Policy Based SLA for Wireless Ad Hoc Networks S.Rajeev PhD Asst.Professor Department of Electronics & Communication Engineering PSG College of Technology Peelamedu, Coimbatore, Tamil Nadu [email protected]

S.N. Sivanandam PhD Professor & Head Department of Computer Science & Engineering PSG College of Technology Peelamedu, Coimbatore, Tamil Nadu [email protected]

K.V.Sreenaath Student Department of Information Technology PSG College of Technology Peelamedu, Coimbatore, Tamil Nadu [email protected]

A.S.Bharathi Manivannan Student Department of Electrical & Electronics Engineering PSG College of Technology Peelamedu, Coimbatore, Tamil Nadu [email protected]

Abstract An Ad Hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any centralized administration or standard support services. In such an environment, it may be necessary for one mobile host to enlist the aid of others in forwarding a packet to its destination, due to the limited propagation range of each mobile host’s wireless transmissions. Since many mobile hosts may be within transmission range of each other, there may be multiple routes for a packet to reach a destination. Thus, there is choice of selecting the best possible service (given many routes) based on many performance metrics constraints. A Service Level Agreement (SLA) (Fankhauser et al., 1999) is a service contract between a customer and a service provider that specifies the forwarding service a customer should receive. SLA is a very static procedure, usually performed manually which is a major drawback as Ad Hoc networks are very dynamic (wireless mobile). Thus there is a compelling need to automate the SLA trading. Previous works fail to address the issue of Policy Based SLA for routing given multiple paths is available. In this paper a mathematical model and a SLA trading algorithm is built as to support decisions on SLA for routing of packets in Ad Hoc networks. The Framework uses a Policy Server to take decisions on the service trading issues. The decision on what to choose and when to choose is written as policies. The policy server takes the decisions by taking appropriate policies from the directory server using LDAP (Wahl et al., 1997) and then the SLA Agent (SLAA) agrees for the service. Keywords: Ad Hoc Networks, Service Level Agreement, Policy Based Networks, Quality of Service, Directory Enabled Networks, Policy Specifications.

1. INTRODUCTION ireless Ad Hoc networks (Ilyas, 2002; Perkins 2000; Toh 2001) are autonomous networks operating either in isolation or as “stub networks” connecting to a fixed infrastructure. Depending on the nodes’ geographical positions, their transceiver coverage patterns, transmission power levels, and cochannel interference levels, a network can be formed and unformed on the fly. Ad Hoc networks

W

have found a growing number of applications: wearable computing, disaster management/relief and other emergency operations, rapidly deployable military battle-site networks, and sensor fields, to name a few. The main characteristics of Ad Hoc networks are: Dynamic topological changes: Nodes are free to move about arbitrarily. Thus, the network topology may change randomly and rapidly over unpredictable times (Chen et al., 1997)

Bandwidth constraints: Wireless links have significantly lower capacity than wired links (Phanse, 2003). Due to the effects such as multiple accesses, multipath fading, noise, and signal interference, the capacity of a wireless link can be degraded over time and the effective throughput may be less than the radio’s maximum transmission capacity. Multi-hop communications: Due to signal propagation characteristics of wireless transceivers, Ad Hoc networks require the support of multi-hop communications; that is, mobile nodes that cannot reach the destination node directly will need to relay their messages through other nodes (Chen et al., 1997). Limited security: Mobile wireless networks are generally more vulnerable to security threats than wired networks (Zhou and Haas, 1999). The increased possibility of eavesdropping, spoofing, and Denial-of-Service (DoS) attacks should be carefully considered when an Ad Hoc wireless network system is designed. Energy constrained nodes: Mobile nodes rely on batteries for proper operation. As an Ad Hoc network consists of several nodes, depletion of batteries in these nodes will have a great influence on overall network performance. Therefore, one of the most important protocol design factors is related to device energy conservation. To support mobile computing in Ad Hoc wireless networks, a mobile host must be able to communicate with other mobile hosts, which may not lie within its radio transmission range. Therefore in order that one mobile host in the Ad Hoc network communicate with the other not lying in its transmission range some other hosts in its transmission range should route the packets from the source to the destination host. The conventional routing protocols used in wired networks cannot be effectively used in Ad Hoc networks (Johnson, 1994). Hence new routing mechanisms are suggested in which may be used for routing in Ad Hoc networks (Zhu and Corson, 2001). Routing issues in Ad Hoc networks are not

considered here and it is beyond the scope of this paper. Since many mobile hosts may be within transmission range of each other, there may be multiple routes for a packet to reach a destination. Therefore the source host should decide as to which route to send the packets to reach its destination. Obviously, the sending host has to decide upon the best optimal route before sending its packets towards the destination. Thus, there should be a service level agreement between the source mobile host and the host which routes the packets to the destination host. Moreover there are certain constraints based on the characteristics on the Ad Hoc network which play a major role in deciding which route is optimal, given there are more routes to reach the destination. The decision on selecting the best possible SLA, based on constraints can be performed by applying a mathematical model given in Section 2 and by applying the Policy based architecture described in Section 3. The rest of the paper is organized as follows: Section 2 describes the mathematical model for the Service level Agreement in Ad Hoc networks. Section 3 gives the Policy based framework for Ad Hoc networks. Policy Specification for SLA trading and Test results are given in Section 4 and Section V. 2. MATHEMATICAL MODEL A mathematical model considered for our framework uses Linear Programming and Simplex Method (Math world) to solve it. The following performance metrics that are crucial for effective SLA trading and choice of route are considered in our model. (a)Bandwidth, (b) Delay, (c) Demand, (d) Packet Loss, (e) Congestion, (f) Queuing Delay, (g) Throughput, (h) Buffer Capacity, (i) Battery Consumption, (j) Mobility Let,

Tij -Total

(maximum)

Bandwidth

(channel

capacity) available from host i to host j . U ij -Bandwidth being used for traffic flow between host i to host j at instant ’ t ’ Rij -Reserved bandwidth from host i to host j . Hence the bandwidth that can be leased to other hosts G ij is given by Gij = Tij − U ij − Rij Let the required bandwidth i.e. the bandwidth consumed by the host k to reach host j through host i be RBij . And, Dij -Delay from host i to host j Cij -Cost of reaching host j through host i . Fij -Fraction of bandwidth bought from host i to

reach host j . The objective here is to minimize the cost of reaching host j through other hosts. (1) Minimise ∑ Fij C ij

constants for the performance metrics can also be set dynamically and SLA negotiated accordingly. Buffer Capacity Bij should not be less than a bearable value given by the constant N=Number of packets that can be buffered (3) Bij ≥ N The time delay D should be set to a limit expressed by a constant ‘ p1 ’ as expected by the ‘ISP k’ which needs the service. The constant ‘ p1 ’ is arrived as derived below (Time Delay) p1 = Propagation Time + Transmission Time + Queuing Delay (+ Setup Time) Propagation Time: Time for signal to travel length of network = Distance/Speed of light Transmission Time = Size/Bandwidth Therefore, we have (4) Dij ≤ p1

i, j

Queuing Delay Qij should not exceed an allowable As stated earlier, Cij in the above equation represents the cost host i charges to reach host j through host i . 2.1 Constraints There are a set of constraints that define the model. The first constraint is that the demand for bandwidth to reach host j through host i , Deij should be less than or equal to the amount of bandwidth host i is ready to offer for cost to reach host j , G ij , DE ij ≤ ∑ Gij

(2)

i, j

The following constraints check if the Service performance metrics in the service offered by the host i to reach host j fall within the predetermined and pre-calculated boundaries as expected by the host k which needs the service. These boundary

limit ‘ p 2 ’expressed as (Queuing Delay) D p 2 = × ( N − 1) 2 where, D -the time delay, N is the Buffer Capacity Qij ≤ p 2

(5)

The Packet Loss Pij for the service provided should not exceed a maximum limit set as constant‘ p 3 ’ and Congestion in the channel offered for service Coij should also be within the acceptable limits represented by the constant ‘ p 4 ’ both of which are arrived as shown below (Biaz and Vaidya, 1999) Tmin = Minimum Inter-Arrival Time observed by the receiver P0 : Out of order packet Pi : Last in-sequence packet received before P0

T g : Time between arrival of packets P0 and Pi .

n : Packets missing between Pi and P0 If ( n + 1)Tmin ≤ T g < ( n + 2)Tmin then n missing packets are lost due to transmission errors and hence ‘ p 3 ’=’ n ’ and (6) Pij ≤ p3

The Battery Consumption BC ij for the offered service should be within the boundary constant ‘ p 7 ’, BC ij ≤ p 7

(10)

The Mobility Factor M ij which gives the idea of Else n missing packets are assumed to be lost due to congestion and hence ‘ p 4 ’=’ n ’ and Co ij ≤ p 4

(7)

Throughput TH ij should be greater than or equal to ‘ p 5 ’ which is given by (Throughput)

p5 = {MSS / RTT } × C /( p ) where MSS - Maximum Segment size in bytes. Typically 1460 bytes RTT -Round Trip Time in seconds, measured by TCP. p - Packet loss C - Constant assumed to be 1. (8) TH ij ≥ p5 The jitter J ij should be within the acceptable limit

how long the host i will be in the transmission range of host j for which packets need to be routed should not smaller than a particular constant represented by ‘ p8 ’ , (11) M ij ≥ p8 This mobility factor M ij plays a crucial role in Ad Hoc networks because the hosts are all mobile. It may be minutes or in any preferred time unit as the case may be. We generally assume that a mobile which has joined the Ad Hoc has more probability of staying in the network than the ones which came earlier than that. But the exact nature of the mobility of a host can be predicted only based on past performances of the mobile. 2.2 Non-Negativity Constraints The following are the non-negativity constraints applied in the model : Cost Cij should always be positive, C ij ≥ 0

(12)

‘ p 6 ’ given by (Schulzrinne et al., 1996) Fraction of bandwidth bought from host i to reach host j , Fij should also be positive, (13) Fij ≥ 0

p6 = p6 + ( D(i − 1, i ) − p6) / 16 given D (i, j ) = ( R j − S j ) − ( Ri − S i ) where S i , S j are sender timestamps for packets i, j and

The bandwidth that can be offered for cost to other hosts by host i should be positive,

Ri , R j are receiver timestamps for packets i, j .

Therefore J ij ≤ p 6

(9)

Gij ≥ 0

(14)

Given the objective i.e. to minimize the agreement cost along with the performance metrics constraints, the proposed linear programming model solved using simplex method suffices for arriving at a suitable agreement for service with other hosts. There are always cases that the above model will fetch more than one solution if there exists. Hence in such cases the decision of choosing the most appropriate of the available solutions should be taken which is described in the next section. 3. POLICY BASED FRAMEWORK FOR SLA IN AD HOC NETWORKS The growing interest in the field of Policy-Based Networking (Lewis, 1996) to monitor and control the access rights of resources in large distributed systems, and in areas like Quality of Service (QoS), Wireless Networks (Rajeev et al., 2003, Rajeev et al., 2004), Network Security, SLA and IP address allocation etc., have identified us to use policy based approaches to SLAs, as SLAs are normally setup manually (Ponnappan et al., 2002). Thus in Ad Hoc networks, the Service Level Agreement for the services between the hosts can be made dynamic as the network is very mobile in nature for which SLAs cannot be set up manually. The mathematical model proposed above will give the hosts in the Ad Hoc networks the dynamicity to set up SLAs . In order to achieve this dynamicity in setting up SLAs each host has to deploy the mathematical model proposed above so that it will be able to choose the agreements which satisfy the constraints given. But it may also be the case that a host may come up with more than one possible agreement satisfying the constraints with the proposed model. Then in such cases the decision on which agreement to choose among the possible agreements is done by means of Policy Based decisions. The architecture for the Policy Based SLAs in Ad Hoc networks is given in the following section. The architecture is designed where at least one host has connectivity with the wired network.

3.1 Architectural Framework of the Policy Based SLA in Ad Hoc networks In order that a decision is taken to choose an agreement among the available options after applying the mathematical model we use the policy based approach. In the architecture shown in Fig.1 policy server is placed in the wired network. Polices are stored in the Directory server. The Ad Hoc ‘host1’ is within the vicinity of both ‘host2’ and ‘host3’. The ‘host4’ is not within the transmission region of ‘host1’. So when ‘host1’ wants to send a packet to ‘host4’, intermediatory hosts, ’host2’ and ‘host3’ help ‘host1’ the connection establishment. Now ‘host1’ uses the mathematical model proposed in Section 2. Assuming that both the host services satisfy the constraints of ‘host1’ it is for the ‘host1’ to choose a service level agreement among the two. In this case since the ‘host1’ is connected to a base station which in turn is connected to the wired network having the policy server, ‘host1’ can query the policy server through the base station and then the leaf access router and edge router. For simplicity we have shown the policy server being connected to the base station through only few hops. But in practice it may be many hops away from it. Once the request reaches the policy server, it takes appropriate policies from the directory server through LDAP. The policy server also communicates with other relevant policy servers such as Time Servers, Certificate Servers and Authentication, Authorization and Accounting (AAA) servers and validates the host providing service by means of certificates and AAA. The policy server takes the decision on whether the host providing the service is an authenticated one and his services are authorized with accountability and certificates. Then the policy server based on the higher level polices stored in the directory server chooses an agreement among the available agreements. The decision on choosing an agreement among the available ones may be done giving more weightage to those performance metrics which affects the

overall performance the most. Over a period of time, the history of the hosts providing the service will be stored; solutions based on a neural network model may be used for finding an optimal solution.

MAX_ ALLOWED_C

MAX_ ALLOWED _BC MIN_ REQUIRED_MF MAX_ ALLOWED_COST

Fig 1.Policy Based Approach through Wired Network Once the policy server chooses an agreement it sends it reply back to ‘host1’ which agrees for the service with the appropriate host. 4. SLA Trading Algorithm Table 1: Parameters for SLA Algorithm UPDATE_PERIOD volume() send bid() accept bid() known dests MIN_REQUIREDBW MAX_ALLOWED_DELAY MAX_ ALLOWED_PL MIN_ REQUIRED_BC MAX_ ALLOWED_QD

MIN_ REQUIRED_TP

MAX_ ALLOWED_JI

Time for updation volume function for an SLA object(time × bandwidth) sends an offered SLA to peer sends an accept message reachability list Minimum required bandwidth expressed in Mbps or in Kbps Maximum allowed delay expressed in unit of time such as seconds or in milliseconds Maximum allowed packet loss expressed as numeral representing the no. of packets Minimum required buffer capacity expressed as numeral repressing the number of packets Maximum allowed queuing

delay expressed in unit of time such as seconds or in milliseconds Minimum required throughput expressed as numeral with each numeral representing throughput in the network Maximum allowed jitter expressed as numeral with each numeral representing jitter severity in the network Maximum allowed congestion factor expressed as numeral with each numeral representing congestion severity in the network Maximum allowed Battery Consumption expresses in terms of mw or in watts Minimum required mobility factor expressed in units of time such as minutes or hours Maximum allowed cost expressed in unit of currency such as rupees or in $

Table 2: SLA Trading Algorithm struct bid { Host_dest, // Destination Host bw, // bandwidth delay, packet_loss, buffer_capacity, queuing_delay, throughput, jitter, congestion, battery_consumption, mobility_factor, cost } process trading () { while (true) { for each d in known_Host_dests { /* buy bids */ if (bw_to_Host(d)>MIN_REQUIREDBW) and (delay_to_Host(d)MIN_ REQUIRED_BC) and (queuing_delay_to_Host(d)MIN_ REQUIRED_TP) and (jitter_to_Host(d)
(congestion_to_Host(d)MIN_MF) and (cost_to_Host(d)
Provisioning algorithm and Profitability Analysis algorithm given in (Savage et al., 1999), does not suit the framework as it takes into consideration very few performance metrics for trading. So provisioning algorithm and Profitability Analysis

algorithm with appropriate enhancements is proposed and given in Table 2. The constants used in Table 2 are given in Table 1 the values for which are derived as explained in section 2.We call the algorithm responsible for the determination of what resources are needed the provisioning algorithm. A passive provisioning algorithm does wait for requests from its customers to select which resources to buy. An active provisioning algorithm tries to forecast future needs. It will then buy resources in advance, before they become scarce. Buying in advanced may be based on statistical information (e.g. previous weeks usage by time of day) or on trend analysis. Once an SLA trader knows it needs to buy some resource from one of its peers, it will have to select one of the bids and buy it. The selection of the bid is made based on the bid’s value for the SLA trader and its price. For bids of equal value, if no special policy is applied, the bid with the lower price will be selected. The SLA trader will also have to evaluate if the selected bid is worth buying using a profitability analysis algorithm. This algorithm does evaluate if by buying that bid, money will be made through the selling of derived services. It is this algorithm which will also ensure that SLA traders won’t build service loops. Trading is done by the method ‘process trading()’ which finds the bid with the highest volume/cost ratio and finds out if that bid is profitable using the ‘is_profitable(bid)’ method and if found profitable accepts the bid using the ‘accept_bid(bid)’ method. Then for each neighbour (Hosts) if bid is not already sent then bid is sent for every UPDATE_PERIOD. The profitability is tested by comparing the bid price with the expected income. Bidding with the neighbouring hosts is done using the ‘make_bid()’ method. 5. POLICY SPECIFICATION FOR SLA TRADING The policy Specification for the SLA trading is given below. The policy specification takes into account the various performance metrics. The policy specification can be run either in the policy

server in the wired network or as a module in the policy service inside the host itself. // On a trading event the action trade sends a popup displaying that the trading is being analyzed to check if all the performance metrics constraints are satisfied with minimal cost based on the mathematical model proposed in section 2.

window.pack(); window.setSize(200,100); window.setLocation(100,50); window.setVisible(true); //the code for the simplex method to solve the Linear Programming model as given in section 2 should be added here }// end of method execute

Table 3: Policy Specification for SLA Trading

6. TEST RESULTS A simulation is performed using the QualNet Network Simulator (QualNet), and using simplex method to solve the linear program model given in the section 2, the SLA Trading algorithm given in section 4 and using the Policy specification given in section 5.

inst oblig/Policies/TradingPolicy { on trading (host_name,required_bandwidth,battery_consumption,Mob ility_factor, delay,battery_consumption,mobility_factor,s,congestion,que uing_delay,buffer_capacity,throughput); subject /PMAs/TradePMA; do trade(host_name,required_bandwidth,battery_consumption, Mobility_factor, delay,battery_consumption,mobility_factor,congestion,queu ing_delay,buffer_capacity, throughput); }

The rule /Policies/Trading Policy will invoke the action trade within the /PMAs/TradePMA’s engine, when the event trading is dispatched to the PMA from the trading event service. The corresponding java code which enforces this policy is given in Table 4. Table 4: Java code for the policy Specification for SLA Trading public void execute(LinkedList params) throws Exception { // parameter0: The string representing the trade that will be considered // For debugging: if (DEBUG) { System.out.println("*******"); System.out.println("Trading the offer of : "+ (String) params.get(0)); } // Pop up the action window JFrame window = new JFrame(); window.setTitle("Trading Console"); JLabel traders = new JLabel("Trader: "+ (String) params.get(0)); JPanel mainPanel = new JPanel(); mainPanel.add("Center",Trader); window.setContentPane(mainPanel);

The test environment has four Ad Hoc hosts from ‘host1’ to ‘host4’ as shown in Fig.1.The total bandwidth, used bandwidth, reserve bandwidth, battery consumption, mobility factor and other performance metrics of the hosts are tabulated below. In the simulation test environment ‘host1’ needs to communicate with ‘host4’ which is not in its transmission range. So both ‘host2’ and ‘host3’ offers the service to ‘host1’. Using the mathematical model proposed in section 2 ‘host1’ decides upon the suitable service among the offers suing the SLA Trading algorithm given in section 4 and the mathematical model given in section 2. Since only the service offered by ‘host2’ adheres to the performance metric constraints, ‘host1’ chooses the service offer of ‘host2’. All the simulation is done with respect to the packet flow from ‘host1’ to ‘host4’. The trade for the Service is decided by using the Simplex method to solve the Linear Programming model and SLA Trading algorithm given in section 4 by which a feasible solution is obtained. The performance constraints of the hosts (Host1- Host 3) are shown graphically from Fig.3 to Fig.6. The constraints imposed by the Host1 for the required service are shown in Fig.7 to Fig.9. According to the constraints given by Host1 for the required service the simplex method and SLA Trading algorithm discussed in section 2 and section 4 are

used and the best bid among the bids offered by the two hosts (Host 2 and Host 3) is selected. Since only the bid for the service offered by Host2 satisfy the constraints of Host 1, SLA between Host1 and Host 2 takes place. The process of solving the LP model by simplex method by adding slack variables is shown graphically in Fig.10 to Fig.12. As only the trade provided by the Host2 satisfies all the constraints with the objective of minimum cost the Service offered by Host2 is agreed upon for trade. From the performance metrics (Figs. 3to 6), and the constraints (Figs. 7, 8 and 9) on performance metrics, the objective of minimizing cost is arrived (Fig. 13). Thus an effective SLA is traded between Host1 and Host2 satisfying the constraints on the performance metrics which affects the service. Table 5: Performance Metrics and other parameters of the hosts Performance Host1 Host2 Host3 Metrics Total Bandwidth Allocated (Mbps)

3

Bandwidth used at instant (Mbps)

2

Reserve Bandwidth (Mbps) Remaining Bandwidth G ij (Mbps) Demand for Bandwidth to reach ‘host4’ (Mbps) Delay (x 10-3/sec)

6 2

5 1

Throughput (x 103 Bits/sec) Buffer Capacity (No. of Packets) Battery Consumption (mWh) Mobility Factorminutes

0

1

3

1

3

100

90

9

10

8

-

8

9

-

25

18

Table 6: Performance Metrics and other parameters of ‘host2’ and ‘host3’ Performance Metrics Host2 Host3 Delay (x 10-3/sec)

2.9

3.1

Packet Loss Factor

0.2

0.3

Congestion Factor

0.3

0.3

Queuing Delay (x 104 sec)

0.2

0.2

4.2

4.2

20

15

8

9

25

18

3.9

4.1

Fraction of Bandwidth that can be given Fij (Mbps)

1

1

2

6

Throughput (x 103 Bits/sec) Buffer Capacity (No. of Packets) Battery Consumption (mWh)

1

100

Mobility Factor (minutes) Jitter (x 10-4sec)

1

0

0

Cost Cij ($)

7

8

10

Packet Loss Factor

7

5

6

The process of solving the mathematical model by simplex method including the values of the slack variables is tabulated from Table 5 to Table 7.

Congestion Factor

30

20

25

Queuing Delay (x 10-4sec)

8

7

10

Table 7: Original and Final value of the Objective Objective

Original Value

Final Value

∑F G C ij

ij

ij

12

6

Ad Hoc Networks ”, PhD Thesis, Virginia Polytechnic Institute and State University, USA, Aug. 2003

4

2

[8]

of ‘host2’

i, j

($) Cost of ‘host2’ ($)

7. CONCLUSION A SLA Framework for Wireless Ad Hoc Networks is modeled using Linear Programming and using the SLA Trading algorithm. The performance metrics crucial for a fairly reasonable model include Bandwidth delay, demand, packet loss, congestion, queuing delay, throughput, buffer capacity, battery consumption, mobility. These models were simulated and tested. The results show significant understanding on the inclusion of SLAs for Wireless Ad Hoc networks, as it affects performance for QoS.

(Zhou and Haas, 1999) L. Zhou and Z. Haas, "Securing Ad Hoc networks," IEEE Network, Vol. 13, No. 6, pp. 24-30, Nov. 1999. (Johnson, 1994) David B. Johnson ,“ Routing in Ad Hoc Networks of Mobile Hosts”, Proc. IEEE Workshop on Mobile Computing Systems and Applications, Dec. 1994.

[9]

[10] (Zhu and Corson, 2001) Chenxi Zhu, M.

Scott Corson, “QoS routing for mobile Ad Hoc networks”, Proc. IEEE Infocom, Jun. 2001. [11] (Math

world) [12] (Lewis, 1996) L. Lewis, “Implementing

REFERENCES [1] (Fankhauser et al., 1999) Fankhauser .G, Schweikert .D, Plattner .B, “Service Level Agreement Trading for the Differentiated Services Architecture”. Swiss Federal Institute of Technology, Computer Engineering and Networks Lab, Technical Report No. 59. Nov. 1999. (Wahl et al., 1997) Wahl, M., Howes, T., and S. Kille, “Lightweight Directory Access Protocol (v3)”, RFC 2251, Dec. 1997. [2]

(Ilyas, 2002) M. Ilyas, ed., The Handbook of Ad Hoc Wireless Networks, CRC Press, December 2002. [3]

(Perkins 2000) C. Perkins, ed., Ad Hoc Networking, Addison-Wesley Publishing, December 2000. [4]

(Toh, 2001) C. K. Toh, Ad Hoc Mobile Wireless Networks: Protocols and Systems, Prentice Hall, December 2001. [5]

(Chen et al., 1997) T.W. Chen, J. T.C. Tsai, and M. Gerla, “QoS Routing Performance in Multihop, Multimedia, Wireless Networks”, Proc. IEEE ICUPC, Vol. 2, pp. 557--61, 1997

[6]

(Phanse, 2003) Kaustubh S. Phanse, “PolicyBased Quality of Service Management in Wireless [7]

Policy in Enterprise Networks,” IEEE Communications Magazine, vol. 34, no. 1, pp. 5055, January 1996. [13] (Rajeev et al., 2003) S. Rajeev, S.N.

Sivanandam, K. Duraivel, Santosh G. Rao, P. Pradeep “Policy Based Provisioning For Wireless Differentiated Services”, Proc. IEEE 12th Annual Symposium on Mobile Computing and Applications, Bangalore,Nov. 2003 [14] (Rajeev et al., 2004) S. Rajeev , S. N.

Sivanandam , Mothi V. Sabaresan, B. Anand, “Frequency Allocation and Priority Handling in Multi-Service Wireless Differentiated Networks”, International Journal of System Modeling and Simulation, Vol. 2, pp.26-29, Jan.2004. [15] (Ponnappan et al., 2002) Appan Ponnappan,

Lingjia Yang, Radhakrishna Pillai.R Peter Braun, “A Policy Based QoS Management System for the IntServ/DiffServ Based Internet”, Proc. Third International Workshop on Policies for Distributed Systems and Networks, 2002 [16] (QualNet)

networks.com>


[17] (Time Delay)

[20] (Throughput)
[18] (Queuing Delay) [19] (Biaz and Vaidya, 1999) Saad Biaz, Nitin H.Vaidya (1999), “Discriminating Congestion Losses from Wireless Losses using Inter-Arrival Times at the Receiver”, Proc. IEEE Symposium ASSET’99, USA

network/packet_loss_calculator/plc_model.html> [21] (Schulzrinne et al., 1996) H. Schulzrinne, S. Casner, R. Frederick, V. Jacobson (1996), “RTP: A Transport Protocol for Real-Time Applications”, RFC 1889, January. [22] (Savage et al., 1999) Stefan Savage, Tom Anderson, Amit Aggarwal, David Becker, Neal Cardwell, Andy Collins, Eric Hoffman, John Snell, Amin Vahdat, Geoff Voelker, John Zahorjan,1999

APPENDIX – I

Fig .3 Bandwidth Metrics

Fig. 4 Bandwidth Metrics of Host1

Fig 5.Performance Metrics-I

Fig.6 Performance Metrics-II

Fig 7. Maximum Value Constraints

Fig. 8 Constraints with Maximum Value

Fig.9 Minimum Value Constraints

Fig.10 Slack Values of Performance Metrics

Fig.11 Slack Values of Performance Metrics

Fig.12 Slack Values of Performance Metrics

Fig.13 Objective

Policy Based SLA for Wireless Ad Hoc Networks

propagation range of each mobile host's wireless transmissions. ... The Framework uses a Policy Server to take decisions on the service trading issues.

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Transmission Power Control in Wireless Ad Hoc Networks
Transmission Power Control in Wireless Ad Hoc. Networks: ..... performance (compared to TDMA and FDMA), CDMA has been chosen as the access technology of choice in cellular systems, including the recently adopted 3G systems.

An Exposure towards Neighbour Discovery in Wireless Ad Hoc Networks
An Exposure towards Neighbour Discovery in Wireless. Ad Hoc Networks. S. SRIKANTH1, D. BASWARAJ2. 1 M.Tech. Student, Computer Science & Engineering, CMR Institute of Technology, Hyderabad (India). 2 Associate Professor. Computer Science & Engineering

QoS routing in ad hoc wireless networks
show this improvement. Index Terms—Ad hoc wireless networks, code division multiple ...... degree in the Department of Computer and Infor- mation Science ...

Mitigating starvation in Wireless Ad hoc Networks: Multi ...
I6. The maximum interference in the worst case: Total_Int = 6 · PNthold . The noise power threshold PNthold : PNthold = PRXthold. 6 · SINRthold . Duc Dang et.

Routing in Ad-Hoc Networks
generate a significant amount of network control traffic when the topology of the network changes frequently. Lastly, packets can .... time, which happens very often in radio networks due to collisions or other transmission problems. In addition, OLS

Latency-Sensitive Power Control for Wireless Ad-hoc ...
must support applications such as multimedia streaming, video- conferencing, and surveillance, with strict latency constraints. In this paper, we ... Note also that our proposed protocol can be implemented on top of contention- based or ...

Stable Topology Control for Mobile Ad-Hoc Networks - IEEE Xplore
Abstract—Topology control is the problem of adjusting the transmission parameters, chiefly power, of nodes in a Mobile. Ad Hoc Network (MANET) to achieve a ...

A SINR-Based MAC Protocol for Wireless Ad Hoc ...
the Dept. of Computer Engineering, Kyung Hee University, Korea (e-mail: {dnmduc ... The minimum arc length between two interfering nodes is. πRNT /3.

Distributed QoS Guarantees for Realtime Traffic in Ad Hoc Networks
... on-demand multime- dia retrieval, require quality of service (QoS) guarantees .... outside interference, the wireless channel has a high packet loss rate and the ...

Routing Architecture for Vehicular Ad-Hoc Networks - Sites
applications of vehicular networks [6], also providing services with the possible link ... Figure 1 is the proposed architecture for VANETs. The routing protocols ...