Accountable Resource Allocation in Broadband Wireless ... - CiteSeerX

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we can define thresholds on the deviation around the mean that is considered normal .... Deficit round-robin vs. proportional fair vs. weighted deficit round-robin.
Accountable Resource Allocation in Broadband Wireless Networks Ravi Kokku, Rajesh Mahindra, Sampath Rangarajan NEC Laboratories America, Inc. Princeton, NJ, USA

{ravik, rajesh, sampath}@nec-labs.com

Varying channel quality between a user and a base station in cellular and broadband wireless access networks leads to varying channel resource usage per Kbps user throughput. In this paper, we present the position that channel variations that are induced by user activity should be explicitly separated from those induced by network deployment and other factors; the variations should be treated differently during resource allocation by MAC schedulers to be accountable to both users and network operators. For instance, while variations induced by user activity generally warrants proportional or slot-based fairness across users, networkinduced variations warrant throughput-based fairness. To enable such customization of fairness metrics on different groups of flows, we propose ARA, a novel accountable resource allocation framework that builds on wireless network virtualization technology. We demonstrate the efficacy of ARA through prototype evaluation on a WiMAX testbed, and present preliminary measurements on categorizing the variations into user-induced and network-induced variations.

Categories and Subject Descriptors C.2.1 [Computer-Communication Networks]: Network Architecture and Design—Wireless Communication

General Terms Design, Experimentation, Performance

1. INTRODUCTION The capacity of a base station in cellular and broadband wireless access networks is a function of the channel quality between the BS and its users; the worse the channel quality, the worse is the capacity. For example, on an IEEE 802.16e WiMAX network testbed, Figure 1 shows the measurements of the maximum client bandwidth achieved in the downlink direction, and corresponding percentage of MAC resource slots used on the WiMAX base station for a static client

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Figure 1: Throughput vs. Resource usage at different channel qualities on an IEEE 802.16e WiMAX base station. with different Modulation and Coding Schemes (MCS). The graph, as expected, shows that more slots are required to achieve the same bandwidth at lower MCS. More importantly, the QPSK line shows that the throughput is only about 4 Mbps with 100% of the base station’s slots (as opposed to 16 Mbps with the highest MCS), indicating that factors reducing channel quality are detrimental to the total base station throughput. Consequently, proportional fair resource allocation is commonly used to tradeoff between base station throughput and fairness across users. While channel quality varies with many uncontrollable parameters, it is also influenced by several characteristics explicitly induced by both users and network operators. Channel variations induced by users include several factors such as change in position, i.e. nomadicity of a particular user with time (over a longer time scale), and mobility of the user (at short time scale). Variations induced by network operators include the density of base station deployments and frequency reuse policies that are detrimental to cell edge users. As mobile networks advance towards supporting data intensive applications, it is increasingly important for mobile network operators (MNOs) to focus on the right accountability of wireless resources. Lack of such accountability unfairly penalizes either the MNOs or the users. Consider the following examples: (1) If a user S starts close to a base station with good channel quality, and moves away while actively receiving data from the network, should he be held accountable (and provided only proportional or slot fairness) for imposing more load on the base station due to bad channel conditions caused by mobility? (2) On the other hand, if the user S is receiving/sending VoIP or video traffic while mobile, should the user continue to receive throughput fairness to avoid disruption in the short-

term, and let the network compensate the extra resource allocation in the long-term? (3) If another user L is static most of the time, but at the cell edge, shouldn’t he receive the same bandwidth (i.e. throughput fairness) as a user M who is closer to the base station, since “which particular user is at a cell edge” is dependent on the network-deployment and not due to a user action? (4) If the user M lives in a multi-storied house, where 80% of the time he accesses the network with good channel quality, and 20% of the time has bad channel quality (e.g. in the basement of the house), is it OK to provide slot- or proportional fairness during the 20% of the time. While it may be acceptable to not explicitly consider the above questions in networks with predominantly voice traffic, the increasing data-intensive usage on wireless networks motivates us to visit these questions. While several channel-aware scheduling proposals in literature do consider variations in channel quality perceived by users, they do so only for exploiting multi-user channel diversity [9, 10, 12, 13, 15, 16]. In most cases, the research proposals and practical implementations degenerate to some form of weighted proportional fairness in the long term. However, they do not consider the cause of variations, and hence do not incorporate accountability appropriately. Further, they are focused on a single scheduling policy for the entire base station. In this paper, our position is that resource management solutions should take into consideration the history of variations in a user’s channel quality and who or what induced them when making fairness decisions. In particular, user induced variations such as those due to mobility and nomadicity, as opposed to network deployment induced variations should be explicitly accounted for during resource allocation. In this paper, we propose an accountability framework that builds on wireless network virtualization technology to (1) provide flexibility of characterizing users into different categories based on the induced variations, and (2) define customized fairness metrics and employ different scheduling policies for each class. We implement and evaluate the framework on a WiMAX network testbed. We then discuss detection techniques for segregating users into different classes. More importantly, replacing the MAC scheduler functionality within a cellular base station with the ARA framework gives base station vendors the flexibility to easily compose or adapt scheduling policies in response to changing traffic trends and operator requirements. In Section 2, we provide the necessary background and explore the factors influencing the channel variations in detail. In Section 3, we present a framework for categorizing users and customizing fairness policies based on channel variations perceived, and demonstrate its efficacy through prototype evaluation on a WiMAX testbed. Section 4 concludes the paper with several interesting research issues.

2. PROBLEM FORMULATION We discuss the need for better accountability of resources in the presence of user induced variations in the context of IEEE 802.16e WiMAX networks. The discussion, however, is generally applicable to other cellular and broadband wireless access networks such as LTE [17], LTE-Advanced [14] and IEEE 802.16m [1]. We only discuss the relevant features of WiMAX in the interest of brevity, and point the interested reader for a quick overview to [6, 18]. A mobile IEEE 802.16e WiMAX base station uses an or-

thogonal frequency division multiple access (OFDMA) frame structure for scheduling downlink and uplink transmissions between the base station and clients. OFDMA enables each frame to be viewed as a set of frequency subchannels on the frequency axis, and timeslots on the time axis. For efficient resource allocation, the base station includes downlink and uplink schedulers that allocate time slots to meet the chosen fairness goals. The exact optimization goals and scheduling algorithms are not part of standards (such as IEEE 802.16e), and left to innovation by the base station vendors and network operators. Any optimizations based on channel quality, QoS, traffic types, etc. are typically incorporated into the scheduling algorithms. In our context, while the different reasons for channel variations such as cell-edge, mobility, nomadicity, interference, etc. are well known, there is no consensus in current networks on how to incorporate the effects systematically into resource allocation. Our position is that channel resource allocations should also consider the history of channel variations on a per-user basis, thereby explicitly holding accountable the factors (e.g., user nomadicity or mobility, network deployment, etc.) responsible for the variations. Recently, So-In et al [18, 19] also argued that different operators may treat such factors differently, and hence designed a generalized weighted fair (GWF) scheduler. GWF applies a weighted combination of bytes served and OFDMA timeslots allocated to users to provide an operator with a tunable fairness framework. To briefly restate, GWF attempts to equalize w ∗ Si + (1 − w) ∗ (Bi /M )

(1)

for all users i over a time interval, where 0 ≤ w ≤ 1 is a weight configurable by a network operator, Si is the number of slots allocated to user i, Bi is the number of bytes served for user i, and M is the number of bytes per slot for the highest MCS. However, they leave it to the network operator to configure the right value of w. We propose that an accountable resource allocation solution should simultaneously employ multiple fairness metrics within a base station, each tuned to suit a different factor responsible for variations. For instance, a base station vendor may instantiate GWF with different values of w for different groups of flows. Note that a number of other schedulers (e.g. those providing proportional fairness) can be used across groups of flows, although we use GWF in this paper. To illustrate our position better, we present several experiments with NEC’s IEEE 802.16e mobile WiMAX base station [3] placed outdoors in a parking lot, which has many parked vehicles and trees that create shadowing at several client locations. For the different experiments, static clients (netbooks with Accton WiMAX cards [4]) are placed at different locations in the parking lot, and a mobile client placed in a car is moved at 25 mph in a rectangular path; the base station is at one corner of the path. The base station transmits on the 2.585- 2.595 GHz channel (10MHz), with a low gain antenna for transmission to restrict the coverage to about 500m at QPSK (line-of-sight). The base station supports several modulation and coding schemes (such as 64QAM-5/6, 16QAM-3/4,QPSK-1/2), and adaptively chooses the appropriate scheme based on CINR feedback and frame loss. All users are assumed to be equal in terms of priority (QoS class) and resource reservations; the arguments in the paper can be easily extended to the case where some users

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Figure 2: Impact of different fairness modes on the base station. For scenarios (a) and (b), we consider one static user close to the base station and one mobile user traversing the rectangular path thrice; both users receive backlogged UDP downlink traffic. For scenario (c), User 1 is close to the base station, and user 2 represents three cases: shadowed by building, in building basement, and at cell edge. are given higher priority or higher resources reservations than others. The NEC base station supports two schedulers that emulate GWF with throughput fairness (w = 0) and slot fairness (w = 1), and also a more sophisticated proportional-fair scheduler. For simplicity, we only use the former two schedulers to motivate ARA. Cellular and broadband wireless access networks support users at a wide range of locations and with wide range of nomadicity and mobility patterns such as a mixture of pedestrian mobility and vehicular mobility at speeds ranging from tens to hundreds of miles per hour. Consider a scenario in which the operator sets w = 0 in Equation 1 to provide throughput fairness to users. Throughput fairness ensures that even cell edge users receive fair amount of bandwidth compared to users closer to the base station. This is desirable since “which users fall at a cell edge” depends on the base station deployment by network operators, and is not user induced. However, as shown in Figure 2(a), if there are some mobile users that are moving away from the base station, throughput fairness reduces the throughput of even the static users of a base station even though the static users do not induce any channel variations. The overall throughput of the base station reduces as a result. Slot fairness (achieved by setting w = 1 in Equation 1) fixes the above problem by making the mobile users use the same number of slots as the static users over a certain time interval. This makes mobile users accountable for the bad channel conditions due to their mobility, while not affecting the static users (in Figure 2(b), static user’s throughput is not affected). However, slot fairness adversely impacts static users at cell edge, users with shadowing such as due to buildings and trees even while static, users in a building basement as opposed to higher floors with better signal reception, etc. in comparison to users closer to the base stations with line of sight conditions (Figure 2(c)). Note that the cumulative impact on the base station may be low or high depending on the amount of time a user is in a shadowed location, or very high (permanent) in case of cell edge users. Similarly, any fixed value of w may be beneficial to some users and not to others. In general, these different situations indicate that no one fairness metric is appropriate in the presence of channel variations, thereby motivating the need for separating users into categories and employing customized fairness settings.

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ARA DESIGN In this section, we describe ARA, a flexible framework for Accountable Resource Allocation. Mainly, ARA groups users at any instant (in comparison to the long-term history) into

Figure 3: ARA Framework. normal and deviant classes based on their behavior. We now describe the framework that allows creating these classes on a base station, and discuss user behavior detection.

3.1

The Framework

ARA builds on wireless network virtualization technology that facilitates employing multiple customized fairness policies simultaneously on a base station. A virtualization solution separates wireless resources into groups, and provides Isolation, Customization and Efficient resource utilization across groups of flows. The reservation (in terms of aggregate throughput or time slots) of each group is configurable by a network operator, and a group scheduler distributes resources in proportion to the reservation of the group. Virtualization helps ARA aggregate variations in channel quality into different categories, and place the corresponding flows in different slices. ARA creates slices for normal, and different deviant behaviors (see Figure 3), maps users initially to the normal slice, and then continuously monitors and places users into one of the slices dynamically based on user behavior. To ensure that any combination of slot and throughput fairness can be provided to flows depending on their group, ARA maintains a moving average of throughput and resource slot usage for each flow. The group or slice scheduler is configured to allocate resources (in terms of time slots) to each group in proportion to the percentage of users in the group. Precisely, we define the group scheduling approach as follows: Let N0 be the

number of users in the normal group, and Nj be the number of users in each of the k deviant groups j, 1 ≤ j ≤ k. Let S be the total number of resource slots at a base station for a unit interval, and Sjexp be an exponentially weighted moving average of slots allocated to each group (including all flows). Let Sjrsv be the slot allocation per group chosen by ARA. Then, Nj Sjrsv = S Pk i=0

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Next, a flow is chosen from within the selected group such that group-specific fairness is ensured; e.g. each slice has the GWF [19] scheduler, with a different value of w. The flexibility provided by virtualization enables the operators to better customize the deviant behavior of users and control the allocation policies, while relieving the base station vendors from implementing multiple customized and complex scheduling solutions for their different customers. For instance, some network operators may treat mobility as a common case since they only support VoIP or video traffic, and prefer to use throughput fairness irrespective of the channel quality; although, the network operator may charge the users higher for throughput fairness to such services. Another operator may choose mobility as a deviant case, and use proportional fairness instead.

3.2 Evaluation Building on our prior work, we implemented a prototype of ARA on a virtualized PicoChip-based WiMAX Base station [11]. The base station can perform both downlink and uplink virtualization. It supports creating different slices with reserved bandwidth or slot allocations, and ensures isolation of resource allocation across slices. In the prototype, users are placed in deviant classes in response to user-induced channel variations, and in normal class otherwise. To demonstrate the flexibility of ARA, we use the following simple setup, primarily for ease of explanation and appreciation of details. We create one normal slice and one deviant slice; flows are initially placed in the normal slice (in time interval T1), and one-by-one moved to the deviant slice at the beginning of time intervals T2 and T3. The deviations are induced by reduction in channel quality due to short-term nomadicity, and hence lead to a change in MCS used for transmission (see Table 1). We consider three clients running backlogged UDP traffic generated using Iperf [2]. We compare ARA to two schedulers providing only throughput-based and only time-based fairness. These emulate two instances of the GWF scheduler (Equation 1) [19], with w = 0 and w = 1 respectively. The results will not be very different from time-based fairness if we use proportional fairness, since the channel is relatively stable for each client in these experiments. Figure 4 represents the throughput achieved by the clients by each of the approaches in different time intervals. Figure 4(a) shows that when all flows are non-deviant (i.e. they

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historically perceive the same channel conditions), throughputbased fairness serves all users equally, whereas time-based fairness penalizes users that are always at bad channel conditions. In this case, ARA behaves like the throughput-based approach to make users equally happy. In Figure 4(b), client 2 deviates from its normal behavior by moving away from the base station after time interval T1. In this case, the first set of bars show that clients 1 and 3 that do not deviate from their normal behavior get affected (by achieving reduced throughput compared to the interval T1) due to throughput-based fairness. The second set of bars show the same problem as in T1 for clients 1 and 3 with time-based fairness; the client with bad channel conditions loses throughput relatively even though it is not deviant. In contrast, ARA ensures that client 2 suffers due to its deviant behavior, where as clients 1 and 3 achieve equal throughput. Finally, in Figure 4(c), both clients 1 and 2 deviate from their normal behavior. Similar observations as above can be made with throughputbased fairness. Observe that when only one client shows non-deviant behavior, time-based approach behaves similar to ARA as seen in Figure 4(c); time-based fairness affects adversely only when more than one client is non-deviant and at different MCS levels. In summary, the graphs show that ARA enables applying different types of accountability to users dynamically.

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Detection of Deviant User Behavior

Several deviant behaviors can be identified by the relative SINR values compared to the long-term historical SINR observed by a user. To illustrate, Figure 5(a) shows the PDF of SINR measurements observed in our testbed by a user in the long term (marked as A), and the PDF of the SINR in the short-term perceived at three locations (B, C and D) that are shadowed locations (due to shortterm nomadicity) relative to A. The line M represents the PDF in the short-term for a scenario in which the user starts from location A moves to location C and returns to A. Figure 5(b) shows the actual SINR profile for the case represented by M. The graph clearly indicates that we can define thresholds on the deviation around the mean that is considered normal behavior, beyond which, a user has deviant short-term mean. This general technique, along with the variance in SINR measurements, can help identify normal users, cell-edge users, users occasionally moving to shadowed locations such as basements, and mobility. For instance, users having short-term mean close to the long-term mean indicates normal users. Users with deviant mean in the short term, along with high variance indicates mobility. In fact, the technique is also useful to detect users with two main static locations of presence (e.g. office and home); the graph would be a bimodal distribution of SINR. Note that the SINR profile (both long term and short term) is collected only when the user actively transmits or receives.

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Figure 5: User Behavior Detection. While these are preliminary measurements to demonstrate the feasibility of user behavior detection, further exploration is required in evaluating the accuracy of the techniques. ARA could also build upon several prediction techniques that already have been proposed. For instance, Balis et al. [7] detect mobile users based on the variation in perceived signal strengths using a maximum likelihood Gaussian estimation method. Choi et al. [8] describe a technique to classify users as cell-interior users or cell-edge users based on achievable capacity estimations. We are currently exploring such techniques for ARA to make detection accurate.

4. CONCLUSION AND FUTURE WORK In this paper, we mainly present the position that channel variations that are induced by user activity should be separated from those induced by network deployments and accounted-for differently by a MAC scheduler to be objective to both users and network operators. We propose an accountability framework to (1) provide flexibility of categorizing users based on the induced variations, and (2) define customized fairness metrics for each class. Understanding the various kinds of variations induced by networks and users also helps in the evolution of a wireless network deployment. For instance, depending on which type of deviant slice is more frequently activated, the network deployment can be modified to enable migration of flows from that deviant slice to the normal slice. ARA will also foster a systematic approach in a variable priced system—a business model that wireless networks are starting to evolve towards [5]. For example, slices providing throughput fairness even with mobility may charge the users a higher price. In general, a virtualization framework facilitates categoryspecific resource management that can be helpful in better managing flows under wide-ranging optimizations available in current and future cellular networks. For instance, with the recently emerging variable pricing data plans [5], users may be categorized into different classes depending on whether they have exceeded their quota, are at 60%, or 90%

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