Taming limits with approximate networking

Information Technology University

(ITU), Pakistan

Junaid Qadir

Post-peak long arc of scarcity

Approximate computing

Figure Credit: “Is “Good Enough” Computing Good Enough?”, Logan Kugler.

Many computing systems and applications can tolerate some loss of quality. With such applications in mind, it makes sense to trade off “precision” for gains in “efficiency”).

Demonstrated results of 50x energy efficiency gain

with an an accuracy loss of only 5 percent

Setting up the stage

The Pareto Principle (also

called the 80/20 rule)

The Tainter Curve

(the downside of too much complexity)

Iatrogenics: the externalities of

technology

Pareto PrincipleThe `law’ of diminishing returns

80/20 RULE: “Among the factors to be considered

there will usually be the vital few and the

trivial many.”

Instead of aiming for 100% ideal networks, we can get good enough services with

considerably less effort (and less negative externality).

Tainter Curve

Dangers of over-technologizing

`Over-technologizing a society makes it

fragile/brittle in the face of future “Black Swans”

Iatrogenics: healer-given problems The externalities of technology

Medicine when overdosed can become a poison.

Figure Credit: “Antifragile: Things That Gain from Disorder.”, Taleb, N. N.

More technology may be less useful!

The mindset of abundance, much like scarcity, brings about its own problems

—such as overconsumption.

Approximate networking (AN)

Existing examples of AN:Best Effort Internet

Bloom Filters;UDP; UDP-Lite

Delay tolerant Networks

If we relax the requirements of “ideal networking”, we can obtain many advantages – e.g., in terms of reducing cost, complexity, and negative harmful

externalities; and in increasing efficiency (e.g., energy efficiency)

The AN simplicity imperativeOccam’s hypothesis:

The simplest model that fits the data is also the most plausible.

Approximate Networking’s RazorThe simplest network

(architecture/protocol/algorithm) that satisfies the desired QoS is the most desirable

approximation.

APPROXIMATE

NETWORKING

In Machine Learning, there’s a regularization technique known as Lasso

that can help build better models by penalizing complexity.

Approximate networking can be thought of as the Lasso of networking that aims to build simpler, more scalable,

networking solutions.

The ``good enough’’ becoming better than a complex ideal

Context-appropriate tradeoffs

(high-tech, low-tech)

In 5G, the aim is to provide seemingly infinite bandwidth (in others as much bandwidth as the user needs). 5G can exploit the great diversity in

application requirements and employ context-appropriate tradeoffs for universal provisioning.

Main tradeoffs:

Latency vs. Throughput

Fidelity vs. Convenience

Throughput vs. Coverage/ Reliability

Coverage vs. Consumed Power

Performance vs. Cost efficiency

Privacy vs. Free Content/ Services

Spectrum of connectivity options:

Context-appropriate tradeoffs

(high-tech, low-tech, or even no-tech)

How to outsource some of things that we do on the current Internet to less-costly and less energy-intensive offline methods (while ensuring that we get enough QoS that is necessary for our applications)?

Why Approximate Networking?• To be anti-fragile/robust to limits and

``undeveloping’’ environments

• For universal coverage (e.g., global access to the Internet for all - GAIA)

• Efficiency and Reducing Cost

• Dealing with resource constraints (such as limited power).

Paradoxically, being somewhat defeatured make

you resilient!When limits will hit us ...

the architectures/ protocols with the bells and the whistles

will be hit much harder.

Approximate solutions may be more sustainable in an unpredictable world.

Open research challenges1. Mitigating the highlighted pitfalls/ challenges2. Multimodal BD4D analytics3. Predictive BD4D analytics4. Combining humans, crowds, and AI5. Unsupervised BD4D analytics

Open questions

1. How do we define context?

2. How do we quantify when our approximation is working and when it is not? • How do we measure the cost of approximation in terms of

performance degradation?

3. How to dynamically control the approximation tradeoffs according to the network condition?

4. How to design proper incentivizes for the service provider and the user so that both act harmoniously for provisioning a customer-centric contextually-appropriate service?

Concluding remarksThe deep-rooted reliance on infrastructure—which itself depends on many exogenously sourced depletable resources (such as energy and materials)—makes the modern society vulnerable to a disruptive collapse.

To cope up with such a disruptive collapse, we have proposed the idea of “approximate networking” that emphasizes the simplicity imperative.

Approximate networking is based on the idea that coping with such a world burdened with limits will require us to adopt context-specific tradeoffs to provide “good enough” service.

junaid.qadir@itu.edu.pk