Eagle: Toward Scalable and Near-Optimal Network-Wide Sketch Deployment in Network Measurement
Authors: 
Xiang Chen, Qingjiang Xiao, Hongyan Liu, Qun Huang, Dong Zhang, Xuan Liu, Longbing Hu, Haifeng Zhou, Chunming Wu, Kui RenAuthors Info & Claims
Pages 291 - 310
Abstract
Sketches are useful for network measurement thanks to their low resource overheads and theoretically bounded accuracy. However, their network-wide deployment suffers from the trade-off between optimality and scalability: (1) Most solutions rely on mixed integer linear programming (MILP) solvers to provide the optimal decisions. But they are time-consuming and can hardly scale to large-scale deployment scenarios. (2) While heuristics achieve scalability, they deteriorate resource and performance overheads. We propose Eagle, a framework that achieves scalable and near-optimal network-wide sketch deployment. Our key idea is to decompose network-wide sketch deployment into sub-problems. Such decomposition allows Eagle to (1) simultaneously optimize switch resource consumption and end-to-end performance (retaining optimality), and (2) incorporate time-saving techniques into sub-problem solving (achieving scalability). Compared to existing solutions, Eagle improves scalability by up to 255× with negligible loss of optimality. It has also saved administrators in a production network days of efforts and reduced the operation time from O(hour) to O(second).
References
[1]
M. Al-Fares, A. Loukissas, and A. Vahdat. A scalable, commodity data center network architecture. ACM SIGCOMM computer communication review, 38(4):63--74, 2008.
[2]
C. Albrecht, A. Merchant, M. Stokely, M. Waliji, F. Labelle, N. Coehlo, X. Shi, and C. E. Schrock. Janus: Optimal flash provisioning for cloud storage workloads. In USENIX ATC, pages 91--102, 2013.
[3]
Amazon CloudWatch. http://aws.amazon.com/cloudwatch/.
[4]
AMD's New 128-Core CPU Elevates Efficiency of Cloud Data Centers. https://www.electronicdesign.com/technologies/embedded/article/21268245/electronic-design-compact-128-core-cpu-elevates-efficiency-of-cloud-data-centers.
[5]
A. Anup, L. Zaoxing, and S. Srinivasan. Heterosketch: Coordinating network-wide monitoring in heterogeneous and dynamic networks. In USENIX NSDI, pages 1--23, 2022.
[6]
M. T. Arashloo, Y. Koral, M. Greenberg, J. Rexford, and D. Walker. Snap: Stateful network-wide abstractions for packet processing. In ACM SIGCOMM, pages 29--43, 2016.
[7]
Barefoot Network. Barefoot Tofino. https://www.barefootnetworks.com/technology/#tofino.
[8]
R. Beckett, R. Mahajan, T. Millstein, J. Padhye, and D. Walker. Don't mind the gap: Bridging network-wide objectives and device-level configurations. In ACM SIGCOMM, pages 328--341, 2016.
[9]
R. Ben Basat, G. Einziger, R. Friedman, M. C. Luizelli, and E. Waisbard. Constant time updates in hierarchical heavy hitters. In ACM SIGCOMM, pages 127--140, 2017.
[10]
B. H. Bloom. Space/time trade-offs in hash coding with allowable errors. Communications of the ACM, 13(7):422--426, 1970.
[11]
P. Bosshart, D. Daly, G. Gibb, M. Izzard, N. McKeown, J. Rexford, C. Schlesinger, D. Talayco, A. Vahdat, G. Varghese, and D. Walker. P4: Programming protocol-independent packet processors. ACM SIGCOMM Computer Communication Review, 44(3):87--95, 2014.
[12]
P. Bosshart, G. Gibb, H.-S. Kim, G. Varghese, N. McKeown, M. Izzard, F. Mujica, and M. Horowitz. Forwarding metamorphosis: Fast programmable match-action processing in hardware for sdn. ACM SIGCOMM Computer Communication Review, 43(4):99--110, 2013.
[13]
T. Bu, J. Cao, A. Chen, and P. P. Lee. Sequential hashing: A flexible approach for unveiling significant patterns in high speed networks. Computer Networks, 54(18):3309--3326, 2010.
[14]
M. Charikar, K. Chen, and M. Farach-Colton. Finding frequent items in data streams. In ICALP, pages 693--703, 2002.
[15]
X. Chen, Q. Huang, P. Wang, H. Liu, Y. Chen, D. Zhang, H. Zhou, and C. Wu. Mtp: Avoiding control plane overload with measurement task placement. In IEEE INFOCOM, pages 1--10, 2021.
[16]
X. Chen, H. Liu, Q. Huang, P. Wang, D. Zhang, H. Zhou, and C. Wu. Speed: Resource-efficient and high-performance deployment for data plane programs. In IEEE ICNP, pages 1--12, 2020.
[17]
X. Chen, H. Liu, Q. Xiao, K. Guo, T. Sun, X. Ling, X. Liu, Q. Huang, D. Zhang, H. Zhou, et al. Toward low-overhead inter-switch coordination in network-wide data plane program deployment. In IEEE ICDCS, pages 370--380, 2022.
[18]
D. R. Choffnes, F. E. Bustamante, and Z. Ge. Crowdsourcing service-level network event monitoring. In ACM SIGCOMM, pages 387--398, 2010.
[19]
G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Finding hierarchical heavy hitters in data streams. In VLDB, pages 464--475, 2003.
[20]
G. Cormode and S. Muthukrishnan. An improved data stream summary: the count-min sketch and its applications. Journal of Algorithms, 55(1):58--75, 2005.
[21]
CPLEX. https://www.ibm.com/analytics/cplex-optimizer.
[22]
M. Dalton, D. Schultz, J. Adriaens, A. Arefin, A. Gupta, B. Fahs, D. Rubinstein, E. C. Zermeno, E. Rubow, J. A. Docauer, et al. Andromeda: Performance, isolation, and velocity at scale in cloud network virtualization. In USENIX NSDI, pages 373--387, 2018.
[23]
Data-center switch silicon developments keep the market future-proof. https://www.telecomtv.com/content/digital-platforms-services/data-center-switch-silicon-developments-keep-the-market-future-proof-39271/.
[24]
D. Dumitrescu, R. Stoenescu, L. Negreanu, and C. Raiciu. bf4: towards bug-free p4 programs. In ACM SIGCOMM, pages 571--585, 2020.
[25]
L. Fan, P. Cao, J. Almeida, and A. Z. Broder. Summary cache: a scalable wide-area web cache sharing protocol. IEEE/ACM Transactions on Networking, 8(3):281--293, 2000.
[26]
A. Fischer, J. F. Botero, M. T. Beck, H. De Meer, and X. Hesselbach. Virtual network embedding: A survey. IEEE Communications Surveys & Tutorials, 15(4):1888--1906, 2013.
[27]
P. Flajolet, É. Fusy, O. Gandouet, and F. Meunier. Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm. In Discrete Mathematics and Theoretical Computer Science, pages 137--156. Discrete Mathematics and Theoretical Computer Science, 2007.
[28]
N. Foster, R. Harrison, M. J. Freedman, C. Monsanto, J. Rexford, A. Story, and D. Walker. Frenetic: a network programming language. In ACM ICFP, pages 279--291, 2011.
[29]
J. Gao, E. Zhai, H. H. Liu, R. Miao, Y. Zhou, B. Tian, C. Sun, D. Cai, M. Zhang, and M. Yu. Lyra: A cross-platform language and compiler for data plane programming on heterogeneous asics. In ACM SIGCOMM, pages 435--450, 2020.
[30]
X. Gao, T. Kim, M. D. Wong, D. Raghunathan, A. K. Varma, P. G. Kannan, A. Sivaraman, S. Narayana, and A. Gupta. Switch code generation using program synthesis. In ACM SIGCOMM, pages 44--61, 2020.
[31]
S. Grant, A. Yelam, M. Bland, and A. C. Snoeren. Smartnic performance isolation with fairnic: Programmable networking for the cloud. In ACM SIGCOMM, pages 681--693, 2020.
[32]
A. Gupta, R. Harrison, M. Canini, N. Feamster, J. Rexford, and W. Willinger. Sonata: Query-driven streaming network telemetry. In ACM SIGCOMM, pages 357--371, 2018.
[33]
Gurobi Optimizer. http://www.gurobi.com.
[34]
M. Hogan, S. Landau-Feibish, M. T. Arashloo, J. Rexford, and D. Walker. Modular switch programming under resource constraints. In USENIX NSDI, pages 1--15, 2022.
[35]
Q. Huang, X. Jin, P. P. Lee, R. Li, L. Tang, Y.-C. Chen, and G. Zhang. Sketchvisor: Robust network measurement for software packet processing. In ACM SIGCOMM, pages 113--126, 2017.
[36]
Q. Huang, P. P. Lee, and Y. Bao. Sketchlearn: relieving user burdens in approximate measurement with automated statistical inference. In ACM SIGCOMM, pages 576--590, 2018.
[37]
Q. Huang, S. Sheng, X. Chen, Y. Bao, R. Zhang, Y. Xu, and G. Zhang. Toward nearly-zero-error sketching via compressive sensing. In USENIX NSDI, pages 1027--1044, 2021.
[38]
Q. Huang, H. Sun, P. P. Lee, W. Bai, F. Zhu, and Y. Bao. Omnimon: Re-architecting network telemetry with resource efficiency and full accuracy. In ACM SIGCOMM, pages 404--421, 2020.
[39]
X. Jin, X. Li, H. Zhang, R. Soulé, J. Lee, N. Foster, C. Kim, and I. Stoica. Netcache: Balancing key-value stores with fast in-network caching. In ACM SOSP, pages 121--136, 2017.
[40]
L. Jose, L. Yan, G. Varghese, and N. McKeown. Compiling packet programs to reconfigurable switches. In USENIX NSDI, pages 103--115, 2015.
[41]
N. Kang, Z. Liu, J. Rexford, and D. Walker. Optimizing the one big switch abstraction in software-defined networks. In ACM CoNEXT, pages 13--24, 2013.
[42]
G. P. Katsikas, T. Barbette, D. Kostic, R. Steinert, and G. Q. Maguire Jr. Metron: Nfv service chains at the true speed of the underlying hardware. In USENIX NSDI, pages 171--186, 2018.
[43]
D. Kim, J. Nelson, D. R. Ports, V. Sekar, and S. Seshan. Redplane: Enabling fault-tolerant stateful in-switch applications. In ACM SIGCOMM, pages 223--244, 2021.
[44]
S. Knight, H. X. Nguyen, N. Falkner, R. Bowden, and M. Roughan. The internet topology zoo. IEEE Journal on Selected Areas in Communications, 29(9):1765--1775, 2011.
[45]
T. Koch, T. Ralphs, and Y. Shinano. Could we use a million cores to solve an integer program? Mathematical Methods of Operations Research, 76:67--93, 2012.
[46]
B. Krishnamurthy, S. Sen, Y. Zhang, and Y. Chen. Sketch-based change detection: Methods, evaluation, and applications. In ACM IMC, pages 234--247, 2003.
[47]
Y. T. Lee and A. Sidford. Efficient inverse maintenance and faster algorithms for linear programming. In IEEE FOCS, pages 230--249, 2015.
[48]
B. Li, K. Tan, L. L. Luo, Y. Peng, R. Luo, N. Xu, Y. Xiong, P. Cheng, and E. Chen. Clicknp: Highly flexible and high performance network processing with reconfigurable hardware. In ACM SIGCOMM, pages 1--14, 2016.
[49]
Y. Li, J. Gao, E. Zhai, M. Liu, K. Liu, and H. H. Liu. Cetus: Releasing p4 programmers from the chore of trial and error compiling. In USENIX NSDI, pages 371--385, 2022.
[50]
Y. Li, R. Miao, C. Kim, and M. Yu. Flowradar: a better netflow for data centers. In USENIX NSDI, pages 311--324, 2016.
[51]
J. Liu, J. H. Wang, and Y. Jiang. Janus: A unified distributed training framework for sparse mixture-of-experts models. In ACM SIGCOMM 2023, pages 486--498, 2023.
[52]
X. Liu, M. Shirazipour, M. Yu, and Y. Zhang. Mozart: Temporal coordination of measurement. In ACM SOSR, pages 1--12, 2016.
[53]
Z. Liu, R. Ben-Basat, G. Einziger, Y. Kassner, V. Braverman, R. Friedman, and V. Sekar. Nitrosketch: Robust and general sketch-based monitoring in software switches. In ACM SIGCOMM, pages 334--350, 2019.
[54]
Z. Liu, A. Manousis, G. Vorsanger, V. Sekar, and V. Braverman. One sketch to rule them all: Rethinking network flow monitoring with univmon. In ACM SIGCOMM, pages 101--114, 2016.
[55]
Z. Liu, H. Namkung, G. Nikolaidis, J. Lee, C. Kim, X. Jin, V. Braverman, M. Yu, and V. Sekar. Jaqen: A high-performance switch-native approach for detecting and mitigating volumetric ddos attacks with programmable switches. In USENIX Security, 2021.
[56]
T. Lukovszki, M. Rost, and S. Schmid. It's a match! near-optimal and incremental middlebox deployment. ACM SIGCOMM Computer Communication Review, 46(1):30--36, 2016.
[57]
A. Mahimkar, C. E. de Andrade, R. Sinha, and G. Rana. A composition framework for change management. In ACM SIGCOMM, pages 788--806, 2021.
[58]
E. C. man Jr, M. Garey, and D. Johnson. Approximation algorithms for bin packing: A survey. Approximation algorithms for NP-hard problems, pages 46--93, 1996.
[59]
G. S. Manku and R. Motwani. Approximate frequency counts over data streams. In VLDB, pages 346--357, 2002.
[60]
J. Marques, K. Levchenko, and L. Gaspary. Intsight: Diagnosing slo violations with in-band network telemetry. In ACM CoNEXT, pages 421--434, 2020.
[61]
J. McClurg, H. Hojjat, N. Foster, and P. Černỳ. Event-driven network programming. In ACM PLDI, pages 369--385, 2016.
[62]
C. Monsanto, N. Foster, R. Harrison, and D. Walker. A compiler and run-time system for network programming languages. In ACM POPL, pages 217--230, 2012.
[63]
C. Monsanto, J. Reich, N. Foster, J. Rexford, and D. Walker. Composing software defined networks. In USENIX NSDI, pages 1--13, 2013.
[64]
M. Moshref, M. Yu, R. Govindan, and A. Vahdat. Dream: dynamic resource allocation for software-defined measurement. ACM SIGCOMM Computer Communication Review, 44(4):419--430, 2015.
[65]
M. Moshref, M. Yu, R. Govindan, and A. Vahdat. Scream: Sketch resource allocation for software-defined measurement. In ACM CoNEXT, page 14, 2015.
[66]
M. Moshref, M. Yu, R. Govindan, and A. Vahdat. Trumpet: Timely and precise triggers in data centers. In ACM SIGCOMM, pages 129--143, 2016.
[67]
H. Namkung, Z. Liu, D. Kim, V. Sekar, and P. Steenkiste. Sketchovsky: Enabling ensembles of sketches on programmable switches. In USENIX NSDI, pages 1273--1292, 2023.
[68]
H. Namkung, Z. Liu, D. Kim, V. Sekar, P. Steenkiste, G. Liu, A. Li, C. Canel, A. A. Philip, R. Ware, et al. Sketchlib: Enabling efficient sketch-based monitoring on programmable switches. In USENIX NSDI, pages 1--17, 2022.
[69]
S. Narayana, A. Sivaraman, V. Nathan, et al. Language-directed hardware design for network performance monitoring. In ACM SIGCOMM, pages 85--98, 2017.
[70]
D. Narayanan, F. Kazhamiaka, F. Abuzaid, P. Kraft, A. Agrawal, S. Kandula, S. Boyd, and M. Zaharia. Solving large-scale granular resource allocation problems efficiently with pop. In ACM SOSP, pages 521--537, 2021.
[71]
A. Newell, D. Skarlatos, J. Fan, P. Kumar, M. Khutornenko, M. Pundir, Y. Zhang, M. Zhang, Y. Liu, L. Le, et al. Ras: Continuously optimized region-wide datacenter resource allocation. In ACM SOSP, pages 505--520, 2021.
[72]
P4C. https://github.com/p4lang/p4c.
[73]
T. Pan, N. Yu, C. Jia, J. Pi, L. Xu, Y. Qiao, Z. Li, K. Liu, J. Lu, J. Lu, et al. Sailfish: Accelerating cloud-scale multi-tenant multi-service gateways with programmable switches. In ACM SIGCOMM, pages 194--206, 2021.
[74]
D. Popescu, N. Zilberman, and A. Moore. Characterizing the impact of network latency on cloud-based applications' performance. 2017.
[75]
J. Reich, C. Monsanto, N. Foster, J. Rexford, and D. Walker. Modular sdn programming with pyretic. Technical Report of USENIX, 2013.
[76]
Y. Shinano, T. Achterberg, T. Berthold, S. Heinz, T. Koch, and M. Winkler. Solving open mip instances with parascip on supercomputers using up to 80,000 cores. In IEEE IPDPS, pages 770--779, 2016.
[77]
A. Sivaraman, A. Cheung, M. Budiu, C. Kim, M. Alizadeh, H. Balakrishnan, G. Varghese, N. McKeown, and S. Licking. Packet transactions: High-level programming for line-rate switches. In ACM SIGCOMM, pages 15--28, 2016.
[78]
V. Sivaraman, S. Narayana, O. Rottenstreich, S. Muthukrishnan, and J. Rexford. Heavy-hitter detection entirely in the data plane. In ACM SOSR, pages 164--176, 2017.
[79]
C. H. Song, P. G. Kannan, B. K. H. Low, and M. C. Chan. Fcm-sketch: generic network measurements with data plane support. In ACM CoNEXT, pages 78--92, 2020.
[80]
H. Soni, M. Rifai, P. Kumar, R. Doenges, and N. Foster. Composing dataplane programs with μp4. In ACM SIGCOMM, pages 329--343, 2020.
[81]
N. Sultana, J. Sonchack, H. Giesen, I. Pedisich, Z. Han, N. Shyamkumar, S. Burad, A. DeHon, and B. T. Loo. Flightplan: Dataplane disaggregation and placement for p4 programs. In USENIX NSDI, pages 571--592, 2021.
[82]
C. Sun, J. Bi, Z. Zheng, H. Yu, and H. Hu. Nfp: Enabling network function parallelism in nfv. In ACM SIGCOMM, pages 43--56, 2017.
[83]
H. Sun, J. Li, J. He, J. Gui, and Q. Huang. Omniwindow: A general and efficient window mechanism framework for network telemetry. In ACM SIGCOMM, pages 867--880, 2023.
[84]
L. Suresh, J. Loff, F. Kalim, S. A. Jyothi, N. Narodytska, L. Ryzhyk, S. Gamage, B. Oki, P. Jain, and M. Gasch. Building scalable and flexible cluster managers using declarative programming. In USENIX OSDI, pages 827--844, 2020.
[85]
L. Tang, Q. Huang, and P. P. Lee. Mv-sketch: A fast and compact invertible sketch for heavy flow detection in network data streams. In IEEE INFOCOM, pages 2026--2034, 2019.
[86]
L. Tang, Q. Huang, and P. P. Lee. Spreadsketch: Toward invertible and network-wide detection of superspreaders. In IEEE INFOCOM, pages 1608--1617, 2020.
[87]
Tofino2. https://www.intel.com/content/www/us/en/products/network-io/programmable-ethernet-switch/tofino-2-series.html.
[88]
S. Venkataraman, D. Song, P. B. Gibbons, and A. Blum. New streaming algorithms for fast detection of superspreaders. In NDSS, pages 1--18, 2005.
[89]
K.-Y. Whang, B. T. Vander-Zanden, and H. M. Taylor. A linear-time probabilistic counting algorithm for database applications. ACM Transactions on Database Systems, 15(2):208--229, 1990.
[90]
P. Wintermeyer, M. Apostolaki, A. Dietmüller, and L. Vanbever. P2go: P4 profile-guided optimizations. In ACM HotNets, pages 146--152, 2020.
[91]
Working With Multiple Objectives. https://www.gurobi.com/documentation/9.0/refman/working_with_multiple_obje.html.
[92]
J. Xing, W. Wu, and A. Chen. Ripple: A programmable, decentralized link-flooding defense against adaptive adversaries. In USENIX Security, 2021.
[93]
Z. Xu, F. Y. Yan, R. Singh, J. T. Chiu, A. M. Rush, and M. Yu. Teal: Learning-accelerated optimization of wan traffic engineering. In ACM SIGCOMM, pages 378--393, 2023.
[94]
T. Yang, J. Jiang, P. Liu, Q. Huang, J. Gong, Y. Zhou, R. Miao, X. Li, and S. Uhlig. Elastic sketch: Adaptive and fast network-wide measurements. In ACM SIGCOMM, pages 561--575, 2018.
[95]
M. Yu, L. Jose, and R. Miao. Software defined traffic measurement with opens-ketch. In USENIX NSDI, pages 29--42, 2013.
[96]
M. Yu, Y. Yi, J. Rexford, and M. Chiang. Rethinking virtual network embedding: Substrate support for path splitting and migration. ACM SIGCOMM Computer Communication Review, 38(2):17--29, 2008.
[97]
K. Zhang, D. Zhuo, and A. Krishnamurthy. Gallium: Automated software middlebox offloading to programmable switches. In ACM SIGCOMM, pages 283--295, 2020.
[98]
M. Zhang, G. Li, S. Wang, C. Liu, A. Chen, H. Hu, G. Gu, Q. Li, M. Xu, and J. Wu. Poseidon: Mitigating volumetric ddos attacks with programmable switches. In NDSS, pages 823--835, 2020.
[99]
Y. Zhang, Z. Liu, R. Wang, T. Yang, J. Li, R. Miao, P. Liu, R. Zhang, and J. Jiang. Cocosketch: high-performance sketch-based measurement over arbitrary partial key query. In ACM SIGCOMM, pages 207--222, 2021.
[100]
P. Zheng, T. Benson, and C. Hu. P4visor: Lightweight virtualization and composition primitives for building and testing modular programs. In ACM CoNEXT, pages 98--111, 2018.
[101]
Y. Zhou, C. Sun, H. H. Liu, R. Miao, S. Bai, B. Li, Z. Zheng, L. Zhu, Z. Shen, Y. Xi, P. Zhang, D. Cai, M. Zhang, and M. Xu. Flow event telemetry on programmable data plane. In ACM SIGCOMM, pages 76--89, 2020.
[102]
H. Zhu, V. Gupta, S. S. Ahuja, Y. Tian, Y. Zhang, and X. Jin. Network planning with deep reinforcement learning. In ACM SIGCOMM, pages 258--271, 2021.
[103]
H. Zhu, T. Wang, Y. Hong, D. R. Ports, A. Sivaraman, and X. Jin. Netvrm: Virtual register memory for programmable networks. In USENIX NSDI, pages 155--170, 2022.
[104]
J. Zhu, K. Zhang, and Q. Huang. A sketch algorithm to monitor high packet delay in network traffic. In APNet, pages 43--49, 2021.
[105]
A. Zulfiqar, B. Pfaff, W. Tu, G. Antichi, and M. Shahbaz. The slow path needs an accelerator too! ACM SIGCOMM Computer Communication Review, 53(1):38--47, 2023.
Index Terms
- Eagle: Toward Scalable and Near-Optimal Network-Wide Sketch Deployment in Network Measurement
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August 2024
1033 pages
ISBN:9798400706141
DOI:10.1145/3651890
- Co-chairs:
- Aruna Seneviratne,
- Darryl Veitch,
- Program Co-chairs:
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- Minlan Yu
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