Active Queue Management F. Baker Internet-Draft R. Pan Intended status: Informational Cisco Systems Expires: March 22, 2015 September 18, 2014 On Queuing, Marking, and Dropping draft-ietf-aqm-fq-implementation-00 Abstract This note discusses implementation strategies for coupled queuing and mark/drop algorithms. 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 March 22, 2015. Copyright Notice Copyright (c) 2014 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 (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. Baker & Pan Expires March 22, 2015 [Page 1] Internet-Draft September 2014 Table of Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 2. Fair Queuing: Algorithms and History . . . . . . . . . . . . 2 2.1. Generalized Processor Sharing . . . . . . . . . . . . . . 3 2.1.1. GPS Comparisons: transmission quanta . . . . . . . . 3 2.1.2. GPS Comparisons: flow definition . . . . . . . . . . 4 2.1.3. GPS Comparisons: unit of measurement . . . . . . . . 5 2.2. GPS Approximations . . . . . . . . . . . . . . . . . . . 5 2.2.1. Definition of a queuing algorithm . . . . . . . . . . 5 2.2.2. Round Robin Models . . . . . . . . . . . . . . . . . 6 2.2.3. Calendar Queue Models . . . . . . . . . . . . . . . . 7 2.2.4. Work Conserving Models and Stochastic Fairness Queuing . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.5. Non Work Conserving Models and Virtual Clock . . . . 9 3. Queuing, Marking, and Dropping . . . . . . . . . . . . . . . 10 3.1. Queuing with Tail Mark/Drop . . . . . . . . . . . . . . . 10 3.2. Queuing with CoDel Mark/Drop . . . . . . . . . . . . . . 10 3.3. Queuing with PIE Mark/Drop . . . . . . . . . . . . . . . 11 4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 12 5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12 6. Security Considerations . . . . . . . . . . . . . . . . . . . 12 7. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 12 8. References . . . . . . . . . . . . . . . . . . . . . . . . . 12 8.1. Normative References . . . . . . . . . . . . . . . . . . 13 8.2. Informative References . . . . . . . . . . . . . . . . . 13 Appendix A. Change Log . . . . . . . . . . . . . . . . . . . . . 14 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 14 1. Introduction In the discussion of Active Queue Management, there has been discussion of the coupling of queue management algorithms such as Stochastic Fairness Queuing [SFQ], Virtual Clock [VirtualClock], or Deficit Round Robin [DRR] with mark/drop algorithms such as CoDel [I-D.nichols-tsvwg-codel] or PIE [I-D.pan-aqm-pie]. In the interest of clarifying the discussion, we document possible implementation approaches to that, and analyze the possible effects and side- effects. The language and model derive from the Architecture for Differentiated Services [RFC2475]. 2. Fair Queuing: Algorithms and History There is extensive history in the set of algorithms collectively referred to as "Fair Queuing". The model was initially discussed in [RFC0970], which proposed it hypothetically as a solution to the TCP Silly Window Syndrome issue in BSD 4.1. The problem was that, due to a TCP implementation bug, some senders would settle into sending a Baker & Pan Expires March 22, 2015 [Page 2] Internet-Draft September 2014 long stream of very short segments, which unnecessarily consumed bandwidth on TCP and IP headers and occupied short packet buffers, thereby disrupting competing sessions. Nagle suggested that if packet streams were sorted by their source address and the sources treated in a round robin fashion, a sender's effect on end-to-end latency and increased loss rate would primarily affect only itself. This touched off perhaps a decade of work by various researchers on what was and is termed "Fair Queuing," philosophical discussions of the meaning of the word "fair," operational reasons that one might want a "weighted" or "predictably unfair" queuing algorithm, and so on. 2.1. Generalized Processor Sharing Conceptually, any Fair Queuing algorithm attempts to implement some approximation to the Generalized Processor Sharing [GPS] model. The GPS model, in its essence, presumes that a set of identified data streams, called "flows", pass through an interface. Each flow has a rate when measured over a period of time; A voice session might, for example, require 64 kbps plus whatever overhead is necessary to deliver it, and a TCP session might have variable throughput depending on where it is in its evolution. The premise of Generalized Processor Sharing is that on all time scales, the flow occupies a predictable bit rate, so that if there is enough bandwidth for the flow in the long term, it also lacks nothing in the short term. "All time scales" is obviously untenable in a packet network - and even in a traditional TDM circuit switch network - because a timescale shorter than he duration of a packet will only see one packet at a time. But it provides an ideal for other models to be compared against. There are a number of attributes of approximations to the GPS model that bear operational consideration, including at least the transmission quanta, the definition of a "flow", the unit of measurement. Implementation algorithms have different practical impacts as well. 2.1.1. GPS Comparisons: transmission quanta The most obvious comparison between the GPS model and common approximations to it is that real world data is not delivered uniformly, but in some quantum. The smallest quantum, in a packet network, is a packet. But quanta can be larger; for example, in video applications it is common to describe data flow in frames per second, where a frame describes a picture on a screen or the changes made from a previous one. A single video frame is commonly on the order of tens of packets. If a codec is delivering thirty frames per Baker & Pan Expires March 22, 2015 [Page 3] Internet-Draft September 2014 second, it is conceivable that the packets comprising a frame might be sent as thirty bursts per second, with each burst sent at the interface rate of the camera or other sender. Similarly, TCP exchanges have an initial window, common values of which include 1, 2, 3, 4 [RFC3390], and 10 [RFC6928], and there are also reports of bursts of 65K bytes at the relevant MSS, which is to say about 45 packets in one burst, presumably coming from TCP Segment Offload (TSO, also called TOE) engines. After that initial burst, TCP senders commonly send pairs of packets, but may send either smaller or larger bursts [RFC5690], and the rate at which they send data is governed by the arrival rate of acknowledgements from the receiver. 2.1.2. GPS Comparisons: flow definition An important engineering trade-off relevant to GPS is the definition of a "flow". A flow is, by definition, a defined data stream. Common definitions include: o Packets in a single transport layer session ("microflow"), identified by a five-tuple [RFC2990], o Packets between a single pair of addresses, identified by a source and destination address or prefix, o Packets from a single source address or prefix [RFC0970], o Packets to a single destination address or prefix, o Packets to or from a single subscriber, customer, or peer [RFC6057]. In Service Provider operations, this might be a neighboring Autonomous System; in broadband, a residential customer. The difference should be apparent. Consider a comparison between sorting by source address or destination address, to pick two examples, in the case that a given router interface has N application sessions going through it between N/2 local destinations and N remote sources. Sorting by source, or in this case by source/destination pair, would give each remote peer an upper bound guarantee of 1/N of the available capacity, which might be distributed very unevenly among the local destinations. Sorting by destination would give each local destination an upper bound guarantee of 2/N of the available capacity, which might be distributed very unevenly among the remote systems and correlated sessions. Who is one fair to? In both cases, they deliver equal service by their definition, but that might not be someone else's definition. Baker & Pan Expires March 22, 2015 [Page 4] Internet-Draft September 2014 2.1.3. GPS Comparisons: unit of measurement And finally, there is the question of what is measured for rate. If the sole objective is to force packet streams to not dominate each other, it is sufficient to count packets. However, if the issue is the bit rate of an SLA, one must consider the sizes of the packets (the aggregate throughput of a flow, measured in bits or bytes). And if predictable unfairness is a consideration, the value must be weighted accordingly. 2.2. GPS Approximations Carrying the matter further, a queuing algorithm may also be termed "Work Conserving" or "Non Work Conserving". A "work conserving" algorithm, by definition, is either empty, in which case no attempt is being made to dequeue data from it, or contains something, in which case it continuously tries to empty the queue. A work conserving queue that contains queued data, at an interface with a given rate, will deliver data at that rate until it empties. A non- work-conserving queue might stop delivering even through it still contains data. A common reason for doing this is to impose an artificial upper bound on a class of traffic that is lower than the rate of the underlying physical interface. 2.2.1. Definition of a queuing algorithm In the discussion following, we assume a basic definition of a queuing algorithm. A queuing algorithm has, at minimum: o Some form of internal storage for the elements kept in the queue, o If it has multiple internal classifications, * a method for classifying elements, * additional storage for the classifier and implied classes, o a method for creating the queue, o a method for destroying the queue, o a method, called "enqueue", for placing packets into the queue or queuing system o a method, called "dequeue", for removing packets from the queue or queuing system Baker & Pan Expires March 22, 2015 [Page 5] Internet-Draft September 2014 There may also be other information or methods, such as the ability to inspect the queue. It also often has inspectable external attributes, such as the total volume of packets or bytes in queue, and may have limit thresholds, such as a maximum number of packets or bytes the queue might hold. For example, a simple FIFO queue has a linear data structure, enqueues packets at the tail, and dequeues packets from the head. It might have a maximum queue depth and a current queue depth, maintained in packets or bytes. 2.2.2. Round Robin Models One class of implementation approaches, generically referred to as "Weighted Round Robin", implements the structure of the queue as an array or ring of queues associated with flows, for whatever definition of a flow is important. On enqueue, the enqueue function classifies a packet and places it into a simple FIFO sub-queue. On dequeue, the sub-queues are searched in round-robin order, and when a sub-queue is identified that contains data, removes a specified quantum of data from it. That quantum is at minimum a packet, but it may be more. If the system is intended to maintain a byte rate, there will be memory between searches of the excess previously dequeued. +-+ +>|1| | +-+ | | | +-+ +-+ | |1| +>|3| | +-+ | +-+ | | | | | +-+ +-+ | +-+ | |1| +>|2| | |3| | +-+ | +-+ | +-+ | A | A | A | | | | | | ++--++ ++--++ ++--++ +->| Q |-->| Q |-->| Q |--+ | +----+ +----+ +----+ | +----------------------------+ Figure 1: Round Robin Queues Baker & Pan Expires March 22, 2015 [Page 6] Internet-Draft September 2014 If a hash is used as a classifier, the modulus of the hash might be used as an array index, selecting the sub-queue that the packet will go into. One can imagine other classifiers, such as using a Differentiated Services Code Point (DSCP) value as an index into an array containing the queue number for a flow, or more complex access list implementations. In any event, a sub-queue contains the traffic for a flow, and data is sent from each sub-queue in succession. 2.2.3. Calendar Queue Models Another class of implementation approaches, generically referred to as "Weighted Fair Queues" or "Calendar Queue Implementations", implements the structure of the queue as an array or ring of queues (often called "buckets") associated with time or sequence; Each bucket contains the set of packets, which may be null, intended to be sent at a certain time or following the emptying of the previous bucket. The queue structure includes a look-aside table that indicates the current depth (which is to say, the next bucket) of any given class of traffic, which might similarly be identified using a hash, a DSCP, an access list, or any other classifier. Conceptually, the queues each contain zero or more packets from each class of traffic. One is the queue being emptied "now"; the rest are associated with some time or sequence in the future. On enqueue, the enqueue function classifies a packet and determines the current depth of that class, with a view to scheduling it for transmission at some time or sequence in the future. If the unit of scheduling is a packet and the queuing quantum is one packet per sub- queue, a burst of packets arrives in a given flow, and at the start the flow has no queued data, the first packet goes into the "next" queue, the second into its successor, and so on; if there was some data in the class, the first packet in the burst would go into the bucket pointed to by the look-aside table. If the unit of scheduling is time, the explanation in Section 2.2.5 might be simplest to follow, but the bucket selected will be the bucket corresponding to a given transmission time in the future. A necessary side-effect, memory being finite, is that there exist a finite number of "future" buckets. If enough traffic arrives to cause a class to wrap, one is forced to drop something (tail-drop). On dequeue, the buckets are searched at their stated times or in their stated sequence, and when a bucket is identified that contains data, removes a specified quantum of data from it and, by extension, from the associated traffic classes. A single bucket might contain data from a number of classes simultaneously. Baker & Pan Expires March 22, 2015 [Page 7] Internet-Draft September 2014 +-+ +>|1| | +-+ | | | +-+ +-+ | |2| +>|2| | +-+ | +-+ | | | | | +-+ | +-+ +-+ | |3| | |1| +>|1| | +-+ | +-+ | +-+ | A | A | A | | | | | | ++--++ ++--++ ++--++ "now"+->| Q |-->| Q |-->| Q |-->... +----+ +----+ +----+ A A A |3 |2 |1 +++++++++++++++++++++++ |||| Flow |||| +++++++++++++++++++++++ Figure 2: Calendar Queue In any event, a sub-queue contains the traffic for a point in time or a point in sequence, and data is sent from each sub-queue in succession. If sub-queues are associated with time, an interesting end case develops: If the system is draining a given sub-queue, and the time of the next sub-queue arrives, what should the system do? One potentially valid line of reasoning would have it continue delivering the data in the present queue, on the assumption that it will likely trade off for time in the next. Another potentially valid line of reasoning would have it discard any waiting data in the present queue and move to the next. 2.2.4. Work Conserving Models and Stochastic Fairness Queuing McKenney's Stochastic Fairness Queuing [SFQ] is an example of a work conserving algorithm. This algorithm measures packets, and considers a "flow" to be an equivalence class of traffic defined by a hashing algorithm over the source and destination IPv4 addresses. As packets arrive, the enqueue function performs the indicated hash and places the packet into the indicated sub-queue. The dequeue function operates as described in Section 2.2.2; sub-queues are inspected in round-robin sequence, and if they contain one or more packets, a packet is removed. Baker & Pan Expires March 22, 2015 [Page 8] Internet-Draft September 2014 Shreedhar's Deficit Round Robin [DRR] model modifies the quanta to bytes, and deals with variable length packets. A sub-queue descriptor contains a waiting quantum (the amount intended to be dequeued on the previous dequeue attempt that was not satisfied), a per-round quantum (the sub-queue is intended to dequeue a certain number of bytes each round), and a maximum to permit (some multiple of the MTU). In each dequeue attempt, the dequeue method sets the waiting quantum to the smaller of the maximum quantum and the sum of the waiting and incremental quantum. It then dequeues up to the waiting quantum, in bytes, of packets in the queue, and reduces the waiting quantum by the number of bytes dequeued. Since packets will not normally be exactly the size of the quantum, some dequeue attempts will dequeue more than others, but they will over time average the incremental quantum per round if there is data present. McKenny or Shreedhar's models could be implemented as described in Section 2.2.3. The weakness of a WRR approach is the search time expended when the queuing system is relatively empty, which the calendar queue model obviates. 2.2.5. Non Work Conserving Models and Virtual Clock Zhang's Virtual Clock [VirtualClock] is an example of a non-work- conserving algorithm. It is trivially implemented as described in Section 2.2.3. It associates buckets with intervals in time, with durations on the order of microseconds to tens of milliseconds. Each flow is assigned a rate in bytes per interval. The flow entry maintains a point in time the "next" packet in the flow should be scheduled. On enqueue, the method determines whether the "next schedule" time is "in the past"; if so, the packet is scheduled "now", and if not, the packet is scheduled at that time. It then calculates the new "next schedule" time, as the current "next schedule" time plus the length of the packet divided by the rate; if the resulting time is also in the past, the "next schedule" time is set to "now", and otherwise to the calculated time. As noted in Section 2.2.3, there is an interesting point regarding "too much time in the future"; if a packet is scheduled too far into the future, it may be marked or dropped in the AQM procedure, and if it runs beyond the end of the queuing system, may be defensively tail dropped. On dequeue, the bucket associated with the time "now" is inspected. If it contains a packet, the packet is dequeued and transmitted. If the bucket is empty and the time for the next bucket has not arrived, the system waits, even if there is a packet in the next bucket. As noted in Section 2.2.3, there is an interesting point regarding the queue associated with "now". If a subsequent bucket, even if it is Baker & Pan Expires March 22, 2015 [Page 9] Internet-Draft September 2014 actually empty, would be delayed by the transmission of a packet, one could imagine marking the packet ECN CE [RFC3168] [RFC6679] or dropping the packet. 3. Queuing, Marking, and Dropping Queuing, marking, and dropping are integrated in any system that has a queue. If nothing else, as memory is finite, a system has to drop as discussed in Section 2.2.3 and Section 2.2.5 in order to protect itself. However, host transports interpret drops as signals, so AQM algorithms use that as a mechanism to signal. It is useful to think of the effects of queuing as a signal as well. The receiver sends acknowledgements as data is received, so the arrival of acknowledgements at the sender paces the sender at approximately the average rate it is able to achieve through the network. This is true even if the sender keeps an arbitrarily large amount of data stored in network queues, and is the basis for delay- based congestion control algorithms. So, delaying a packet momentarily in order to permit another session to improve its operation has the effect of signaling a slightly lower capacity to the sender. 3.1. Queuing with Tail Mark/Drop In the default case, in which a FIFO queue is used with defensive tail-drop only, the effect is therefore to signal to the sender in two ways: o Ack Clocking, pacing the sender to send at approximately the rate it can deliver data to the receiver, and o Defensive loss, when a sender sends faster than available capacity (such as by probing network capacity when fully utilizing that capacity) and overburdens a queue. 3.2. Queuing with CoDel Mark/Drop In any case wherein a queuing algorithm is used along with CoDel [I-D.nichols-tsvwg-codel], the sequence of events is that a packet is time-stamped, enqueued, dequeued, compared to a subsequent reading of the clock, and then acted on, whether by dropping it, marking and forwarding it, or simply forwarding it. This is to say that the only drop algorithm inherent in queuing is the defensive drop when the queue's resources are overrun. However, the intention of marking or dropping is to signal to the sender much earlier, when a certain amount of delay has been observed. In a FIFO+CoDel, Virtual Clock+CoDel, or FlowQueue-Codel Baker & Pan Expires March 22, 2015 [Page 10] Internet-Draft September 2014 [I-D.hoeiland-joergensen-aqm-fq-codel] implementation, the queuing algorithm is completely separate from the AQM algorithm. Using them in series results in four signals to the sender: o Ack Clocking, pacing the sender to send at approximately the rate it can deliver data to the receiver through a queue, o Lossless signaling that a certain delay threshold has been reached, if ECN [RFC3168][RFC6679] is in use, o Intentional signaling via loss that a certain delay threshold has been reached, if ECN is not in use, and o Defensive loss, when a sender sends faster than available capacity (such as by probing network capacity when fully utilizing that capacity) and overburdens a queue. 3.3. Queuing with PIE Mark/Drop In any case wherein a queuing algorithm is used along with PIE [I-D.pan-aqm-pie], RED, or other such algorithms, the sequence of events is that a queue is inspected, a packet is dropped, marked, or left unchanged, enqueued, dequeued, compared to a subsequent reading of the clock, and then forwarded on. This is to say that the AQM Mark/Drop Algorithm precedes enqueue; if it has not been effective and as a result the queue is out of resources anyway, the defensive drop algorithm steps in, and failing that, the queue operates in whatever way it does. Hence, in a FIFO+PIE, SFQ+PIE, or Virtual Clock+PIE implementation, the queuing algorithm is again completely separate from the AQM algorithm. Using them in series results in four signals to the sender: o Ack Clocking, pacing the sender to send at approximately the rate it can deliver data to the receiver through a queue, o Lossless signaling that a queue depth that corresponds to a certain delay threshold has been reached, if ECN is in use, o Intentional signaling via loss that a queue depth that corresponds to a certain delay threshold has been reached, if ECN is not in use, and o Defensive loss, when a sender sends faster than available capacity (such as by probing network capacity when fully utilizing that capacity) and overburdens a queue. Baker & Pan Expires March 22, 2015 [Page 11] Internet-Draft September 2014 4. Conclusion To summarize, in Section 2, implementation approaches for several classes of queueing algorithms were explored. Queuing algorithms such as SFQ, Virtual Clock, and FlowQueue-Codel [I-D.hoeiland-joergensen-aqm-fq-codel] have value in the network, in that they delay packets to enforce a rate upper bound or to permit competing flows to compete more effectively. ECN Marking and loss are also useful signals if used in a manner that enhances TCP/SCTP operation or restrains unmanaged UDP data flows. Conceptually, queuing algoritms and a mark/drop algorithms operate in series, as discussed in Section 3, not as a single algorithm. The observed effects differ: defensive loss protects the intermediate system and provides a signal, AQM mark/drop works to reduce mean latency, and the scheduling of flows works to modify flow interleave and acknowledgement pacing. Certain features like flow isolation are provided by fair queueing related designs, but are not the effect of the mark/drop algorithm. The authors think highly of queuing algorithms, which can ensure certain behaviors, but in this context believe that coupling queuing and marking or dropping in an AQM (mark/drop) discussion is unwarranted and masks issues with the mark/drop algorithm in question. 5. IANA Considerations This memo asks the IANA for no new parameters. 6. Security Considerations This memo adds no new security issues; it observes on implementation strategies for Diffserv implementation. 7. Acknowledgements This note grew out of, and is in response to, mailing list discussions in AQM, in which some have pushed an algorithm the compare to AQM marking and dropping algorithms, but which includes Flow Queuing. 8. References Baker & Pan Expires March 22, 2015 [Page 12] Internet-Draft September 2014 8.1. Normative References [RFC2475] Blake, S., Black, D., Carlson, M., Davies, E., Wang, Z., and W. Weiss, "An Architecture for Differentiated Services", RFC 2475, December 1998. 8.2. Informative References [DRR] Microsoft Corporation and Washington University in St. Louis, "Efficient fair queueing using deficit round robin", ACM SIGCOMM 1995, October 1995, . [GPS] Xerox PARC, University of California, Berkeley, and Xerox PARC, "Analysis and simulation of a fair queueing algorithm", ACM SIGCOMM 1989, September 1989, . [I-D.hoeiland-joergensen-aqm-fq-codel] Hoeiland-Joergensen, T., McKenney, P., Taht, D., Gettys, J., and E. Dumazet, "FlowQueue-Codel", draft-hoeiland- joergensen-aqm-fq-codel-00 (work in progress), March 2014. [I-D.nichols-tsvwg-codel] Nichols, K. and V. Jacobson, "Controlled Delay Active Queue Management", draft-nichols-tsvwg-codel-02 (work in progress), March 2014. [I-D.pan-aqm-pie] Pan, R., "PIE: A Lightweight Control Scheme To Address the Bufferbloat Problem", draft-pan-aqm-pie-02 (work in progress), September 2014. [RFC0970] Nagle, J., "On packet switches with infinite storage", RFC 970, December 1985. [RFC2990] Huston, G., "Next Steps for the IP QoS Architecture", RFC 2990, November 2000. [RFC3168] Ramakrishnan, K., Floyd, S., and D. Black, "The Addition of Explicit Congestion Notification (ECN) to IP", RFC 3168, September 2001. [RFC3390] Allman, M., Floyd, S., and C. Partridge, "Increasing TCP's Initial Window", RFC 3390, October 2002. Baker & Pan Expires March 22, 2015 [Page 13] Internet-Draft September 2014 [RFC5690] Floyd, S., Arcia, A., Ros, D., and J. Iyengar, "Adding Acknowledgement Congestion Control to TCP", RFC 5690, February 2010. [RFC6057] Bastian, C., Klieber, T., Livingood, J., Mills, J., and R. Woundy, "Comcast's Protocol-Agnostic Congestion Management System", RFC 6057, December 2010. [RFC6679] Westerlund, M., Johansson, I., Perkins, C., O'Hanlon, P., and K. Carlberg, "Explicit Congestion Notification (ECN) for RTP over UDP", RFC 6679, August 2012. [RFC6928] Chu, J., Dukkipati, N., Cheng, Y., and M. Mathis, "Increasing TCP's Initial Window", RFC 6928, April 2013. [SFQ] SRI International, "Stochastic Fairness Queuing", IEEE Infocom 1990, June 1990, . [VirtualClock] Xerox PARC, "Virtual Clock", ACM SIGCOMM 1990, September 1990, . Appendix A. Change Log Initial Version: June 2014 Authors' Addresses Fred Baker Cisco Systems Santa Barbara, California 93117 USA Email: fred@cisco.com Rong Pan Cisco Systems Milpitas, California 95035 USA Email: ropan@cisco.com Baker & Pan Expires March 22, 2015 [Page 14]