Sangtae Ha

CUBIC: a new TCP-friendly high-speed TCP variant

CUBIC is a congestion control protocol for TCP (transmission control protocol) and the current default TCP algorithm in Linux. The protocol modifies the linear window growth function of existing TCP standards to be a cubic function in order to improve the scalability of TCP over fast and long distance networks. It also achieves more equitable bandwidth allocations among flows with different RTTs (round trip times) by making the window growth to be independent of RTT -- thus those flows grow their congestion window at the same rate. During steady state, CUBIC increases the window size aggressively when the window is far from the saturation point, and the slowly when it is close to the saturation point. This feature allows CUBIC to be very scalable when the bandwidth and delay product of the network is large, and at the same time, be highly stable and also fair to standard TCP flows. The implementation of CUBIC in Linux has gone through several upgrades. This paper documents its design, implementation, performance and evolution as the default TCP algorithm of Linux.

Joint VM placement and routing for data center traffic engineering

Todays data centers need efficient traffic management to improve resource utilization in their networks. In this work, we study a joint tenant (e.g., server or virtual machine) placement and routing problem to minimize traffic costs. These two complementary degrees of freedom-placement and routing-are mutually-dependent, however, are often optimized separately in todays data centers. Leveraging and expanding the technique of Markov approximation, we propose an efficient online algorithm in a dynamic environment under changing traffic loads. The algorithm requires a very small number of virtual machine migrations and is easy to implement in practice. Performance evaluation that employs the real data center traffic traces under a spectrum of elephant and mice flows, demonstrates a consistent and significant improvement over the benchmark achieved by common heuristics.

Incentivizing time-shifting of data: a survey of time-dependent pricing for internet access

The tremendous growth in demand for broadband data is forcing ISPs to use pricing as a congestion management tool. This changing landscape of Internet access pricing is evidenced by the elimination of flat rate data plans in favor of usage-based pricing by major wired and wireless operators in the US and Europe. But simple usage-based fees suffer from the problem of imposing costs on all users, irrespective of the network congestion level at a given time. To effectively reduce network congestion, appropriate incentives must be provided to users who are willing to time-shift their data demand from peak to off-peak periods. These pricing incentives can either be static (e.g., two-period daytime/nighttime prices) or computed dynamically (e.g., dayahead pricing, real-time pricing). Data plans that offer such incentives to consumers fall under the category of time-dependent pricing (TDP). Many ISPs across the world are currently exploring various forms of TDP to manage their traffic growth. This article first outlines the sources of today¿s challenges, and then discusses current trends from regulatory and technological perspectives. Finally, we review representative pricing proposals for incentivizing the time-shifting of data.

Impact of background traffic on performance of high-speed TCP variant protocols

This paper examines the effect of background traffic on the performance of existing high-speed TCP variant protocols, namely BIC-TCP, CUBIC, FAST, HSTCP, H-TCP and Scalable TCP. We demonstrate that the stability, link utilization, convergence speed and fairness of the protocols are clearly affected by the variability of flow sizes and round-trip times (RTTs), and the amount of background flows competing with high-speed flows in a bottleneck router. Our findings include: (1) the presence of background traffic with variable flow sizes and RTTs improves the fairness of most high-speed protocols, (2) all protocols except FAST and HSTCP show good intra-protocol fairness regardless of the types of background traffic, (3) HSTCP needs a larger amount of background traffic and more variable traffic than the other protocols to achieve convergence, (4) H-TCP trades stability for fairness; that is, while its fairness is good independent of background traffic types, larger variance in the flow sizes and RTTs of background flows causes the protocol to induce a higher degree of global loss synchronization among competing flows, lowering link utilization and stability, (5) FAST suffers unfairness and instability in small buffer or long delay networks regardless of background traffic types, and (6) the fairness of high-speed protocols depends more on the amount of competing background traffic rather than its rate variability. We also find that the presence of high-speed flows does not greatly reduce the bandwidth usage of background Web traffic.

When the price is right: enabling time-dependent pricing of broadband data

In an era of 108% annual growth in demand for mobile data and

Individualization for Education at Scale: MIIC Design and Preliminary Evaluation

10/GB overage fees, Internet Service Providers (ISPs) are experiencing severe congestion and in turn are hurting consumers with aggressive pricing measures. But smarter practices, such as time-dependent pricing (TDP), reward users for shifting their non-critical demand to off-peak hours and can potentially benefit both users and ISPs. Although dynamic TDP ideas have existed for many years, dynamic pricing for mobile data is only now gaining interest among ISPs. Yet TDP plans require not only systems engineering but also an understanding of economic incentives, user behavior and interface design. In particular, the HCI aspects of communicating price feedback signals from the network and the response of mobile data users need to be studied in the real world. But investigating these issues by deploying a virtual TDP data plan for real ISP customers is challenging and rarely explored. To this end, we carried out the first TDP trial for mobile data in the US with 10 families. We describe the insights gained from the trial, which can help the HCI community as well as ISPs, app developers and designers create tools that empower users to better control their usage and save on their monthly bills, while also alleviating network congestion.

Scalable Multi-Class Traffic Management in Data Center Backbone Networks

We present the design, implementation, and preliminary evaluation of our Adaptive Educational System (AES): the Mobile Integrated and Individualized Course (MIIC). MIIC is a platform for personalized course delivery which integrates lecture videos, text, assessments, and social learning into a mobile native app, and collects clickstream-level behavioral measurements about each student as they interact with the material. These measurements can subsequently be used to update the students user model, which can in turn be used to determine the content adaptation. Recruiting students from one of our Massive Open Online Courses (MOOCs), we have conducted two preliminary trials with MIIC, in which we found (i) that the majority of students (70 percent) preferred MIIC overall to a one-size-fits-all (OSFA) presentation of the same material, (ii) that the mean level of engagement, when quantified as the number of pages viewed, was statistically higher (by 72 percent) among students using MIIC than among OSFA, and (iii) that the integrated multimedia learning features were generally favorable among the students (e.g., 87 percent found the videos helpful).

Clarifying Fog Computing and Networking: 10 Questions and Answers

Large online service providers (OSPs) often build private backbone networks to interconnect data centers in multiple locations. These data centers house numerous applications that produce multiple classes of traffic with diverse performance objectives. Applications in the same class may also have differences in relative importance to the OSPs core business. By controlling both the hosts and the routers, an OSP can perform both application rate-control and network routing. However, centralized management of both rates and routes does not scale due to excessive message-passing between the hosts, routers, and management systems. Similarly, fully-distributed approaches do not scale and converge slowly. To overcome these issues, we investigate two semi-centralized designs that lie at practical points along the spectrum between fully-distributed and fully-centralized solutions. We achieve scalability by distributing computation across multiple tiers of an optimization machinery. Our first design uses two tiers, representing the backbone and classes, to compute class-level link bandwidths and application sending rates. Our second design has an additional tier representing individual data centers. Using optimization, we show that both designs provably maximize the aggregate utility over all traffic classes. Simulations on realistic backbones show that the 3-tier design is more scalable, but converges slower than the 2-tier design.

DiffQ: Differential Backlog Congestion Control for Wireless Multi-hop Networks

Fog computing is an end-to-end horizontal architecture that distributes computing, storage, control, and networking functions closer to users along the cloud-to-thing continuum. The word “edge” may carry different meanings. A common usage of the term refers to the edge network as opposed to the core network, with equipment such as edge routers, base stations, and home gateways. In that sense, there are several differences between fog and edge. First, fog is inclusive of cloud, core, metro, edge, clients, and things. The fog architecture will further enable pooling, orchestrating, managing, and securing the resources and functions distributed in the cloud, anywhere along the cloud-to-thing continuum, and on the things to support end-to-end services and applications. Second, fog seeks to realize a seamless continuum of computing services from the cloud to the things rather than treating the network edges as isolated computing platforms. Third, fog envisions a horizontal platform that will support the common fog computing functions for multiple industries and application domains, including but not limited to traditional telco services. Fourth, a dominant part of edge is mobile edge, whereas the fog computing architecture will be flexible enough to work over wireline as well as wireless networks.

Do mobile data plans affect usage? Results from a pricing trial with ISP customers

In this demo, we showcase DiffQ - a congestion control protocol inspired by theoretical cross-layer optimization approaches. DiffQ can support congestion control for network flows that use either single-path or opportunistic multi-path routing. Our demo will focus on the performance in single-path routing environments, where contemporary end-point congestion control algorithms like TCP face severe unfairness or even starvation. This is primarily due to the interaction of such protocols with MAC layer unfairness. We demonstrate micro (5 flows) as well as macro-evaluations (60 flows) of such cases. Our demo is conducted on WiSeNet - a 70-node wireless mesh test-bed hosted in the computer science building at NCSU. Distributed over a 100,000 sq ft building, this is one of the largest test-bed installations both in terms of number of nodes and coverage area, hence an ideal testing ground for such scenarios. Experimental results like throughput, MAC-layer statistics, delay, routing path flaps and network buffer overflows are recorded and displayed in real-time and enable a birds eye-view of the entire network status and allow us to point out various phenomenon as they happen.