Approximate Networking for Universal Internet Access
1.1. What Is Approximate Computing?
1.2. What Is Approximate Networking?
1.3. Why Adopt Approximate Networking?
1.3.1. Affordable Universal Internet (GAIA)
1.3.2. Diversity of User & Application Profiles
1.3.3. The Pareto Principle (80–20 Law): The Power of “Good Enough”
1.3.4. Need of Energy Efficiency
1.4. Contributions of This Paper
2. Approximate Networking Technologies
2.1. Approximate Networking: Old Wine in a New Bottle?
2.2. Approximate Networking Hardware
2.3. Approximate Networking Software: Algorithms
2.4. Approximate Networking Protocol Stack
- Optional multi-layer integrity check support: currently, the different network layers perform redundant checksums (e.g., TCP over Wi-Fi uses three separate checksums, namely, the TCP layer, the IP layer, and the link layer). In an approximate networking context, it is useful to permit some errors in approximate payloads.
- Partial integrity checking for critical data (e.g., addresses and ports must be precise): it is typical in networking to discard erroneous packets that have been received with checksum errors. Both TCP and UDP discard erroneous packets (TCP also asks for a retransmission to ensure reliability). However, in the spirit of approximation, partial errors in non-critical data can be tolerated. UDP-Lite  is an example transport protocol that performs partial integrity checking through the use of a configurable checksum (which specifies how many bits are protected by checksum).
- Application-provided approximation specification, and switching between these specifications, for a given socket at the level of different layers. As an example work, Selective Approximate Protocol (SAP)  allows applications to coordinate with multiple networks layers to accept potentially damaged data. The authors of SAP, which is built over UDP-Lite, have reported a 30% speedup for an error-tolerant file transfer application over Wi-Fi.
3. Context-Appropriate Approximate Networking Trade-Offs
3.1. Trade-Offs in Networking
- Fidelity versus affordability/convenience: a lot of research has shown that customers are willing to sacrifice considerable fidelity for a more convenient and accessible service . The notion of fidelity matches with the QoS/ Quality of Experience (QoE) concept. Convenience subsumes concepts such as the cost, accessibility/availability, and simplicity of the service. The fact that users are willing to tradeoff fidelity for convenience and affordability is an extremely important insight for our topic.
- Latency versus throughput: it is well known in literature that throughput-optimal solutions can compromise performance in terms of delay . The Sneakernet concept, long known in networking folklore (“Never underestimate the bandwidth of a station wagon full of tapes hurtling down the highway.”—Andrew Tanenbaum, 1981) is the embodiment of the latency-throughput trade-off. In a similar vein, DTN routing protocols also tradeoff latency for throughput and connectivity (i.e., DTN Bundles can achieve the same throughput as IP protocols but with longer latency).
- Throughput versus coverage/reliability: in wireless networks, there is a tradeoff between the throughput and the coverage (and the reliability) of a transmission, i.e., for higher-rate transmissions, the coverage area is typically smaller and the bit error rate higher. The idea of approximate networking can be used to provide context-appropriate QoS to 5G users , by provisioning higher rates to users and applications where feasible and desired, while still allowing everyone access to basic connectivity (allowing users who are currently offline to come online).
- Coverage versus consumed power: in wireless networks, the coverage of a transmission is directly proportional to the transmission power. Since nodes do not need to communicate at all times, researchers have proposed putting to sleep parts of the infrastructure, such as the base transceiver station (BTS) of cellular systems, to save on energy costs.
- Other trade-offs: many innovative solutions are able to improve performance by inventing a new tradeoff. For example, Vulimiri et al. discovered that an interesting way to reduce latency is to tradeoff some additional capacity or redundancy (i.e., the authors showed that latency can be reduced by by initiating redundant operations across diverse resources and using the first complete response) . Future approximate networking solutions can derive much utility by focusing on discovering new ways of developing context-appropriate new trade-offs.
3.2. Leveraging Approximation
3.3. How Can We Visualize the Trade-Offs?
3.4. Open Questions
- Which approximation to apply where in the hardware/software stack, and to which degree, such that the end-to-end QoS requirements are fulfilled?
- How to estimate end-to-end error degradation due to approximations?
- How do we quantify when our approximation is working and when it is not?
- How to measure success in managing the service quality/ accessibility tradeoff?
- How do we measure the cost of approximation in terms of performance degradation?
- How to dynamically control the approximation trade-offs according to the network condition?
- Can the degradation models for approximation errors and channel errors be consolidated?
4. Case Study: Approximate 5G Networks in Rural/Low Income Areas
5. Discussion Issues
5.1. Zero Rating and Net Neutrality
What’s Better: Approximate or Zilch?
5.2. HCI Issues: User Perceptions of the Approximation
Conflicts of Interest
- The World Bank. World Development Report 2016: Digital Dividends; The World Bank: Washington, DC, USA, 2016; ISBN 978-1-4648-0671-1. [Google Scholar]
- Subramanian, L.; Surana, S.; Patra, R.; Ho, M.; Sheth, A.; Brewer, E. Rethinking Wireless for the Developing World. IRVINE IS BURNING 2006, 43–48. Available online: http://conferences.sigcomm.org/hotnets/2006/subramanian06rethinking.pdf (accessed on 7 December 2017).
- Onireti, O.; Qadir, J.; Imran, M.A.; Sathiaseelan, A. Will 5G See Its Blind Side? Evolving 5G for Universal Internet Access. In Proceedings of the GAIA ’16 2016 Workshop on Global Access to the Internet for All, Florianopolis, Brazil, 22–26 August 2016. [Google Scholar]
- Qadir, J.; Sathiaseelan, A.; Wang, L.; Crowcroft, J. Taming Limits with Approximate Networking. In Proceedings of the Second Workshop on Computing within Limits, Irvine, CA, USA, 8–10 June 2016. [Google Scholar]
- Han, J.; Orshansky, M. Approximate computing: An emerging paradigm for energy-efficient design. In Proceedings of the 2013 18th IEEE European Test Symposium (ETS), Avignon, France, 27–30 May 2013; pp. 1–6. [Google Scholar]
- Shafique, M.; Hafiz, R.; Rehman, S.; El-Harouni, W.; Henkel, J. Cross-layer approximate computing: From logic to architectures. In Proceedings of the 53rd Annual Design Automation Conference (ACM), Austin, TX, USA, 5–9 June 2016; p. 99. [Google Scholar]
- El-Harouni, W.; Rehman, S.; Prabakaran, B.S.; Kumar, A.; Hafiz, R.; Shafique, M. Embracing approximate computing for energy-efficient motion estimation in high efficiency video coding. In Proceedings of the 2017 Design, Automation & Test in Europe Conference & Exhibition (DATE), Lausanne, Switzerland, 27–31 March 2017; pp. 1384–1389. [Google Scholar]
- Wadhwa, A.; Madhow, U.; Shanbhag, N.R. Slicer Architectures for Analog-to-Information Conversion in Channel Equalizers. IEEE Trans. Commun. 2017, 65, 1234–1246. [Google Scholar] [CrossRef]
- Schläfer, P.; Huang, C.H.; Schoeny, C.; Weis, C.; Li, Y.; Wehn, N.; Dolecek, L. Error resilience and energy efficiency: An LDPC decoder design study. In Proceedings of the IEEE 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, Germany, 14–18 March 2016; pp. 588–593. [Google Scholar]
- Mishra, A.K.; Barik, R.; Paul, S. iACT: A Software-Hardware Framework for Understanding the Scope of Approximate Computing. In Proceedings of the Workshop on Approximate Computing Across the System Stack (WACAS), Salt Lake City, UT, USA, 2 March 2014. [Google Scholar]
- Nair, R. Big data needs approximate computing: Technical perspective. Commun. ACM 2015, 58, 104. [Google Scholar] [CrossRef]
- Agrawal, A.; Chen, C.Y.; Choi, J.; Gopalakrishnan, K.; Oh, J.; Shukla, S.; Srinivasan, V.; Venkataramani, S.; Zhang, W. Accelerator Design for Deep Learning Training: Extended Abstract. In Proceedings of the 54th Annual Design Automation Conference 2017 (ACM), Austin, TX, USA, 18–22 June 2017; p. 57. [Google Scholar]
- Esmaeilzadeh, H.; Sampson, A.; Ceze, L.; Burger, D. Architecture support for disciplined approximate programming. ACM SIGPLAN Notices 2012, 47, 301–312. [Google Scholar]
- Chippa, V.; Chakradhar, S.; Roy, K.; Raghunathan, A. Analysis and characterization of inherent application resilience for approximate computing. In Proceedings of the 2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC), Austin, TX, USA, 29 May–7 June 2013. [Google Scholar]
- Kugler, L. Is good enough computing good enough? Commun. ACM 2015, 58, 12–14. [Google Scholar] [CrossRef]
- Rehman, S.; El-Harouni, W.; Shafique, M.; Kumar, A.; Henkel, J. Architectural-Space Exploration of Approximate Multipliers. In Proceedings of the 2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Austin, TX, USA, 7–10 November 2016. [Google Scholar]
- Venkataramani, S.; Chakradhar, S.T.; Roy, K.; Raghunathan, A. Computing approximately, and efficiently. In Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition, Grenoble, France, 9–13 March 2015; pp. 748–751. [Google Scholar]
- Ransford, B.; Ceze, L. SAP: An Architecture for Selectively Approximate Wireless Communication. arXiv, 2015; arXiv:1510.03955. [Google Scholar]
- Ransford, B.; Sampson, A.; Ceze, L. Approximate Semantics for Wirelessly Networked Applications. Available online: https://sampa.cs.washington.edu/wacas14/papers/ransford.pdf (accessed on 7 December 2017).
- Affordability Report 2014 by Alliance for Affordable Internet. Available online: http://bit.ly/1BXTS0X (accessed on 7 December 2017).
- Koch, R. The 80/20 Principle: the Secret to Achieving More with Less; The Crown Publishing Group: New York, NY, USA, 2011. [Google Scholar]
- Bouch, A.; Kuchinsky, A.; Bhatti, N. Quality is in the eye of the beholder: Meeting users’ requirements for Internet quality of service. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems, The Hague, The Netherlands, 1–6 April 2000; pp. 297–304. [Google Scholar]
- Raghavan, B.; Ma, J. Networking in the long emergency. In Proceedings of the 2nd ACM SIGCOMM workshop on Green networking, Toronto, ON, Canada, 15–19 August 2011; pp. 37–42. [Google Scholar]
- Sathiaseelan, A.; Crowcroft, J. LCD-Net: Lowest cost denominator networking. ACM SIGCOMM Comput. Commun. Rev. 2013, 43, 52–57. [Google Scholar] [CrossRef]
- Pentland, A.; Fletcher, R.; Hasson, A. Daknet: Rethinking connectivity in developing nations. Computer 2004, 37, 78–83. [Google Scholar] [CrossRef]
- Outernet by Alliance for Affordable Internet. Available online: https://www.outernet.is (accessed on 7 December 2017).
- Tyson, G.; Sathiaseelan, A.; Ott, J. Could we fit the Internet in a Box? In Embracing Global Computing in Emerging Economies; Springer: New York, NY, USA, 2015. [Google Scholar]
- Mittal, S. A survey of techniques for approximate computing. ACM Comput. Surv. 2016, 48, 62. [Google Scholar] [CrossRef]
- Sampson, A.; Baixo, A.; Ransford, B.; Moreau, T.; Yip, J.; Ceze, L.; Oskin, M. Accept: A Programmer-Guided Compiler Framework for Practical Approximate Computing; University of Washington Technical Report UW-CSE-15-01; University of Washington: Washington, DC, USA, 2015. [Google Scholar]
- Sampson, A.; Dietl, W.; Fortuna, E.; Gnanapragasam, D.; Ceze, L.; Grossman, D. EnerJ: Approximate data types for safe and general low-power computation. ACM SIGPLAN Not. 2011, 46, 164–174. [Google Scholar] [CrossRef]
- Esmaeilzadeh, H.; Sampson, A.; Ceze, L.; Burger, D. Neural acceleration for general-purpose approximate programs. In Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), Vancouver, BC, Canada, 1–5 December 2012; pp. 449–460. [Google Scholar]
- Jokela, P.; Zahemszky, A.; Esteve Rothenberg, C.; Arianfar, S.; Nikander, P. LIPSIN: Line speed publish/subscribe inter-networking. ACM SIGCOMM Comput. Commun. Rev. 2009, 39, 195–206. [Google Scholar] [CrossRef]
- Talla, V.; Kellogg, B.; Ransford, B.; Naderiparizi, S.; Smith, J.R.; Gollakota, S. Powering the next billion devices with Wi-Fi. Commun. ACM 2017, 60, 83–91. [Google Scholar] [CrossRef]
- Jouppi, N. Google Supercharges Machine Learning Tasks with TPU Custom Chip. Available online: cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-withcustom-chip.html (accessed on 7 December 2017).
- Esmaeilzadeh, H.; Sampson, A.; Ceze, L.; Burger, D. Towards neural acceleration for general-purpose approximate computing. In Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture, Vancouver, BC, Canada, 1–5 December 2012; pp. 449–460. [Google Scholar]
- Mazahir, S.; Hasan, O.; Hafiz, R.; Shafique, M.; Henkel, J. An area-efficient consolidated configurable error correction for approximate hardware accelerators. In Proceedings of the 53rd ACM/EDAC/IEEE Design Automation Conference (DAC), Austin, TX, USA, 5–9 June 2016; pp. 1–6. [Google Scholar]
- Shafique, M.; Ahmad, W.; Hafiz, R.; Henkel, J. A low latency generic accuracy configurable adder. In Proceedings of the 52nd ACM/EDAC/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, 8–12 June 2015; pp. 1–6. [Google Scholar]
- Baker, C.E.; Starke, A.; Xing, S.; McNair, J. Demo Abstract: A Research Platform for Real-World Evaluation of Routing Schemes in Delay Tolerant Social Networks. arXiv, 2017; arXiv:1702.05654. [Google Scholar]
- Sermpezis, P.; Spyropoulos, T. Not all content is created equal: Effect of popularity and availability for content-centric opportunistic networking. In Proceedings of the 15th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Philadelphia, PA, USA, 11–14 August 2014; pp. 103–112. [Google Scholar]
- Larzon, L.A.; Degermark, M.; Pink, S.; Jonsson, L.E.; Fairhurst, G. The Lightweight User Datagram Protocol (UDP-Lite). RFC 3828. Available online: https://tools.ietf.org/html/rfc3828 (accessed on 7 December 2017).
- Shelby, Z.; Hartke, K.; Bormann, C. The Constrained Application Protocol (CoAP); RFC 7252; Internet Engineering Task Force: Fremont, CA, USA, 2014; Available online: https://tools.ietf.org/html/rfc7252 (accessed on 7 December 2017).
- Krishnan, D.R.; Quoc, D.L.; Bhatotia, P.; Fetzer, C.; Rodrigues, R. Incapprox: A data analytics system for incremental approximate computing. In Proceedings of the 25th International Conference on World Wide Web, Montreal, QC, Canada, 11–15 April 2016; pp. 1133–1144. [Google Scholar]
- Gupta, A.; Könemann, J. Approximation algorithms for network design: A survey. Surv. Oper. Res. Manag. Sci. 2011, 16, 3–20. [Google Scholar] [CrossRef]
- Gandhi, R.; Kim, Y.A.; Lee, S.; Ryu, J.; Wan, P.J. Approximation algorithms for data broadcast in wireless networks. IEEE Trans. Mob. Comput. 2012, 11, 1237–1248. [Google Scholar] [CrossRef]
- Varghese, G. Network Algorithmics; Chapman & Hall/CRC: Boca Raton, FL, USA, 2010. [Google Scholar]
- Vazirani, V.V. Approximation Algorithms; Springer: New York, NY, USA, 2013. [Google Scholar]
- Broder, A.; Mitzenmacher, M. Network applications of Bloom filters: A survey. Internet Math. 2004, 1, 485–509. [Google Scholar] [CrossRef]
- Maney, K. Trade-Off: Why Some Things Catch On, and Others Don’t; The Crown Publishing Group: New York, NY, USA, 2010. [Google Scholar]
- Bertsekas, D.P.; Gallager, R.G.; Humblet, P. Data Networks; Prentice-Hall International: Upper Saddle River, NJ, USA, 1992; Volume 2. [Google Scholar]
- Vulimiri, A.; Godfrey, P.B.; Mittal, R.; Sherry, J.; Ratnasamy, S.; Shenker, S. Low latency via redundancy. In Proceedings of the ACM CoNEXT 2013, Santa Barbara, CA, USA, 9–12 December 2013; pp. 283–294. [Google Scholar]
- Rondeau, T.W.; Bostian, C.W. Artificial Intelligence in Wireless Communications; Artech House: Norwood, MA, USA, 2009. [Google Scholar]
- Van Mieghem, P.; Vandenberghe, L. Trade-Off Curves for QoS Routing. In Proceedings of the INFOCOM 2006. 25th IEEE International Conference on Computer Communications, Barcelona, Spain, 23–29 April 2006. [Google Scholar]
- Chang, Y.C.; Chang, C.J.; Chen, K.T.; Lei, C.L. Radar chart: Scanning for satisfactory QoE in QoS dimensions. IEEE Netw. 2012, 26, 25–31. [Google Scholar] [CrossRef]
- ICT Facts and Figure 2017. International Telecommunication Union. Available online: http://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2017.pdf (accessed on 7 December 2017).
- Eriksson, M.; van de Beek, J. Is anyone out there? 5g, rural coverage and the next 1 billion. IEEE ComSoc Technology News (CTN). 2015. Available online: https://www.comsoc.org/ctn/anyone-out-there-5g-rural-coverage-and-next-1-billion (accessed on 7 December 2017).
- Chiaraviglio, L.; Blefari-Melazzi, N.; Liu, W.; Gutiérrez, J.A.; van de Beek, J.; Birke, R.; Chen, L.; Idzikowski, F.; Kilper, D.; Monti, P.; et al. Bringing 5G in Rural and Low-Income Areas: Is it Feasible? IEEE Commun. Stand. Mag. 2017, 1, 51–57. [Google Scholar] [CrossRef]
- Smith, D. The Truth about Spectrum Deployment in Rural America; Technical Report; Mobile Future: Washington, DC, USA, 2015; Available online: http://mobilefuture.org/wp-content/uploads/2015/03/031615-MF-Rural-Paper-FINAL.pdf (accessed on 7 December 2017).
- State of Connectivity 2015 A Report on Global Internet Access. Internet.org by Facebook. Available online: https://fbnewsroomus.files.wordpress.com/2016/02/state-of-connectivity-2015-2016-02-21-final.pdf (accessed on 7 December 2017).
- Hasan, Z.; Boostanimehr, H.; Bhargava, V.K. Green cellular networks: A survey, some research issues and challenges. IEEE Commun. Surv. Tutor. 2011, 13, 524–540. [Google Scholar] [CrossRef]
- Chen, Y.; Zhang, S.; Xu, S.; Li, Y.G. Fundamental trade-offs on green wireless networks. IEEE Commun. Mag. 2011, 49. [Google Scholar] [CrossRef]
- Wang, W.; Shen, G. Energy efficiency of heterogeneous cellular network. In Proceedings of the 2010 IEEE 72nd Vehicular Technology Conference Fall (VTC 2010-Fall), Ottawa, ON, Canada, 6–9 September 2010; pp. 1–5. [Google Scholar]
- Fehske, A.; Fettweis, G.; Malmodin, J.; Biczok, G. The global footprint of mobile communications: The ecological and economic perspective. IEEE Commun. Mag. 2011, 49. [Google Scholar] [CrossRef]
- Chih-Lin, I.; Rowell, C.; Han, S.; Xu, Z.; Li, G.; Pan, Z. Toward green and soft: A 5G perspective. IEEE Commun. Mag. 2014, 52, 66–73. [Google Scholar]
- Duan, Q.; Ansari, N.; Toy, M. Software-defined network virtualization: An architectural framework for integrating SDN and NFV for service provisioning in future networks. IEEE Netw. 2016, 30, 10–16. [Google Scholar] [CrossRef]
- Chiaraviglio, L.; Blefari-Melazzi, N.; Liu, W.; Gutierrez, J.A.; Van De Beek, J.; Birke, R.; Chen, L.; Idzikowski, F.; Kilper, D.; Monti, J.P.; et al. 5G in rural and low-income areas: Are we ready? In Proceedings of the ITU Kaleidoscope: ICTs for a Sustainable World (ITU WT), Bangkok, Thailand, 14–16 November 2016; pp. 1–8. [Google Scholar]
- Qadir, J.; Sathiaseelan, A.; Wang, L.; Crowcroft, J. Resource Pooling for Wireless Networks: Solutions for the Developing World. arXiv, 2016; arXiv:1602.07808. [Google Scholar]
- Heimerl, K.; Hasan, S.; Ali, K.; Brewer, E.; Parikh, T. Local, sustainable, small-scale cellular networks. In Proceedings of the Sixth ICTD Conference (ICTD’13), Cape Town, South Africa, 7–10 December 2013; pp. 2–12. [Google Scholar]
- Hasan, S.; Heimerl, K.; Harrison, K.; Ali, K.; Roberts, S.; Sahai, A.; Brewer, E. GSM whitespaces: An opportunity for rural cellular service. In Proceedings of the 2014 IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN), McLean, VA, USA, 1–4 April 2014; pp. 271–282. [Google Scholar]
- HUAWEI Reveals the Future of Mobile AI at IFA 2017. Available online: http://consumer.huawei.com/en/press/news/2017/ifa2017-kirin970/ (accessed on 2 September 2017).
- Aliu, O.G.; Imran, A.; Imran, M.A.; Evans, B. A survey of self organisation in future cellular networks. IEEE Commun. Surv. Tutor. 2013, 15, 336–361. [Google Scholar] [CrossRef][Green Version]
- Global Internet User Survey 2012 by Interent Society. Available online: https://www.internetsociety.org/internet/global-internet-user-survey-2012/ (accessed on 7 December 2017).
- Bates, S.L.; Bavitz, C.T.; Hessekiel, K.H. Zero Rating & Internet Adoption: The Role of Telcos, ISPs, & Technology Companies in Expanding Global Internet Access; Berkman Klein Center for Internet & Society Research Publication: Cambridge, MA, USA, 2017; Available online: http://nrs.harvard.edu/urn-3:HUL.InstRepos:33982356 (accessed on 7 December 2017).
- Pahwa, N. It’s a battle for internet freedom. The Times of India. 2015. Available online: https://blogs.timesofindia.indiatimes.com/toi-edit-page/its-a-battle-for-internet-freedom/ (accessed on 7 December 2017).
- Bouch, A.; Sasse, M.A. It ain’t what you charge, it’s the way that you do it: A user perspective of network QoS and pricing. In Proceedings of the Sixth IFIP/IEEE International Symposium on Integrated Network Management, 1999, Distributed Management for the Networked Millennium, Boston, MA, USA, 24–28 May 1999; pp. 639–654. [Google Scholar]
- Belshe, M. More Bandwidth Doesnt Matter (Much); Google Inc.: Mountain View, CA, USA, 2010. [Google Scholar]
- Fall, K.; McCanne, S. You don’t know jack about network performance. Queue 2005, 3, 54–59. [Google Scholar] [CrossRef]
|Reference||Task||Brief Summary||How Approximation Is Used to Increase Performance|
|Sampson et al. ||Approximation-based Compiler Framework||Introduces a compiler framework for practical approximate computing.||The approximation compiler framework substantially improves the end-to-end performance with little quality degradation.|
|Sampson et al. ||Language of Approximate Computing||Proposes a programming language model (EnerJ) for approximate computing||An approximate data type for low power consumption devices is proposed.|
|Esmaeilzadeh et al. ||Programmable Accelerator||Proposes a new class of neural programmable unit (NPU) accelerator that uses approximate computing to get better performance and energy efficiency.||A general purpose approximate computing NPU saves 3× more energy and speeds up the process by 2.3×.|
|Jokela et al. ||Multicast Forwarding||LIPSIN incorporate Bloom filter properties for large scale topic based Publish-Subscribe systems.||Bloom filters reduce the forwarding table size, and increase multicast forwarding efficiency, at the cost of small false positives.|
|Talla et al. ||Network Hardware Approximation||Power over Wi-Fi delivers power to low-power sensors and network devices.||A new approximate-computing-enabled energy harvesting design that provides far-field power delivery to Wi-Fi enabled is provided.|
|Jouppi et al. ||Custom Hardware Chip for Machine Learning (ML)||Google’s Tensor Processing Unit (TPU) provides tolerance for reduced computational precision in ML programs.||Google is using TPUs in datacenters since 2016, thereby achieving better-optimized ML performance per watt.|
|Esmaeilzadeh et al. ||Neural Processing Unit (NPU)||NPU’s software and hardware design is presented.||With learning, code transfer, and approximate computing enabled instruction set architecture, 2× performance and energy-saving improvement is achieved.|
|Mazahir et al. ||Consolidated Error Correction (CEC)||CEC: Correction is applied to errors accumulated from several additions.||CEC is used in Approximate Hardware Accelerators for area saving and speed enhancement.|
|Shafique et al. ||Low Latency Adder||Low latency generic accuracy configurable hardware combined with error recovery circuit for applications requiring high accuracy.||Adder provides a better accuracy, area and speed tradeoff as compared to previous counterparts.|
|Mishra et al. ||Approximate Computing Toolkit||Intel’s approximate computing (iACT) toolkit comprises a run-time compiler and a simulated hardware testbed.||Intel’s iACT is a approximate computing toolkit designed for promoting industry and academia research.|
|Baker et al. ||Opportunistic Communication for Delay Tolerant Networks||A routing platform for delay-tolerant social networks.||Packets from source to destination reaches in cooperative communication fashion.|
|Sermpezis et al. ||Opportunistic Communication||Describes how content-centric applications perform in opportunistic scenarios.||QoS of content-centric networks is improved by approximating delays, content popularity and availability.|
|Rehman et al. ||Architectural Exploration of Approximate Multipliers||Using variants of approximate/accurate adders/ multipliers and approximate LSBs for exploring apace of approximate multipliers.||Open Source Library for further Research and Development of approximate Computing at higher abstraction level of HW/SW stack.|
|Esmaeilzadeh et al. ||Architectural Support for Approximate Programming||A new ISA extension which provides approximate operations and storage, due ti which energy is saved at the cost of small degradation in accuracy.||When proposed scheme is tested with several applications up to 43% energy is saved.|
|Larzon et al. ||Flexible Best Effort Protocol||Proposes a UDP variant called UDP-Lite that uses partial checksums.||UDP-Lite allows for error tolerance and this approximation can significantly improve the network throughput.|
|Shelby et al. ||Best Effort Protocol||Proposes a best-effort application layer protocol for constrained devices.||Constraint application protocol uses UDP and UDP-Lite as the underlying approximation transport layer protocol to facilitate error tolerance.|
|Ransford et al. ||Cross Layer Approximation Protocol||Selective Approximate Protocol (SAP) enables network applications to receive potentially damaged network data.||Approximation introduced in SAP increased the throughput and reduce the retransmission rate of wireless communication networks.|
|Krishnan et al. ||Incremental Approximation Algorithm||An incremental approximate computing algorithm (IncApprox) is presented for network and Twitter data analytics.||IncApprox combines incremental and approximate computing paradigms to achieve 2.1× the throughput achieved by either.|
|Gupta et al. ||Approximation Algorithms||Approximation algorithms for network design are presented.||Different emerging solutions for minimum spanning tree problem using different approximation assumption are discussed.|
|Gandhi et al. ||Approximation Algorithms||A one-to-all approximate wireless broadcasting algorithm is presented.||An approximate solution is proposed for an NP-Complete optimization problem with routing, scheduling and QoS applications.|
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Qadir, J.; Sathiaseelan, A.; Farooq, U.B.; Usama, M.; Imran, M.A.; Shafique, M. Approximate Networking for Universal Internet Access. Future Internet 2017, 9, 94. https://doi.org/10.3390/fi9040094
Qadir J, Sathiaseelan A, Farooq UB, Usama M, Imran MA, Shafique M. Approximate Networking for Universal Internet Access. Future Internet. 2017; 9(4):94. https://doi.org/10.3390/fi9040094Chicago/Turabian Style
Qadir, Junaid, Arjuna Sathiaseelan, Umar Bin Farooq, Muhammad Usama, Muhammad Ali Imran, and Muhammad Shafique. 2017. "Approximate Networking for Universal Internet Access" Future Internet 9, no. 4: 94. https://doi.org/10.3390/fi9040094