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Introduction
Performance Analysis of Basic Protocol Mechanisms
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Computer Science
Embedded Systems

A Reliable Message Delivery Protocol for Mobile Agents

Profile image of Doug MontgomeryDoug Montgomery

2000, Lecture Notes in Computer Science

https://doi.org/10.1007/978-3-540-45347-5_17
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12 pages

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Abstract

The abstractions and protocol mechanisms that form the basis for inter-agent communications can significantly impact the overall design and effectiveness of Mobile Agent systems. We present the design and performance analysis of a reliable communication mechanism for Mobile Agent systems. Our protocols are presented in the context of a Mobile Agent system called AGNI ¢. We have developed AGNI communication mechanisms that offer reliable peer-to-peer communications, and that are integrated with our agent location tracking infrastructure to enable efficient, failure-resistant networking among highly mobile systems. We have analyzed the design parameters of our protocols using an in-situ simulation approach with validation through measurement of our prototype implementation in real distributed systems. Our system assumptions are simple and general enough to make our results applicable to other Agent systems that may adopt our protocols and/or design principles. £ 1 Introduction Mobile Agents are a convenient and powerful paradigm for structuring distributed systems. Using the Agent paradigm, work can be assigned to sequential, event-driven tasks that cooperate with each other to solve a distributed problem. In such systems, Agents roam the network accessing distributed information and resources while solving pieces of the problem. During the course of these computations Mobile Agents need to communicate among themselves to exchange state and status information, control and direct future behavior, and report results. The abstractions and protocol mechanisms that form the basis for inter-agent communications can significantly impact the overall design and effectiveness of Mobile Agent systems. Numerous approaches to inter-Agent communications are possible including RPC and mailboxes [3]. In this work we present a simple, ordered, reliable, one-way message protocol on top of which other abstractions can easily be built. Reliable, ordered one-way communication mechanisms greatly simplify the construction of most distributed applications. In traditional distributed applications, TCP [15] provides such services. Through decades of experience and re-engineering, TCP has evolved into a protocol that is highly effective at providing reliable end-toend data delivery over the conditions found in today's Internet (e.g., link failures, variable latencies, congestion loss). In this paper, we examine how to build a TCP-like reliable communication mechanism for Mobile Agent systems. The first question to be addressed is "Why we don't use the existing TCP?" We argue that Mobile Agent systems impose new communication requirements and problems that are not adequately addressed by conventional TCP, nor its potential minor variants. In particular, we are concerned with building failure resistant, rapidly re-configurable distributed systems. We view these system properties as a primary motivation for dynamic Agent creation and mobility mechanisms, and as posing significant requirements on inter-Agent communications mechanisms. As such, we require that Agent systems be able to (1) detect and recover from failures in the end-to-end transport mechanism and (2) accommodate efficient communication among mobile end-points. Neither of these capabilities can be provided using standard TCP. In the remainder of this paper we present the design and performance analysis of a reliable communication mechanism for Mobile Agent systems. Our protocol is presented in the context of a Mobile Agent system called AGNI, whose general design and capabilities have been described earlier [13]. Our communication mechanism ¢ AGNI stands for Agents at NIST and is also Sanskrit for fire. £ This work was supported in part by DARPA under the Autonomous Negotiation Teams (ANTS) program (AO # 99-H412/00). The work described in this paper is a research project and not an official US. Government endorsement of any product or protocol.

Figures (7)
Fig. 1. Simulated versus actual time per message and packet ratio. Both end-points are fixed. Packet drop is effected by random drop with uniform probability. Figure 1 shows an example of the fit between real and simulated systems for the two quantities of interest in this paper which are : (1) the ratio of packets sent to the packets delivered which is a measure of the packets lost in transit and (2) the average time to consume a packet which is a measure of the throughput.
Fig. 1. Simulated versus actual time per message and packet ratio. Both end-points are fixed. Packet drop is effected by random drop with uniform probability. Figure 1 shows an example of the fit between real and simulated systems for the two quantities of interest in this paper which are : (1) the ratio of packets sent to the packets delivered which is a measure of the packets lost in transit and (2) the average time to consume a packet which is a measure of the throughput.
Fig. 2. Simulated versus actual time per message and packet ratio one end-point fixed and one end-point moving. Packet dro percentage is 0.
Fig. 2. Simulated versus actual time per message and packet ratio one end-point fixed and one end-point moving. Packet dro percentage is 0.
Fig. 3. Effect of location latency on messages dropped per move. Early messages are cached for 1 second at the receiver. The sender remains fixed. The move interval is the number of messages per move of the receiver. a running estimate of expected round trip time to each target site for each open MStream and recompu ing it on each acknowledgement using a smoothing estimator similar to the one employed by TCP [6] ( i. RTT; = RTT;_1 + a* MeasuredRTT ). As in TCP, we use an a value of 0.75. The expected round-tri time includes the time for the handler to run as the acknowledgement only returns after the handler is execute at the target MStream. If the acknowledgement does not arrive in the expected time, the sender attempts a r transmission of the message. This is re-tried k (currently set to 3) times before the sender sends the message the Location Manager to be forwarded to the new destination for the M Stream and drops its congestion windor cwnd[ receiver] to 1. The Location Manager forwards the message to the current Site where the receive resides and sends a notification back to the sender informing it about the new location of the receiver. Both th combined and the lazy schemes adopt this forwarding strategy. However, in the combined scheme, in additio to this forwarding service, the Location Manager informs all Sites that have an open MStream handle for th MStream in motion, immediately as soon as a movement takes place. Location update messages can be delayed in transit, or lost. The delay in location notifications is important t consider as it affects the performance of message delivery in highly mobile systems. We have a classic case ¢ scalability versus responsiveness and it is important to study the effect of this tradeoff to determine the cost ¢ scalable structures. Note that we do not rely on the Sites propagating location information themselves for tw reasons : (1) the Sites are assumed to be unreliable and the links are lossy so the location update message ma be lost and never reach the senders (2) if multiple Sites are sending location update information, the informatio may be out of sync and lead to instability for rapidly moving targets. Our first simulation scenario involves a single sender and a single receiver that is moving randomly between set of locations. The sender sends messages to the receiver who moves after m messages. The receiver move round-robin between 10 locations. Each time the receiver moves, the location manager sends a notification the move to the senders. We examine the effect of delaying this notification on the average time to for eac message and on the number of retransmissions per move. We present these results for two cases - a perfect lin with no loss and a lossy link with 5% drop in the Figure 3. The interactions between the various mechanisrr are a bit more complex than one might expect at the outset. For example, note that in the case of the rapidl moving endpoint (move interval of 5) the sender window never gets a chance to open up so there is no messag pipeline. As the receive window never gets filled, fewer messages get dropped at the receiver. The messag drop percentages are thus lower than in the other cases as no messages are dropped when the receiver moves especially with the low location latencies.
Fig. 3. Effect of location latency on messages dropped per move. Early messages are cached for 1 second at the receiver. The sender remains fixed. The move interval is the number of messages per move of the receiver. a running estimate of expected round trip time to each target site for each open MStream and recompu ing it on each acknowledgement using a smoothing estimator similar to the one employed by TCP [6] ( i. RTT; = RTT;_1 + a* MeasuredRTT ). As in TCP, we use an a value of 0.75. The expected round-tri time includes the time for the handler to run as the acknowledgement only returns after the handler is execute at the target MStream. If the acknowledgement does not arrive in the expected time, the sender attempts a r transmission of the message. This is re-tried k (currently set to 3) times before the sender sends the message the Location Manager to be forwarded to the new destination for the M Stream and drops its congestion windor cwnd[ receiver] to 1. The Location Manager forwards the message to the current Site where the receive resides and sends a notification back to the sender informing it about the new location of the receiver. Both th combined and the lazy schemes adopt this forwarding strategy. However, in the combined scheme, in additio to this forwarding service, the Location Manager informs all Sites that have an open MStream handle for th MStream in motion, immediately as soon as a movement takes place. Location update messages can be delayed in transit, or lost. The delay in location notifications is important t consider as it affects the performance of message delivery in highly mobile systems. We have a classic case ¢ scalability versus responsiveness and it is important to study the effect of this tradeoff to determine the cost ¢ scalable structures. Note that we do not rely on the Sites propagating location information themselves for tw reasons : (1) the Sites are assumed to be unreliable and the links are lossy so the location update message ma be lost and never reach the senders (2) if multiple Sites are sending location update information, the informatio may be out of sync and lead to instability for rapidly moving targets. Our first simulation scenario involves a single sender and a single receiver that is moving randomly between set of locations. The sender sends messages to the receiver who moves after m messages. The receiver move round-robin between 10 locations. Each time the receiver moves, the location manager sends a notification the move to the senders. We examine the effect of delaying this notification on the average time to for eac message and on the number of retransmissions per move. We present these results for two cases - a perfect lin with no loss and a lossy link with 5% drop in the Figure 3. The interactions between the various mechanisrr are a bit more complex than one might expect at the outset. For example, note that in the case of the rapidl moving endpoint (move interval of 5) the sender window never gets a chance to open up so there is no messag pipeline. As the receive window never gets filled, fewer messages get dropped at the receiver. The messag drop percentages are thus lower than in the other cases as no messages are dropped when the receiver moves especially with the low location latencies.
Fig. 4. The top figure shows the sequence number of sender and receiver with a location latency of 0.1 sec. and the bottom figure shows sequence numbers of sender and receiver with a location latency of 1.0 seconds. Sender is producing a message once every 0.01 seconds and the receiver handler runs for 0.001 seconds. Link latency is 0.01 seconds. The effect of increased pipeline disruption with higher location latency is evident. new location again when the receiver has arrived. This effect is illustrated in Figure 4 which shows the effect of different propagation delays of the Location Manager on the sequence numbers of the messages sent by the sender and consumed by the receiver. The effect is as expected. A higher location latency results in the pipeline being disrupted for a longer period of time and hence greater loss and reduced performance.
Fig. 4. The top figure shows the sequence number of sender and receiver with a location latency of 0.1 sec. and the bottom figure shows sequence numbers of sender and receiver with a location latency of 1.0 seconds. Sender is producing a message once every 0.01 seconds and the receiver handler runs for 0.001 seconds. Link latency is 0.01 seconds. The effect of increased pipeline disruption with higher location latency is evident. new location again when the receiver has arrived. This effect is illustrated in Figure 4 which shows the effect of different propagation delays of the Location Manager on the sequence numbers of the messages sent by the sender and consumed by the receiver. The effect is as expected. A higher location latency results in the pipeline being disrupted for a longer period of time and hence greater loss and reduced performance.
Fig. 5. Opportunistic Caching - Sender is producing a message every 0.005 seconds and receiver is consuming a message ever 0.001 seconds. Location latency is 0.5 seconds. The connections loss-free. this optimization displays a step behavior. After a threshold, holding packets for additional time does not yield greater benefit as the pipeline starvation effect caused by the move is already masked beyond this threshold.
Fig. 5. Opportunistic Caching - Sender is producing a message every 0.005 seconds and receiver is consuming a message ever 0.001 seconds. Location latency is 0.5 seconds. The connections loss-free. this optimization displays a step behavior. After a threshold, holding packets for additional time does not yield greater benefit as the pipeline starvation effect caused by the move is already masked beyond this threshold.
Fig. 6. The top figure shows the sequence number of sender and receiver without opportunistic caching, and the bottom figure shows the sequence number plot with 0.1 seconds cache residency. Sender is producing a message every 0.01 seconds and the receiver's append handler runs for 0.001 seconds seconds. Location latency is 0.5 seconds. Link latency is 0.01 seconds.
Fig. 6. The top figure shows the sequence number of sender and receiver without opportunistic caching, and the bottom figure shows the sequence number plot with 0.1 seconds cache residency. Sender is producing a message every 0.01 seconds and the receiver's append handler runs for 0.001 seconds seconds. Location latency is 0.5 seconds. Link latency is 0.01 seconds.
Fig. 7. Effect of receiver window size on throughput and message loss.
Fig. 7. Effect of receiver window size on throughput and message loss.

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