Quality of Service Scheduling based on Utility Prediction

Quality of Service Scheduling based on Utility Prediction Simone Redana

Antonio Frediani, Antonio Capone

RTP PT Radio System Technology Nokia Siemens Networks Munich, Germany [email protected]

Dipartimento di Elettronica e Informazione Politecnico di Milano Milan, Italy [email protected]

Abstract—Next generation wireless systems are expected to provide broadband access service and efficient support to multimedia applications. To overcome the limitation due to harsh propagation environments and the scarce spectrum availability, advanced transmission techniques based on multicarrier schemes and adaptive modulation are commonly considered key elements. However, in order to fully exploit these transmission schemes, scheduling mechanisms able to take advantage of the diverse channel conditions experienced by users and guarantee at the same time quality of service, are of paramount importance. In this paper we first propose a scheduling scheme that increases the achievable throughput by combining multiuser diversity with channel prediction. Then we extend the proposed scheme to incorporate quality of service requirements of multimedia services. The proposed solution has been validated within the framework of the IST WINNER project by means of system level simulations. Scheduling, multiuser diversity, channel prediction, utility function, Quality of Service (QoS)

I. INTRODUCTION The scarce radio spectrum allocated to mobile wireless communication, coupled with the continuous growth of user population, requires novel strategies for efficient use of available radio resources. This issue is getting more critical due to the shift of many applications to multimedia platforms. Quality of Service (QoS) provisioning for multimedia applications over wireless networks requires that user performance requirements are taken into account. Central is the representation of multimedia connections in terms of multiple user flows, each one with its own QoS requirements. Opportunistic scheduling schemes that exploit multiuser diversity through adaptive transmission mechanisms can increase the spectrum efficiency of wireless access and provide a means for guaranteeing quality. In recent years multiuser diversity has gained a lot of attention as a mean to increase spectral efficiency [1]. An important goal of future wireless networks is the achievement of a fair bandwidth allocation in order to better support multimedia services. The proportional fair scheduling approach aims at finding a compromise between fairness and high

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network throughput [2]. Moreover, channel prediction can improve the achievable throughput, as studied in [3][4]. In this paper we present the Utility based Predictive Scheduler (UPS) and its capabilities to serve users with different QoS requirements. The proposed UPS scheduler extends the Predictive Proportional Fair (PPF) scheduler, described in [3][4], by substituting the static system utility function with a tunable one. This work has been developed within the framework of the IST WINNER project, which aims at developing a broadband wireless access system able to adapt to a whole range of different conditions ranging from indoor to base coverage urban scenarios. It is expected to provide high data-rates and low latency, allowing the satisfaction of tight QoS requirements [5]. This paper is organized as follows. In section II we describe the current state of the art on QoS scheduling. In section III, we illustrate our UPS scheduler and a methodology for mapping QoS requirements and parameters of the scheduler. Simulation results are presented in section V for the WINNER system (section IV), while section VI concludes the paper, also addressing future works. II.

QUALITY OF SERVICE SCHEDULER

An optimal scheduler should differentiate between the QoS available classes, optimize each service to its specific needs (measured as user satisfaction) and maximize the use of the radio resources. Several scheduling schemes have been proposed in literature, however those based on proportional fair [2][3][4] and utility functions [6][7] have attracted more attention in the last years. The first one allows the implementation of schedulers that find a compromise between user fairness and high network throughput, while the second one provides a concept for measuring the user satisfaction. An appropriate channel feedback is obviously essential for adaptive resource scheduling. Channel state information can be used not only to estimate the current channel conditions, but also to predict its quality in the near future. Recent works have shown that being able to exploit channel prediction could significantly increase the efficiency of resource allocation performed by the scheduler [4]. For example, if we know that

the channel quality of a user is going to decrease in the next time slots, it might be more convenient providing him/her some transmission opportunities than to other users whose channels are getting better, even if pure QoS considerations would suggest otherwise. An interesting algorithm using channel prediction is the Predictive Proportional Fair (PPF) scheduler proposed in [3][4]. Based on the well known Proportional Fair (PF) scheduler [2], it differs in that, instead of at the current time slot, the rate is evaluated at the end of a channel prediction window. The decision is based on both the past assignments and the current allocation vector for the time slots of the prediction window. Such an allocation vector is modified at each time slot by an iterative procedure trying to maximize the allocation quality. The authors also suggest that the impact of future time slots in the scheduling decision could be weighted differently, e.g. in order to take into account uncertainties in the channel prediction. Another interesting approach, which is recently gaining popularity in the literature on resource scheduling, is based on utility functions. Originally developed and applied within the economics field, these are functions, usually non-linear, measuring the level of satisfaction of a user (or data stream) according to the value of one or more parameters [6][7]. By combining the utility levels of the users in the system, it is possible to derive an overall evaluation of the current resource assignment. By evaluating multiple potential allocations, a scheduling decision can be performed. It should be noted, however, that most traditional scheduling algorithms can be written as utility based schedulers with simpler, linear utility functions. While the non linearity makes theoretical performance estimation hard, utility functions can help make decisions by performing simple parameters measures, making them a powerful tool for the resource scheduling when multiple complex QoS requirements are involved. It is clear that the choice of an appropriate utility function has a fundamental impact on the resulting system performances. In particular, all parameters of interest (e.g. throughput, latency etc.) should be appropriately mapped to the utility function. III. UTILITY BASED PREDICTIVE SCHEDULER In this section, we propose the Utility based Predictive Scheduler (UPS) that focuses on QoS exploiting the prediction of channel state information and adopting the utility concept. In particular, the proposed scheduler extends the Predictive Proportional Fair (PPF) scheduler by substituting the system utility function with a tunable one. Moreover, we propose a methodology for mapping parameters of the utility function into QoS requirements, expressed in terms of minimum rate (Rmin), maximum sustained rate (Rmax) and maximum latency (Lmax). The scheduling problem solved by UPS can be also formulated as a dynamic programming problem for which analytic tools are available for proving convergence and optimality. However, we aim at designing a scheduler to be

implemented in a real system, therefore the analysis of the dynamic programming is out of the scope of our work.

observation

ω(j)

prediction

window W

window L

j k-W k

frame

k+1

…

j

… k+L-1

Figure 1. Observation and prediction windows

Let us consider the current time slot k, a prediction and an observation window of L and W time slots respectively, as depicted in Fig. 1. Assuming the scheduling vector is known not only in the observation window {i ( j )}kj =−1k −W but also in the prediction window {i ( j )}kj += kL+−11 , the scheduling decision i(k) at current time slot k is taken as described in section III.A. In section III.B, we illustrate how the predicted scheduling vector is computed in the window L. We have applied the utility function concept [6][7] in order to select the scheduling solution i(k) for the time slot k; in particular the utility function depicted in Fig. 2 is well suited for our scope, details on the utility functions assumed can be found in section III.C. A. User Scheduling The scheduling decision i(k) at time slot k is taken assuming ω(j) in Fig. 1 as a possible assignment of weights to the resource allocation at time j (k-W≤j≤k+L-1). Time slots closer to the current one k have a higher weight. For past allocations, this is due to the higher importance of the current achieved transmission rate with respect to the past one, while for the future allocations it is due to the higher uncertainty in the channel prediction and consequently in the allocation. Let us consider the current time slot k, and the set of active users u∈{1,…,N}. The user scheduling i(k) at time slot k is computed as follows: 1.

for each user u the rate Rucurrent is computed as predicted at time slot k assuming that time slot k is not assigned;

Rucurrent = ∑ j = k −W ω ( j ) ⋅ Ru ( j ) + ∑ j = k +1 ω ( j ) ⋅ Ru ( j ) k −1

k + L −1

(1)

where Ru(j) is the transmission rate of user u in time slot j, which is equal to 0 if the time slot j is not assigned to user; 2.

for each user u the predicted rate Runew is computed assuming the slot k assigned to the user and Ru(k) is the transmission rate;

R unew = R ucurrent + Ru (k )

(2)

3.

the slot is assigned to the user u with the maximum utility increase ΔU on the curve depicted in Fig. 2 and the allocation in the current slot k is obtained (i(k)=u).

U(R)

β ΔU

C. Parameter Tuning of Utility Function The utility function depicted in Fig. 2 is well suited for mapping parameters used by the UPS scheduler and QoS requirements, in particular: •

Rmin represents the minimum rate that is requested by a user flow, i.e. the scheduler has to be able to satisfy bandwidth requests at least equal to Rmin;

•

Rmax represents the maximum sustainable rate, i.e. the peak information rate;

•

Lmax represents the maximum latency between the reception of a packet and its transmission.

Parameter α in Fig. 2 can be used to prioritize users affected by higher experienced latency, e.g. it can be a function of the ratio between the experienced latency and the maximum latency supported by the specific user flow.

α

ΔR Rmax

Rmin Rucurrent

R

Runew

Figure 2. Utility increase ΔU due to user rate increase ΔR

B. Predicted Scheduling Vector In this section we present how the scheduling vector

{i ( j )}kj+=kL+−11

used for the user scheduling illustrated in subsection III.A, is predicted in the window L. It is computed recursively (see Fig. 3) by applying to the time slot j the same algorithm illustrated in section III.A, beginning from the last slot j=k+L-1 and decreasing j until the time slot j=k+1 is scheduled. During the processing of time slot j the first part of j −1 the predicted scheduling vector {i (l )}l =k is the result of the algorithm applied for the user scheduling at the previous k + L −1 scheduling time (k-1), while the remaining part {i (l )}l = j +1 has already been updated by the user scheduling procedure at the current time slot k (as shown in Fig. 3). L

… k

k+1

… j

k+L-1

slot allocation order Figure 3. Scheduling in the prediction window L

Our scheduler aims at assigning user transmissions at time slot k on the basis of the prediction of users scheduling in a time window from k to k+L-1. At current time slot k it schedules the users whose channel dynamics, according to the predictor, would make QoS provisioning using future time slots more difficult.

⎛ Lexp erienced ⎞ ⎟ ⎟ ⎝ Lmax ⎠

α = f ⎜⎜

(3)

Parameter β in Fig. 2 can be used to configure different priorities among users, e.g. it can be function of the ratio between the user priority P and the maximum priority Pmax.

⎛ P ⎞

⎟⎟ β = g ⎜⎜ ⎝ Pmax ⎠

(4)

Therefore, parameters Rmin, Rmax, Lmax and P are configured for each user according to QoS requirements and priorities; parameter β is computed one time for each active user. They change only if QoS requirements or priorities change. On the contrary, parameter α varies over the time according to the experienced latency Lexperienced and it is different for each user. The scheduler works with a number of tunable utility functions equals to the number of active users N. IV.

SYSTEM MODEL

The proposed Utility based Predictive Scheduler (UPS) has been implemented and assessed by means of a system level simulator based on the ns-2 tool [8]. The scenario parameters were derived from the specifications of the IST WINNER project [9]. In section IV.A the simulated system model is illustrated, providing an overview of the IST WINNER project specifications. In section IV.B the UPS scheduler is adapted to Orthogonal Frequency Multiple Access (OFDMA) physical layer. Simulation results are presented and commented in section V. A. WINNER System Model The IST WINNER project aims at developing a standard for the next generation of broadband wireless access networks capable of providing QoS support to multimedia streams originated by users operating in different scenarios, ranging from indoor to microcellular and base urban coverage. In order to achieve the required performances and flexibility it supports advanced technologies such as adaptive modulation and

coding, MIMO multiple antenna transmissions and multi-hop relaying. These characteristics make it the ideal testing ground for the proposed UPS scheduler. Two physical layer modes (PLMs) exist, one operating in FDD and the other one using TDD. They both assume OFDM access in downlink and different flavors of Generalized Multi Carrier (GMC) in uplink. The TDD PLM main application scenarios are indoor and micro-cellular, allowing simpler channel feedback thanks to the channel reciprocity between downlink and uplink. On the other hand, the high propagation delays in base coverage urban scenarios make the FDD PLM more suitable, since it requires much lower guard times. At MAC layer the time-frequency resources are divided into super-frames of 5.9 ms each. They include a preamble for cell-wide signaling and synchronization and a data portion. The data part is further divided into 8 frames, each having both a downlink and an uplink portion. These portions are not necessarily symmetric, and one could receive more resources according to traffic considerations. At the frame time scale (0.6912 ms), the resource scheduling is performed, which can be dynamic and adaptive using short-term channel quality feedback. At the super-frame time scale a long-term cell-wide resource partitioning is performed. The basic resource allocation unit is a time-frequency tile called chunk, comprising a give number of sub-carriers and OFDM symbols. In particular, the parameters used in our simulation are those of the base coverage urban scenario described in [9]. It implements a FDD PLM on a pair of 50 MHz bands around the 3.7/3.95 GHz carrier frequencies. Each sub-band comprises 1024 sub-carriers, with guard times of 3.2 μs. Each chunk includes 12 OFDM symbol periods and 8 sub-carriers, while every frame comprises 2 chunks in the time domain and 128 ones in the frequency domain on each band. Having the frames time duration of 0.6912 ms, each super-frame includes 8 frames, or equivalently 16 chunks, in the time dimension; however, for overhead considerations it is assumed that both the time chunks on the same frequency receive the same allocation [9]. B. User Scheduling and Predicted Scheduling Vector in OFDMA In this section we apply the algorithm presented in sections III.A and III.B to the OFDMA physical layer. Let us consider the current time slot k, the set of active users u∈{1,…,N}, the set of available chunks m∈{1,…,M} at time slot j (with j=k,…,k+L-1). The user scheduling i(j,m) for chunk (j,m) at time slot j is computed as follows: 1. for each user u the rate Rucurrent is computed as predicted at time slot k assuming that chunks m∈{1,…,M} at time slot j are not assigned; current u

R

= ∑l =k −W ∑m=1ω (l ) ⋅ Ru (l , m) + j −1

M

+ ∑l = j +1 ∑m=1ω (l ) ⋅ Ru (l , m) k + L −1

M

(5)

where Ru(l,m) is the transmission rate of user u at time l in chunk m, which is equal to 0 if the chunk m at time l is not assigned to the user;

2.

a chunk m* at time j is randomly selected within the set of non allocated chunks;

3.

for each user u the predicted rate Runew is computed assuming the chunk m* at time j is assigned to the user and Ru(j,m*) is the transmission rate;

Runew = Rucurrent + Ru ( j , m*) 4.

5.

(6) the chunk is assigned to the user u with the maximum utility increase ΔU on the curve depicted in Fig. 2 (i(j,m*)=u);

the rate Rucurrent for the selected user is updated with Runew, the chunk m* is now considered assigned and the algorithm returns to point 2 until all chunks at time j are assigned; if all chunks are assigned the allocation at time j is obtained. V.

PERFORMANCE E VALUATION

Simulation results are obtained assuming the same QoS parameters (Rmin, Rmax, and Lmax) and priority P for all users. We also assumed that parameter α remains fixed during the simulation because the objective of the following analysis has been evaluating the properties of the tunable utility function described in section III.C. Dynamic utility functions that change according to experienced latency, as well as the study of functions f(•) and g(•), are left for further investigations. In Tab. 1 it is shown the system throughput for the Utility based Predictive Scheduler (UPS) assuming size of prediction window equal to L=8 and different values of parameters α and β. Tab. 1 also reports the fairness, computed using Jain’s fairness index [10]. Results obtained using Proportional Fair (PF), Predictive Proportional Fair (PPF) and Max SNR schedulers are also provided for comparison. A prediction window L=8 corresponds to a WINNER super-frame. The analysis of this parameter is left for further studies since the ideal channel predictor we have assumed would have suggested that better performances could have been obtained with larger prediction windows. For the same reason we have assumed in the simulations ω(j)=1 for kW≤j≤k+L-1. The optimization of parameter L and weights ω(j) are left for further studies since they depend on the characteristics of the channel predictor, which is out of scope of this work. TABLE I. THROUGHPUT AND FAIRNESS OF UTILITY BASED PREDICTIVE SCHEDULER (UPS) FOR DIFFERENT VALUES OF PARAMETERS. COMPARISON WITH PROPORTIONAL FAIR (PF), PREDICTIVE PROPORTIONAL FAIR (PPF) AND MAX SNR.

Scheduler

α

β

UPS UPS UPS PF PPF Max SNR

60° 80° 85° -

180° 160° 155° -

Throughput [Mbps] 141 122 116 112 126 155

Fairness Index 0.7 0.74 0.77 0.765 0.768 -

In Tab. 1 we can observe that if the parameter α is increased the scheduler decreases the achievable system throughput since it has to satisfy requirements of flows that are suffering from an increasing of latency. Therefore, the parameter can be used to increase the priority of traffic flows according to latency constraints. Results in Tab. 1 also show that the throughput decreases with a decreasing value of the parameter β while the fairness increases. Therefore the parameter β can be used to control the tradeoff between system throughput and the user priorities. Moreover, decreasing the value of parameter β the behavior of the scheduler approaches performance of proportional fair scheduler in terms of fairness. We know from [3][4] that by exploiting the channel prediction the PPF scheduler is able to increase the throughput while achieving the same fairness provided by the PF scheduler. Our simulations confirm these results, as shown in Tab. 1. UPS scheduler provides the same fairness with a lower network throughput but QoS requirements are satisfied, as show in Fig. 4.

Cumulative Density Function (CDF)

The cumulative density function of user rate for the UPS and PPF schedulers is plotted in Fig. 4; both with size of prediction window equal to L=8. The PF scheduler is also plotted as reference for comparison. We can see that the proposed UPS scheduler exploiting the channel prediction and tunable utility function is able to guarantee the minimum rate to all users with probability equal to 1 (Rmin has been set to 2.8 Mbps in these simulations). 1 PF PPF UPS

0,8 0,6 0,4 0,2 0 0

1

2 3 Rate per user [Mbps]

4

5

Figure 4. Comparison among different schedulers in terms of cumulative density function of the user rate

VI. CONCLUSIONS We have shown how the channel prediction can be used by a scheduler, referred as Utility based Predictive Scheduler (UPS), not only to increase the throughput but also to satisfy

QoS requirements. The proposed scheduler is useful for multimedia applications where user flows are associated to different QoS constraints, making it particularly useful for future wireless networks where multimedia services are expected to be natively supported. In fact, employing utility functions described by a simple set of parameters UPS can be able to efficiently allocate user flows with different QoS requirements. Further investigations will consist of studying how tunable parameters of the utility functions dynamically change over the time according to measures of experienced latencies by users. Moreover, the introduction of realistic channel predictors will require the analysis of the optimal prediction window size L and weights ω(j). ACKNOWLEDGMENT This work has been performed in the framework of the IST project IST-4-027756 WINNER II, which is partly funded by the European Union. The authors would like to acknowledge the contributions of their colleagues, although the views expressed are those of the authors and do not necessarily represent the project. REFERENCES [1]

R. Knopp, P. Humblet, Information Capacity and Power Control in Single-Cell Multiuser Communication, in proceeding of ICC 1995, Seattle, June 1995. [2] P. Viswanath, D. Tse, R. Laroia, Opportunistic Beamforming Using Dumb Antenna, IEEE Transaction on Information Theory, June 2002. [3] H. J. Bang, T. Ekman, D. Gesbert, A Channel Predictive Proportional Fair Scheduling Algorithm, in proceeding of SPAWC 2005, New York, June 2005. [4] H. J. Bang, T. Ekman, D. Gesbert, Channel Predictive Proportional Fair Scheduling, IEEE Transaction on Wireless Communication, submitted the 20th of September 2006. [5] IST-4-027756 WINNER II, “D6.11.1: Revised WINNER system requirements,” June 2006. [6] G. Song, Y. Le, Cross-Layer Optimization for OFDM Wireless Networks – Part I: Theoretical Framework, IEEE Transaction on Wireless Communication, March 2005. [7] G. Song, Y. Le, Cross-Layer Optimization for OFDM Wireless Networks – Part I: Algorithm Development, IEEE Transaction on Wireless Communication, March 2005. [8] K. Fall, K. Varadhan, “ns notes and documentation,” The VINT Project, UC Berkley, LBL, USC/ISI, and Xerox PARC, 1999. Avail. from http://www.isi.edu/nsnam/ns. [9] IST-4-027756 WINNER II, “D6.13.7 Test scenarios and calibration case issue 2,” December 2006. Available from https.//www.ist-winner.org. [10] R. K. Jain, D.-M. W. Chiu, and W. R. Hawe, A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer Systems, DEC Research, Technical Report, September 1984.