process, balancing domains reduce the complexity of the process. 1. Introduction. Low earth orbit (LEO) satellite systems enable affordable access to fiber-like ...
Traffic Load Balancing in Low Earth Orbit Satellite Networks Yun Sik Kim, Young-Ho Bae, Youngjae Kim, and Chul Hye Park Korea Telecom Wireless Communication Research Laboratory 17 Woomyun Sucho Seoul, Korea ysk @rcunix.kotel.co.kr
earth with a nearly constant speed [ 11. Due to the moving coverage iregions of individual satellites, the terminals on the ground may not stay in a single coverage region of the satellite during the communication. Thus, a LEO satellite needs to transfer the ground terminals to other satellites whose coverage regions contain the ground terminals. This event is called handover in LEO satellite systems. The vi.sibility period of a satellite is the maximum time duration that a ground terminal resides in the coverage region of a satellite and can directly communicate with that satelliite. Thus, the handover of the ground terminal is performed every visibility period. The visibility period of a typical LEO satellite is around 10 minutes. Since, the velocity of satellite is mush faster than that of the ground terminals, most of the handovers are caused by the mobility of the LEO satellite instead of the ground terminals. In a LEO satellite network, ISLs make it possible for LEO satellites to handoff calls between satellites. Each satellite has ISLs with other satellites in the same or adjacent (orbit planes. This interconnection arrangement forms a non-hierarchical mesh network. Since each individual satellite moves along their orbit plane and the earth rotates on its axis, the traffic distribution of a LEO satellite system is unbalanced and time-variant. The service area of a LEO satellite may cover sparsely populated areas such as desert or sea as well as densely populated areas: then the total traffic load of the satellite covering densely populated areas becomes much higher than that of its neighbor satellite (e.g., one that flies over an ocean). In addition, the traffic load of the ISL over cities becomes higher than that of the ISL over the ocean. The service area may cover the bright side of the earth, that implies day, as well as the dark side as the earth rotates. This will be one unique traffic feature of LEO satellite system. This feature results in an important biased traffic distribution problem for the LEO satellite systems. The network must continually adapt to these changing conditions to provide better quality of service. In this paper, we propose a traffic load balancing scheme
Abstract One of the unique traffic features of low earth orbit (LEO) satellite networks is time-variant and nonuniform load distribution. This feature results in a locally biased congestion problem f o r the LEO satellite systems. In this paper, we propose a traJjcic load balancing scheme to resolve the congestion problem in such traffic scenarios. The proposing scheme makes use of near-neighbor residual bandwidth information to apportion excess bandwidth f r o m congested satellites to their underloaded neighbors in the network. Each traffic load balancing process is performed on the domain basis. When the residual bandwidth of the path by way of the target satellite is less than a prespecifed amount, calls routed over the path can be migrated to the alternative paths of the same balancing domain. By decreasing the size of the network graph being considered in load balancing process, balancing domains reduce the complexity of the process.
1. Introduction Low earth orbit (LEO) satellite systems enable affordable access to fiber-like telecommunication services to institutions and individuals anywhere in the world. This ability to deliver fiber-like, broadband, digital transmission capability, regardless of location, distinguishes the LEO system from other existing communications systems. A number of LEO satellite systems have been propped [l-31. LEO satellite systems are based on constellations with several circular common period orbits of low altitude. This low altitude enables short end-to-end delays and low power requirements for both satellites and the ground terminals. In addition, inter-satellite links (ISLs) make it possible to route a connection through the satellite network without using any terrestrial resources. LEO satellites move along their orbits in reference to the
0-8186-9014-3/98 $10.000 1998 IEEE
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It should be explicitly noted in this context that the traffic load balancing concept presented herein is indeed developed on top of Iridium like dynamic ISL constellations, but it is definitely not the Iridium proprietary concept. Each satellite has up and down links for communication with ground terminals and eight ISLs. This paper is organized as follows : In section 2 , we present network model proposing traffic load balancing scheme based on. In Section 3, we present the traffic load balancing process. In this section we present load balancing necessity determination, call migration procedure for load balancing and its complexity. Finally, we conclude this paper in section 4.
to resolve the congestion problem in such traffic scenarios. Performance of LEO satellite communication systems in nonuniform traffic distribution has been introduced and studied in [4-61. In [6], a review of the LEO systems from this viewpoint, and an examination of its effects on network performance has been performed. Although several routing and handover rerouting schemes in LEO satellite networks have been proposed [7-111, few of them have considered explicitly the issue of dynamically changing time-variant nonuniform traffic load, despite its importance. In [71, they proposed a handover rerouting scheme called Footprint Handover Re-route Protocol(FHRP), The FHRP puts emphasis on maintaining the optimality of the initial route without performing a routing algorithm after satellite handovers. However, the FHRP assumes that the traffic load distribution is uniform. Other studies [9- 1 I ] proposed pre-determined allpairs routing in ATM-based LEO satellite networks with time-variant topology, operating in a connection-oriented mode. It should be noticed that the evaluation of dynamically changing traffic load distribution cannot be included in these kinds of pre-operational path searchapproach. In [9], the static location dependent traffic distribution is considered as a parameter in their cost function. In [IO], they proposed routing scheme using pre-determined routing table based on time-variant network topology and static location dependent traffic distribution map. In [ 113, the time-variant nonuniform traffic distribution problem is introduced, but they do not specify any single algorithm for the traffic adaptive routing. It is clear that only the complete end-to-end handover rerouting may achieve the best path without congestion problem, but, in general, the complete end-to-end rerouting is infeasible in practice. If a complete end-toend routing considering dynamic traffic load distribution is performed whenever the handover occurs, as a result end-to-end routing for all of the calls is performed in every visibility period, around I O minutes. It would incur excessive signaling and computational overhead. Moreover, for the traffic adaptive routing or handover rerouting, the current traffic load distribution of entire network has to be maintained in each satellite, which results in another considerable overhead. The proposing traffic load balancing scheme is a highly distributed local approach which makes use of near-neighbor residual bandwidth information to apportion the excess bandwidth of congested ISLs to underloaded neighbor lSLs in the system. Throughout this paper, only systems employing ISLs are considered, and Iridium will be used as an example, and we assume that the LEO satellite network uses a simple shortest path routing as an initial routing scheme and uses a handover rerouting scheme such as the FHRP.
2. Network Model Let us consider arbitrary meshed LEO network with following characteristics. The number of satellites, N is constant, and the number of ISLs for a satellite is K. The network uses high speed switching technology based on the ATM technology operating in a connection-oriented mode. We can model a LEO satellite network as a directed graph,
G = ( V , E ) , V = { v , , v ~ , v ~ ,....., , v ~ v n } , IV I= N E c { { v ,' V I } I V [ , V I E and if j } f : E + ( p , (p20
v
A satellite corresponds to a vertex and an ISL corresponds to an edge. Thus, the number of edges incident with a vertex v is equal to the number of ISLs, K. 9 is attribute values for each edge. Since each link carries different calls in each of its two opposite directions, we consider each edge { i, j ) E E consisting of two directed edges:
(i, j ) and (',i) . We consider two attributes for each directed edge, distance and residual bandwidth. For simplicity, we assume that all directed edges have the same distance in our model. A traffic load balancing process consists of three phases: 1) Residual Bandwidth Evaluation 2) Load Balancing Necessity Determination 3) Call Migration The residual bandwidth evaluation of ISLs is a problem that can be executed independently on each individual satellite. Our focus is on the necessity determination and call migration phases, the second and third phases, of the traffic load balancing process. As LEO system works, the traffic load is distributed unbalanced fashion due to the reason that we mentioned in the previous section. To prevent call blocking result from this traffic distribution, the congestion in network must be detected and appropriate traffic load balancing strategy should be used to resolve the problem.
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During the load balancing necessity determination phase, a decision is made as to whether or not to invoke the load balancing process. Since our objective is to minimize call blocking probability, a decision on whether or not to perform load balancing is made based on the value of the residual bandwidth of each direction of ISLs. The residual capacity, r of a directed edge is defined as the difference between the maximum capacity and current bandwidth utilization of the edge, respectively.
b,,,
and
and the path via node
bCurren, ,
r = 'ma, - 'current In general, the directed edges
We represent a unique path from s to t by
(i, j ) , and ( j ,i)
(1)
different residual bandwidth of r ( ( i ,j ) ) ,and r ( ( j , i ) ) , since different calls are routed over these edges. A decision on whether or not to perform is made based on the value of r relative to a predefined residual bandwidth threshold. The traffic load balancing is necessity if the residual bandwidth is less than the threshold. The responsibility of invoking the traffic load balancing process may either be authorized to all satellites in the network or only to the designated satellites containing the necessary information. It is desirable to distribute the responsibility to multiple points in the network. This may be accomplished by partitioning the system into independent groups of satellites called balancing domains. The load balancing necessity decision is based solely on information pertaining to those satellites within each domain. By decreasing the size of the network graph being considered in the traffic load balancing process, the balancing domains reduce the complexity of necessity determination as well as the complexity of the call migration. In this paper, each of the balancing domain is in charge of the traffic load balancing to resolve the congestion of the path via the node located in its center. At this time, the target node of the traffic load balancing is denoted by objective node. Thus, the number of domain is equal to the number of satellites in system. A balancing domain @ consists of the objective node, its near-neighbors, and all links between those nodes. The subset of G is organizing a balancing domain @ . 0 is the set of nodes in the domain except the objective node, and I @ I= K . For each call, we denote the source node in the domain by s, and the destination node in the domain by t hereafter. The part of the end-to-end path that lie in the domain is identified by pair of s and t via the objective node is denoted by objective path. For a pair of s and t, there is a single objective path. Thus, the set of objective paths is denoted by
Pa , and I Pa I= K ( K - 1).
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by
pp( s , t ) . The
alternative path for an objective path is defined as the path that has the same s and t as those of the objective path, and that satisfies the distance condition. When an objective path is congested, calls over it can be detoured over an alternative path. The difference between the distance of objective path and its alternative path is denoted by 0 . The distance condition for an alternative path is true if the value of O is less than the prespecified value
have
pE@
p(~,t),
Orhrrrho/d
.
'
O Othrrshold (2) The distanlce condition on the alternative paths enables avoidance of QoS degradation and loops in the resultant paths. Hence, the bigger value of Orhrr,h,,ld yield more even distribution of traffic that result in lower call blocking probability, while the path of migrated calls can include wasteful loops and the migrated calls may go through more communication delay. The role of the call migration phase is first, to apportion the excess bandwidth of the congested objective path and to determine the amount of bandwidth to be transferred to the alternative paths, and second, to diffuse the excess bandwidth across the entire system if alternative paths within the domain is congested. The concept of the balancing domain reduces the overhead of the traffic load balancing process, but does not ensure a bandwidth apportion for the entire system. This is accomplished by overlapping domains, whereby excess load can be diffused from more congested domains into lightly loaded ones. The proposing scheme employs overlapping domains to achieve global load distribution. Our traffic load balancing scheme is purely distributed, local and asynchronous. Each satellite acts independently, apportioning excess load to deficient neighbors. The load balancing information of each objective satellite is limited to only the residual bandwidth information from satellites within its own domain. Each of the satellites in network manages ithe residual bandwidth for each direction of adjacent ISLs, informs its near-neighbors of their residual bandwidth levels, and updates this information periodically.
3. Traffiic Load Balancing Process The necessity of call migration to alternative path is determine'd by the residual bandwidth of objective links. If the residual bandwidth of an objective link is less than a prespecifiled threshold, Rrhrrshold , it proceeds to apportion excessive loads to the deficient alternative paths.
r,"
'
ps,+,,,
(3)
Rrhresho/d
where r," is the residual bandwidth of the objective path
P(sz,~-lJi,t+1) ri+l,,
p . The frequency of the call migration is determined by the value of Rrhrrshold. To prevent QoS degradation due
P(S,,;-1 .S,,,+I)
rs,_,,,
to frequent call migration, it would be better to keep the value of
8 - 1 4
a
has
= 28 >
residual
ps,-,,;( q - 1 9
and
bandwidth Si,i+l)
= 22. Thus, the excess bandwidth
of has is
,+I)
= 10, and the amount of bandwidth apportioned to each alternative path is, respectively, p:,: ,-I J, , + I ) = 8 and g[('l )-I ,'! ( + I ) = 2 . The resultant
RthreJhold small.
The call migration phase is started by first computing the average residual bandwidth of the objective path and its alternative paths for each objective path. The average residual bandwidth of the domain for an objective path p , according to the following formula,
1-1
residual P(',
rs,,
(-1
2 s )
bandwidth ,+I
of
I
the
objective
path
is
1 = 20 ,
K
1 E [ R i ]= -
