Ravi Kokku

NVS: a substrate for virtualizing wireless resources in cellular networks

This paper describes the design and implementation of a network virtualization substrate (NVS ) for effective virtualization of wireless resources in cellular networks. Virtualization fosters the realization of several interesting deployment scenarios such as customized virtual networks, virtual services, and wide-area corporate networks, with diverse performance objectives. In virtualizing a base stations uplink and downlink resources into slices, ssr NVS meets three key requirements-isolation, customization, and efficient resource utilization-using two novel features: 1) NVS introduces a provably optimal slice scheduler that allows existence of slices with bandwidth-based and resource-based reservations simultaneously; and 2) NVS includes a generic framework for efficiently enabling customized flow scheduling within the base station on a per-slice basis. Through a prototype implementation and detailed evaluation on a WiMAX testbed, we demonstrate the efficacy of ssr NVS. For instance, we show for both downlink and uplink directions that ssr NVS can run different flow schedulers in different slices, run different slices simultaneously with different types of reservations, and perform slice-specific application optimizations for providing customized services.

CellSlice: Cellular wireless resource slicing for active RAN sharing

We present the design and implementation of Cell-Slice, a novel system for slicing wireless resources in a cellular network for effective Radio Access Network (RAN) sharing. CellSlice is a gateway-level solution that achieves the slicing without modifying the basestations MAC schedulers, thereby significantly reducing the barrier for its adoption. Achieving slicing with a gateway-level solution is challenging, however, since resource scheduling decisions occur at the basestations at fine timescales, and these decisions are not visible at the gateways. In the uplink direction, CellSlice overcomes the challenge by indirectly constraining the uplink schedulers decisions using a simple feedback-based adaptation algorithm. For downlink, we build on the technique used by NVS, a native basestation virtualization solution, and show that effective downlink slicing can be easily achieved without modifying basestation schedulers. We instantiate a prototype of CellSlice on a Picochip WiMAX testbed. Through both prototype evaluation and simulations, we demonstrate that CellSlices performance for both remote uplink and remote downlink slicing is close to that of NVS. CellSlices design is access-technology independent, and hence can be equally applicable to LTE, LTE-Advanced and WiMAX networks.

InSite: QoE-aware video delivery from cloud data centers

The Internet is witnessing a rapid increase in video traffic. Due to the scalability and the cost-savings offered by cloud-computing, Internet video service providers are increasingly delivering their content from multi-tenant cloud data centers. One of the major challenges faced by such a video service provider is the management of the Quality-of-Experience (QoE) of the end-users in the presence of Variable Bit Rate (VBR) video flows, time varying network conditions in the Internet, and the bounded egress bandwidth provided by the data center. To this end, we present InSite, a light-weight and easy-to-deploy solution for managing the QoE of a set of video flows of a service provider, which are served from a data center. InSite is deployed at the egress of a data center, between the video servers and the clients, and manages the video flows that are transmitted over TCP. The solution uses a novel generalized binary search technique to concurrently search for the appropriate flow rates for a set of flows, with the goal of maximizing the QoE-fairness across the flows, as opposed to TCP-fairness. The search takes into account the total egress bandwidth allocated for the set of video flows at the data center, the unknown and possibly time-varying capacities of any remote bottleneck links, and the playout buffer sizes of the video flows. The solution is also designed to operate with minimal modifications to the video servers and the clients. In our evaluations using extensive ns-3 simulations and a testbed implementation for serving videos over TCP, we observe that deploying InSite achieves several folds reduction in playout stalls over a system without InSite.

Idea: A System for Efficient Failure Management in Smart IoT Environments

IoT enabled smart environments are expected to proliferate significantly in the near future, particularly in the context of monitoring services for wellness living, patient healthcare and elderly care. Timely maintenance of failed sensors is of critical importance in such deployments to ensure minimal disruption to monitoring services. However, maintenance of large and geographically spread deployments can be a significant challenge. We present Idea that significantly increases the vtime-before-repair for a smart home deployment, thereby reducing the maintenance overhead. Specifically, our approach leverages the facts that (a) there is inherent sensor redundancy when combinations of sensors monitor activities of daily living (ADLs) in smart environments, and (b) the impact of each sensor failure depends on the activities being monitored and the functional redundancy afforded by rest of the heterogeneous sensors available for detecting the activities. Consequently, Idea identifies homes that need to be fixed based on expected degradation in ADL detection performance, and optimizes maintenance scheduling accordingly. We demonstrate that our approach leads to 3--40 times fewer maintenance personnel than a scheme in which failed sensors are fixed without considering their impact.

Trajectory aware macro-cell planning for mobile users

We handle the problem of efficient user-mobility driven macro-cell planning in cellular networks. As cellular networks embrace heterogeneous technologies (including long range 3G/4G and short range WiFi, Femto-cells, etc.), most traffic generated by static users gets absorbed by the short-range technologies, thereby increasingly leaving mobile user traffic to macro-cells. To this end, we consider a novel approach that factors in the trajectories of mobile users as well as the impact of city geographies and their associated road networks for macro-cell planning. Given a budget k of base-stations that can be upgraded, our approach selects a deployment that improves the most number of user trajectories. The generic formulation incorporates the notion of quality of service of a user trajectory as a parameter to allow different application-specific requirements, and operator choices. We show that the proposed trajectory utility maximization problem is NP-hard, and design multiple heuristics. We evaluate our algorithms with real and synthetic datasets emulating different city geographies to demonstrate their efficacy. For instance, with an upgrade budget k of 20%, our algorithms perform 3-8 times better in improving the user quality of service on trajectories when compared to greedy location-based base-station upgrades.

People in motion: Spatio-temporal analytics on Call Detail Records

The data about how people move in a city can be potentially used by various enterprises and government organizations to strategically optimize their operations and maximize their revenue. However, fine-grained and real-time data is currently unavailable to the enterprises. We believe that Cellular Network operators can deliver such data and insights to enterprises. Call records collected in the networks embed a wealth of information about where, when and how a large fraction of the city moves. However, this information is untapped; a majority of the cellular operators are not deriving spatio-temporal insights or monetizing the data that is already available. In this paper, we demonstrate “People in Motion”: an end-to-end Hadoop-based system with a library of spatio-temporal algorithms that operates on the call record data to derive business insights. We identify the hangouts and trajectories of users with different interests. Finally, we demonstrate a visual analytics tool that facilitates business users to compute, compare and contrast the importance of spatial regions at different times for different categories of users.

Async: De-congestion and yield management in cellular data networks

We design and implement a novel system called Async, which enables a mobile network operator (MNO) to efficiently manage the growth of mobile data by leveraging the delay-elastic nature of certain applications and the price-sensitive nature of certain users. Specifically, Async introduces an alternate “asynchronous” content-delivery paradigm for heavy content (e.g., videos), and facilitates an MNO to negotiate with users a delay in delivery in exchange for appropriate incentives. The MNO uses the negotiated delays to actively manage Async flows to reduce congestion and improve the quality-of-experience (QoE) of both delayed and regular flows. We show that in comparison to state-of-the-art, Asyncs network-based flow management enhances QoE for more than 30% of the regular flows, with up to 60% improvement in per-flow QoE metric, while still meeting the negotiated delivery times of 95% of the delayed flows. Async also lowers the delivery times of delayed flows by ∼67% and significantly increases robustness to traffic unpredictability. Our design is robust to disconnections and does not require any modifications to existing network infrastructure and protocols. Our prototype deployment (using Apaches mod_proxy and an Android app) on live networks confirms Asyncs efficacy in meeting EDTs for diverse deployment scenarios.

Location assisted handoffs in dense cellular networks

We conduct an extensive measurement study in an operational cellular network in dense urban settings and identify two key sources of inefficiencies in standard 3GPP handoff technique: wrong timing of handoff and sub-optimal base station association at a point. Based on our study, we propose a novel handoff technique that leverages (1) historical geo-tagged signal strength data from visible base stations at typical mobile user locations and (2) knowledge of the user trajectory. First, we formulate the handoff problem as a bi-criteria optimization problem and present a dynamic programming formulation that provides pareto-optimal solutions that maximize overall signal strength along user trajectory for a range of handoffs. Using an efficient utility based selection technique, we select a solution that balances the tradeoff between reducing number of handoffs and increasing the overall signal strength experienced by the user. Next, we present a much faster greedy heuristic that augments the solution obtained from the dynamic programming formulation when optimizing user performance in the presence of dynamic network load at each base station. A key contribution of our work is to show that striking a tradeoff between the two objectives is much harder in dense macro cellular base station deployments and an effective handoff technique should balance both these objectives well. We show that our technique takes a well balanced approach and achieves more than 25% reduction in the number of handoffs over the standard 3GPP handoff technique in an operational cellular network without incurring reduction in average signal strength, and even improving it by 1dB/sec in some cases. Finally, we propose a novel 3GPP standards compliant trajectory-aware location based handoff protocol and discuss the practical implementation aspects in cellular networks.

On the estimation of available bandwidth in broadband cellular networks

Over-the-top estimation of available bandwidth (AB) in a network path has been well studied for wired networks. The AB of a path denotes its slack capacity, i.e., the bandwidth available for use in the path without impacting the existing traffic. This estimation problem has been receiving attention only recently in cellular networks, which are increasingly becoming one of the main modes of access for a large number of applications. In this paper, we discuss the challenges posed by the problem, and why existing techniques developed for wired networks cannot be applied. We show that, interestingly, it may not even be feasible to estimate AB using over-the-top approaches under certain conditions, even when the wireless channel and traffic conditions are non-varying. We then present a novel AB estimation technique for cellular networks, which typically use proportional fair scheduling at base stations. When the wireless channel and traffic conditions are non-varying, our technique can accurately determine AB when it exceeds the “fair share” due to a new flow. We also extend the basic technique for estimation under conditions that are time-varying. The proposed methods can as well be used when one or more bottleneck links in a network path are fair-scheduled using algorithms such as weighted-fair queueing. We evaluate our methods using simulations and over operational networks, and present the results. In simulations, our technique is capable of detecting AB close to 90% of the time under feasible conditions even with bursty traffic.

SERA: A hybrid scheduling framework for M2M transmission in cellular networks

Trends show that machine-to-machine (M2M) devices are going to grow by orders of magnitude, far surpassing the number of mobile devices. This unprecedented scale and the fact that M2M traffic typically consists of many small-sized transmissions make the data and signaling overhead of introducing M2M traffic into cellular networks a big concern. Fortunately, it is possible to exploit certain unique characteristics of M2M traffic, like periodicity and delay tolerance in its scheduling, to alleviate these concerns. In this paper, we propose SERA - a two-level Scheduled Randomization framework, which does precisely this, and efficiently integrates M2M traffic into cellular networks. Broadly, SERA consists of (i) a central controller that defines certain coarse-level transmission parameters to govern M2M traffic in the next scheduling period and (ii) a simple distributed randomized algorithm at each M2M device that governs fine-grained transmission decisions within the period. Using experiments and analyses, we show that compared to existing techniques for M2M traffic management, SERA can lower peak traffic load by 30-40%, bring down the total time spent under congestion by 30-40%, and that these gains are robust to errors in traffic prediction.