- freely available
Future Internet 2017, 9(4), 94; https://doi.org/10.3390/fi9040094
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
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|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.|
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