We present BurstTracker, the first tool that developers can use to detect if the LTE downlink is the bottleneck for their applications. BurstTracker is driven by our discovery that the proprietary LTE downlink schedulers running on LTE base stations allocate resources to users in a way that reveals if a user's downlink queue runs empty during a download. We demonstrate that BurstTracker works across Tier-1 cellular providers and across a variety of network conditions. We also present a case study that shows how application developers can use this tool in practice. Surprisingly, with BurstTracker, we find that the LTE downlink may not be the bottleneck for video streaming on several Tier-1 providers, even during peak hours at busy locations. Rather, transparent TCP middleboxes deployed by these providers lead to downlink underutilization, because they force Slow-Start Restart. With a simple workaround, we improve video streaming bitrate on busy LTE links by 35%.

Load on cellular towers is one of the key metrics that cellular service providers monitor as part of their operational and management tasks. Increased load on the towers can lead to congestion, which in turn can severely degrade quality of service perceived by users. Hence, it is of great interest to cellular service providers to minimize the maximum load at cell towers, and thereby minimize chances of congestion in the event of a sudden increase in load due to user demand changes. This goal can be achieved by proactive load balancing among neighboring cell towers, i.e., proactively identify opportunities to balance the load through re-binding of users from heavily loaded cell towers to lightly loaded neighboring towers. In this paper, we propose a new tool Libra to effectively assess the impact of load balancing related parameter changes. Libra provides an objective measure of the degree of load imbalance across multiple network locations and identifies if the measure improves or degrades after parameter changes. Our evaluation of Libra using real-world data collected from a large cellular provider demonstrates its effectiveness in accurately capturing the degree of imbalance at multiple cell towers.

We present xFabric, a novel datacenter transport design that provides flexible and fast bandwidth allocation control. xFabric is flexible: it enables operators to specify how bandwidth is allocated amongst contending flows to optimize for different service-level objectives such as minimizing flow completion times, weighted allocations, different notions of fairness, etc. xFabric is also very fast, it converges to the specified allocation one-to-two order of magnitudes faster than prior schemes. Underlying xFabric, is a novel distributed algorithm that uses in-network packet scheduling to rapidly solve general network utility maximization problems for bandwidth allocation. We evaluate xFabric using realistic datacenter topologies and highly dynamic workloads and show that it is able to provide flexibility and fast convergence in such stressful environments.

The FCC mandate of allowing TV Whitespaces for unlicensed access has the potential for dramatic improvements in wireless access data rates. We argue that an ideal MAC should account for diverse user-location and spectrum dependent channel rates to provide fair data rates and efficient utilization. Furthermore, due to limited tunable bandwidth of a radio and fragmented spectrum, the AP should support multiple radios. We make the following contributions by designing a MAC for wireless LAN access over TV Whitespace. (i) We propose an architecture and beaconing mechanism to enable such a MAC. Our MAC is an evolution of 802.11 MAC. (ii) We propose an algorithm that chooses the Whitespaces for the different radios of the AP and assigns clients to the radios. Our algorithm has provable guarantee and is near-optimal in many scenarios. (iii) Extensive simulation over OMNET platform demonstrates the benefit of our design over a frequency and client-location agnostic Wi-Fi-like MAC. The typical throughput gain is 30-76 percent, whereas, the reduction in collisions is up to 80 percent. (iv) We implemented a proof-of-concept prototype (by modifying madWiFi drivers) that demonstrates feasibility of our design, robustness to temporal variation of available spectrum, and system throughput.

Recent trends suggest that cognitive radio based wireless networks will be frequency agile and the nodes will be equipped with multiple radios capable of tuning across large swaths of spectrum. The MAC scheduling problem in such networks refers to making intelligent decisions on which communication links to activate at which time instant and over which frequency band. The challenge in designing a low-complexity distributed MAC, that achieves low delay, is posed by two additional dimensions of cognitive radio networks: interference graphs and data rates that are frequency-band dependent, and explosion in number of feasible schedules due to large number of available frequency-bands. In this paper, we propose MAXIMAL-GAIN MAC, a distributed MAC scheduler for frequency agile multi-band networks that simultaneously achieves the following: (i) optimal network-delay scaling with respect to the number of communicating pairs, (ii) low computational complexity of O(log2(maximum degree of the interference graphs)) which is independent of the number of frequency bands, number of radios per node, and overall size of the network, and (iii) robustness, i.e., it can be adapted to a scenario where nodes are not synchronized and control packets could be lost. Our proposed MAC also achieves a throughput provably within a constant fraction (under isotropic propagation) of the maximum throughput. Due to a recent impossibility result, optimal delay-scaling could only be achieved with some amount of throughput loss . Extensive simulations using OMNeT++ network simulator shows that, compared to a multi-band extension of a state-of-art CSMA algorithm (namely, Q-CSMA), our asynchronous algorithm achieves a 2.5x reduction in delay while achieving at least 85% of the maximum achievable throughput. Our MAC algorithms are derived from a novel local search based technique.

Unlike their cellular counterparts, Wi-Fi networks do not have the luxury of a dedicated control plane that is decoupled from the data plane. Consequently, Wi-Fi struggles to provide many of the capabilities that are taken for granted in cellular networks, including efficient and fair resource allocation, QoS and handoffs. The reason for the lack of a control plane with designated spectrum is that it would impose significant overhead. This is at odds with Wi-Fi's goal of providing a simple, plug-and-play network.

There are two emerging trends in the mobile data world. First, mobile data is exploding at a rapid rate with analysts predicting 25-50X growth by the year 2015. The second trend is that users are demanding greater degree of flexibility in selecting their operators at fine timescales. Across Asia, dual-SIM phones have become popular, while Apple is rumored to be designing a Universal SIM that will allow iPhone users to toggle between different operators. This latter trend points towards an impending disruption in wireless service models which could also be the need of the hour from the spectrum shortage perspective.

Cisco's NetFlow collector (NFC) is a powerful example of a real-world product that supports multiple aggregate queries over a continuous stream of IP flow records. NFC enables a plethora of network management tasks like traffic demands estimation, application traffic profiling, etc. In this paper, we investigate two computation sharing techniques for enabling streaming applications such as NFC to scale to hundreds of queries. Our first technique instantiates certain intermediate aggregates which are then used to generate the final answers for input queries. Our second technique coalesces the filter conditions of similar queries and uses the coalesced filter to pre-filter stream data input to these queries. Using these techniques, we propose a heuristic to compute a good query plan and perform extensive simulations to show that our heuristic delivers a factor of over 3 performance improvement compared to a naive approach.

IEEE 802.16e WiMAX is a promising new technology for broadband access networks. Amongst the class of applications that can be supported is real time video services (such as IPTV, broadcast of live events etc.). These applications are bandwidth hungry and have stringent delay constraints. Thus, scalable support for such applications is a challenging problem. To address this challenge, we consider a combination of approaches using multicast, layer encoded video and adaptive modulation of transmissions. Using these, we develop algorithms to ensure efficient, fair and timely delivery of video in WiMAX networks. The corresponding resource allocation problem is challenging because scheduling decisions (within a WiMAX base station) are performed in real-time across two dimensions, time and frequency. Moreover, combining layered video with appropriate modulation calls for novel MAC algorithms. We model the multicast resource allocation problem in WiMAX and demonstrate this problem to be NP-hard. We present a fast greedy algorithm that is (i) provably within a constant approximation of the optimal solution (based on a metric that reflects video quality as perceived by the user), and (ii) performs within 87-95% of the optimal as demonstrated by realistic simulations. We also demonstrate that our algorithm offers a 25% improvement over a naive algorithm. Moreover, in terms of the average rate received by each user, our algorithm out-performs the naive algorithm by more than 50%.