TCP vs. UDP Performance Evaluation for CBR Traffic On Wireless ...

TCP vs. UDP Performance Evaluation for CBR Traffic On Wireless Multihop Networks S. Giannoulis, C. Antonopoulos, E. Topalis, A. Athanasopoulos, A. Prayati, S. Koubias Applied Electronics Laboratory, Department of Electrical & Computer Engineering University of Patras, Rio Campus, Greece (sgiannoulis, cantonop, topalis, athan, prayati, koubias)@ee.upatras.gr

Abstract1- Multimedia applications over wireless networks become more and more demanding with respect to Quality of Service (QoS) and power consumption. As network behavior is a very important factor affecting the overall system performance, the protocol stack needs to be studied regarding Quality of Service (QoS) management. Power saving is another feature that network functionality must provide as mobility yields for battery self-sufficiency. This paper discusses QoS and power management at the transport layer of the protocol stack. More precisely, TCP and UDP are studied as different transport layer alternatives and their performance is evaluated with respect to QoS (i.e. mean/instant throughput as well as max and mean delay) and power behavior under multimedia-like streaming conditions.

I. INTRODUCTION The successful addition of a stream to a loaded network depends on its characteristics like the network capacity, the load fluctuations, the bandwidth needs of the stream, and the medium access control. Mechanisms, like Differentiated services and Integrated services, exist to provide connection quality reservation but most guarantees are probabilistic, unless the used communication lines are dedicated to a number of known applications (companies). Best effort techniques adapt bandwidth needs to bandwidth availability, assuring for sufficiently low load a bandwidth use close to optimal. To guarantee a specific absolute quality of service (QoS), the load on the network needs to be known. The statement leads to the need for modeling network behaviour in order to perform optimization techniques with respect to QoS vs. power trade-offs. Multimedia applications are becoming more and more popular on the Internet and many of these applications have special requirements related to the handling of their data over the network. Protocols have been developed to supply these applications with a certain QoS characteristics, however these protocols have been

1 The work reported here was performed as part of the ongoing research

Program PYTHAGORAS II and funded by the European Social Fund (ESF), in particular by the Operational Program for Educational and Vocational Training II (EPEAEK II).

designed for the fixed Internet and are invalid in a mobile scenario. The integration of mobility management protocols and QoS protocols is of vital importance due to the growing market for mobile multimedia applications. A weak point when it comes to mobility is the amount of data lost during handoff, since the available mechanisms are not able to handle handover information on time and cannot guarantee QoS, leading to lost power consumption for their recovery. At the transport layer, when it comes to handling energy efficiency and unnecessary retransmissions, problems would rise if one or more intermediated links were error prone. To alleviate the problem, several approaches have been proposed. Protocol optimizations attempts are made for reducing the energy consumption of wireless LAN interfaces, based on the observation that, the transport protocol, which implements flow control to regulate the network traffic [1]. Network interface dynamic power management, reduces power by monitoring run-time parameters in the transport protocol, coarse-granularity idle periods. It has been shown that error correlation degrades energy performance of TCP [2]. Since then, several attempts have tried to improve the energy efficiency of TCP. TCP can be made aware of non-congestion-related losses, improving both performance and energy. Techniques used for this purpose are local retransmissions, split connections, and additional forward error correction. Power-aware TCP can also be used to make the sender transmit in a predictable manner [3]. By making the sender transmit data in bursts with sufficient separation to one another, the receiver is provided the opportunity to sleep in the idle periods. Tuning TCP’s parameters is the most straightforward way to improve its efficiency. The default parameters of TCP have been consciously designed to sacrifice throughput, in exchange for fair sharing of bandwidth on congested networks. One of the most critical parameters is certainly the TCP buffer size, and techniques for determining the optimal size for performance are reported in [4]. The TCP buffer size can also be dynamically adjusted to the connection and server characteristics through dynamic right-sizing [5] and buffer size allocation [6]. The energy savings obtained are over

and above those possible using MAC layer techniques, and the proposed techniques can result in beneficial energy vs. performance trade-offs. The dynamic right-sizing technique dynamically and automatically determines the best buffer size, and hence flow-control window size in TCP. The major benefits of dynamic right-sizing include improved memory and network performance. Buffer size allocation uses a local optimization scheme that dynamically adjusts the TCP send-buffer size to the connection and server characteristics and a global optimization scheme that divides a certain amount of buffer space among all active TCP connections. The main advantage of this scheme is that it enables to accurately estimate the minimum amount of buffers a connection needs in order to achieve maximum throughput. In the case, where the amount of buffer space needed by TCP connections is larger than the size of the server buffer pool, two allocation schemes for global optimization are used, namely Round Robin and Priority Queuing. When high-speed network communications come into play, TCP/IP packet processing within the sink nodes becomes a major bottleneck in delivering fast computing [8]. Another approach to this problem is to implem1ent the TCP/IP packet processing by custom hardware, namely TCP/IP Core [9]. In this paper, a performance evaluation of TCP vs. UDP is presented, handling network heterogeneity and QoS tenability. This work focuses on the study of critical transport layer parameters and their impact on QoS and power consumption of the overall system. In section 2, the network topology and system particularities are presented, while in section 3 simulation results are discussed. Finally, conclusions are reported in section 4. II. NETWORK TOPOLOGY AND APPLICATION SETUP Two different network topologies are considered for a thorough study of the transport layer behaviour regarding traffic handling, power management and QoS provisioning; a random topology of 49 wireless stations (WS) and a 7x7 grid topology, as illustrated in Fig. 1 and Fig. 2 respectively.

Figure 1. Random network topology

It is important to notice that nodes’ density in the random topology pattern, assures continuous connectivity between all WSs. Moreover, for the grid topology pattern, the transmission range of each node covers only the

Figure 2. Grid network topology

horizontal and vertical adjacent nodes. The arrows in Fig. 2 fully depict this assumption. The offered bandwidth over the wireless medium is 2Mbps. Two different data traffic patterns are considered during the simulations, involving 5 and 10 data flows respectively. The data traffic type is CBR over UDP and TCP. Furthermore, packet size is 512 bytes and transmission rate was set to be 300kbps for each flow separately. Table I illustrates the pairs of the sending and the receiving nodes for both traffic patterns. TABLE I. RECEIVING / SENDING NODE PAIRS

Traffic Pattern 5 flows

10 flows

Sending Node No 8 36 22 38 40 8 36 22 38 40 35 41 9 47 14

Receiving Node No 40 12 26 10 12 40 12 26 10 11 15 19 32 31 43

Simulation time for all simulations was 200 sec. Four different scenarios were taken into consideration, based on two data traffic a pattern of five and ten data flows on the two different topologies respectively. According to the first pattern, all CBR flows are active during the whole simulation (200sec). The latter data flow pattern deals with random on-off intervals for each CBR flow. The duration of each on-off interval is randomly chosen in the range of 1 to 10 sec. With the usage of this scenario realistic data traffic conditions are simulated. The metrics measured in order to examine and evaluate the performance of transport layer protocols are the following. First, the mean as well as the instant throughput of each flow regarding all traffic patterns. Second, the mean, maximum and minimum end-to-end delay of the network. Hence, the transport layer protocols

can be compared and evaluated. Finally, the power consumption for the wireless stations is examined. III.SIMULATION RESULTS Simulations results were processed with Excel and are shown in Fig.3 to Fig.10. They are grouped to show the network’s performance of the Grid and Random topology respectively. The mean instant throughput is presented in Fig.3. Two data traffic patterns were simulated; five and ten data flows respectively with steady and random on-off intervals over TCP and UDP. The 5-flow steady traffic pattern over UDP produces the best mean instant throughput, as UDP does not require acknowledgement. On the other hand, TCP has worse performance compared to UDP and hence poor mean throughput value.

Fig.5, illustrates the Mean Instant Throughput of random topology. The results are more or less similar to the grid patterns due to the fact that UDP requires no acknowledgement. Fig.6 displays the results derived from ns2, concerning the mean instant delay, regarding all TCP/UDP random patterns. Generally, UDP is significantly faster trading-off the loss of small fragments, imposing the need for a specific functionality guessing this loss at the receiver side. However, as shown in the Fig. 6, UDP patterns have higher delays. Throughput 66 61 56 51

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Results concerning the mean instant delay, regarding all grid topology patterns are presented in Fig. 4. UDP is significantly faster over TCP, but the downside is that sometimes small fragments can be lost, and thus a software-based method guessing losses is required at the receiving end. It should also be noted that the UDP patterns have higher delays.

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Figure 4. Mean Instant Delay of Grid topology for various traffic patterns

In Fig.7, the results derived from ns2 concerning the max and mean delay with 5-flow and 10-flow TCP/UDP patterns, for both steady and variable traffic patterns in grid topology, are illustrated. The max delays for the steady traffic pattern, over both TCP and UDP, are a lot shorter in 5-flow patterns than in 10-flow patterns. On the other hand, the divergence level of max delays in variable traffic patterns, over both TCP and UDP is shorter. Arithmetically speaking, the best performance is achieved, for both max and mean delay metrics, in the 5flow steady traffic pattern over TCP. Furthermore, the worst network performance is measured in the 10-flow

steady traffic pattern over UDP. UDP, unlike TCP, has no congestion mechanisms and no self-checking mechanism to ensure that data is received, or received in order. Thus, TCP provides more reliable connection-oriented delivery and is suitable for hard real time applications. Additionally, UDP is more appropriate for sending limited amounts of data per packet and it is usually better for gaming, voice conferencing, and other low-latency applications.

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Figure 10. UDP power consumption

UDP packets. The 5-flow steady pattern had the worst performance, due to the maximum average delay. The distribution of the power consumption for both UDP and TCP regarding the grid topology patterns is provided in Fig. 9 and 10 respectively. It is clear that grid wireless networks with the same network characteristics using the TCP dissipate bigger amount of energy from their nodes compared to those using UDP. In TCP, when a packet is dropped, but the next packet makes it through, the kernel will withhold that packet until the earlier packet can be resent.

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Figure 8. Max/Mean delay total statistics of random topology for various traffic patterns

Fig.8 displays the results derived from ns2 concerning the max and mean delay with 5-flow and 10-flow TCP/UDP patterns, for both steady and variable traffic patterns in random topology. As far as mean delays are concerned, TCP has better performance, nevertheless that the TCP 5-flow scenario had the highest delay. On the other hand, the mean delay is significant increased in S7

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Summarizing, in this paper, the transport layer behavior regarding TCP and UDP protocols was evaluated. Several simulation scenarios in ns2, over different network topologies and data flow patterns were carried out. QoS characteristics are valuated for low and heavy traffic in order to characterize throughput, delay and power consumption behavior of the above protocols. Simulation results show that TCP suffers on multihop wireless routes, managing to deliver minimum amount of packets on destination. Therefore, fewer data packets can be sent over the multihop wireless routes compared to UDP protocol. Furthermore, delay is short, because the transmit window of TCP was minimal. On the other hand, UDP achieved better results in throughput, although its mean delay was higher compared to TCP. The reason UDP is faster than TCP is because there is no form of flow control or error correction which also explains the fact that delay over UDP is higher compared to TCP. Finally, as power is concerned, TCP is a more power consuming protocol than UDP, due to the complexity of TCP’s structure, i.e. acknowledgment packets must be send, as well as packet retransmissions are forced.

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