P2P Survey

This is a survey paper (by the authors of Chord) discussing the various schemes available for lookup. This is a sweet and short paper with a nice flow introducing the problem, discussing the solutions and talking about open issues.

Some of the approaches like a central database and broadcast are obviously out of the window for the scale that is being aimed for p2p systems. But given this is a survey paper, I would have wished the authors gave it a little more credit – these approaches do work in many limited but useful settings. The dismissal of the “superpeer” idea irked me. I think in practice, this approach should work very well and might actually provide better “guarantees” if superpeers were maintained by some commercial entity. To me, the p2p era became a “bubble” mainly because such un-sexy approaches weren’t given much importance (fair enough, you can’t get them published!).

The lookup problem has been addressed using ring-based approaches (Chord) and tree data structures (Pastry, Tapestry and Kademlia). Differences between them include distance functions that are combination of being symmetric and unidirectional, cost of node join/failure operations and topology-aware routing.

This whole space seemed a little disconnected from reality – who really is the customer for such smart and sophisticated lookup schemes? At some point, it comes across as over-optimization and complexity that we can do without. That said, I think there were quite a few positives and overall an interesting area.

I would like this paper to be substituted with some paper that shows a real system based on any of these schemes. If a survey paper is a must, my vote is for “The impact of DHT routing geometry on resilience and proximity” at SIGCOMM 2003.

Chord

This is a celebrated paper from the p2p era and addresses a fundamental problem with p2p applications – given a key, locate the node that is responsible for the key. This problem has wider applicability, as the values associated with the key can be application-specific. The authors do a nice job of presenting some potential applications – I especially liked the time-shared storage and combinatorial search breaking examples.

Chord is based on consistent hashing that has a nice property of balancing values across the image space. In my opinion, showing the community the value of consistent hashing in the then important p2p space (though I believe it was a touch magnified) was a crucial contribution. Each node and key is assigned a unique identifier by the consistent hashing function. I found the representation of the space as a “ring” to be quite beautiful. Every node is responsible for a range of key values and keeps pointers (in its finger table) to nodes at distances that exponentially increase. Queries can be resolved in log(n) time. A beautiful intuitive scheme – genius!

While Chord in its original form can be faulted for not considering the underlying topology in its allocation, we should remember that this was the beginning of the p2p storm. Follow-on’s have been published where people take into account such optimizations too. An interesting question is what happens to the guarantees of consistent hashing when such optimizations take place – the allocation is not something completely under its control.

Chord has been used in multiple contexts and is also applicable in datacenters. While it can be argued that storage of state isn’t that big a deal, I still think there is value in having to store lesser state and be more resilient to node failures and key redistribution (given that DCs mostly have machines with commodity hardware).

I would have liked its results to be compared to Pastry and OceanStore; the contrasting in the related works section came across as hand-waving (“other details").

Overall, I think it was a very nice paper (I believe it is the most cited in the last decade or something) and is an obvious keeper!

Active network vision and reality

Active networks allow users to inject code into the nodes in the network. This aids in building a range of customized applications and generally seems a nice idea. Applications can have control over their routing, QoS etc. I would have liked the paper to provide a better sense of the things active networks promised (in terms of concrete applications). The security (or lack of it) aspect jumps at your face – external (and untrusted) code running in your machine seems a bad idea! While there has been a ton of work done that tries to address it, I think this is a fundamental flaw why active networks didn’t see the light of the day.

This paper is based on observations and feedback from the ANTS system. The system uses capsule-based forwarding. Capsules contain code that can either be actually present in the packet or referenced locally (taking advantage of caching). Accessibility is an important goal where code from untrusted users should not do any harm to users of other services. The authors discuss techniques to guard against malicious code but also do some hand-waving by saying that this is analogous to malicious users in the Internet monopolizing bandwidth. It sort of seems to be saying, “There are bad guys anywhere you go…that doesn’t mean dealing with them is the highest priority”. Not the most convincing given that the proposal is to run arbitrary code in other machines.

For the most part, I tried not to cloud my reading with my skepticism for active networks. Were there any useful concepts that came out of active networks that people use in other areas? For example, did network management and upgrades become easier and elegant with active networks? That would be a good discussion to have in class – While active networks as a whole did not make much progress, what concepts of it became useful.

I didn’t like this paper but I would vote for keeping it on the reading list for that specific reason. People should have the chance to read it and make their call.

Resilient Overlay Networks

RON is a fix for distributed Internet applications to detect and recover from path outages and degraded performance. The main goal of RON is to enable a group of nodes to communicate with each other in the face of problems in the underlying Internet paths. This group of nodes are connected to each other in the RON. They aggressively calculate the quality of the paths and exchange information among them using link-state routing. Path metrics are obtained using active probing as well as passive measurements.

I liked the fact that RON gives applications control over the routing protocol. Applications can register RON router and RON membership manager that implement the routing protocol and the consequent decisions. This spirit is similar to the class project we are working on – ask the needs of the application and take decisions on network interfaces accordingly. While this might not be ideal for every single application, the ceding of control is a fundamental shift; applications that do not want it can use a default module. That said I am not a fan of frameworks/architectures that suggest that applications have to be re-written. Also, an interesting extension would be to offer applications the need to monitor custom parameters and not just use throughput, latency etc. that are available.

Their motivation scenario where an ISP deploys a RON to provide better customer services seemed compelling. I wonder if ISPs actually use it – unlike many other motivating scenarios for overlay networks, this one is actually motivating! :-) But I think they blame BGP a little too much - it's the mess that BGP gets in with policies that results in its sorry performance sometimes. It is not clear how RON interacts with BGP policies...

The scalability aspect (or the lack of it) raises some questions. What happens if RON actually becomes very popular and everybody deploys one? It is not clear if aggressive probing by the numerous RONs would do the network any good. I suspect not but an evaluation that points out the cases when it won’t would be nice to see. Another aspect is fairness - isn't RON being unfair to non-RON users by being very aggressive?

Their result that two-hop paths are good enough to route across most outages seems surprising and non-obvious. I generally prefer identifying worst-case scenarios too where the system would not work.

Overall, I have no doubt that this paper should be retained! It was a good read and introduces many important points and concepts. I get a feeling that most of these important papers that we are reading are out of MIT and that is noteworthy ;-)

XORs in The Air: Practical Wireless Network Coding

Ah, this paper that has a mechanism true to its name! Routers use the broadcast medium in wireless networks to estimate packet reception probabilities and optimally encode the packets to minimize transmissions. The idea is clearly explained in Figure 3 of the paper. Given the knowledge of which nodes have received which of the packets, the router can accordingly encode the packets and rely on the nodes’ ability to decode the packets. The routing algorithm, COPE, uses the ETX metric to estimate the probability of which of the nodes have received the packets. I didn’t quite understand their explanation of why reception reports won’t work. The authors mention that during periods of high congestions, reception reports are likely to get lost. But arguably, the potential for any smart encoding is going to be limited under high contention as the ETX values are going to very low too (estimating that dynamically is another major headache!).

The coding+MAC gain of COPE was interesting. MAC protocols aim to achieve fairness among all the nodes though the load on the routers and the end-nodes are vastly different. COPE reduces the effects of this “unfair” fairness (!) and also helps in draining the router queues faster.

COPE increases the throughput of UDP flows significantly, but not much for TCP.

I like COPE better than ExOR as it seems applicable in a more practical scenario. We should definitely keep it in the reading list.

ExOR: Opportunistic Multi-Hop Routing for Wireless Networks

This paper uses the inherent broadcast mechanism in wireless networks to increase throughput of multi-hop wireless networks. I think the idea of late-binding in routing decisions is very smart. Traditional routing protocols choose the best route beforehand. ExOR broadcasts every packet with a “forwarder list”. The list contains the set of packets that are likely to be closest to the destination. But since other packets hear it too, we inherently end up with redundancy in routes.

ExOR operates on batch of packets. The source node includes a list of candidate forwarders for each packet, essentially an ordered list of nodes prioritized by their distance to the destination. All nodes that receive the packets wait till the end of the batch. Using a timer for synchronization, nodes transmit the packets they have received in order of priority. This keeps moving forward till the packet reaches the destination. The forwarding list is estimated by the source by having a “link-state” of the entire network. The metric used is ETX. ETX ensures that this seemingly crazy method of broadcasting at every step is making forward progress towards the destination. After 90% of the transfer is complete, ExOR falls back on traditional routing mechanisms. Overall, I think this is a very smart idea and the fact that they got it to work on real networks is impressive.

By design, this mechanism increases latency. Hence it is not suited for streaming, web etc. Given that the only practical example of a multi-hop network that I know of is RoofNet (or networks that increase the coverage of a wireless network), and Internet access typically consists of web accesses, I wonder how useful ExOR is in practice.

All this variable latency per packet might leave TCP totally confused! If TCP isn’t happy with ExOR, this makes its case even weaker. What are the other scenarios? Maybe applications that work over intermittent networks…

Also, this idea seems unsuited to scenarios that concentrate on power optimization. If nodes mostly sleep and wake up only periodically as in sensor nodes or PSM in Wi-Fi (does it make sense in ad hoc Wi-Fi?), ExOR is not too useful.

If you buy into the idea of multi-hop wireless networks, I think ExOR is a good paper to keep. I vote for it.

Btw can somebody tell me what has ExOR got to do with the XOR operator? J

A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing Protocols

Simulations are the basis for testing most new proposals, and this paper extends ns2 to include a realistic model of the physical layer where the signal attenuates based on the distance between nodes, a MAC on top of it and a few other things. The signal attenuates depending on the horizontal distance between nodes, but I wonder if there is a component that has got to do with the height of the object above the ground (the vertical distance). Anyway, this is a reasonable model and the authors proceed to test the performance of the available ad hoc routing protocols using their enhanced simulator. This extension in itself is quite valuable and it would have been nice for them to spend more time describing the details of their implementation (it was hardly a page!).

The routing protocols evaluated are DSDV, TORA, DSR and AODV. DSDV is a hop-by-hop distance vector routing protocol that requires each node to periodically broadcast routing updates, guaranteeing loop-free routing. TORA is based on the link reversal algorithm. It discovers routes on demand, and minimized communication overheads by localized reactions. DSR uses source-routing. AODV is a mixture of DSR and DSDV. DSR and AODV are familiar from undergrad days when I used to work on ad hoc routing protocols. The question I had then (and still have now) is the practical significance of such ad hoc routing protocols. It gets even more unclear with assumptions of mobility. Where exactly in real-life do these scenarios crop up? While they definitely seem interesting problems to solve, I am yet to get the significance of these problems in real-life.

Now on to their methodology. The mobility pattern they use is “random waypoint” – nodes move to another random point with certain velocity. This seems a reasonable model but I fear if this would lead to designing protocols for the worst case scenario. Clearly, nobody moves randomly in reality and hence one can do a whole bunch of optimizations to predict routes. Let’s see if there are interesting insights into how this has affected MANET routing protocols in class…

Results: (1) DSDV-SQ and DSR use routes very close to optimal, (2) TORA has a high overhead and (3) DSDV-SQ is not very sensitive to node mobility.

The little paragraph where they talk about not using TCP raises more questions than answers. Probably a tone that said “While our current results are valuable for , evaluations with TCP are part of future work” would have been better. Given that a significant majority of practical applications run over TCP, the interaction would have been definitely interesting. That said, since I don’t know where MANETs are actually used in practice, this might be a moot point.

While I admit that my reading was clouded by my lack of understanding about the real-world significance of MANETS, I didn’t find this paper worth keeping. I would vote against it.

A High Throughput Path Metric for Multi-Hop Wireless Networking

Minimum hop-count is the metric widely used for route selections. This is sort of a natural extension from the wired world where a link is treated as up/down. The wireless world requires a more nuanced dealing and this paper starts off with experimental results showing why hop-count often doesn’t pick the route with the best throughput. Links have varying losses and asymmetry. Picking a route based on hop-count is clearly meaningless especially under churn.

ETX to the rescue! Expected Transmission Count (ETX) tries to estimate the number of link-layer retransmissions. Dedicated link probe packets are sent to calculate the delivery ratio of the link. Each node broadcasts probes periodically, and nodes calculate the ratio of the number of packets received to the number of packets that would have been transmitted, in a window. Inverse of the products of the delivery ratios in both directions is the ETX of the link. ETX of a route is the sum of the link metrics. The authors show the elegance of their metric by easily retro-fitting their metric into existing routing protocols.

I had a few questions on the paper:

1. How does the metric function when different node pairs have links with different bit-rates? I guess we could incorporate this information into ETX and calculate the end-to-end throughput for route selection.

2. While the authors have been clear that load balancing is not what they are targeting, I wonder how different ETX would behave when the network is loaded.

3. While on load, a sensitivity analysis on the significance of w, the window used for calculating the ETX of the link would be interesting. Like most systems this is a parameter to be “tuned” – high values would make it less susceptible to transient changes while low values would make it agile.

4. I wasn’t clear on how “interference between successive hops in a multi-hop path” is captured by ETX.

5. ETX seems to give equal weightage to delivery ratios in both directions – can we optimize this? I am not sure how link layer acknowledgements work, but is every packet acknowledged? If not, shouldn’t I assign more weight to the forward delivery ratio?

Overall, I vote to keep this paper in the reading list. It presents a landmark shift and also encourages discussion with a bunch of related/unanswered questions.