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Yang, Member, IEEEAbstract—New applications based on cloud computing, such as data synchronization for large chain departmental stores and banktransaction records, require very high-speed data transport.
Note that the servers in the cloudare often responsible for multiple projects or multipletasks [15], [16], so that a waste of memory in one applica-tion could signi?cantly impair the performance of otherapplications. Our results indicatethat Rada outperforms the static buffer scheme in termsof throughput, memory footprint, and fairness in all thescenarios that we have explored.Note that this paper is an extended version of ourprevious paper presented at ICCCN 2010 [33], whichpresents an overview of our dynamic buffer adaptationidea and a brief evaluation with a high-speed protocoldeployed in our testbed. Although a number of high-bandwidth networks have been built, existingtransport protocols or their variants over such networks cannot fully exploit the network bandwidth. Our experiments show that the?xed-size application level buffer employed in the receiver side is a major cause of this de?ciency. Moreover, a larger buffer usually requires morecomplicated data structures, resulting in higher over-head in buffer management. In a situation where thememory at the receiver side becomes heavily loaded, alarger buffer can even result in memory swapping andserious performance drop (this is further demonstratedin Section 5.3).
Section 3 describesthe details of the Rada algorithm, including the EMAaided buffer adaptation decision, the LAICD schemefor controlling the adaptation extent, and the memoryadaptive buffer adaptation with WMF. Therefore, an optimal or near optimalbuffer size is needed to maximize the throughput, whileminimizing the memory footprint. Section 4 presentsthe design of Rada and Section 5 presents the evaluationresults of Rada in various scenarios.
In this paper, we propose Rada, a dynamic receiving buffer adaptation scheme for high-speeddata transfer.
We de?ne the optimalbuffer size loptimal asloptimal = min{l, l ? Lmax thoughput},where Lmax throughput means the set of static buffer sizesthat can achieve the maximum transfer throughput.We conducted several experiments demonstrating thatthe optimal receiving buffer size varies during runtime,depending on factors including the transfer data size,the available memory in the receiver, and the number ofsimultaneous transfers 1. Section 6 reviewsrelated work, and Section 7 concludes this paper.2 BASIC IDEA OF RADAAs described in the previous section, the buffer size mustbe adapted dynamically according to condition varia-tions.
Rada employs an Exponential Moving Average aided scheme to quantify the data arrival rate and consumption rate inthe buffer.

A straightforward approach isthus to adapt the receiving buffer size dynamically withthe change in such parameters.There have been existing works on adapting the TCPsending buffer based on network feedbacks in terms of achange in packet loss [22], [23], [25], queuing delay [21]and bandwidth-delay product (BDP) [26], [28].
Based on these two rates, we develop a Linear Aggressive Increase Conservative Decrease scheme to adjust the buffersize dynamically. Thus, itis dif?cult to adapt the buffer size for a given speci?ccondition, as so many different conditions must be con-sidered. Moreover, a Weighted Mean Function is employed to make the adjustment adaptive to the available memory in thereceiver. Theoretical analysis is provided to demonstrate the rationale and parameter bounds of Rada. Both types of buffer adap-tation algorithms aim at adapting the sending buffer, anduse the perceived performance metrics such as packetloss, queuing delay and BDP as feedbacks to adjust thesending buffer size during runtime. We implement Rada in a Linux platform and extensively evaluate its performance ina variety of scenarios. Incast, a buffer issue of switchesin data center networks has attracted great interest re-cently. As cloud sites are oftengeographically distributed, they need very high-speeddata communication for tasks such as data synchroniza-tion and data mining [10]. It refers to the phenomenon when one clientsimultaneously requests data from multiple servers, aburst of TCP traf?c will overwhelm the switch buffer1.
For example, some researchtestbeds such as Open Cloud Testbed [15] and Ultra-Science Net (USN) [16] need a transfer speed of up tomultiple Gbps to meet the demands of large-scale scienceapplications. The corresponding results are demonstrated in Section 5.and thus the TCP throughput will decrease signi?cantlydue to packet loss and TCP timeout. Enterprise cloud applications, such as datasynchronization for large chain department stores, alsodemand high-speed data transfer [17].Although a number of dedicated high-bandwidth net-works have been constructed, including JGN2plus [18],UCLP [19] and DRAGON [20], Transmission ControlProtocol (TCP) fails to perform well in these high-speednetworks [5]. To address this?xed-size hardware buffer issue inside a data center, anumber of approaches [1], [2], [3], [4], [6], [11], [14], [27]have been proposed to control the buffer occupation toavoid buffer over?ow as well as maximize throughput.Note that the buffer in Incast is the hardware bufferof switches inside a data center. Thus, TCP variants [21], [22], [23] for high-speed networks have been proposed to improve thetransfer throughput over these networks. However, the bufferbottleneck we address in this paper refers to the receivingbuffer for high-speed data transfer between two serversfrom two data centers.

However, mostof TCP variants run at the transport layer and needkernel modi?cation for implementation, so that large-scale deployment is dif?cult.
Rada employs a rate detection based approachto circumvent the complexity of taking all these fac-tors into account, and it merges the factor of availablememory into the adaptation later. Zhou are with the Department of Com-puter Science and Technology, Tsinghua University, China. Fu is with the Institute of Computer Science, Georg-August-Universityof Goettingen, Germany. These results are further con?rmed by theexperimental evaluation.3) To merge the factor of available memory intothe adaptation, we introduce a Weighted Mean Function(WMF) [37] with time-varying weights to adjust the adap-tation extent.
The time-varying weights are based on thetradeoff between the transfer demand and the memoryavailable during runtime.
Theoretical analysis showsthat this scheme achieves the expected mergence effect.We implement the Rada algorithm in Linux and eval-uate its effectiveness in various scenarios with differenttransfer size, different available memory, and different2. Adaptation extent means how much the buffer size is increased ordecreased in each adaptation operation.IEEE TRANSACTIONS ON COMPUTERSThis article has been accepted for publication in a future issue of this journal, but has not been fully edited. It runs at theapplication level and can be easily deployed to facilitatebulk data transfers in high-bandwidth networks such asUSN [16], JGN2plus [18], UCLP [19] and DRAGON [20].However, these UDP based high-speed protocols stillcannot fully utilize these high-bandwidth networks.
Inthese protocols, the receiver employs a ?xed-size appli-cation level buffer to hold the data received before de-livering it to upper layer protocols. As the buffer size is?xed, the buffer could be exhausted in protocols withouta precise ?ow control mechanism, such as [24]. In thiscase, the newly arrived packets would be discarded andthe throughput would decease dramatically. Protocolswith precise ?ow control, such as [5], ensure that thebuffer would not be exhausted, but they have to employa conservative mechanism to send data at a speed belowa certain threshold.

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