Ihsan Ayyub Qazi

Minimizing flow completion times in data centers

For provisioning large-scale online applications such as web search, social networks and advertisement systems, data centers face extreme challenges in providing low latency for short flows (that result from end-user actions) and high throughput for background flows (that are needed to maintain data consistency and structure across massively distributed systems). We propose L2DCT, a practical data center transport protocol that targets a reduction in flow completion times for short flows by approximating the Least Attained Service (LAS) scheduling discipline, without requiring any changes in application software or router hardware, and without adversely affecting the long flows. L2DCT can co-exist with TCP and works by adapting flow rates to the extent of network congestion inferred via Explicit Congestion Notification (ECN) marking, a feature widely supported by the installed router base. Though L2DCT is deadline unaware, our results indicate that, for typical data center traffic patterns and deadlines and over a wide range of traffic load, its deadline miss rate is consistently smaller compared to existing deadline-driven data center transport protocols. L2DCT reduces the mean flow completion time by up to 50% over DCTCP and by up to 95% over TCP. In addition, it reduces the completion for 99th percentile flows by 37% over DCTCP. We present the design and analysis of L2DCT, evaluate its performance, and discuss an implementation built upon standard Linux protocol stack.

On the Design of Load Factor based Congestion Control Protocols for Next-Generation Networks

Load factor based congestion control schemes have shown to enhance network performance, in terms of utilization, packet loss and delay. In these schemes, using more accurate representation of network load levels is likely to lead to a more efficient way of communicating congestion information to hosts. Increasing the amount of congestion information, however, may end up adversely affecting the performance of the network. This paper focuses on this trade-off and addresses two important and challenging questions: (i) How many congestion levels should be represented by the feedback signal to provide near-optimal performance? and (ii) What window adjustment policies must be in place to ensure robustness in the face of congestion and achieve efficient and fair bandwidth allocations in high bandwidth-delay product (BDP) networks, while keeping low queues and negligible packet drop rates? Based on theoretical analysis and simulations, our results show that 3-bit feedback is sufficient for achieving near-optimal rate convergence to an efficient bandwidth allocation. While the performance gap between 2-bit and 3-bit schemes is large, gains follow the law of diminishing returns when more than 3 bits are used. Further, we show that using multiple levels for the multiplicative decrease policy enables the protocol to adjust its rate of convergence to fairness, rate variations and responsiveness to congestion based on the degree of congestion at the bottleneck. Based on these fundamental insights, we design multi-level feedback congestion control protocol (MLCP). In addition to being efficient, MLCP converges to a fair bandwidth allocation in the presence of diverse RTT flows while maintaining near-zero packet drop rate and low persistent queue length. These features coupled with MLCPs smooth rate variations make it a viable choice for many real-time applications. Using extensive packet- level simulations we show that the protocol is stable across a diverse range of network scenarios. A fluid model for the protocol shows that MLCP remains globally stable for the case of a single bottleneck link shared by identical round-trip time flows.

Friends, not foes: synthesizing existing transport strategies for data center networks

Many data center transports have been proposed in recent times (e.g., DCTCP, PDQ, pFabric, etc). Contrary to the common perception that they are competitors (i.e., protocol A vs. protocol B), we claim that the underlying strategies used in these protocols are, in fact, complementary. Based on this insight, we design PASE, a transport framework that synthesizes existing transport strategies, namely, self-adjusting endpoints (used in TCP style protocols), innetwork prioritization (used in pFabric), and arbitration (used in PDQ). PASE is deployment friendly: it does not require any changes to the network fabric; yet, its performance is comparable to, or better than, the state-of-the-art protocols that require changes to network elements (e.g., pFabric). We evaluate PASE using simulations and testbed experiments. Our results show that PASE performs well for a wide range of application workloads and network settings.

Congestion Control using Efficient Explicit Feedback

This paper proposes a framework for congestion control, called Binary Marking Congestion Control (BMCC) for high bandwidth-delay product networks. The basic components of BMCC are i) a packet marking scheme for obtaining high resolution congestion estimates using the existing bits available in the IP header for Explicit Congestion Notification (ECN) and ii) a set of load-dependent control laws that use these congestion estimates to achieve efficient and fair bandwidth allocations on high bandwidth-delay product networks, while maintaining a low persistent queue length and negligible packet loss rate. We present analytical models that predict and provide insights into the convergence properties of the protocol. Using extensive packet-level simulations, we assess the efficacy of BMCC and perform comparisons with several proposed schemes. BMCC outperforms VCP, MLCP, XCP, SACK+RED/ECN and in some cases RCP, in terms of average flow completion times for typical Internet flow sizes.

Loss Differentiation: Moving onto High-Speed Wireless LANs

A fundamental problem in 802.11 wireless networks is to accurately determine the cause of packet losses. This becomes increasingly important as wireless data rates scale to Gbps, where lack of loss differentiation leads to higher loss in throughput. Recent and upcoming high-speed WLAN standards, such as 802.11n and 802.11ac, use frame aggregation and block acknowledgements for achieving efficient communication. This paper presents BLMon, a framework for loss differentiation, that uses loss patterns within aggregate frames and aggregate frame retries to achieve accurate and low overhead loss differentiation. Towards this end, we carry out a detailed measurement study on a real testbed to ascertain the differences in loss patterns due to noise, collisions, and hidden nodes. We then devise metrics to quantitatively capture these differences. Finally, we design BLMon, which collectively uses these metrics to infer the cause of loss without requiring any out-of-band communication, protocol changes, or customized hardware support. BLMon can be readily deployed on commodity devices using only driver-level changes at the sender-side. We implement BLMon in the ath9k driver and using real testbed experiments, show that it can provide up to 5× improvement in throughput.

On achieving low latency in data centers

Todays data centers face extreme challenges in providing low latency for online services such as web search, social networking, and recommendation systems. Achieving low latency is important as it impacts user experience, which in turn impacts operator revenue. However, most current congestion control protocols approximate Processor Sharing (PS), which is known to be sub-optimal for minimizing latency. In this paper, we propose Router Assisted Capacity Sharing (RACS), a data center transport protocol that minimizes flow completion times by approximating the Shortest Remaining Processing Time (SRPT) scheduling policy, which is known to be optimal, in a distributed manner. With RACS, flows are assigned weights which determine their relative priority and thus the rate assigned to them. By changing these weights, RACS can approximate a range of scheduling disciplines. Through extensive ns-2 simulations, we demonstrate that RACS outperforms TCP, DCTCP, and RCP in data center environments. In particular, it improves completion times by up to 95% over TCP, 88% over DCTCP, and 80% over RCP. Our results also show that RACS can outperform deadline-aware transport protocols for typical data center workloads.

Congestion control with multipacket feedback

Many congestion control protocols use explicit feedback from the network to achieve high performance. Most of these either require more bits for feedback than are available in the IP header or incur performance limitations due to inaccurate congestion feedback. There has been recent interest in protocols that obtain high-resolution estimates of congestion by combining the explicit congestion notification (ECN) marks of multiple packets, and using this to guide multiplicative increase, additive increase, multiplicative decrease (MI-AI-MD) window adaptation. This paper studies the potential of such approaches, both analytically and by simulation. The evaluation focuses on a new protocol called Binary Marking Congestion Control (BMCC). It is shown that these schemes can quickly acquire unused capacity, quickly approach a fair rate distribution, and have relatively smooth sending rates, even on high bandwidth-delay product networks. This is achieved while maintaining low average queue length and negligible packet loss. Using extensive simulations, we show that BMCC outperforms XCP, VCP, MLCP, CUBIC, CTCP, SACK, and in some cases RCP, in terms of average flow completion times. Suggestions are also given for the incremental deployment of BMCC.

A View from the Other Side: Understanding Mobile Phone Characteristics in the Developing World

Mobile devices are becoming increasingly dominant in the developing world. However, there is little insight into the characteristics of devices being used in such regions. Using a dataset of ~0.5 million subscribers from one of the largest cellular operators in Pakistan, we analyze the characteristics of cell phones based on different features (e.g., CPU, memory, and cellular interface). We identify potential device-level bottlenecks for Internet access and analyze the security implications of the phones being used. To aid the analysis of cell phones, we propose abstractions (e.g., connectivity, capacity, and device security) and cluster phones based on these abstractions. Our analysis reveals interesting insights for improving mobile web performance.

On the coexistence of transport protocols in data centers

The emergence of cloud data centers has led to the design of customized transport protocols such as Data Center TCP (DCTCP). These protocols improve the performance of cloud applications by explicitly accounting for the unique network and traffic characteristics in data centers. However, such protocols have only been evaluated under greenfield deployment scenarios, where the entire data center is assumed to use the same protocol, which may not always be desirable or feasible. This leads to scenarios where these protocols coexist with TCP and thus share the same network resources. This paper considers such scenarios and presents a comprehensive study of the coexistence of DCTCP and TCP. In particular, we evaluate their bandwidth sharing properties under different active queue management schemes (AQM) including RED, DCTCP AQM, and CHOKe. Our results show that under the DCTCP AQM, DCTCP can starve TCP flows. This problem is mitigated through the use of RED, however, significant unfairness remains. Interestingly, we find that CHOKe exacerbates this unfairness. We show that a modified version of CHOKe considerably improves fairness by more accurately penalizing dominating flows.

Towards a Redundancy-Aware Network Stack for Data Centers

In this paper, we make a case for a redundancy-aware network stack (RANS) for data centers. In RANS, applications expose information about replicas to the network, which in turn, uses duplicate requests to improve performance of typical applications by enabling them to effectively avoid stragglers. At the heart of RANS is the use of duplicate-aware scheduling, which ensures that duplicate-requests do not overload the system and disturb any primary requests. We highlight the challenges and opportunities present at different layers of RANS, from new interfaces that capture replicas and their semantics, to in-network mechanisms that deal with duplicates. Our preliminary evaluation shows the promise of duplicate-aware scheduling in improving performance of typical data center applications.