QoS Optimization of In-elastic Flows Stripped over Multiple Asymmetric Channels in Mobile

Networks Syed Zubair Ahmad, Muhammad Abdul Qadir

Center of Semantic and Distributed Computing Mohammad Ali Jinnah University, Islamabad campus

Islamabad, Pakistan {szubair, aqadir}@jinnah.edu.pk

Mohammad Saeed Akbar Advanced Computer & GIS Lab, Faculty of Agriculture,

University of Agriculture Faisalabad, Pakistan

saeedakbar@jinnah.edu.pk Abstract—In this paper, we analyze the impact of end-to-end (E2E) delay variation on stripped sessions, whose packets are distributed over multiple links of multi-mode mobile terminal (MMT). The results of this analysis signify the importance of minimized out-of-sequence (OOS) arrival of packets belonging to a stream, while traversing through multiple radio networks to reach their destination. We, then propose a multi-server delay-budget ordered (MDO) scheduling scheme, that schedules back-logged packets on multiple interfaces of MMT according to their residual delay-budget metrics. In the proposed scheme, the im-pact of E2E delay variation on in-order packet arrival has been minimized through a link ranking function that reduces the im-pact of delay fluctuations. The simulation results show a consis-tently high performance metrics for the scheme with respect to end-to-end delay variations, packet drop rate, E2E throughput and in-order arrival which are highly desirable for interactive/ live, and buffered video streams running on MMT devices in uplink or/and downlink paths.

Keywords-component; multi-server delay-budget ordered scheduling, Quality-of-service, out-of-sequence packet arrival, capacity aggregation, one-way trip time estimation

I. INTRODUCTION A robust and dependable communication infrastructure is

essential for emerging ubiquitous mobile computing paradigm, but due to inconsistent channel characteristics of wireless links, dependability is difficult to be guaranteed. The location updates during mobility with respect to DNS and other naming services are still not fast enough to support anything more than nomadic mobility. It is, therefore important to reduce the impact of channel inconsistencies on QoS-aware application through some contingency plans that could provide service guarantees without depleting scarce network resources. The capacity ag-gregation (CAG) or bandwidth aggregation (BAG) of multiple interfaces available in an MMT has been considered as one feasible way of providing seamless service guarantees over less reliable wireless channels, particularly during mobility[5][6]. Although CAG has sufficient potential to be used in achieving desired service levels and its best case results are available in literature, the average and worst case scenarios need thorough investigation to identify its key challenges and make it more useful, not only for MMT itself, but also for other devices op-erating in the close vicinity.

The variability in the wireless link conditions is one such aspect that causes large E2E delay variations and may reduce the accuracy of link selection decision process during data striping based CAG. The variations in E2E delay may also have roots in asymmetric channel characteristics of multiple available channels, while sending a single steam on multiple channels. Such variations eventually cause increased probabili-ty of out-of-sequence (OOS) arrival of packets at the destina-tion, and may lead to added complexity for packet re-ordering [9] or may lead to higher packet drop rate. Both of these factors have catastrophic impact on QoS provisioning that needs de-tailed investigation and concrete solutions.

Some of the issues relating to BAG/CAG in wireless net-works have been studied by researchers in recent past. The Earliest Delivery Path First (EDPF) scheduling approach pro-posed in [6] is one such scheme that has been tested for video sessions under relatively static link conditions, in the downlink path. This approach is highly effective when the proposed proxy is located at the base-station (BS). In case, the proxy has certain hop count distance from the BS, (as may be the case in mobile IP home agent); the variation in path delay can serious-ly impair scheduling order, resulting in higher OOS arrival at the destination. The same is also applicable to the time-slotted EDPF (TS-EDPF) scheme proposed in [4]. These schemes also assume a single backlogged session, which may not be realistic model in case of multi-sessions belonging to different service classes. Surplus round robin (SRR) and some of its variants have also been evaluated for the data striping over multiple links [7]. These round robin(RR) schemes are superior for fairness guarantee amongst the contending sessions, but not specifically designed for multi-server operation, and don’t have sufficient information to arbitrate links between contending sessions. The multi-path loss-tolerant (MPLOT) protocol, pro-posed in [5], uses transport protocol semantics to achieve high-er bandwidth in heterogeneous wireless environment. This scheme uses packet drop rate, transmission window size and E2E delay for data striping. This approach can absorb varia-tions in link metrics, but due to upper layer semantics, it uses multiple sub-streams for performance gains but may reduce the fairness property of other concurrently running sessions.

In this paper, we present a novel scheduling scheme that can serve multiple traffic classes over heterogeneous wireless channels; with a proactive link monitoring approach, based on

9781-4244-3941-6/09/$25.00 ©2009 IEEE

variations in E2E delay estimation of available links. We use a simple link ranking model that models link delay fluctuations and schedule traffic on the basis of delay-budget (DB) of each backlogged packet. The proposed MDO scheduling achieves fairness amongst backlogged session in addition to improved in-order arrival. Such services may be very useful for mobile applications that manage QoS at their own, without service guarantees of the underlying network(s). This approach is also very suitable for mobile QoS provisioning; where renegotiation overhead during mobility (particularly vertical handover) is not cost effective. In such scenarios, QoS reservation may be per-formed over the fixed path; like home agent (HA) and corres-ponding node (CN) and multipath CAG is used to achieve QoS on the mobile (changing) path. The ingress router of the QoS assured path needs in-order packet arrival [1]. The rest of the paper is organized as follows. The section II describes the sys-tem model of the proposed scheme. Section III discusses the proposed MDO scheduling scheme. Section IV describes the simulation setup and results. We conclude with some future research direction in section V.

II. SYSTEM MODEL Figure 1 shows system model of proposed scheme. It high-

lights three contributors of overall E2E communication scena-rio. These include MMT sender, Network convolution re-sponse, and the receiver buffers. The MMT sender is the core processing component where the link monitoring and schedul-ing is performed. An MMT can act as a sender or receiver or both at the same time, but for the sack of simplicity we study it as sender only. The provision of MDO at the remote senders or any of the intermediate proxy such as home agent (HA) in mobile IP model enhances performance of MMT as a parallel receiver. The link monitoring component helps in ranking a link with respect to, its available capacity (in terms of bytes), E2E delay with its variations and packet drop-rate. The MDO

performs the multi-link scheduling in accordance to the DB of each back-logged packet, along-with token bucket regulator (TBR). The multiple service queues represent multiple class-of-services (CBR, VBR etc.), one queue per session to ensure fair allocation of link resources in combination with DB of head-of-line (HoL) packet of each queue.

The Network convolution response provides the E2E delay with variation on different paths. The variation of each path is accumulated in terms of standard deviation to find the va-riance in delay characteristics of each path. The convolution response of queuing and delay behavior of multiple hops in the E2E path is modeled by the variation in E2E delay and packet loss rate. The receiver’s buffers hold packets arrived for each session. The study of OOS arrival is conducted in these buffers. The multiple session buffer shown in Figure 1 need not be on the same terminal and each one may reside on separate terminals. Each remote receiver may also include an optional remote link monitoring and ranking component, in case it desires to send packets towards the MMT on multiple paths (having prior knowledge of multimode capability of receiver). In general, this component resides in the proxy at HA or any other data-striping point, en-route MMT.

III. PROPOSED SCHEDULING SCHEME In this section we describe proposed MDO scheduling

scheme that aggregates capacity of multiple wireless interfaces of an MMT, to satisfy QoS needs of ongoing sessions. First, we analyze the probabilistic OOS arrival against E2E delay variations and its relation with packet-drop at different buffer sizes. Its worth-mentioning here that buffer management at routers is expensive in terms of resource utilization and scala-bility of service provisioning. The analysis of OOS and pack-et-drop leads to device a ranking function that performs assi-milation of E2E delay variation to minimize OOS arrival.

Receive Buffers at Receiver(s)

Minimized OoS Arrival, Buffer Occupancy, and QoS maximization of

each flow

Remote Link Monitoring & Ranking (Capacity Estimation, delay characte-

ristics log, Packet Drop log etc.)

MMT Sender Node

Link 1(Delay Statistics, Capaci-ty, Delay variation, Drop rate)

Link 2(Delay Statistics, Capaci-ty, Delay variation, Drop rate)

Link N (Delay Statistics, Capaci-ty, Delay variation, Drop rate)

Link Status Link monitoring Multi-source Packets

Link Monitoring & Ranking (Capacity Estimation, delay charac-teristics log, Packet Drop log etc.)

Multi-server Delay-budget ordered (MDO) Scheduler

Multiple Classes of Service with different buffer, delay

requirements

Network Convolution Response

E2E Delay, E2E variance, drop rate of flow 1

E2E Delay, E2E variance, drop rate of flow 2

E2E Delay, E2E variance, drop rate of flow 3

E2E Delay, E2E variance, drop rate of Flow 4

E2E Delay, E2E variance, drop rate of flow 5

UDP channel for link status exchange

Figure 1: System model of capacity aggregation

A. Analysis of OOS and Packet Drop

Figure 2 shows plot of probabilistic OOE2E delay variation. A logarithmic trend lmentally observed values that produces mierror is also shown in the graph. It is noticearrival is around 1% at delay variation valuethe mean E2E value, over a short span of tirival rises sharply above 7% E2E delay variato 20 % value within increase of 8% E2E deter this point, plot saturates to approximatcrease model. It is not common that a pardelay variation in the above 20% ranges, buseeable that in situations like data striping anpossibility may not be ruled-out. It is thereconsider these ranges while using data-stripp

One important aspect of delay variation packet drop-rate at flow-state managing ingceiver application. Assuming that packets different paths at an ingress and experiencetion in the realizable range of 7 % to 20%, thdrop-rate (PD) at the receiving application; aing rates ( ) in seconds is plotted in Figure that PD is very high in case of small valsignificantly low for higher buffering rateE2E delay variation.

Figure 3: A probabilistic packet drop against E2E delay variatio

% PD

E2E Delay variation (%)

E2E delay variation (%)

% OOS

Figure 2: A Probabilistic plot of OOS arrival against E2E

OS arrival against ine of the experi-nimum regression able that the OOS

es of within 5% of ime. The OOS ar-ations and reaches elay variation. Af-te logarithmic in-rticular path faces ut it is highly fore-nd handovers, such efore, important to ping protocols.

could be the high gress routers or re-

are arriving from e E2E delay varia-he possible packet at different buffer-3. It is noticeable

lues, whereas it is es, even at higher

ons at different buffer time

The larger buffering time aters can be highly expensive icost and scalability. Similarlyalso add processing complexitybuffer occupancy issues, as ditive and live video transmissioing time and are desirable to olieu of this analysis, it seems hE2E delay variation and OOS termediate node to ensure QoSeo applications.

B. Link Ranking Function The impact of E2E delay va

possibly higher PD can be caapplication, particularly interactherefore, important to cater E2less path while performing CAthis purpose, we add a link msystem architecture for CAG, way trip time for nodes. Table 1 summarizes symranking function and algorithmings for each links for multipdestinations, but for simplicityThe estimated valuecharacteristics w.r.t. E2E delarate. The E2E delay variation DB status of each arriving pawell as remotely through a UDdeparted packets. The mean amathematically related in a lo

of link that is given i

The positive or negative skthe delay variation is accrued athe link . The vallogged packets which is discuss

C. MDO Scheduling Service

The proposed MDO scheduliing OOS arrival at the receivertipath stream forwarding scenasurplus round robin (SRR) schattribute amongst contending QThe proposed scheme is basedand effective one-way trip timlink to the destination. A brief scenario is described now. Leeach with a possibly distinct Dscheduling process is fast enou

1 UDP packet is sent periodically to cstatus of each packet belonging to a strstatistical mean and variance of E2E de

E delay variations

at the flow-state managing rou-in terms of resource utilization, y, a larger buffering time may y and cost due to reordering and scussed in [9]. Further, interac-

on may not afford larger buffer-operate with lower values. In highly desirable to minimize the

arrival at the destination or in-S provisioning for real-time vid-

ariation on the OOS arrival and atastrophic to the QoS enabled ctive & live-video streams. It is 2E delay variation of each wire-AG/BAG of multiple links. For onitoring process in the overall which estimates effective one-link between communicating mbols and notations used in the

m. There may be multiple read-ple sessions running for diverse y we use only one such value. es are based on the recent past ay variations and packet drop is recorded locally through the

acket or as given in [8][10]; as P packet-pair exchange1, for the and variance of E2E delay are ogarithmic function to evaluate in (1).

, (1)

kew , in accordingly to the mean value of lue is used for scheduling back-sed in the next sub-section.

ing scheme is focused on reduc-r due to delay variations in mul-arios. It is partly inspired by the heme that has superior fairness QoS enabled traffic sources [7]. d on DB of backlogged packets me of each wireless

f summary of the pre-scheduling et there are ongoing sessions, DB value . It is assumed that ugh so as the backlogged traffic ollect information about the arrival time ream. In response the receiver sends the elay.

of each queue never exceed one maximum length packet. It is also assumed that the token allocated to each flow is equal to the minimum packet length, on the worst. For simplicity, the backlogged queues are maintained in order of their DB value (in ascending order). The queues are served in their defined order irrespective of their current token value. This means that a queue shall be served irrespective of its token counter (pro-vided it has non-negative value) to minimize processing com-plexity. In case, backlogged packet is larger than the token value, it is still served at its turn and the token value in all cas-es, is subtracted by the packet length. This ensures that an over serviced queue shall not be served in sub-sequent scheduling round(s) unless token counter attains non-negative integer value. This particular feature is inherited from SRR [7].

TABLE 1: TABLE OF NOTATIONS &VARIABLES USED IN AND SCHEDULING

Symbol Description

Number of available RAT Length of packet

Maximum allowed length of packet Cumulative capacity of all links at time Number of queues The backlogged packet at queue with length Set of all backlogged packets at time . Weight of the queue at time as per service require-

ment and available cumulative capacity Delay budget of queue at time in ms

Token value of queue at time Variance of of link

Mean of link Effective one-way trip time of link for a given desti-

nation, as derived from (1) Index of with the best effective

Frame cycle time (Frame duration) of link Cumulated output queues after scheduling with representing output queue of link at time .

The decision regarding allocation of a particular link slot to backlogged packet is based on the minimum amongst all active links. Hence, smallest DB valued packet is scheduled on the best valued link and the value of the link is incremented by the Frame duration of the link. The purpose of this addition is to indicate that the next probable schedule on this link shall be expensive in terms of E2E delay by a minimum of time units. The time elapsed after the completion of scheduling round automatically removes the added factor in the next round.

The token generation process for each backlogged queue is based on TBR with constraint parameters where is token generation rate (equivalently; traffic arrival rate), in ac-cordance with the service requirement of the queue and con-straint by the available data rate. The represent the maxi-mum bucket size to constraint burst transmission. Figure 4 gives the pseudo-code representation of the propose MDO al-gorithm. The token generation process and clock management is kept separate from the scheduling algorithm. In the next sub-section we describe some properties of MDO.

D. Properties of MDO The proposed MDO scheme has some salient features that

enhance its utility in multi-server heterogeneous channel communication resource utilization. The proposed scheduling algorithm has a complexity of , which is desirable in network traffic scheduling. The availability of aggregation of multiple links increases its service quality and rate. The main advantage of MDO scheduling lies in OOS minimization, scheduling latency and fairness which are briefly described in the following text.

1) Minimized OOS The link monitoring service helps in minimizing OOS arriv-

al of the packets belonging to same stream. The monitoring function works independently; resulting in swift adaptation of scheduling process in accordance with the physical link changes; without adding complexity. Since each queue is giv-en best available link, the chances of its in-order ar-rival are improved. The ranking function takes a convolution approach of network delay responses to absorb variation in E2E delay of a path to low ranges. In case, the value of a link is low, the probability of more than one backlogged packets be scheduled on it is high, which improve in-order arrival. This scenario is represented mathematically in (2)

(2)

The situation shown in (2) indicates that all backlogged packets may also be scheduled on a single link, at a time , when (2) holds. This scenario induced high probability of packets belonging to each session being scheduled over a sin-gle link. This can also increase in-order arrival probability and can be specifically useful during handover operations. In case, delay variations amongst the path differ only slightly, that can be the most useful scenario for CAG. Ideally, a variation of less than frame size achieves effective load-balancing.

1. Input:

2. Output: Schedule for all backlogged packets

3. ;choose index of best OTT link

4. ; The first backlogged queue(Highest priority)

5. ;Repeat until all queues served

6. ; serve queues with positive Tt

7.

8.

9. ;minimum delay till next sche-dule

10.

11.

12. Increment ; Service next queue

13. ;choose ;;index of best OTT link

14.

Figure 4:Multi-server temporal-order scheduling (MDO) algorithm

2) Service Latency

The proposed MDO scheduling scheme belongs to work-conserving, latency-rate (LR) scheduler, as it would be busy scheduling if there are backlogged packets with assured quali-ty. Using analysis techniques given in [2], the service rate

can be modeled by (3)

(3)

where represents normalized latency of service in the presence of multiple servers and approximates to , when is the service latency in case of a single server and servers may serve packets simultaneously. The is the arrival rate constraint by TBR of flow , is the time of arrival of backlogged packet of stream , and is any time between start and completion of service. It can be noticed that the service latency of MDO is better than RR scheduling schemes, due to multiple server operation and improves as the number of server increase. Since we are not considering the case of packet striping i.e. a single packet is stripped over multiple links, the delay is solely dependent on the packet size. The single server latency, can be represented as given in (4).

(4)

where and represent packet length and maximum packet length, respectively. Hence, in case of multiple back-logged packets, the maximum latency of a backlogged packet can be represented as given in (5)

(5)

where is the number of concurrent streams backlogged, in their order of priority and is the number of concurrent serv-ers in service. The is the maximum service delay expe-rienced by flow , under multi-server operation. It indicates that due to multiple servers, a concurrent execution of algo-rithm with proper synchronization techniques such as sema-phores and monitors can significantly reduce service latencies.

3) Fairness The fairness of the scheme can be represented by range

bounding maximum latency faced by any two or more simul-taneously backlogged packets. On the basis of (5), it is easy to prove that the maximum latency of two streams, during the overlapped wait interval is bounded in the val 0 , as given in (6)

(6)

The (6) shows that the delay in service for any two overlapped backlogged packet is upper bounded by . This also indicates that fairness increases as the number of available channels increase.

IV. SIMULATIONS In the simulation setup, scheduling component defined for

the proposed system is independently developed and tested in Matlab and ns-2. We have used three TDMA based radio

access networks (RANs) to test the scheduling scheme. Due to our focus on E2E delay variations, the experiments are de-signed to have wide ranged values of delay variations. The convolution network response is modeled through individual router’s statistical processing models for input and output queues. This provided more realistic picture of E2E delay re-sponse. The E2E path has been modeled through 13 routers, which is the average hop count for any E2E communication channel over Internet. The wired-line delays in the wireless networks have been accumulated by the service time of router at the individual network’s gateway for Internet. The video server used at MMT node is trace driven by traffic classifica-tion of DiffServ [1]. The mean values of E2E delay has been in the range of 150-180 milliseconds, with delay variation modeled with exponential distribution. The metrics of interest in simulation study has been OOS, PD and throughput at dif-ferent levels of E2E delay variations.

A. Simulation Results

Figure 5 plots OOS behavior of MDO under diverse ranges of E2E delay variations. The graph shows a reasonably ac-ceptable value of 7% OOS arrival at 10% E2E delay variation. The OOS percentage is much lower at below 10% delay varia-tions. This is a significant improvement over the results shown in Figure 3, where OOS steeply increase to 20%, at around 15% delay variation. In this plot (Figure 5), OOS is around 13% at 18% delay variation. The delay variations are normally consistent if major part of a stream flows through a single link, but seriously fluctuates as data stripping is in use, as is the case in MDO. The delay variation represents convolu-tion response of multi-hop network path and around 5% delay variations are common in normal network operation. The proactive approach of MDO towards curbing OOS through calculating for each available path reduces the impact of E2E delay variation. The DB based scheduling of MDO is the second reason for reduced OOS arrival under volatile de-lay variations.

Figure 6 plots PD against delay variations at different buf-fering capacity under MDO scheduling. The buffering ( ) is shown in terms of time equivalent of bytes, as per path capaci-ty. The graph shows minimum drop rate for higher buffering rate, equivalent to 0.02 seconds, whereas it is worst for lower buffering of 0.001 seconds. The impact of OOS arrival, in conjunction with buffering size represents a complex scenario where a tradeoff between resource utilization and service qual-ity has to be resolved. Further, this result shows that in case of relatively low delay variation (up to 10%) the packet drop rate is not alarmingly high. The PD values are taken over a long stream and don’t reflect PD rate. The result also shows that MDO is highly useful for both interactive/live and stored vid-eo transmission. This assessment particularly holds, in case where delay variation doesn’t exceed 10% of the mean values.

Figure 7 shows a plot of throughput against PD at different buffering scale. The graph shows that higher buffering size ( ) produces higher throughput values at lower PD percentages; which is highly favorable for video streaming applications. In

the worst case delay variation scenario, the %for 0.001s and 0.02s buffer duration respenoticeable that the throughput of smaller buficantly lower than corresponding values at hThe main reason behind this lower throughvalues is the reduced transmission through Mlower buffer sizes. The 1% PD occurs at arorival and a faster transmission rate can incrhigher values. This result also highlightsMDO under multiple types of service classes

Figure 5: A plot of OOS arrival of proposed MDO schevariation

Figure 6: A plot of packet drop against E2E delay variating rates ( represent time in second’s equivalen

V. CONCLUSIONS

In this paper, we have proposed a novthat aggregates heterogeneous channels of mto achieve better QoS provisions for interacfered video streams. The proposed scheme ieral purpose multi-server scheduling algoritof backlogged packets along with effective

of each available channel, to achievebility of in-order arrival of packets at the recfocus of this research has been to evaluate ilay variations on the multi-channel paths of rogeneous wireless networks. We have usedis adaptive in terms of handling E2E del

Delay Variation (%)

E2E Delay variation (%)

%OOS

%PD

%PD is 17% & 9% ectively. It is also ffer sizes is signif-

higher buffer sizes. hput at similar PD MDO scheduler, at ound 8% OOS ar-rease PD to much s effectiveness of s.

eme against E2E delay

tion at different buffer-nt to buffering)

vel MDO scheme multi-mode devices ctive/live and buf-is based on a gen-thm that uses DB one-way trip time

e improved proba-ceiver. The prime impact of E2E de-f a session in hete-d an approach that lay variation; that

could lead to serious degradatreceiver end. The OOS arrivalthe analysis has been conductearrival has also been studied variation of OOS arrival can cbuffering duration on the PD iless buffering approach in mulpacket drop.

The results of the researchtions can cause higher OOS artastrophic impact on the overlead to poor resource utilizatioour future work, we plan to tholer characteristics of MDO sche

Figure 7: A plot of throughput againsscheme (with bu

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