessed: Jan. 23, 2023. [Online]. Available: https://www-01.ibm.com/software/commerce/optimization/cplex-optimizer
[150]
V. Kalavri, J. Liagouris, M. Hoffmann, D. Dimitrova, M. Forshaw, and T. Roscoe, “Three steps is all you need: Fast, accurate, automatic scaling decisions for distributed streaming dataflows,” in Proc. 13th USENIX Symp. Oper. Syst. Design Implement. (OSDI), Oct. 2018, pp. 783–798.
[151]
N. Hidalgo, D. Wladdimiro, and E. Rosas, “Self-adaptive processing graph with operator fission for elastic stream processing,” J. Syst. Softw., vol. 127, pp. 205–216, May 2017.
[152]
M. Lindeberg and T. Plagemann, “A study on migration scheduling in distributed stream processing engines,” in Proc. 23rd Int. Conf. Distrib. Comput. Netw., 2022, pp. 50–61.
[153]
J. F. C. Kingman, “The single server queue in heavy traffic,” in Mathematical Cambridge Philosophical Society, vol. 57. Cambridge, U.K.: Cambridge Univ. Press, 1961, pp. 902–904.
[154]
C. E. Rasmussen, “Gaussian processes in machine learning,” in Summer School on Machine Learning. Berlin, Germany: Springer, 2003, pp. 63–71.
[155]
Weka.” Accessed: Aug. 28, 2021. [Online]. Available: https://weka.cms.waikato.ac.nz/
[156]
MOA.” Accessed: Aug. 28, 2021. [Online]. Available: https://www.cs.waikato.ac.nz/ml/weka/
[157]
S. B. Taieb, G. Bontempi, A. F. Atiya, and A. Sorjamaa, “A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition,” Exp. Syst. Appl., vol. 39, no. 8, pp. 7067–7083, 2012.
[158]
RocksDB.” Accessed: Jan. 23, 2023. [Online]. Available: https://rocksdb.org/
[159]
M. Abbasi, A. Shahraki, and A. Taherkordi, “Deep learning for network traffic monitoring and analysis (NTMA): A survey,” Comput. Commun., vol. 170, pp. 19–41, Mar. 2021.
[160]
G. M. Dias, B. Bellalta, and S. Oechsner, “A survey about predictionbased data reduction in wireless sensor networks,” ACM Comput. Surveys, vol. 49, no. 3, pp. 1–35, 2016.

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image IEEE Communications Surveys & Tutorials
IEEE Communications Surveys & Tutorials  Volume 26, Issue 1
Firstquarter 2024
746 pages

Publisher

IEEE Press

Publication History

Published: 07 November 2023

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 21 Dec 2024

Other Metrics

Citations

View Options

View options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media