Network Working Group F. Devetak Internet-Draft S. Kapoor Expires: September 14, 2017 Illinois Institute of Technology March 13, 2017 Dynamic MultiPath Routing draft-kapoor-rtgwg-dynamic-multipath-routing-00 Abstract In this draft we consider dynamic multipath routing and introduce two methods that use additive increase and multiplicative decrease for flow control, similar to TCP. Our first method allows for congestion control and re-routing flows as users join in or leave the network. As the number of applications and services supported by the Internet grows, bandwidth requirements increase dramatically so it is imperative to design methods to ensure not only that network throughput is maximized but also to ensure a level of fairness in network resource allocation. Our second method provides fairness over multiple streams of traffic. We drive the multiplicative decrease part of the algorithm with link queue occupancy data provided by an enhanced routing protocol. Status of This Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet- Drafts is at http://datatracker.ietf.org/drafts/current/. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." This Internet-Draft will expire on September 14, 2017. Copyright Notice Copyright (c) 2017 IETF Trust and the persons identified as the document authors. All rights reserved. This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents Devetak & Kapoor Expires September 14, 2017 [Page 1]

Internet-Draft Dynamic MultiPath Routing March 2017 (http://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License. 1. Introduction Internet packet traffic keeps growing as the number of applications and services it supports as well as their bandwidth requirements explode. It has then become necessary to find ways to ensure that network throughput is maximized. In this draft we propose dynamic multi-path routing to improve network throughput. Multipath routing is important, not only for throughput but also for reliability and security. In multipath routing, improvements in performance are achieved by utilizing more than one feasible path [M75]. This approach to routing makes for more effective network resource utilization. Various research on multipath routing have addressed network redundancy, congestion, and QoS issues [CRS99] [ST92]. Prior work on multipath routing includes work on bounding delays as well as delay variance [DSAK11] [SKDA13]. The prior work is primarily from the viewpoint of static network design but, in practice, congestion control is necessary to prevent some user flows from being choked due to link bottlenecks. Single path routing implementations of TCP achieve that by rate control on specified paths. TCP is able to handle elastic traffic from applications and establishes a degree of fairness by reducing the rate of transmission rapidly upon detecting congestion. Regular TCP has been shown to provide Pareto-optimal allocation of resources [PU12]. However, unlike the single path approach of TCP, we consider multipath routing with associated issues of path selection and congestion. We may note that multipath TCP (MPTCP) has been studied extensively [RG10] [GWH11] [RH10] [AP13] with a number of IETF proposals [M05] [M06] [M07] [M08]. Prior work on multipath TCP is defined over a specific set of paths and the choice of paths or the routing is independent of congestion control; determining the right number of paths thus becomes a problem. The variation of throughput with the number of paths has been illustrated in [RG10] [GWH11] Along with consideration of congestion, we also need to ensure a level of fairness in network resource allocation. Factoring fairness into the protocol is important in order to prevent some user's flows from suffering due to bottlenecks in some links. Based on mathematical optimization formulations, we consider route Devetak & Kapoor Expires September 14, 2017 [Page 2]

Internet-Draft Dynamic MultiPath Routing March 2017 determination methods that ensure fairness where all users can achieve at least a minimum percentage of their demand. We introduce an algorithm that uses additive increase and multiplicative decrease for flow control and we have experiments to illustrate its stability and convergence. The algorithm may be considered as a generalization of TCP. We have performed an extensive set of simulations using the NS-3 simulation environment. In our implementation we drive the multiplicative decrease part of the algorithm using queue occupancy data at each outgoing network link, with that data provided by an enhanced routing protocol. For a more in-depth evaluation of the algorithm's performance we simulated not only the fairness algorithm but also a version of the same without the fairness component. We also performed and compared simulations using standard TCP and TCP with ECMP enabled. 2. Joint Routing and Congestion Control: Preliminaries The Joint Routing and Congestion Control utilizes the Link State of the network. The algorithm utilizes a price variable that models congestion at each link and a variable that models the fairness coefficient. The fairness coefficient is used to establish the same percentage of traffic is being routed for multiple source-sink pairs. 2.1. The Price function Each edge of the network has a price function associated with it, referred to as P(e). The price function measures the congestion on the link. The price function lies in the interval [0,1] and is 0 if the edge occupancy is low and 1 if the edge occupancy is high. In theory the edge occupancy is given by f(e)/C(e) where f(e) is the amount of traffic on the link and C(e) is the capacity of the link. In practice, the edge occupancy is measured by the congestion in the queue serving the link. The price function increases as the congestion grows. This function's values will be referred to by a price variable on link e which is denoted by PBQ(e) 2.2. The Fairness function The price function is complemented by an "increase" function, i.e. a variable that regulates the amount of traffic changes based on the fraction of traffic of the commodity that has been routed. This function, th values represented by the variable PBF(s-t), is used to model the fairness. This variable is related to the source- Devetak & Kapoor Expires September 14, 2017 [Page 3]

Internet-Draft Dynamic MultiPath Routing March 2017 destination pair, denoted by s-t, whose requirements are being satisfied. The variable PBF(s-t) starts with an initial value and goes down to zero as the requirement of s-t is being increasingly met. The increase in PBF is dictated by a fairness co-efficient, Gamma. The formula for PBF(s-t) is 1- y(s-t)/[Gamma *d(s-t)] where Gamma is the fairness co-efficient, d(s-t) is the demand and y(s-t) is the amount of requirement that is being met. 3. Joint Routing and Congestion Control: Algorithm We present the details of the algorithms below 3.1. Preliminaries Let T be the time interval used to increment or modify routing. We use two coefficients for each path Pi 1. Additive increase coefficient: A positive value ai by which we increment the flow on a path at each iteration xi(t) = xi(t-T ) + ai 2. Multiplicative decrease coefficient: The coefficient bi that we apply to decrement flows: xi(t) = (1-bi)xi(t-T ) where x, t, T are the same as above. We utilize multiple methods for calculating bi: METHOD 1: bi may be computed as follows: 1. bi =0 if no edge on the path Pi is congested. 2. bi =0.5 if one edge on the path Pi is congested. 3. bi= 1.0 if more than one edge on the path Pi is congested METHOD 2: bi may be computed as follows: 1. bi = 1-1/2^c where c is the number of congested edges. 3.2. The Basic Multi-path Algorithm We propose two routing mechanisms. The first, presented here,is a basic mechanism that is primarily based on multiplicative decrease and additive increase Devetak & Kapoor Expires September 14, 2017 [Page 4]

Internet-Draft Dynamic MultiPath Routing March 2017 In the first routing method, for each commodity c, let P(c) be the set of paths being used. If any of the paths Pi is congested, the flow on the path is reduced using the multiplier (1-bi). Next, the shortest path is found to push additional flow requirements if they exist. An additive flow of value ai, which is chosen to be a constant independent of i, is pushed on that path. At the end of each time interval T FOR each commodity c: Calculate commodity flow FOR each flow path, i, of commodity c Calculate bi IF {demand is met and bi = 0} No change in path flow ELSE Apply coefficient bi to decrease path flow ENDIF ENDFOR IF {demand not met} Find shortest path for commodity c IF{shortest Path is new} Add new path to list ENDIF Increment shortest path flow by a ENDIF ENDFOR If the number of paths used is excessive then no new paths need be generated. 3.3. The Multi-path Algorithm with Fairness In order to ensure that different source-sink pairs are treated fairly, the coefficient bi for path Pi is chosen as PBQ - PBF with two components: a congestion component PBQ and a fairness component PBF . 1. PBQ is calculated as bi before; 2. PBF is calculated using the formula PBF(s-t) = 1-Total_current_FLOW(S-t)/(Gamma* Demand(s-t)) where Gamma is the fairness parameter and DEMAND(s-t) is the demand. Devetak & Kapoor Expires September 14, 2017 [Page 5]

Internet-Draft Dynamic MultiPath Routing March 2017 In the second routing method, again, for each commodity c, let P(c) be the set of paths being used. If any of the paths is congested, the flow on the path Pi is reduced using the multiplier (1-bi). The key difference is in the computation of bi. bi is not uniform across various user requests but is dependent on the fraction of flow of that commodity that is already being serviced by the network. Having reduced congestion, if it exists, a shortest path is found to push additional flow requirements if they exist. Again an additive flow of value ai, which in our current implementation is chosen to be a constant independent of i, is pushed on that path. At the end of each time interval T FOR {each commodity} Calculate commodity c flow FOR {each path i} Calculate PBQ and PBF IF {demand is met} IF {NO Congestion} No change in path flow ELSE bi = max (0, PBQ-PBF) flow on path i, xi(t) = (1-bi)*xi(t-T)) ENDIF ELSE IF {NO Congestion} bi = -PBF ELSE bi = max (0, PBQ-PBF) ENDIF xi(t) = a + (1-bi)*xi(t-T)) ENDIF ENDFOR Recalculate Commodity flow IF {flow change in any path AND demand not met} Find shortest path IF {shortest path is new} Add new path to list ENDIF Increment shortest path flow by a ENDIF ENDFOR If the number of paths become excessive then they can be curtailed. At that stage no additional flow is pushed until congestion is relieved. Devetak & Kapoor Expires September 14, 2017 [Page 6]

Internet-Draft Dynamic MultiPath Routing March 2017 4. Conclusion We implemented [1] a discrete time version of the two algorithms using the NS-3 simulation environment. We modeled the network topology on the network of a large service provider, with link capacities proportional to capacities in the actual physical network. In our implementation we used, for routing, a combination of link- state routing protocol and source routing. For the link-state part we augmented the NS-3 implementation of the OSPF routing protocol by adding link queue occupancy to the data exchanged by nodes in Link State Advertisement (LSA) messages, a minimal increase in LSA data. That allows for more sophisticated monitoring of network status: if the queue occupancy for one of more links of a path exceeds a given threshold we conclude that the path is experiencing congestion and that the multiplicative decrease has to be applied to adjust the allocation of flow to the paths. The additive increase is applied at each iteration, if demand is not met, to augment the sending rate. The source node uses OSPF to find the shortest path to the destination and, based on available network data, builds a source- routing vector that is inserted in the packet and used by intermediate nodes to route the packet to the destination. To implement the source-routing function we augmented the NS-3 Nix- Vector protocol that builds the source-routing vector from the list of nodes to be traversed, list that is obtained from OSPF. The main process is iterative as we refresh LSA at a fixed interval: for our simulations we experimented with updating LSAs every 50 ms and 500 ms. In conclusion we found that our algorithm with fairness provides throughput improvement over both regular TCP and TCP with ECMP. In addition, its ability to discover additional path dynamically eliminates the need to set a preselected set of paths, allowing the spreading of the traffic load amongst a wider but still reasonable set of paths. The results may be found at www.cs.iit.edu/~kapoor/papers/reducerate.pdf . 5. References 5.1. References [M75] Maxemchuk, N., "Dispersity Routing", IEEE ICC, 1975. [CRS99] Cidon, I., Rom, R., and Y. Shavitt, "Analysis of multi- path routing", IEEE/ACM Trans on Networking pages 885-896, 1999. Devetak & Kapoor Expires September 14, 2017 [Page 7]

Internet-Draft Dynamic MultiPath Routing March 2017 [ST92] Suzuki, H. and F. Tobagi, "Fast bandwidth reservation with multiline and multipath routing in atm networks", Proceedings of IEEE Infocom pages 2233-2240, 1992. [DSAK11] Devetak, F., Shin, J., Anjali, T., and S. Kapoor, "Minimizing path delay in multipath networks", IEEE ICC, 2011. [SKDA13] Devetak, F., Anjali, T., Shin, J., and S. Kapoor, "Concurrent multipath routing over bounded paths: Minimizing delay variance", Globecom 2013 , 2013. [PU12] Popovic, M., Upadhyay, U., Le Boudec, J., Khalili, R., and N. Gast, "Mptcp is not pareto-optimal: performance issues and a possible solution", Proceedings of the 8th international conference on Emerging networking experiments and technologies , 2012. [RG10] Barre, S., Greenhalgh, A., Wischik, D., Handley, M., Raiciu, C., and C. Pluntke, "Data center networking with multipath tcp.", 9th ACM SIGCOMM , 2010. [GWH11] Greenhalgh, A., Wischik, D., Handley, M., and C. Raiciu, "Design, implementation and evaluation of congestion control for multipath tcp.", 8th USENIX conference on Networked systems design and implementation , 2011. [RH10] Raiciu, C., Handley, M., Barre, S., and O. Bonaventure, "Experimenting with multipath tcp.", Proceedings of the ACM SIGCOMM 2010 conference , 2010. [AP13] Amer, P. and F. Pang, "Non-renegable selective acknowledgments (nr-sacks) for mptcp.", Proceedings of the 2013 27th International Conference on Advanced Information Networking and Applications Workshops , 2013. [M05] Internet Engineering Task Force, , "Coupled congestion control for multipath transport protocols", RFC 6356 , 2011. [M06] Internet Engineering Task Force, , "Multipath tcp (mptcp) application interface considerations", RFC 6897 , 2013. [M07] Internet Engineering Task Force, , "Tcp extensions for multipath operation with multiple addresses", RFC 6824 , 2013. Devetak & Kapoor Expires September 14, 2017 [Page 8]

Internet-Draft Dynamic MultiPath Routing March 2017 [M08] Internet Engineering Task Force, , "Architectural guidelines for multipath tcp development", RFC 6182 , 2011. 5.2. URIs [1] http:www.cs.iit.edu/~kapoor/papers/reducerate.pdf Authors' Addresses Fabrizio Devetak Illinois Institute of Technology 10W 31 Street Stuart Building Chicago, IL 60565 US Sanjiv Kapoor Illinois Institute of Technology 10W 31 Street Stuart Building Chicago, IL 60565 US Phone: +1 312 567 5329 Email: kapoor@iit.edu URI: http:www.cs.iit.edu/~kapoor Devetak & Kapoor Expires September 14, 2017 [Page 9]