Automatic Flow Distribution and Management in Heterogeneous Networks Yi Sun, Yuming Ge, Shan Lu
Eryk Dutkiewicz
Jihua Zhou
Institute of Computing Technology, P.R China {sunyi, geyuming, lushan}@ict.ac.cn
Macquarie University, Australia
[email protected]
Chongqing Jinmei Communication Ltd. P.R.China
[email protected]
Abstract—With the development of heterogeneous networks, multimode terminals are becoming more and more popular. However, when there are several different kinds of sessions requiring transmission simultaneously, how to distribute these sessions among the available access networks according to the different features of the flows and the current link conditions of the candidate networks is a new challenge. In this paper, we propose a new solution to the flow distribution problem for multimode terminals. Our proposal, Automatic Flow Distribution (AFD), includes a network selection algorithm located at the terminals and an admission control algorithm located at the access points of the networks. Consequently, the terminals and the networks can cooperate with each other and realize automatic flow distribution among the different available access networks. We utilize the notion of priority, ensuring that the more important sessions have preferential use of the network resources. In addition, in order not to excessively deteriorate the transmission performance of the lower priority flows, a probabilistic suspension scheme is introduced. Finally, the AFD method utilizes the concept of “entropy” to automatically compute the weights of different attributes which influence the flow distribution decision making, thus avoiding the users’ difficulty to specify the weights manually. T
T
networks manually, their only concern being whether the sessions can achieve better QoS, for a lower overall cost in terms of money and energy. Therefore, a management scheme is required, which can automatically and reasonably make the decisions of the flow distribution among the different available access networks and ensure that the more important sessions have preferential use of the network resources. In this paper, we propose a new solution to the flow distribution problem in heterogeneous networks. Adopting the same assumption as other similar proposals, we suppose that the terminals use multiple transceivers, each doing a different job simultaneously. Our solution includes a network selection algorithm located at the terminals as well as an admission control algorithm located at the access points of the networks. The organization of the paper is as follows. Section 2 introduces related research work. Section 3 describes our proposal in detail. Section 4 presents our simulation results showing the performance gains made possible by our solution and Section 5 summarizes the paper and presents our conclusions. II.
I.
INTRODUCTION
In recent years, with the development of wireless communication technology and the increase in user demand for seamless access to mobile networks, a series of different kinds of wireless access networks have emerged. Currently, one issue on the composition of the Next Generation Mobile Network (NGMN) that has been agreed on is that NGMN is a multiple access mode integrated all-IP based framework [1].
RELATED WORK
In [2] Chen and Yang pointed out that the future 4G network is a multiple access mode integrated framework, and divided the process of flow distribution on multimode terminals into three stages: network discovery, network selection and seamless handover. They then identified each attribute that would influence the flow distribution decision, and used these attributes to drive a weighted selection process to choose the best network.
Multimode mobile terminals enable users to enjoy convenient, fast and seamless access to heterogeneous networks. The utilization of multimode terminals relieves users of the burden of buying and carrying different types of terminals. Moreover, when there are several different kinds of sessions requiring transmission simultaneously, a multimode terminal can distribute these session flows among the available interfaces according to the different features of the flows, the current conditions of the candidate access networks, and the preference of the users, thereby guaranteeing the QoS for different types of sessions and reducing the communication monetary cost and power consumption.
Adamopoulou and Koutsorodi carried out a series of research work on flow distribution algorithms for multimode mobile terminals. In [3, 4], they proposed a flow distribution algorithm, which mainly made the decision based on the four attributes: the QoS requirement of the session flows, the network operator, the access technology type and the communication cost. Users were required to specify the weight for each of the attributes and then the objective function for the flow distribution decision making was derived based on the weighted sum of these attributes. The objective function is computed for every requested/running flow. The candidate network with the maximum value of the objective function was then selected as the final choice for that flow.
But unfortunately, in practice users have no interest in performing the task of flow distribution among different access
In [5] Isaksson and Fiedler utilized an AHP (Analytic Hierarchy Process) method [6] to solve the flow distribution
978-1-4244-4148-8/09/$25.00 ©2009 This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE "GLOBECOM" 2009 proceedings.
problem of multimode terminals. AHP is a general class of models which deal with decision making problems in the presence of a number of attributes. The authors constructed the hierarchical structure by breaking down the flow distribution problem into several decision elements, finally resulting in the ranking of the different candidate networks through the process of pair-wise comparison, weight estimation and consistency checking. The authors of [7] implemented the flow distribution scheme into a middleware platform—SALOME (Situation And Location aware MiddlEware). Compared with other methods, the scheme in SALOME considers more attributes to be involved in the flow distribution decision making, including communication cost, power consumption, cell coverage, security, reliability and bandwidth degradation. The paper also proposed a way to normalize all these different parameters, with the objective function being the weighted sum of all these normalized parameters. Summarizing the above proposals, two problems are still not yet properly solved. (1) Inability to distinguish sessions. The current proposals can not distinguish different types of sessions, and do not ensure that more important sessions have the preferential use of the network resources when the traffic intensity is high. (2) High requirements on the users. The above proposals all rely on users to specify the weights of the multiple decision attributes. However, most users are not familiar with the meanings of these attributes, and therefore it is hard for them to set the weights properly. Aiming at these two problems, we proposed a flow distribution algorithm called PAWES in [8]. PAWES distinguished different types of sessions, ensuring that the more important sessions have preferential use of the network resources. In addition, PAWES includes a Lagrange multiplier method to automatically determine the weights of different attributes. As a result, users are not required to specify these weights manually. III.
PROPOSED SCHEME
In this paper, we propose a new method, Automatic Flow Distribution (AFD) to handle the automatic flow distribution and management problem in heterogeneous networks. Compared with PAWES, AFD has the following advantages. Firstly, it includes a network selection algorithm at the multimode terminals as well as an admission control algorithm at the access points of the networks. Thus, with the cooperation of the terminal and the network, AFD can achieve higher performance than that of PAWES. Secondly, in PAWES we designed an automatic weight generation mechanism to relieve users of the burden of specifying the weights of different decision attributes manually. However, the weight generation mechanism in PAWES utilized Lagrange multiplier theory and imported a lot of complex operations such as partial differential operations. In AFD, we also propose an automatic weight generation mechanism, which is based on entropy theory and is much less computationally intensive. Thirdly, PAWES requires multimode terminals having the knowledge of the current available bandwidths of different access networks. However, it is difficult for the terminals to get the available bandwidth information in a timely manner, because this bandwidth depends on the number of terminals that are
sharing this bandwidth. In contrast, AFD moves the task of bandwidth checking to the access points of the candidate networks, making it easier for implementation. Next, we demonstrate the AFD method in detail. A. Architecture Model of the Multimode Teminal The architecture model of the multimode terminal to support AFD is illustrated in Fig. 1. In our model, there are four functional modules. z
Monitor Monitors are responsible for inspecting and measuring the current conditions of the access networks as well as the devices. They report their collected information (e.g. the flow variations at the terminal, the delay jitter and packet loss of the networks, the usage of different radio interfaces, the remaining power energy) to the Decision Engine, so that the Decision Engine can make reasonable and timely decisions. z
Rule Database Rule Database maintains the rules to help the Decision Engine to make flow distribution decisions. The rules include the priorities and specific QoS requirements of different types of sessions, the preferences of users and the warning information of the devices. The rules in the database can be updated either through a simple GUI interface or by receiving broadcast management messages from the network. z
Decision Engine Decision Engine is the most important module in our architecture model. It locates in the network layer of the device, and is mainly used to make the flow distribution decisions according to the data from the Monitors and rules in the Rule Database. It collects necessary information by two ways: from the Monitors on the terminal for flow usage, radio interface usage, signal strength, delay and packet loss etc. or from the periodical broadcast messages of the Access Points for the available network conditions. In addition, the QoS requirements for different types of flows are stored in the Rule Database. Thereby, combined with the current link conditions, qualified networks for different flows can be selected by the Decision Engine. z
Decision Execution Decision Execution module initiates the signaling message exchanging process for the flow transmissions after it receives the decisions from the Decision Engine. In addition, when the flows need to hand over between different access networks, the Decision Execution module is responsible for transmitting the handover signaling messages. B. Network Selection Algorithm at the Multimode Terminal Decision Engine is the key module in our architecture model, and it contains a network selection algorithm. Let X={x1, x2, x3…xn} be the set of session flows at the multimode terminals. Let Y={y1, y2, y3…ym} be the set of access networks currently available, and each access network yj is described using a set of k attributes yj={yj,1, yj,2, yj,3…yj,k}. All these attributes (eg. dealy, packet loss, communication cost) will be considered in the network selection decision making for the
978-1-4244-4148-8/09/$25.00 ©2009 This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE "GLOBECOM" 2009 proceedings.
attributes form an m×k matrix B=(bj,l)m×k Then the entropy of the lth attribute Hl is defined as: m
H l = −λ ∑ f j ,l ln f j ,l
l = 1, 2,3...k
(2)
j =1
where
f j ,l =
Fig.1. Architecture model of the multimode terminal
flows. yj,l represents the lth attribute of the jth available network. Let W={w1, w2, w3…wk} be the vector of weights corresponding to the different attributes. Then the network selection problem for the flows at the multimode terminals can be presented as finding a mapping between set X and set Y, and satisfy:
yoptimal _ i = argmax N j j =1...m
for each xi ∈ X
(1)
⎧ ⎪∑wl = 1 s.t. ⎨ l =1 ⎪x ⎩ i,delay ≥ y j ,delay , xi, jitter ≥ y j , jitter , xi,loss ≥ y j ,loss
b j ,l
λ=
m
∑b
1 ln m
(3)
j ,l
j =1
Finally, the entropy weight of the lth attribute wl can be computed by Equation 4. W={w1, w2, w3…wk} forms the weight vector for the different decision attributes.
wl =
1 − Hl k
n − ∑ Hi
l = 1, 2, 3...k
(4)
i =1
k
(1.1) (1.2)
The objective function (1) aims at finding the most appropriate network yoptimal_i in the current available network set Y for each session flow xi in the set X. Nj is a variable measuring the distance between the selected network to the theoretically optimum network and the definition of it is given later. A large value of Nj means being very close to the optimum solution. Constraint (1.2) means that the selected network must meet the QoS requirements of the session flow (eg. xi,delay is the delay requirement of the session xi,; yj,delay is the delay of the network yj). Here, we adopt the definition of QoS in ITU [9], using delay, jitter and packet loss to measure the QoS of the sessions. The network selection for each flow at the multimode terminals should consider multiple attributes such as network conditions, communication cost and QoS of the flow. In order to make the different types of attribute parameters comparable with each other, firstly according to their numerical ranges we classify and normalize these parameters using the method described in reference [8]. Another preparation work is to reasonably determine the weights of these different attributes. Existing proposals by other researchers all rely on users to specify the weights manually. However, most of the users lack the related background knowledge, thus it is very difficult for them to give the appropriate weights of multiple attributes. In this paper, we demonstrate an innovative mechanism to automatically compute the weights of different attributes. We utilize the concept of “entropy” [10] in information theory to determine the weights of different attributes. Entropy is a measurement of uncertainty and it can also be used to measure the amount of effective information contained in the data. Suppose that there are m candidate networks and each network has k attributes, thus the candidate networks and the
Based on the above equations, it can be concluded that the attribute weights determined by entropy in this paper have the following desirable features: 1) The entropy weight wl is inversely proportional to the entropy of the network attribute, and satisfies 0≤wl≤1, k
∑w
l
= 1.
l =1
2)
If all the candidate networks have the same value in one attribute, then the corresponding entropy weight for this attribute reaches 0. It means that there is no difference in these attribute values among all the candidate networks, thereby this attribute supplies little useful information and can be discarded. 3) If there exists a big difference in the values for a specific attribute among all the candidate networks, then the corresponding entropy weight of this attribute is large. This means that the attribute supplies a lot of useful information and should be paid more attention to. Finally, the weight generated by entropy can multiply an adjusting coefficient to adapt the different networks’ demand. The coefficients for different networks are stored in the Rule Database. The pseudo-code of the network selection algorithm at the multimode terminals is illustrated in Fig. 2. It can be concluded that the time complexity of the network selection algorithm in AFD is O(nm2k2), better than that in PAWES [8] 2 2 2 O(n m k ), where n is the number of flows, m is the number of candidate networks and k is the number of the network attributes. The network selection algorithm in AFD utilizes the notion of “priority”, whereby each session is assigned a priority according to some heuristic rules: realtime sessions have higher priorities than non-realtime sessions; interactive sessions have higher priorities than non-interactive sessions; handover sessions have higher priorities than new sessions.
978-1-4244-4148-8/09/$25.00 ©2009 This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE "GLOBECOM" 2009 proceedings.
Procedure Initialization ( ) //initialize the algorithm { determine the priorities of different types of sessions; queue the sessions in descending order of priorities; } Procedure Distribution ( ) //distribute the flows to the appropriate access networks {
while (the session queue is not empty){ get and delete the first session flow from the queue; calculate the feasible solution set for the session flow according to its QoS requirements; if (the feasible solution set is not empty){
derive the decision matrix C=(cj,l)m×k, where cj,l=wl× bj,l and wl th is the weight of the l attribute. Next, we construct the + + + theoretically best solution C and worst solution C , C ={c1 , + + + c2 , c3 …ck }, C ={c1 , c2 , c3 …ck }.
cl+ = max{c j ,l | j = 1, 2...m} = wl max{b j ,l | j = 1, 2...m} = wl bl+ cl− = min{c j ,l | j = 1, 2...m} = wl min{b j ,l | j = 1, 2...m} = wl bl−
For each feasible candidate network j, we compute its + distance to the theoretically best solution dj and its distance to the theoretically worst solution dj using Equation 5 and 6. Finally, the variable of adjacency Nj can be computed using Equation 7. k k
d +j = ∑ (c j ,l − cl+ ) 2 = ∑ wl2 (b j ,l − bl+ ) 2 j = 1, 2,3...m (5) l =1
l =1
k
k
l =1
l =1
d −j = ∑ (c j ,l − cl− ) 2 = ∑ wl2 (b j ,l − bl− ) 2 j = 1, 2,3...m (6)
//normalize the attribute parameters Normalization ( ); //compute the weights of the different attributes Auto_Weight_Generation ( ); //rank the order of the different candidate networks TOPSIS( ); } else Report_Error ( ); } } Fig.2. Pseudo-code of the network selection algorithm in AFD
transmission at the multimode terminal and the flows are queued in descending order of priority, thus the items at the head of the queue are more important and should be dealt with first. For each flow, the decision process is divided into 2 stages. In the first stage, the networks which can not meet the QoS requirements of the flow (Constraint 1.2 in Equation 1) are removed from consideration, and all the remaining candidate networks are selected to form the feasible solution set for the flow. In the second stage, we use the auto weight generation mechanism described above to compute the weights of the multiple attributes, and utilize the TOPSIS method to rank the different candidate networks for the flow. TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) [11] is a traditional method to solve the multiple attribute decision problem. The basic idea is to construct the theoretically best and worst solutions for the problem, respectively, and then try to select the solution which is closest to the theoretically best solution and farthest from the theoretically worst solution. In this paper, we utilize TOPSIS to rank the different feasible access networks for each flow at the multimode terminal. Its main procedures are as follows. Suppose there are m candidate networks in the feasible solution set and each network has k attributes. Therefore, the candidate networks and the attributes form an m×k matrix B=(bj,l)m×k. We then multiply the matrix B and the attribute weight vector W to
Nj =
d −j d +j + d −j
j = 1, 2,3...m (7)
As can be seen from Equation 7, the variable of adjacency Nj indicates how close the selected network is to the theoretically best solution and how far it is from the theoretically worst solution. Thus, Nj is the variable in the objective function (Equation 1). The larger the value of Nj, the better the solution. For each flow we should rank the different candidate access networks in descending order of the value of Nj and try our best to distribute the flow to the access network with a larger value of Nj. C. Admission Control Algorithm at the AP of the Network AFD also includes an admission control algorithm at the Access Points (AP) of the networks. The pseudo-code of the admission control algorithm is illustrated in Fig. 3. As illustrated in Fig. 3, the flow admission control algorithm also relies on the concept of priority. For each new flow, the algorithm first checks whether the network has enough available bandwidth to accommodate this new flow. If it does, the new flow is admitted into the network. Otherwise, the algorithm verifies whether it can admit this new flow through suspending some lower priority non-realtime flows. In order to make the high priority flows having preferential use of the network resources and simultaneously not excessively deteriorating the transmission performance of the lower priority flows, the algorithm only stops the lower priority flows and admits the new higher priority flow with a given probability ρ. The exact value of ρ can be dynamically adjusted according to the current conditions of the network. D. The Procedures of the AFD Method We take a simple example to illustrate the main procedures of the AFD method (shown in Fig. 4). The Monitors on the terminal collect the necessary information and send it to the Decision Engine. The Decision Engine runs the network selection algorithm, and gets an
978-1-4244-4148-8/09/$25.00 ©2009 This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE "GLOBECOM" 2009 proceedings.
Procedure Admission_Control ( ) // main function on the AP { while (1) { wait for a new flow admission request; bandwidth = extract the bandwidth requirement of the new flow; priority = extract the priority of the new flow; Admission_Check (priority, bandwidth); }
Fig.4. Procedures of the AFD method
} Procedure Admission _Check (priority, bandwidth) {
if (the current available bandwidth >= bandwidth ){ admit the flow; } else { bandwidth_occupation = compute the sum of the bandwidth occupied by lower priority non-realtime flows; if (bandwidth
