Locality-Aware Clustering Application Level Multicast for Live ...

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 27, 319-336 (2011)

Locality-Aware Clustering Application Level Multicast for Live Streaming Services on the Internet KANCHANA SILAWARAWET AND NATAWUT NUPAIROJ Department of Computer Engineering Chulalongkorn University Bangkok, 10300 Thailand With the rapid growth of Internet, media streaming plays an important role for the growing demand of media service. Application Level Multicast (ALM) has emerged as a key alternative to enable broadcasting the streaming media over the Internet. Several studies have proposed solutions to construct ALM multicast trees with various aspects such as end-to-end delay optimization, quick joining delay, small maintenance join/leave overhead, and balance multicast tree. However, these approaches focus on the optimization of a simple measurement, end-to-end delay, while ignoring other factors such as bandwidth consumptions, which can be very critical for large-scale ALM. In addition, the tier-based nature of the Internet further increases the difficulties of constructing scalable ALM multicast tree. This paper proposes a hierarchical structure approach called Locality-Aware Clustering (LAC), which utilizes the knowledge of network topology to construct an efficient ALM multicast tree. Based on simulation studies, LAC algorithm can reduce the overlay stress by more than 80% comparing to traditional algorithms such as ZIGZAG and MBMT/MSMT while maintaining low overlay delay even when there are large number of nodes. This simulation results show that LAC algorithm can construct ALM multicast trees that are more localized, efficient and scalable than the traditional approaches. Keywords: locality-aware clustering, application level multicast, landmark, bandwidth localization, bandwidth efficiency

1. INTRODUCTION The success of live multimedia services on the Internet, such as event broadcasting, Internet TV, and Internet radio, has increased the amount of multimedia traffics drastically. Many content providers adopt media streaming technology such that an end user can start watching the media as soon as the download starts. Nevertheless, these services rely on the network capability to satisfy traffic requirements such as bandwidth and jitter delay. This usually requires support from underlying network equipment. Application Level Multicast (ALM) has emerged as a key alternative to enable broadcasting the live streaming media on the Internet without any support from network equipment. ALM typically requires an efficient and scalable structure of unicast communication to support a large number of users. To be a good ALM structure for live streaming services, the structure has to meet two goals; (1) bandwidth efficiency, which deliver the streaming to the end-host with sufficient bandwidth that matches the requirements of the streaming application, (2) scalability, which minimize system resource usage to sustain the large number of users. To satisfy these goals, many ALM overlay structures, which are usually tree-based so called “multicast tree”, are proposed. Many works have Received December 22, 2008; revised September 9, 2009; accepted November 18, 2009. Communicated by Rong-Hong Jan.

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been focusing on optimizing the overlay construction problem in many aspects, such as the delay [1, 2], total number of hops to forward the packet to all destinations [1], join/ leave overhead [1, 3], link stress [1, 4, 5], and tree bandwidth maximization [5]. Some algorithms [1, 6] consider group of node or cluster level, instead of the node level. Using the cluster level, the structure can reduce the join/leave overhead and consume less network resource in transferring data. Examples of cluster based approaches are NICE [6] and ZIGZAG [1]. ZIGZAG is very famous as it aims to construct overlay network based on latency optimization while limits the out-degree of each node to a constant bound. Thus, ZIGZAG can construct multicast trees that can be deployed on the Internet as the bandwidth availability is limited. Nevertheless, it cannot fully utilize the network resources as all links are not the same. Limiting out-degree of all nodes to a certain constant number is too pessimistic. To utilize the underlying network resources effectively, several topology-aware approaches have been proposed. In general, topology-aware approaches, such as MSMT/ MBMT [5], have to measure all physical links in the network. This can be very impractical for large-scale network such as the Internet as it is almost impossible to ping all nodes in the network and massive amount of information must be exchanged. In addition, the network environment is very dynamic. Thus, the measured information can become obsolete very quickly and re-measurements are needed. This paper proposes a Locality-Aware Clustering ALM (LAC) that can construct a multicast tree with good scalability, bandwidth efficiency, and practicality for live streaming services. To utilize the fact that the Internet spans several countries, links of the Internet are tier-based by nature as they can be classified into transit links and stub links. Hence, a good ALM approach focuses on the bandwidth localization, which is to minimize the bandwidth consumption on the transit links and utilize more bandwidths on the stub links. Moreover, this paper also proposes a simple geographical information called landmark to classify the links. By using landmarks, LAC can construct a multicast tree with maximum bandwidth localization and minimal latency measurements. The performance of the approach is evaluated using simulation. The simulation results show that LAC algorithm demonstrates better overlay utilization and offers better scalability than other traditional ALM approaches. The organization of this paper is as followed. Section 2 defines all necessary models, including physical model, logical model, ALM model and ALM performance metrics. Section 3 proposes Locality-Aware Clustering algorithm. This study evaluates and analyzes LAC algorithm by comparing with ZIGZAG, MSMT/MBMT through simulation in section 4. Section 5 discusses the related work and finally section 6 concludes the paper.

2. MODELS AND ASSUMPTIONS 2.1 Physical Model In general, a physical network is represented as a general graph G = (V, E), where V denotes a set of nodes and E denotes a set of physical links. A node in physical network, vi, can be either a host or a router. For a physical link e(vi, vj) ∈ E, which is a link between vi and vj, there are two associated costs, link delay and link bandwidth represented

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by d(e(vi, vj)) and bw(e(vi, vj)), respectively. Let the application bandwidth, denoted by abw(e(vi, vj)), to be the bandwidth that an application uses for transmitting data over a physical link e(vi, vj), including all duplicated packets on that particular link. The utilization of a link, denoted by u(e(vi, vj)), is ratio of application bandwidth and the link bandwidth. Thus,

u (e(vi , v j )) =

abw(e(vi , v j )) bw(e(vi , v j ))

.

(1)

Typically, the effectiveness of a streaming application is usually limited by the bottleneck link, which is the link being utilized the most. The bottleneck link, denoted by BL, is the link with maximum utilization. In this case,

u ( BL) =

max {u (e(vi , v j ))}.

e ( vi , v j ) ∈ E

(2)

In reality, Internet is the networks of networks. Thus, it exhibits the characteristics of the tier-based environment [7], such as domestic tier and international tier. Therefore, to represent the real network such as Internet, this paper will extend the physical network model with transit-stub architecture [8]. This architecture consists of two domain categories, Transit-Domain (TD) and Stub-Domain (SD). Transit-domain is consisting of international-level Transit-Nodes (TN) acting as connection points between transit-nodes and stub-domains. Thus, the transit-domain provides Internet backbone to connect between the two transit-nodes, as well as, between a transit-node to stub-domains via transit link or inter-area link. For stub-domain, all nodes in the same domain are assumed to be in nearby physical location or area. Thus, these nodes communicate other nodes in the same stub-domain via stub links. In this study, the data traffics within a stub-domain are called stub traffic and the traffics within the transit-domain or between a transit-node and stubdomain are called transit traffic. The extended model for a physical network G = (V, E) is formally defined as followed. Suppose the network has n stub-domains. Let ai be a stub-domain and Vi be a set of nodes in ai. V0 is defined as the set of transit nodes. The set of nodes V can be partitioned into n subsets, V = ∪in=1 Vi . In addition, a set of all links E composes of a set of transit links Et, which is the collection of links connecting transit-nodes with other transit nodes or stub-domains, and a set of stub links Es, which is the collection of links connecting nodes in the same stub-domains. Then, the set of link E is Et ∪ Es. Suppose et is a transit link and es is a stub link. This works make general assumptions that bw(et) < bw(es). Fig. 1 shows an example of Internet transit-stub architecture with 4 stub-domains and one transit-domain. Based on this model, nodes and links in physical topology will be classified into transit-domain and stub-domain. This process requires the measuring of node information such as delay and bandwidth using many popular tools such as traceroute, pathchar [9], Max-Delta [10]. However, these approaches usually take long time to measure the information, require frequent measurement, and consume a lot of resources. To simplify the identification process, this study utilizes the fact that nodes in the same domain should be in the nearby geographical area. Thus, area identification can be done easily such as asking user to provide the area information or inquiring from some standard Internet services

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Fig. 1. Physical network.

Fig. 2. Logical network.

such as IPwhois [11]. These location identification approaches may not be 100% accurate. However, large-scale location information such as country code should have quite minimal error rates. In this study, the country code from IPwhois service will be used to identify the area of node, such as th, jp, and tw that represents Thailand, Japan, and Taiwan, respectively. The obtained country code will be converted to the code number, called “landmark”, that is the running number from 1 − n country code. The landmark is used for 2 purposes. First, it is used for stub-domain identification. Let l(vi) be a landmark of the node vi. For any node vi and vj, if l(vi) is equal to l(vj), both nodes are considered to be in the same stub-domain. Second, the landmark is used to identify the type of network link. For a link e(vi, vj), if vi and vj have the same landmark and both nodes are not belong to the transit-domain, the link e(vi, vj) is considered to be a stub link. Otherwise, it is a transit link. Fig. 1 presents a sample network consisting of 17 end-hosts (vs, v1 − v16) and 8 routers (R1 − R8). Let a1 − a4 are the country codes of hosts obtained by using IPwhois service. The set of links E composes of stub links, e.g., e(vs, R5), and transit links, e.g., e(R1, R2). 2.2 Logical Model In general, most applications rely on logical network model as they utilize point-topoint communication or end-to-end paths between nodes, while ignoring the physical components such as routers. Based on a physical model G = (V, E), a logical network model is a complete graph, Go = (Vo, Eo) where nodes in the logical network (Vo) are all hosts (excluding routers) from G. For vi and vj ∈ Vo, each logical link or end-to-end path p(vi, vj) ∈ Eo, Eo is the set of physical links that constitutes the shortest path between vi and vj. Fig. 2 shows a logical network derived from the physical network in Fig. 1. The costs in logical network model include path delay and path bandwidth, which are defined as follows, pd ( p (vi , v j )) =

∑

{d (e(vi , v j ))},

(3)

min

{bw(e(vi , v j ))}.

(4)

e (vi ,v j ) ∈ p (vi ,v j )

pbw(p(vi , v j )) =

e(vi ,v j ) ∈ p (vi ,v j )

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Fig. 3. Packet flow of IP multicast.

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Fig. 4. Packet flow of ALM.

2.3 Application Level Multicast Model Streaming applications often require multicast services for delivering their streaming feeds. Typically, multicast services for streaming can be based on either IP multicasting or Application Level Multicasting (ALM). The main concept of ALM is to implement the multicast service at the application level. At this level, ALM utilizes pointto-point communication to create overlay network atop of logical network model. This enables ALM to become independent from physical network topology. Note that this study focuses on the ALM with single source. Thus, the ALM structure in this study is assumed to be based on tree. Figs. 3 and 4 show the differences between IP multicast and ALM. The arrow dash lines represent the packet flows on the transit link, while the arrow dot lines represent the packet flows on the stub link. Obviously, IP multicast mechanism relies on the routers to forward data packets from the sender to receivers without duplication. On the contrary, ALM utilizes unicasts to deliver packets. Thus, some unicasts may share the same physical link. This can lead to multiple sending of the same packet over this link, called link stress. The link stress of a physical link e(vi, vj), denoted by st(e(vi, vj)), refers to the number of duplicated packets on that link. This paper defines the stress on transit link to be the maximum stress of all transit links on the multicast tree. The stress on stub stress is the maximum stress of all stub links, and the overlay stress is the maximum stress of all links. Given an ALM tree, T = (Go, vs, D, S). Let Go be a logical network model. Let vs be the source which is the root of the tree and let D be the set of nodes in Go which are the destinations of ALM traffic. Streaming data traffic from the source to all destinations may require multiple unicasts. For a destination vi ∈ D, a streaming path s(vs, vi) constitutes the path between vs and vi. SP is the set of all streaming paths in the tree. Streaming path delay and streaming path bandwidth are defined as followed. The streaming path delay, denoted by sd(s(vs, vi)), is the sum of end-to-end delay of the logical link along the streaming path. The streaming path bandwidth, denoted by sbw(s(vs, vi)), is the minimum path bandwidth on the streaming path. Thus: sd (s(vs , vi )) =

∑

{pd (p(vs , vi ))},

(5)

p (vs ,vi ) ∈ s (vs ,vi )

sbw(s(vs , vi )) =

min

p(vs ,vi ) ∈ s (vs ,vi )

{pbw(p(vs , vi ))}.

(6)

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Fig. 5. Overlay network.

Fig. 6. The relationship of the three models.

Fig. 5 presents the overlay network of the ALM tree in Fig. 4. To perform multicasting, vs establishes unicast connections to v1, v3, v6, v7, and v16. After the nodes receive the data, they forward the data to other members, such as v1 to v2 and v3 to v4. The stress on transit link and stub link are 4 and 2, respectively. Finally, the overlay stress is 4. In the overlay network, the overlay delay, denoted by od(T), is the maximum streaming path delay comparing to all of them on the overlay network. The overlay bandwidth, denoted by obw(T), is the minimum streaming path bandwidth of the overlay network. Thus, od (T ) =

max {sd (s(vs , vi ))},

s (vs ,vi ) ∈ SP

obw(T ) =

max {sbw(s(vs , vi ))}.

s (vs ,vi ) ∈ SP

(7) (8)

Fig. 6 summarizes the relationships between the Physical model, the Logical model, and the ALM model. Some ALM algorithms base entirely on physical model, while some others focus only on logical model. With the introduction of landmark in the extended physical model, LAC algorithm can utilize both physical model and logical model to construct ALM trees with good performance. ALM performance matrices are discussed in the next section. 2.4 ALM Performance Metrics To evaluate the effectiveness of ALM on the Internet, this study uses the following metrics: link stress, overlay utilization, transit byte ratio, and overlay delay. The definitions of these metrics are as follows, 1. Link stress: This metric measures the quantity of the flooding packets. To utilize network bandwidth effectively, the stress must be minimized, especially at the link with small bandwidth like a transit link and highly-congestion link like a bottleneck link. The transit stress, denoted by stt, and the bottleneck link stress, denoted by stb, are defined as follows,

LOCALITY-AWARE ALM FOR INTERNET LIVE STREAMING

stt =

max {st (e(vi , v j ))},

e ( vi , v j ) ∈ Et

stb = max {st (e( BL))}. e ( BL ) ∈ E

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(9) (10)

2. Overlay utilization: As mentioned earlier, the bottleneck link is very critical to the streaming application. Thus, the utilization of the bottleneck link is used to represent the overlay utilization. Therefore, u(T) = u(BL).

(11)

3. Transit byte ratio: This metric is a metric that shows the quality of localization-aware. It is used to quantify the quality of traffic localization by considering traffic in the stub links. The transit byte ratio, tbr, is the ratio of total bytes, tb, on the transit links by total bytes of all links.

tbr (T ) =

∑

{tb(e(vi , v j ))}

∑

{tb(e(vi , v j ))}

e ( vi ,v j ) ∈ Et

(12)

e ( vi , v j ) ∈ E

For good localization, the ALM should avoid using the transit links. Thus the transit byte ratio should be closer to 0. In addition, the stub byte ratio, denoted sbr(T), is defined as follows, sbr(T) = 1 − tbr(T).

(13)

4. Overlay delay: This metric is the maximum streaming path delay in the ALM tree. In this assumption, the overlay delay is not quite critical for the live streaming services. The delay just has to be within the pre-defined bound. Hence, the overlay delay is not the main metric in this evaluation.

3. LOCALITY-AWARE CLUSTERING (LAC) In this section, a new algorithm called Locality-Aware Clustering (LAC) is proposed to construct an efficient overlay tree for live streaming over the Internet. LAC algorithm uses landmark information to partition nodes and links into groups and constructs the overlay tree accordingly. 3.1 Locality-Aware Clustering Hierarchical Structure

Traditionally, ALM overlay trees are constructed based on the optimization of the overlay delay. This may be significant in some ALM applications such as network game and video conference [12]. In some other applications such as live streaming, some delays within bound are usually acceptable and scalability becomes an important issue as it needs to support large number of audiences. To solve this problem, a new algorithm called

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Locality-Aware Clustering (LAC) is proposed to construct an efficient overlay tree for live streaming. LAC structure is a tree of clusters, similar to the structure constructed by ZIGZAG and NICE, where the source is the root node. The size of the cluster is limited to be 3k, where k is a constant. This is to avoid the overhead of streaming data and protect the lost of members in merging cluster [6]. In each cluster, there are one Header Node (HN) and one Distributor Node (DN). Other nodes in the cluster are considered member nodes or Other Nodes (ON). For a cluster Ci, the definitions of these nodes are as follows, 1. Header Node (HNi): HNi is responsible for receiving streaming feed from the parent cluster and forwards the streaming feed to its child, which is the distributor node. This node is also a member of the parent cluster. For the top cluster, the source or the root of the tree acts as both header node and distributor node. 2. Distributor Node (DNi): DNi is responsible for receiving streaming feed from the HN of this cluster and distributing data to other members of the cluster. DN also maintains the membership of the cluster. In addition, DN is the backup of HN. Thus, when HN fails, DN will become HN and a new DN is elected from one of the members. 3. Other node (ON): The rests of members are called the Other Nodes (ONs), which are the children of DN. ONs receive data from DN. If the cluster has child clusters, HNs of the child clusters are also ONs of this cluster. In addition, we define {ON}i to be a set of all ONs in Ci. Fig. 7 shows an example of an LAC tree that generate from the physical network in Fig. 1. The tree has three overlapped clusters, C0, C1, and C2. C0 is the root cluster, which also includes the source and the root node (vs). vs acts as both HN0 and DN0 of C0 and v8 is the only ON in this cluster. In C1, there are three members (vs, v1, and v2). vs is an HN1, v1 is the DN1, and v2 is the only ON. In C2, the HN2 is v8, the DN2 is v9, and v10, v12, v16, v3, v5 are the members of {ON}2. Based on the structure, the streaming feed comes from the source node vs to its only child in C0, v8. Upon receiving data, v8 as HN2 forwards the streaming feed to DN2 (v9), which forwards the feed to all {ON}2. In addition, acting as HN1, vs also forwards its data to v1, which is DN1. Although, LAC structure is based on ZIGZAG and NICE, LAC structure is more efficient because it combines the information from physical model and logical model to implement the traffic localization concept called “Locality-Aware”. This concept bases on the fact that the communication between two nodes within the same stub domain should be more efficient than those across two different domains. Thus, the concept focuses on constructing a cluster such that stub links (or local links) are preferred over tranC0 HN0, DN0 HN1

ON0

Vs a1

HN2

DN1 V1

Vs: Sender HN: Header Node DN: Distributor Node ON: Other Node V9 a4 a1- a4: Landmark

V8 a4

DN2

a1 ON2

ON1

C1

V2

V10

V12

V16

V3

V5

a1

a4

a4

a4

a2

a2

C2

Fig. 7. An example of LAC tree.

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sit links as intra-cluster links. This minimizes the stresses in the transit links, which lead to better performance and greater scalability. Considering the LAC structure, DN has to communicate with many ONs. If DN and ON are on the same physical area, they can communicate directly via stub links. With this concept, LAC algorithm tries to group nodes with the same landmark into the same cluster. This will be done when a new node joins the tree. 3.2 Joining a New Node

To join a new node, LAC joining algorithm, presented in Fig. 8, is used to find the target cluster. Based on the localization, LAC joining algorithm selects the target cluster (Cx) by keeping nodes in the same area within the same cluster. Suppose a new node vi want to join the structure. If the vi is the source node, it becomes the root of LAC multicast tree. Otherwise, vi will requests all DN list and their landmarks from the DN of the root cluster (DN0) to construct a shortlist of the near-by clusters. A near-by cluster is a cluster that shares the same landmark with vi. If there are more than one cluster in the near-by cluster shortlist, vi will select the Cx which is not full and has the smallest latency. Note that if all clusters in the shortlist are full, vi will select the Cx based on the latency only. If the near-by cluster shortlist is empty, vi measures the latency from vi to all DNs in the multicast tree and joins the cluster with the smallest latency. The algorithm of LAC: Joining new node 1. If (source(vi)) 2. root = vi 3. else 4. if (#C == 0) 5. HN0 = DN0 = root 6. else DNx = FindCx(vi, l(vi)) 7. end if 8. JoinNode(DNx, vi, pd(DNx, vi)) 9. if (#Member(Cx) > 9) 10. SplittingAndMerging(Cx) 11. end if 12. end if Fig. 8. The algorithm of joining newcomer.

When the target cluster is selected, vi joins as a child of the DN of the target cluster. LAC joining algorithm then evaluates the size of the cluster. If the cluster is too large (more than 3k), the cluster is partitioned by splitting and merging algorithm. Fig. 9 shows an example of joining nodes to the multicast tree. Suppose the multicast tree already has 3 nodes, as demonstrated in Fig. 9 (a), and there are 7 new nodes, v9, v10, v8, v1, v2, v5, and v12, want to join the multicast tree. When the first 6 nodes join the multicast tree, all nodes will join the root cluster, C0, as presented in Fig. 9 (b). When v12 joins the multicast tree in Fig. 9 (c), the root cluster becomes overflow as its size is 10 (including the root node), which is more than 3k (k = 3). In this case, the cluster will be partitioned into 2 clusters by calling SplittingAndMerging in line 10.

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(a) vs, v16, v3 joining. (b) Cluster with maximum members. (c) Cluster overflows. Fig. 9. An example of joining nodes in LAC tree construction.

The algorithm: SplittingAndMerging 17. In the left of sp 1. sortLandmark(l({ON}x)) 18. If (#C > 1) 2. sortNoOfNodeInLandmark(#(l{ON}x)) 3. sortLatency(pd({ON}x) ) 19. for child (i = 0 to sp − 1) 4. sp = position(findDiffLandmark({ON}x)) 20. clearChild(DNx, vi) 5. In the right of sp 21. end for 6. HNy = child(DNx, sp) 22. else 7. DNy = child(DNx, sp + 1) 23. DNx = child(HNx, 0) 8. For child (i = sp + 2 to n) 24. for child (i = 1 to sp − 1) 9: addChild(DNy) = child(DNx, vi) 25. addChild(DNx) = child(HNx, vi) 10. end for 26. end for 11. If (l(HNy) == l(DNx)) 27. addChild(HNx) = DNx 12. addChild(DNx) = HNy 28. end if 13. else 29. end of left of sp 14. addChild(ParentOfCx) = HNy 15. end if 16. end of right of sp Fig. 10. The algorithm of SplittingAndMerging.

When the cluster is too large, there are two stages of the SplittingAndMerging procedure. (1) Splitting the overflow cluster into two clusters, and (2) merging the new cluster to the multicast tree. Fig. 10 shows the detail of SplittingAndMerging algorithm. Suppose Cx is too large. Splitting Cx will perform in 3 steps, sorting the {ON}x, which is the set of all ONs of Cx (lines 1-3), finding the splitting position to form a new cluster (called sp, line 4), and selecting the HN and DN for the new cluster (lines 6-7, 23). In the sorting stage, LAC first group {ON}x by their landmarks, then sort the group of landmark by the number of nodes in decreasing order, and finally re-sort these nodes in any landmark by latency in increasing order. Note that the group of ONs with the same landmark of DNx will stay on the left side of the list. Then the splitting position is assigned by choosing the first child, whose landmark is different from the others. If all children have the same landmark, the cluster is split in halves. Using the splitting position, HNx, DNx, and all ONs on the left side of the splitting position remain in Cx. In this case, HNx and DNx remain unchanged. The ONs on the right side of the list will form a new cluster, Cy. The first two nodes in the right side of the list will be chosen as new HNy and the DNy. To prevent the new cluster from being too small, the splitting position must be in between the third and the sixth child of the list. To complete the process, Cy will be merged to the multicast tree using the merging function in lines 11-15. In merging process, the landmark of HNy is compared to the

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landmark of the DNx. If the both landmark are the same, the HNy will join Cx and become the child of the DNx. Otherwise, the HNy will become a child of the parent cluster of Cx. In this case, Cx and Cy become siblings. Once merged, if the parent cluster becomes overflow, the parent cluster will be split and merged in similar fashion. There is a special case when splitting the root cluster, C0. Since the C0 is the root cluster, it has no parent cluster. In this case, all clusters in the multicast tree will be rearranged by renaming all clusters and creating a new C0. Suppose the C0 is full. It will be split C0 and form C1 similar to normal splitting process. Note that the root node still remains as HN0 and DN0. After splitting, the tree must be re-arranged. To re-arrange the tree, the names of all clusters in the multicast tree will be increased by one. Thus, C0 becomes C1, etc. A new C0 is created from the HN1 (after being renamed), or the root node, and the HN2 (after being renamed). For the new C0, the root node will become the HN0 and DN0 and the HN2 will become the only ON of C0. Fig. 11 shows the example of splitting and merging cluster. Fig. 11 (a) shows C0 after the {ON}0 are sorted by their landmarks, number of nodes in each landmark, and latencies. Then the ONs will form a list in order to choose the splitting position. In C0, the first position that the landmarks are different is the third position, so the splitting position of this cluster is the node v8. Thus, the C0 is split. HN0 and DN0 (vs), as well as, all ONs from the left side of the splitting position {v1, v2} remain in C0. HN0 and DN0 remain unchanged. The remaining ONs ({v8, v9, v10, v12, v16, v3, v5}) form a new cluster C1. The first

(a) C0 in splitting process.

(c) C2 overflows.

(b) LAC tree after merging C2.

(d) C2 in splitting process.

(e) LAC tree after merging C3. Fig. 11. An example of splitting and merging cluster.

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Fig. 12. ALM with locality-aware.

Fig. 13. Overlay network.

two nodes in the list, v8 and v9, are chosen as the HN1 and the DN1, respectively. Since the C0 is split, all clusters in the multicast tree must be re-arranged. C0 is renamed to be C1. C1 is renamed to be C2. A new C0 is created from HN1 (vs) and HN2 (v8). The root node becomes HN of the new C0. Similarly, Fig. 11 (c) shows LAC tree after v7, v4, and v6 join the C2 and then the cluster is overflow. C2 is partitioned into C2 ({v8, v9, v10, v12, v16}), C3 ({v3, v5, v4, v7, v6}) as illustrated in Fig. 11 (d). The new cluster C3 is merged to the multicast tree and HN3 becomes a child of C0, which is also the parent cluster of C2 in Fig. 11 (e). Finally, Figs. 12 and 13 show an example multicast tree generated by LAC algorithm. Using localization strategy, LAC tree is constructed such that most streaming traffics have been transmitted over the stub links leading to fewer stresses in the transit links. In this example, the transit stress of this tree is 2, which is lower than the transit stresses of the tree generated by ZIGZAG in Fig. 5. 3.4 Leaving Node

There are two possible cases of node leaving the multicast tree, voluntary and involuntary (e.g. node crashes). When a node leaves voluntarily from a cluster in LAC tree, the leaving process is invoked. By considering the type of the leaving node, there are three cases: 1. If the leaving node is the HN of the cluster, the DN of that cluster is promoted to be a new HN. The ON node with the shortest latency from the new HN will become the new DN. 2. If the leaving node is the DN of the cluster, the ON node with the shortest latency from the existing HN will become the new DN. 3. If the leaving node is the ON, no action is necessary. After node leaves from the cluster, if the cluster size is not between k and 3k, all nodes in the cluster must join a cluster whose HN has the same landmark as the HN of the undersize cluster. After joining, the joined cluster may be split again if the cluster size is overflow. In the case that a node crashes, if the crashed node is an ON, this will not affect the leaving process. If the crashed node is either HN or DN, however, part of the multicast

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tree is disconnected. To prevent this problem, a heart-beat mechanism must be implemented between HN and DN. Once a crash is detected, the leaving process is invoked, similarly to a node leaves voluntarily. 3.5 Practical Issues

To implement LAC algorithm, node’s memory requirement, clustering time, and packet loss must be considered. In LAC algorithm, cluster information during normal operation is distributed among clusters. In this case, HN and DN must maintain the information of their own cluster. Since cluster size is limited to 3k, the memory requirements of maintaining cluster information during normal operation is quite minimal. Memory requirements during clustering time, however, require special consideration. In general, node’s memory requirement and clustering time are quite related, especially during the joining period. In LAC algorithm, the time consuming step of the joining process is to construct a shortlist of near-by clusters. This requires examining the landmark information of DN of each cluster starting from the root cluster and goes further down in the multicast tree. To shorten this construction process, high-level nodes such as the root node and HN of the high-level clusters may cache cluster information of their child clusters. Since only landmark information of each DN is needed to represent each cluster, caching child clusters’ information for shortlist construction is not memory-demanding. Packet loss is possible during the leaving process. For voluntarily leaving, the leaving node will not leave right away. It will wait until the leaving process is complete and then actually leave. For involuntarily leaving, the leaving node is crashed. Thus, it cannot wait until the leaving process is complete. In this case, other nodes in the cluster will be disconnected from the multicast tree and the streaming service is interrupted. To minimize the length of interruption period, the heart-beat mechanism between HN and DN must be frequent enough in order to detect the crash as soon as possible. The other practical issue is the accuracy of location identification. Using country code as a landmark may not always be accurate as the IPWhois service may return a country code that is not corresponding to the actual location where the node is located. The inaccurate landmark information can lead LAC to construct an inefficient multicast tree. However, since the landmark is based on a country code, which is very large-scale, it is very unlikely that this information is incorrect. In addition, LAC algorithm has been designed such that it can tolerate this problem because latency is also considered during the joining process. If the landmark is incorrect, the DN that is claimed to be in the same area of the joining node will have longer latency than other DNs in the shortlist whose landmark are correct. Thus, the joining node will choose to join the DN with shorter latency, which should have a correct landmark. At the worst, LAC will perform as good as other cluster-based algorithms such as NICE and ZIGZAG if the inaccuracy rate is too high.

4. PERFORMANCE EVALUATION 4.1 Simulation Environment

To evaluate LAC algorithm, simulations using NS2 simulator are conducted. The physical network topologies in all simulations are randomly generated based on the tran-

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sit-stub architecture GT-ITM [8]. Each network composes of a transit-domain with five transit-nodes. Each transit node connects to three stub-domains and each stub-domain has 199 nodes. The size of physical network is 1000 nodes. Furthermore, 50-500 nodes are randomly chosen to form multicast trees. Table 1. Testing condition. Parameter Propagation Latency (msec) Link Bandwidth (Mbps) Message size (bytes) Streaming rate (Kbps)

Stub Tier 0.2-0.5 80-100 1000 250

Transit Tier 9-20 8-10 1000 250

Table 1 lists the parameters and their values being used in this simulation. The bandwidth distribution and end-to-end latency are the measurement results of [13]. The Bandwidth distribution is randomly assigned between 80 to 100 Mbps for stub link and 8 to 10 Mbps for transit link. All cluster size is limited at 3k where k = 3. Each streaming is 5 sec. long, and the streaming rate is 250 Kbps, which is quite typical for both audio and video streaming applications. Average values of simulating ALM trees on three different physic-cal topologies are being used in all performance comparison. 4.2 Simulation Results

As discussed in section 2.4, stress is considered the most important metric for evaluating ALM. To achieve good scalability on the Internet, stress in the transit links must be minimized. Fig. 14 shows the overlay stresses of all algorithms with different number of nodes. For ALM trees with 500 nodes, the overlay stress of ZIGZAG, MSMT/MBMT, and LAC, are 182, 52, and 9, respectively. The result shows that LAC performs quite well, especially when the number of nodes is large. LAC trees can out-perform ZIGZAG and MBMT/ MSMT by 95% and 82%, respectively. Fig. 15 compares the stresses in the transit links and in the stub links of ALM trees generated by LAC and MBMT algorithms. With localization strategy, LAC tries to utilize the stub links as much as possible. Thus, the stresses in transit links of LAC tree remain quite low. On the contrary, the stresses in the transit links of MBMT increase as ALM tree becomes larger. This is because MBMT first uses a latency-oriented spanning tree as a basis to construct an ALM tree. Then, MBMT uses link utilization as criteria to optimize the tree. With this approach, the ALM trees constructed by MBMT are basically latency-optimized, which may not be stress-optimized. To provide a clear explanation, let’s consider a simple example. In an MBMT tree, suppose a node vi is a parent of 3 other nodes. And suppose these 3 nodes are in the same area and vi belongs to the different area. Since all 3 nodes are in the same area, the latencies between these nodes and vi should be almost the same. Thus, for a spanning tree, these 3 nodes will link to the same parent, vi. In this case, the connections between parent and all 3 children will be over the same transit link, which lead to more stresses in the transit link. On the contrary, LAC will group these 3 nodes into the same cluster and have only one node connecting to vi. In this case, the transit stress is minimized.

LOCALITY-AWARE ALM FOR INTERNET LIVE STREAMING

Fig. 14. Overlay stress.

Fig. 16. Overlay delay.

Fig. 18. Overlay utilization on bottleneck link.

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Fig. 15. Transit stress and stub stress.

Fig. 17. Transit bytes ratio.

Fig. 19. Stress on bottleneck link.

Fig. 16 shows the overlay delay. The results indicate that the overlay delay of LAC tree is less than the overlay delay of other trees in almost all of network sizes. Since LAC relies on the stub links, which have much lower latency than transit links, the overlay delay of LAC scheme is minimized. Fig. 17 shows the transit byte ratio as number of nodes change. The results confirm the characteristics of LAC algorithm that it utilizes the stub links more than the transit links. Comparing to other algorithms, LAC has the lowest transit bytes ratio, 0.043, while ZIGZAG has the largest transit byte ratio, 0.134. This means less data is transferred on the transit links of LAC tree. Thus, with the same number of nodes in the multicast tree, LAC is the most bandwidth efficient. Fig. 18 investigates the scalability of the ALM tree on the Internet by evaluating the overlay utilization on the bottleneck link. This results show the performance of ALM tree

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when taking the bottleneck link of each tree into consideration. In this case, the lower is better. As number of nodes increases, the overlay utilization on the bottleneck link of all algorithms increases. It also quite clear that LAC can out-perform all algorithms as its bottleneck links is quite minimal. This is because LAC scheme tries to place a new node in the cluster that matches its landmark first, while other algorithms ignore this fact and utilize either the minimum spanning tree or the shortest path algorithms. Thus, for these algorithms, the new node may ends up on different physical area of its peers. Fig. 19 confirms the previous results. LAC focuses on minimizing stresses on the bottleneck links and thus it has less stresses and utilizes bandwidth very effectively.

5. RELATED WORK Various ALM overlay construction have been proposed for streaming services [1-6]. They can be classified into two categories, tree-based (ZIGZAG [1], STAG [3], MSMT/ MBMT [5], and NICE [6]), and mesh based (IOO [2] and SHM [4]). IOO [2] proposed an inter-overlay optimization scheme using mesh-based structure aiming for constructing efficient paths for different overlay, guaranteeing streaming service quality, and improving resource utilization. SHM [4] constructs the hierarchical structure that composes of Rendezvous Point (RP) and domain. The structure is a global share tree based on IP Multicast Island that does not require extra detection mechanism between end-host during the member join. Some structures [1, 3, 5, 6] construct multicast trees using the underlying network information such as delay, bandwidth, and location information. These algorithms are considered topology-aware. STAG [3] proposed a tree construction that uses transit-stub network topology on top of physical network. This mechanism minimizes the overhead of the joining process, has low relative delay penalty, and reduce link stress. As this algorithm focuses mainly on latency, it is not bandwidth efficient. NICE [6] and ZIGZAG [1] construct a hierarchical cluster of nodes with each cluster having a “head” represent in the higher layer. In these algorithms, nodes are being grouped into a cluster to reduce the control overhead and allow fast joining process. NICE was designed to support network architecture with limited bandwidth. Its cluster size is bounded by a constant to avoid the bottleneck at the distributor. ZIGZAG is an extension of NICE with two additional techniques, cluster size balancing and capacity-based switching. It aims to minimize the overheads during joining and leaving processes, as well as, reducing the number of network hops. However, ZIGZAG assumes all nodes are similar in term of latency and bandwidth availability. Unfortunately, this is not applicable for the Internet. MSMT/MBMT [5] optimizes ALM tree based on the underlying network topology. Using complete information of network topology, the algorithm focuses on minimizing stress in any link and generates an ALM tree with high tree bandwidth and low link stress with low penalty in end-to-end delay. However, obtaining information required by MSMT/ MBMT, complete network topology, is not quite practical.

6. CONCLUSION This paper proposes an overlay construction for live streaming application on the

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Internet, called Locality-Aware Clustering or LAC. Based on the assumption of tier-based networking structure, LAC combines the physical model and logical model to construct an efficient ALM structure for the Internet using landmark and end-to-end delay. This paper evaluates the performance of LAC scheme by comparing with ZIGZAG, MSMT/ MBMT using simulation. The experimental results clearly show that LAC tree can outperform all other algorithms by more than 80% when comparing overlay stress and by more than 30% when comparing the utilization on bottleneck link. In addition, the overlay delay of LAC tree is minimal. The simulation results also indicate that the effectiveness of LAC mechanism is due to its localization strategy as LAC relies on the stub tier, which can greatly reduce the bandwidth requirements on the transit tier, whose bandwidth is usually quite limited. Therefore, LAC algorithm can generate a bandwidth-efficient ALM structure with low latency and good scalability for live streaming. The future works will extend LAC algorithm to support multiple source services.

REFERENCES 1. D. A. Tran, K. A. Hua, and T. T. Do, “ZIGZAG: An efficient peer-to-peer scheme for media streaming,” in Proceedings of International Conference on Computer Communications, 2003, pp. 1283-1292. 2. X. Liao, H. Jin, Y. Liu, and L. M. Ni, “Scalable live streaming service based on inter-overlay optimization,” IEEE Transactions on Parallel and Distributed Systems, 2007, pp. 1663-1674. 3. J. Cui, Y. He, and L. Wu, “More efficient mechanism of topology-aware overlay construction in application-layer multicast,” in Proceedings of International Conference on Networking, Architecture, and Storage, 2007, pp. 31-36. 4. S. Lu, J. Wang, G. Yang, and C. Guo, “SHM: Scalable and backbone topology-aware hybrid multicast,” in Proceedings of IEEE Conference on Computer Communications and Networks, 2007, pp. 699-703. 5. X. Jin, W. P. K. Yiu, S. H. G. Chan, and Y. Wang, “On maximizing tree bandwidth for topology-aware peer-to-peer streaming,” IEEE Transactions on Multimedia, Vol. 9, 2007, pp. 1580-1592. 6. S. Banerjee, B. Bhattacharjee, and C. Kommareddy, “Scalable application layer multicast,” in Proceedings of ACM SIGCOMM, 2002, pp. 205-217. 7. H. Haddadi, M. Rio, G. Iannaccone, A. Moore, and R. Mortier, “Network topologies: inference, modeling, and generation,” IEEE Communications Surveys and Tutorials, Vol. 10, 2008, pp. 48-69. 8. E. W. Zegura, K. Calvert, and S. Bhattacharjee, “How to model an Internetwork,” in Proceedings of IEEE Conference on Computer Communication, 1996, pp. 594-602. 9. V. Jacobson, Pathchar, http://www.caida.org/tools/utilities/others/pathchar/. 10. X. Jin, W. P. K. Yiu, S. H. G. Chan, and Y. Wang, “Network topology inference based on end-to-end measurements,” IEEE Journal on Selected Areas in Communications, Vol. 24, 2006, pp. 2182-2195. 11. The APNIC Whois Database, http://wq.apnic.net/apnic-bin/whois.pl. 12. S. Hajime, S. Hiroki, and T. Yoshito, “P2P grouping for the delay sensitive applications,” IEIC Technical Report, Vol. 106, 2006, pp. 13-18.

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13. K. Silawarawet and N. Nupairoj, “Minimizing multicast communication latency in WAN-based environment,” in Proceedings of International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed, 2006, pp. 145-150.

Kanchana Silawarawet received the B.E. in Computer Engineering from Siam University in 1996, and the M.S. degree in Computer Science from Chulalongkorn University in 2002. She has been working as a Lecturer at Department of Computer Engineering, Siam University for 5 years and currently pursuing the Ph.D. degree in Computer Engineering at Chulalongkorn University. Her research interests include multicast, parallel and distributed computing, and grid computing.

Natawut Nupairoj received the B.E. in Computer Engineering from Chulalongkorn University in 1990, M.S. and Ph.D. in Computer Science from Michigan State University in 1993 and 1998, respectively. Since 1998, he has been a Lecturer at Department of Computer Engineering, Chulalongkorn University. His research interests include grid computing, distributed system, and service oriented architecture.