Impact of Scheduling and Dropping Policies on the ...

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

Impact of Scheduling and Dropping Policies on the Performance of Vehicular Delay-Tolerant Networks João A. Dias1, João N. Isento1, Vasco N. G. J. Soares1,2, and Joel J. P. C. Rodrigues1 1 Instituto de Telecomunicações, University of Beira Interior, Portugal 2 Superior School of Technology, Polytechnic Institute of Castelo Branco, Portugal [email protected], [email protected], [email protected], [email protected] Abstract—— Vehicular Delay-Tolerant Networks (VDTNs) are a disruptive network architecture based on delay-tolerant network paradigm, gathering contributions from opportunistic and cooperative networks, and optical burst switching paradigm. VDTNs assume out-of-band signaling and handle non-real time applications with a low cost network infrastructure. In VDTNs, vehicles are opportunistically exploited to carry data between terminal nodes, enabling network connectivity under unreliable conditions with unstable links and where a contemporaneous end-to-end path may not exist. To address this problem VDTN combines routing schemes that replicate bundles at contact opportunities, with long-term bundle storage. However, this combination increases the resources consumption (e.g., bandwidth, storage) and may affect the performance of the entire network. To improve network performance different scheduling and dropping policies can be used. This paper studies the impact of different scheduling and dropping policies on the performance of a VDTN laboratory testbed using Epidemic and Spray and Wait (binary variant) routing schemes. It was shown that network performance increases, in terms of delivery ratio and delivery delay, when these scheduling and dropping policies are based on the bundle lifetime criteria. Keywords- Vehicular Delay-Tolerant Networks; Scheduling Policies; Dropping Policies; Performance Analysis; Testbed I.

INTRODUCTION

Delay-tolerant networks (DTNs) [1] were initially proposed to deal with interplanetary connectivity, and later extended to terrestrial environments. It enables communications in environments characterized by sparse and intermittent connectivity, long or variable delay, asymmetric data rate, high latency, and even no end-to-end connectivity. This architecture implements a store-carry-and-forward paradigm where a source node originates a message (i.e., a bundle) and stores it while a contact is not available. When a source node is in contact with an intermediate node, a message is forwarded if this intermediate node is thought to be more close to the destination node. This message is stored in the intermediate node that carries it until a new contact opportunity. This process is repeated and the message will be relayed hop by hop until the destination [2]. DTN concept has been extended to vehicular networks, an application scenario where vehicles provide a low cost message relaying service in environments where the telecommunications infrastructure is not available. Vehicular

networks are an example of opportunistic networks where vehicles move around the network communicating with each network nodes in order to collect messages from source nodes and deliver them to sink nodes. Examples of some potential applications for these networks include the notification of traffic conditions (unexpected jams), free parking spots, advertisements [3], cooperative vehicle collision avoidance [4] and may also be used to gather information collected by vehicles (like the road pavement defects) [5]. Vehicular networks have also been proposed to implement transient networks to benefit developing communities and disaster recovery networks [6-8]. Vehicular delay-tolerant networks (VDTNs) [9] have been proposed as an innovative approach to generalize the DTN architecture applied to transit networks, gathering some contributions from DTN architectural principles, vehicular networks and optical burst switching networks (data packets aggregation under network layer and out-of-band signaling). Contrary to the DTN architecture that overlays a protocol layer called bundle layer over the transport layer, VDTN architecture assumes the bundle layer placement under the network layer in order to aggregate incoming IP packets into large packets, called data bundles. Another important contribution of the VDTN architecture is the separation of the control plane and the data plane. Network nodes use a low-powered, long-range, low bandwidth control plane link connection to exchange control messages out-of-band. These messages contain information related to node type, geographical location, current path, velocity, data plane link range, power status, buffer constraints, bundle format and size, delivery options, security requirements, among others. This control information is used to setup a data plane connection that uses a high-powered, short-range, and high bandwidth link. Data bundles are exchanged using this data plane link connection. VDTN appears as an alternative to provide low-cost asynchronous data communication and Internet access on developing countries or isolated regions, enabling non-real time services, such as electronic mail, Web access, telemedicine, environmental monitoring, and other data collection applications. An example of a VDTN scenario used for rural connectivity is illustrated in Figure 1. It consists in three node types: terminal nodes, relay nodes, and

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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

mobile nodes. Terminal nodes act as access points to the VDTN network, representing the network edge. They are located in isolated regions providing connection to end-users allowing them to exchange data using non real-time services. It is assumed that one or more of these nodes have direct access to the Internet. Relay nodes are fixed devices, located at crossroads with low-power requirements and store-andforward capabilities. They allow mobile nodes to ““save”” and pickup data. Relay nodes increase the number of contacts opportunities and therefore contribute to increase the bundles delivery ratio while decreasing their delivery delay, as discussed in [9, 10]. Mobile nodes (e.g. vehicles) are responsible for physically transporting the data between the terminal nodes. Mobile nodes can exchange data with one another and with the stationary relay nodes.

Fig. 1 - Example of a VDTN applied to a rural connectivity scenario.

VDTN networks must deal with specific connectivity issues that characterize vehicular networks, such as the high mobility of vehicles and highly dynamic network topology, short contact durations, disruption intermittent connectivity, significant loss rates, node density, and frequent network partition. To deal with these problems, the store-carry-andforward routing DTN-based paradigm may be complemented by routing strategies that replicate bundles along network nodes in order to increase their delivery probability and decrease their delivery delay. However, this approach may cause contention for networks resources like bandwidth and storage. This situation emphasizes the importance of scheduling and dropping policies to optimize the overall performance of the network. In this paper, a VDTN laboratory testbed is used to set-up a scenario where different combinations of scheduling and dropping policies [11] are applied to two routing protocols (Epidemic [12] and Spray and Wait [13]), and their impact on the network performance is evaluated. The remainder of the paper is organized as follows. Section II elaborates on the background about scheduling and

dropping policies for VDTNs. Section III presents our VDTN laboratory testbed, while Section IV focuses on the performance analysis of the proposed approaches. Finally, Section V concludes the paper and points some directions for future work. II.

BACKGROUND

In networks with intermittent connectivity constrains, such as VDTNs, routing schemes have to find ways to increase the bundle delivery probability and to reduce the bundle average delay. A possible solution may be the replication and bundles storage during long periods of time. Although some routing schemes follow this approach [14], these strategies may not be efficient when network resources are limited (e.g. buffer constraints) [15]. To cope with this situation and maintain network performance, efficient scheduling and dropping policies are needed. Scheduling policies are used to select the order by which bundles should be sent when two network nodes have an opportunity for data exchange [11]. The following three scheduling policies are considered in this work: first-in firstout (FIFO), Random, and Remaining Lifetime Descending Order (RL-DESC). In a FIFO scheduling policy, bundles are sorted by the order they were received. Using a Random scheduling policy bundles are sorted by a random order. With RL-DESC scheduling policy bundles are sorted taking into account their remaining time-to-live (TTL). Bundles with a longer remaining TTL are scheduled to be sent first because they have a higher probability to reach its final destination. Dropping policies are used to select which bundles must be dropped when buffer congestion occurs [11]. This work considers three dropping policies: Head Drop, Random, and Remaining Lifetime Ascending Order (RL-ASC). In a Head Drop dropping policy, the bundle stored for the longest period of the time in a node’’s buffer is dropped first. In a Random dropping policy, a bundle is randomly selected and dropped from the buffer. In a RL-ASC dropping policy, bundles are dropped depending on its TTL (time-to-live). Bundles with a smaller TTL are dropped first because they have less probability to reach its final destination before TTL expires. Figure 2 illustrates these scheduling and dropping policies.

Fig. 2 –– Illustration of the used scheduling and dropping policies mechanisms. Numbers inside boxes represent the remaining bundle TTL.

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

III.

VDTN LABORATORY TESTBED

This section describes the VDTN@Lab, a testbed created to demonstrate the VDTN architecture together with its services, protocols, and applications in a laboratorial environment. This section includes two sub-sections as follows. The first sub-section presents the testbed while the second describes the network scenario created to evaluate the impact of different scheduling and dropping policies in the performance of a VDTN. A. Testbed Design The VDTN@Lab testbed is composed by desktop/laptop computers and robotic cars. Desktop computers, like iMacs (Intel(R) Core(TM) 2 Duo 2.66GHz + 4GB RAM), are used to emulate terminal and relay nodes. Mobile nodes (e.g. vehicles) are emulated through LEGO MINDSTORMS NXT robots in conjunction with HP Mini laptops. These robots are programmed with several mobility models (e.g. bus movement or random movement across roads), allowing the performance evaluation of services and protocols under different movement patterns. Due to its lightweight, HP Mini laptops (Intel(R) Atom 1.66GHz + 1GB RAM) are carried by these Lego robotic cars. These computers provide network interfaces, like Bluetooth and IEEE 802.11b/g technologies, and storage capabilities to emulate a real VDTN and its data communications. Some software modules were created in C# programming language and deployed in the network nodes to emulate the VDTN protocol stack and applications. They were developed using the .NET Framework for running in desktops and laptops with Microsoft Windows 7 operating system. In addition, the software modules also provide management tools and advanced statistics reports. B. Network Scenario The scenario that was set up to demonstrate the use of the VDTN testbed has a dimension of 36,5m2. It consists in three terminal nodes, four mobile nodes and two relay nodes. Terminal nodes are placed at different points (edges) of the laboratory. Mobile nodes follow different pre-defined paths in order to emulate bus routes. Mobile node 1 follows a yellow path, mobile node 2 and 4 follow a black path and mobile node 3 a white path. Relay node 1 is placed on the road intersection with mobile node 1 and 2. Relay node 2 is deployed on the road intersection with mobile nodes 2, 3, and 4. The network node’’s buffers have different capacities according to their roles in the network. Terminal nodes have a buffer with a 50MB of capacity, relay nodes 75MB, and mobile nodes 25MB. Data bundles are generated with a time interval of 20 seconds. They have random source and destination terminal nodes, and its size is uniformly distributed between 250 KB and 2 MB. Bundles have a timeto-live (TTL) that change between 5, 10, 15 and 20 minutes, across testbed experiments. This increase of the TTL value leads to have more bundles stored at the network node’’s

buffers during larger periods of time. Therefore, more bundles will be exchanged between network nodes. This increases the probability of buffer overflow occurrence. Two DTN routing schemes applied to VDTNs are considered: Epidemic, and Spray and Wait. Epidemic is a flooding-based routing protocol where nodes exchange the bundles they do not have. In an environment with infinite network resources this protocol provides an optimal solution. Binary spray and wait protocol creates a number of copies (N) to be transmitted (““sprayed””) per bundle. Any node A that has more than 1 bundle copies and encounters any other node B that does not have a copy, forwards to B N/2 bundle copies and keeps the rest of the bundle copies to it. Nodes carrying only a copy of a bundle, can only forward it to its final destination. In this network scenario is considered N=3. Experiments were conducted assuming three different combinations of scheduling and dropping policies, as presented at Table I. Performance metrics considered in this study are the bundle delivery probability (measured as the relation of the number of unique delivered messages to the number of messages sent) and the bundle average delay (measured as the time between bundles creation and delivery). TABLE I. COMBINATION OF SCHEDULING AND DROPPING POLICIES USED ON THE TESTBED EXPERIMENTS.

Name

Dropping Policy

Scheduling Policy

FIFO

Head Drop

FIFO

Remaining Lifetime

RL-ASC

RL-DESC

Random

Random

Random

Each testbed experiment runs along an hour, and it is considered a fully cooperative opportunistic environment. Figure 3 presents photos of the VDTN testbed laboratory and all the above-mentioned nodes interactions and behaviors.

Fig. 3 –– Photos of the VDTN testbed.

PERFORMANCE EVALUATION

This section studies the performance evaluation of the above-presented VDTN testbed when the combination of the described scheduling and dropping policies is enforced on Epidemic, and Spray and Wait routing protocols. A. Performance Analysis of Epidemic Routing Protocol The study starts with the performance evaluation of scheduling and dropping policies for Epidemic routing protocol. The Remaining Lifetime combination (Table I) presents the best results in terms of bundle delivery probability, across all the experiments, as may be seen in Figure 4. It presents gains of 12%, 6%, 7%, and 7% (for bundle TTLs equal to 5, 10, 15, and 20 minutes) when compared to the FIFO combination, and approximately 3%, 4%, 9%, and 10% when compared to the Random combination. Figure 5 shows that same combination also contributes to decrease the bundles average delay. When compared to the FIFO combination, for the considered bundles TTL, bundles arrive to its final destination approximately 62, 106, 192, and 302 seconds sooner. Comparing Remaining Lifetime to the Random combination, bundles arrive approximately 49, 76, 128, and 189 seconds sooner. This behavior is caused by storage and bandwidth constraints that limit the number of bundles being carried, and the number of bundles exchanged at a contact opportunity. Forwarding bundles with a longer remaining TTL first and dropping first those who’’s TTL is smaller increases bundles probability of being delivered and decreases their average delay.

Delivery Probability (%)

85 80 75 FIFO

70 65 60

Remaining Lifetime

55

Random

50 45 40 5

10

15

20

Bundle TTL (minutes)

Fig. 4. Bundle delivery probability as function of bundle time-to-live for Epidemic routing protocol.

600 500 400

FIFO

300 200

Remaining Lifetime

100

Random

0 5

10

15

20

Bundle TTL (minutes)

Fig. 5. Bundle average delay as function of bundle time-to-live for Epidemic routing protocol.

B. Performance Analysis of Binary Spray and Wait Routing Protocol This subsection analyzes the Binary Spray and Wait routing protocol. By definition, Spray and Wait limits the number of sprayed bundle copies. This origin less bandwidth utilization and less congestion at nodes buffers. However, similar to Epidemic routing, a Remaining Lifetime combination (Table I) applied to this routing protocol improves the overall VDTN network performance in terms of delivery ratio and bundle average delay. 90 Delivery Probability (%)

IV.

Bundle Average Delay (seconds)

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

85 80

FIFO

75 Remaining Lifetime

70 65

Random

60 55 50 5

10

15

20

Bundle TTL (minutes)

Fig. 6. Bundle delivery probability as function of bundle time-to-live for Spray and Wait routing protocol.

Figure 6 confirms that the Remaining Lifetime combination increases about 7%, 6%, 3%, and 4% the bundle delivery probability for the considered bundle TTL of 5, 10, 15, and 20 minutes, when compared with FIFO combination, and about 5%, 6%, 7%, and 9% when compared to the Random combination. The gains observed in the bundle delivery probability performance metric are attenuated when bundles have a bigger TTL. Nevertheless, increasing the TTL reinforces the improvement on average delay that was introduced by the Remaining Lifetime combination. Figure 7 shows that bundles will arrive to its final destination approximately 18, 48, 93, and 105 seconds sooner for the

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

Bundles Average Delay (seconds)

considered TTL values, when compared to the FIFO combination and about 11, 36, 52, and 60 seconds sooner, when compared to the Random combination.

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400 350 FIFO

300 250

Remaining Lifetime

200

Random

[3]

[4]

150 100 5

10

15

20

[5]

Bundles TTL (minutes)

Fig. 7. Bundle average delay as function of bundle time-to-live for Spray and Wait routing protocol.

V.

[6]

CONCLUSIONS AND FUTURE WORK

This paper evaluated the impact of different combinations of scheduling and dropping policies on the performance of a vehicular delay-tolerant network (VDTN) emulated in a laboratorial testbed, called VDTN@Lab. The main goal of the study was the evaluation of different policies combinations and finds the best that should improve the bundle delivery ratio and reduce the bundle delivery delay. Three different combinations (FIFO, Random, and Remaining Lifetime) were enforced on two routing protocols (Epidemic and Spray and Wait). The observed results of the testbed experiments were presented and discussed. It was shown that scheduling and dropping policies should consider the bundles TTL as a criterion in order to improve the network performance. In the considered network scenario, Spray and Wait performs better than Epidemic. This work intends to provide a starting point for future studies on new scheduling and dropping policies for VDTN networks. New proposals of these policies will be evaluated in this laboratory testbed. The study may also be extended in the future, considering network scenarios with different available network resources, and also changing the mobile nodes’’ mobility patterns. ACKNOWLEDGEMENTS Part of this work has been supported by the Instituto de Telecomunicações, Next Generation Networks and Applications Group (NetGNA), Portugal, in the framework of the VDTN@Lab Project, and by the Euro-NF Network of Excellence of the Seventh Framework Programme of EU, in the framework of the Specific Joint Research Project VDTN.

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