This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE Globecom 2010 proceedings.
Mitigating Load Imbalance in Wireless Mesh Networks with Mixed Application Traffic Types Amr Alasaad
Sathish Gopalakrishnan
Victor C.M. Leung
Email:
[email protected]
Email:
[email protected]
Email:
[email protected]
Electrical and Computer Engineering Department The University of British Columbia
Abstract—Wireless Mesh Networks (WMNs) have been widely deployed as a new communication paradigm that can provide innovative services for a community (neighborhood, campus, etc.). WMNs are capable to provide both delay-sensitive services such as Voice over Internet Protocol (VoIP) and delay-insensitive services such as peer-to-peer file sharing. Network routing protocols in WMNs often employ the minimum-hops routing metric to provide Quality of Service (QoS) for the delay-sensitive traffic. However, this approach results in unbalanced traffic load at network mesh routers. In this paper, we propose the WMNBalance: a content lookup algorithm for peer-to-peer file sharing over wireless mesh networks. The WMN-Balance enhances the content lookup routing on the overlay network, that involves mesh routers which support peer-to-peer file sharing, to mitigate the network unbalanced load.
I. I NTRODUCTION Wireless mesh networks (WMNs) are deployed in areas with limited access to the wired networks. The dominant design approach for such deployments is a two-tier architecture, wherein an access tier connects mobile end-user computing terminals (Mesh Clients, MCs) to mostly stationary mesh nodes (Mesh Routers, MRs), and the mesh nodes form a multihop wireless backhaul tier that routes data packets between mesh clients within the WMN and between mesh clients and few number of gateway nodes that are wired to the Internet. Wireless mesh networks are capable to provide both delaysensitive services (e.g. Voice over Internet Protocol (VoIP), Email, and streaming) and delay-insensitive services such as Peer-to-Peer (P2P) file sharing [1]. Similar to the Internet and as the mentioned innovative services suggest, we project forward and expect P2P file sharing to be a major service to be carried out in WMNs. Network routing protocols in WMNs often use the minimum-hops (shortest path) routing metric to route delaysensitive traffic between mesh routers. This approach provides QoS to the delay-sensitive traffic but results in unbalanced network traffic load and traffic congestion at mesh routers located in the geographically central mesh network deployment region [2]. Furthermore, most existing content lookup algorithms used for P2P file sharing such as Chord [3] and MeshChord [4] also lead to unbalanced traffic load when the underlying network routing protocol employs the minimum-hops routing metric [5]. This is due to the fact that many network-level
(physical) routes that are mapped from the overlay links tend to cross the geographic center of network deployment region through mesh routers which are often highly congested. What makes the situation worse in wireless mesh network compared to the Internet (wired networks) is contention and interference between mesh routers. When several neighboring mesh routers communicate over a wireless channel, not only contention for the channel is high, but communication is also affected by interference from other neighboring mesh routers [6]. Packet collision and packet retransmission exacerbate the unbalanced load issue because it adds to the traffic load at the congested mesh routers. Unbalanced load is a major cause of performance degradation in WMNs. Mesh clients connected to highly loaded mesh routers experience lower QoS than those connected to relatively less loaded routers [2]. We propose the WMN-Balance: a content lookup algorithm for P2P file sharing over wireless mesh network that mitigates the network unbalanced load (Section III). The algorithm presented in this paper complements our prior work in which we proposed a combined architecture and framework for P2P file sharing over WMNs [7], [8]. The WMN-Balance algorithm is motivated by the assumption that the network routing protocol in a WMN employs the minimum-hops routing metric scheme to route the network traffic which include both delay-sensitive traffic and delay-insensitive traffic. Therefore, we believe that rather than modifying the underlying network routing protocol (i.e. proposing congestion-aware routing metric or solving a multi-metric routing problem at the IP mesh routers) to mitigate the network unbalanced load, we can simply enhance the content lookup routing algorithm on the overlay network that involves mesh routers which support P2P file sharing. Basically, a location-aware finger peers selection scheme is employed in WMN-Balance to detour P2P traffic away from the geographic center of the WMN deployment region over physical routes that do not traverse this congested region. We show by means of extensive packet-level simulations that the proposed WMN-Balance algorithm mitigates the network-level unbalanced load and improves the performance of the large-scale P2P file sharing over WMNs in terms of content lookup latency and files download time (Section IV) and improves the WMNs performance in terms of overall
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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE Globecom 2010 proceedings.
throughput (rate of unique packets received at mesh clients). To the best of our knowledge, this is the first work that proposes an application layer algorithm to mitigate unbalanced traffic load in WMNs. II. R ELATED W ORK Many major operators have already considered the wireless mesh network as a technology for their wireless city initiatives (Houston wireless networks [9]). The proliferation of P2P communication provides a mean to sharing information between end users. In P2P file sharing, content lookup is defined as the process which maps required filenames to the IP addresses of peers that store them (content providers). Structured content lookup algorithms typically rely on the maintenance of virtual overlay topologies (virtual ring), that involves all participating peers, and distributed hash tables (DHTs) for locating contents on the overlay network such as Chord [3]. Chord is based on the idea of mapping both peers IDs and contents IDs (keys) into the same ID space using a hashing function. Chord maintains virtual unit ring where each key resides on the peer with the smallest ID larger than the key. Every peer manages keys between its own ID and the ID of its predecessor in the virtual ring. A lookup operation for a key (k) routes the inquiry to the peer p which is responsible for that key in the virtual ring. Peer p upon receiving the inquiry message return the IP address of the peer which is sharing the required content. To speed up the lookup operation on the virtual ring, every peer maintains a table of up to m distinct peers (fingers). The ith finger of peer p is the peer which has the smallest ID larger than: p’s ID+2i−1 , where (1 ≤ i ≤ m). Only few studies have addressed the deployment of P2P file sharing application on wireless mesh networks [4], [10]. Recently, Canali et al. proposed a specialization for Chord algorithm for P2P content lookup over WMNs called MeshChord [4]. MeshChord exploits the availability of the wireless infrastructure (stationary mesh routers) to realize locationaware ID assignment to the mesh nodes. Each content is assigned an ID which is mapped into [0,1] interval using a hashing function. Each mesh router with physical coordinates (x, y) is assigned an ID in the unit ring interval such that any pair of mesh routers which are physically close are assigned close IDs in the virtual unit ring. This mechanism achieves a close correspondence between the virtual overlay topology and the physical network topology. MeshChord has shown to achieve better content lookup performance compared to Chord. However, an important issue such as network unbalanced load which is introduced to the underlying network was not addressed. In prior work, we designed a network topology aware system that benefits from the support for the P2P applications at mesh routers and achieves significant improvements for both the P2P file sharing performance in realistic WMN scenarios and the overall WMN performance [7]. We also proposed a combined architecture and efficient schemes for P2P streaming applications over WMNs [8]. The proposed algorithm in
this paper complements our prior work and mitigates the unbalanced traffic load in WMNs. III. T HE WMN-BALANCE ALGORITHM The basic setup we consider is a planned WMN deployment over an approximately squared area and consisting of many stationary mesh routers that are almost equally separated. Within the mesh network, data is communicated over wireless links and often over multiple hops. This assumption is feasible because WMNs are usually deployed in regular territories to provide services for a community in moderate size areas. The current approach to implementing peer-to-peer file sharing systems involves all participating peers in an overlay network that is transparent to the actual topology of network connections. Mobility of peers and excessive communication cost (overhead) render the deployment of this approach over WMNs infeasible. The proposed content lookup algorithm (WMN-Balance) adopts a two-tier hierarchy for peers in WMNs. The lower tier comprises of mesh clients which provide shared contents to the P2P overlay, while the upper tier is composed of stationary mesh routers which construct the overlay and implements the WMN-Balance algorithm for content lookups within the WMN. The architecture aims to decouple contents providers (mobile mesh clients) from participating in the overlay. The architecture we espouse requires cooperation from mesh networks operators. Such a co-operation needs to consider incentives for mesh network operators in participating in P2P activities and incentives for end users in adapting the system. Reducing P2P overhead traffic and better quality of service for participating peers (e.g. lower fraction of failed lookups and less lookup latency) can provide sufficient incentives. The WMN-Balance algorithm is a content lookup algorithm aims to minimize number of network-level packets that traverse the geographical central area of WMN deployment region, which are introduced to the network by P2P content lookup traffic. The squared WMN deployment region, where s is the side of the deployment region, is segmented into four smaller regions with equal size as shown in Fig. 1(a). Each mesh router with physical location coordinate (x, y) located in Region i (i ∈ {1, 2, 3, 4}) is assigned IDlocal in interval [0,1] based on MeshChord’s geographical mapping function [4]. Mesh routers are then assigned new (global) IDs according to mesh routers location in the WMN deployment region. Namely, IDlocal assigned to a mesh router located in Region i is mapped to ID = 0.25 ∗ IDlocal + 0.25 ∗ (i − 1) (Fig. 1(a)). The terms mesh routers and peers are used interchangeably in this paper. In the WMN-Balance, a location-aware finger peers selection scheme is proposed to mitigate the unbalanced traffic in the WMN. Each peer maintains a finger table which contains the finger peers IDs and their IP addresses. Namely, every peer located in Region h (h ∈ {1, 2, .., 12}) (Fig. 1(b)) maintains a table of up to two distinct finger peers. Finger peer IDs for mesh routers in Regions h are depicted in TABLE I, where k is a content’s key to be resolved. For example, assume a
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TABLE I F INGERS ’ ID S FOR PEERS IN REGION (h) Region (h) 1 2 3 4 5 6 7 8 9 10 11 12
0.0 ≤ k ≤ 0.25 − − ID − 0.25 ID − 0.25 − 0.25 ∗ IDlocal (x, y + s ) + 0.25 4 ID − 0.5 ID − 0.5 ID − 0.5 0.25 ∗ IDlocal (x − s , y) + 0.5 4 ID − 0.25 ID − 0.25
0.25 < k ≤ 0.5 ID + 0.25 ID + 0.25 − − 0.25 ∗ IDlocal (x, y + s ) 4 − ID − 0.5 ID − 0.5 ID + 0.25 ID + 0.25 0.25 ∗ IDlocal (x + s , y) + 0.75 4 ID − 0.5
(a) Peers’ IDs assignment in the (b) Location-aware finger peers in WMN-Balance the WMN-Balance Fig. 1.
WMN deployment region
0.5 < k ≤ 0.75 ID + 0.5 0.25 ∗ IDlocal (x − s , y) 4 ID − 0.25 ID− 0.25 ID + 0.5 ID + 0.5 − 0.25 ∗ IDlocal (x, y − s ) + 0.75 4 − − ID − 0.25 ID − 0.25
0.75 < k ≤ 1.0 ID + 0.5 ID + 0.25 0.25 ∗ IDlocal (x + s , y) + 0.25 4 ID + 0.5 ID + 0.5 ID + 0.5 0.25 ∗ IDlocal (x, y − s ) + 0.5 4 − ID + 0.25 ID + 0.25 − −
i = 3. Namely, peer p routes the lookup message on the overlay to the peer with the smallest ID larger than k. The proposed scheme requires peers to be aware of their physical locations which can be accomplished manually upon setting up the network or by using GPS receivers. To account for dynamic join/leave of mesh routers, the following procedures is implemented: When a mesh router (peer) p joins the network, it initializes its predecessor and successor peers in the WMN-Balance overlay and its fingers table. This is can done by sending a request to any peer, which p is aware of, currently joining the network in p’s Region i and in other deployment Regions i (hook peers). IV. E VALUATION
peer j has physical location coordinates (x, y) and located in Region h = 2. The y th finger for peer j (1 ≤ y ≤ 2) when (0.5 ≤ k ≤ 0.75) is the peer which has the smallest ID larger than: 0.25 ∗ IDlocal (x − s4 , y), where IDlocal (x, y) can be computed as: ⎧ (y− s2 ) (y− s2 ) δ ⎨ x.δ . is even s 2 + s , if ( ) δ δ s IDlocal (x, y) = ( s2−x).δ y− s 2 ) (y− δ 2 2 ⎩ 2s 2 + . s , if is odd (2) δ δ 2 (1) The finger peers are used to route network-level packets, that are mapped from the overlay content lookup traffic, exchanged between peers physically located in different Regions i, around the center of network deployment region through those selected finger peers. Since the underlying network routing protocol uses minimum-hop routing metric protocol (e.g. Dynamic Source Routing (DSR) or Ad hoc On-Demand Distance Vector Routing (AODV)), the location-aware finger peers scheme in WMN-Balance mitigates the traffic at mesh routers in the central of the network deployment region. Referring to Fig. 1(b), when a lookup operation for a key (k) (0.5 100 (Fig. 3(a)). Fig. 3(b) shows
20
40 60 Percentile
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(b) Network-level load distribution (m = 100). Fig. 3.
Network unbalanced load in presence of background traffic
traffic load distribution in the network in the presence of background traffic when m = 100. 10% of peers in Chord carried higher than 5.5x amount of traffic compared to the traffic carried by the minimally loaded peer while 4.5x in MeshChord and only 2x in WMN-Balance. In the remaining set of simulations, we investigate the impact of the WMN-Balance algorithm on the content lookup performance, in terms of lookup latency, in the presence of background traffic. Moreover, we investigate the impact of the WMN-Balance on the overall WMN performance, in terms of network throughput, and compare the results with Chord and MeshChord algorithms. Contents lookup latency is defined as the time elapsed between the instance the query is invoked at a peer p and the instance the key is resolved at peer p. We present the lookup latency for all algorithms under investigation in the presence of background √ traffic with increasing CBR rate (Fig. 4). We selected m pairs of source/destination mesh routers uniformly at random and established constant bit rate (CBR) flows between them with varied rate. We observe that Chord needs longer time to lookup required contents due to lack of geographical mapping between overlay topology and true network topology. Another interesting observation is that when the network is lightly loaded, MeshChord outperforms WMNBalance in terms of lookup latency. This is due to the fact that network-level lookup packets in WMN-Balance traverse longer physical route. However, for moderate to high traffic load in the WMN, the mesh routers located in the central region become highly congested. Therefore, contention and
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6
interference between packets and packet retransmission result in higher lookup latency in MeshChord compared to WMNBalance.
Aggregate Throughput (bps)
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(a) Average total traffic throughput at destinations for all TCP flows (m = 100).
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Fig. 4.
80 100 120 CBR rate (Kbps)
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Avergae file download time (second)
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Lookup latency (m = 100)
To show the impact of our WMN-Balance algorithm on the performance of the WMN in terms of overall network throughput (received throughput at end-users), we simulated a network consisting of 100 mesh routers (m). We selected 10 pairs of source/destination uniformly at random and established TCP flow between them. TCP flows are existed in the network with P2P file sharing application. TCP flows were used to deliver a file with 10 MBytes size to destinations using the DSR network routing protocol that uses the minimum hop routing metric. TCP is used as a transport layer protocol for reliable data delivery. We show the average total throughput for all TCP flows with increasing peers average query rate (average content query per second per peer q = t−1 req ) (Fig. 5(a)). We also computed the average file download times (Fig. 5(b)). We observed that mesh clients can achieve higher throughput and less file download time when peers in the WMN use the WMN-Balance algorithm, particularly at higher content query rate, due to the fact that WMN-Balance mitigates the traffic load on the congested mesh routers. V. C ONCLUSION In prior work, we designed a network topology aware system that benefits from the support for the P2P applications at mesh routers and achieves significant improvements for both the P2P file sharing performance and the overall WMN performance. In this paper, we complemented our prior work and introduced the WMN-Balance: a content lookup algorithm for P2P resource sharing over WMNs. So far we considered routing P2P traffic on the WMN-Balance overlay. In future work, we would like to investigate routing other delay-insensitive traffic on the WMN-Balance overlay network and compare the network traffic load with other conventional data delivery schemes. ACKNOWLEDGMENT This work was supported in part by NSERC under Discovery Grant RGPIN 342751-07.
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500 400 300 200 100
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(b) Average file download time for all TCP flows (m = 100). Fig. 5.
WMN-Balance algorithm impact on the WMN performance
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