We are increasingly reliant on cellular data services for many types of day-to-day activities, from hailing a cab, to searching for nearby restaurants. Geo-location has become a ubiquitous feature that underpins the functionality of such applications. Network operators can also benefit from accurate mobile terminal localization in order to quickly detect and identify location-related network performance issues, such as coverage holes and congestion, based on mobile measurements. Current implementations of mobile localization on the wildly-popular Android platform depend on either the Global Positioning System (GPS), Android's Network Location Provider (NLP), or a combination of both. In this paper, we extensively study the performance of such systems, in terms of its localization accuracy. We show through real-world measurements that the performance of GPS+NLP is heavily dependent on the mobility of the user, and its gains on localization performance is minimal, and often even detrimental, especially for network round-trip delays up to 1s. Building upon these findings, we evaluate the efficacy of using Tattle, a cooperative local measurement-exchange system, and propose Delay-Adjusted U-CURE, a clustering algorithm that greatly improves the localization performance of both GPS-only, and GPS+NLP techniques, without keeping expensive system states, nor requiring any location anchors nor additional instrumentation, nor any external knowledge that is not available programmatically to application designers. Our results are promising, demonstrating that median location accuracy improvements of over 30% is achievable with just 3 co-located devices, and close to 60% with just 6 co-located devices. These findings can be used by operators to better manage their networks, or by application designers to improve their location-based services.

Cellular service subscribers are increasingly reliant on cellular data services for all kinds of mobile applications. Oftentimes, when subscribers experience frustratingly high network delays and timeouts, they like to know whether their experiences are shared by other users nearby. The question that is often asked is essentially this: “is it just me, or do others around me face the same problem?” In this paper, we describe how we use Tattle, a distributed real-time participatory sensing and monitoring framework, to glean network performance information from users nearby. Tattle relies on recent advances in peer-to-peer device networking, such as Wi-Fi Direct, Bluetooth Low Energy, and Apple's iBeacon, to exchange key snippets of diagnostic information using very low-power, very short-range local-area wireless interfaces, between participating devices. We propose and develop a robust statistical algorithm, based on quantile regression, which identifies key points in time where a device experiences high delays and outages that are not observed by its neighbors, and decides if the device is performing “normally”, or “abnormally”. This directly answers the “me, or others?” question. We demonstrate and validate the efficacy of our system through real-world measurements of network delay, consisting of over 7,300 time-series that comprises over 443,500 data samples, using commodity smart devices attached to two different providers' networks.

The operating environment of cellular networks can be in a constant state of change. One Singaporean operator expressed difficulty with the coverage assertion (CA) problem of whether regulated minimum coverage is met, especially in urban areas. Currently, the operator manually appraises coverage through laborious and expensive walk/drive-tests. In this paper, we propose Tattle, a distributed, low-cost and comprehensive cellular network measurement collection and processing framework. We exemplify Tattle by leveraging on participating UEs to report on network coverage in real-time. Tattle exploits wireless local-area interfaces to exchange RSCP measurements amongst devices to preserve the co-locality of readings and conserve power. We propose U-CURE, a clustering algorithm which considers sample location uncertainty and the knowledge of device co-location to remove erroneously localized readings. We develop a prototype app on the Android™ platform as a proof-of-concept of the Tattle framework. We then use the Tattle framework to perform extensive RSCP measurement collection and processing in various areas in Singapore, collecting over 3.78 million readings. We present visualizations of mean signal coverage and RSCP CDFs for various areas of interest. The latter is a key output of Tattle, which helps operators to appraise coverage and solve the CA problem by relying on subscriber measurements, instead of expensive, laborious and limited-scale walk-/drive-tests.

With wireless energy transfer, a mobile node can operate perpetually without having a wired connection to charge its battery. In this paper, we present a quality of service (QoS) aware data transmission and wireless energy transfer for the mobile node. The node can request for wireless energy transfer or transmit a packet when the node is in a coverage area of an access point. The node supports service differentiation for different type of traffic (i.e., low and high priority data). To meet the QoS requirement of each traffic type, we present the performance modeling and optimization framework. The objective is to maximize the throughput, while the packet loss probabilities are maintained below the target levels. The optimal policy for the node is obtained from solving the constrained Markov decision process (CMDP). In addition, we present an application of the framework to a data mule for collecting, carrying, and transmitting data from sensors to the access point.

MapReduce has become the dominant programming model for processing massive amounts of data on cloud platforms. More and more enterprises are now utilizing hybrid clouds, consisting of private infrastructure owned by themselves and public clouds such as Amazon EC2, to process their spiky MapReduce workloads, which fully utilize their own on-premise resources while outsourcing the tasks only when needed. With disparate workloads of different MapReduce tasks, an efficient scheduling mechanism is in need to enable efficient utilization of the on-premise resources and to minimize the task outsourcing cost, while meeting the task completion time requirements as well. In this paper, a fine-grained model is described to characterize the scheduling of heterogeneous MapReduce workloads, and an online algorithm is proposed for joint task admission control into the private cloud, task outsourcing to the public cloud, and VM allocation to execute the admitted tasks on the private cloud, such that the time-averaged task outsourcing cost is minimized over the long run. The online algorithm features preemptive scheduling of the tasks, where a task executed partially on the on-premise infrastructure can be paused and scheduled to run later. It also achieves desirable properties such as meeting a pre-set task admission ratio and bounding the worst-case task completion time, as proven by our rigorous theoretical analysis.

We expect femto-cells to be massively and densely deployed in the future. Numerous existing works on femto-cell interference management assume that the local topology of interfering femto-cells can be sufficiently approximated through sensing, if not already known in advance. We show that this assumption results in poor throughput performance in dense femto-cell networks. For some cell-edge users, using conventional sensing in dense deployments can result in almost 50 times less instantaneous throughput, as compared to having oracular knowledge of interference topology. This sub-optimality is caused by “far-away” interferers. These are femto-cells that are deployed just far enough such that their presence will not be detected by conventional sensing. We then introduce a mobile sensing scheme to detect these “far-away” interferers by exploiting the inherent mobility of femto-cell users. We show through packet-level simulation that this sensing scheme is able to better approximate the interference topology. This results in significantly improved performance over conventional sensing, in dense deployment scenarios.

Erasure coding has been proposed in distributed storage systems for both high data reliability and low storage redundancy. With virtualization, virtual machines (VMs) are essentially defined by software and by their memory states, and erasure coding can be used in the same manner as in storage for high reliability and availability. Each VM can backup its memory state to a hot spare, and multiple memory states are coded at the hot spare to provide data reliability and low redundancy. This poses a new set of challenges. Both synchronization and recovery of the VMs consume significant bandwidth which may impede the performance of a data center. To guarantee high availability, recovery from a machine failure (decoding) at the hot spare must be fast. When (de)coding is done in-network, computation can be distributed over hosts, routers or switches and bandwidth for synchronization and recovery can be reduced significantly. We show the conditions needed to support optimal rates for synchronization and recovery for many-to-one backup in arbitrary networks. Furthermore, we propose routing and code construction algorithms that run in polynomial-time.

Through the recent emergence of joint resource and network virtualization, dynamic composition and provisioning of time-limited and isolated virtual infrastructures is now possible. One other benefit of infrastructure virtualization is the capability of transparent reliability provisioning (reliability becomes a service provided by the infrastructure). In this context, we discuss the motivations and gains of introducing customizable reliability of virtual infrastructures when executing large-scale distributed applications, and present a framework to specify, allocate and deploy virtualized infrastructure with reliability capabilities. An approach to efficiently specify and control the reliability at runtime is proposed. We illustrate these ideas by analyzing the introduction of reliability at the virtual-infrastructure level on a real application. Experimental results, obtained with an actual medical-imaging application running in virtual infrastructures provisioned in the experimental large-scale Grid'5000 platform, show the benefits of the virtualization of reliability.

Virtualization is a key enabler to autonomic management of hosted services in data centers. We show that it can be used to manage reliability of these virtual entities with virtual backups. An architecture is proposed to autonomously manage and allocate the physical resources, ensure reliability guarantees, and manage the pools of virtual backups for failure recovery and resource conservation. It is fault tolerant by design so that component failures do not bring down the entire data center.

Latency is a critical concern in interactive or delay-sensitive services such as interactive IPTV and VoIP. This is especially so when using network coding as a means to conserve bandwidth in these bandwidth-hungry services. In practical network coding, packets are coded in batches and thus suffer a large average delay per packet when packets get decoded after the whole batch is received. A larger batch size, however, also gives the highest bandwidth savings. In this paper, we analyze the achievable upper and lower bounds of the average delay per packet as well as packet loss rates due to finite-sized queue in a multicast downlink transmission from the system and client perspectives. We validate our analysis with simulation results and characterize the queueing and transmission delays, and packet loss performance with respect to (i) the maximum size of a batch and (ii) packet arrival rates. We find that random linear coding is an upper bound in delay performance and other hybrid network coding method might achieve better delay gains.

Network coding is a method that promises to achieve the min-cut capacity in multicasts. However, pushing towards this gain in throughput comes with two sacrifices. Delay suffers as the decoding procedure requires buffering and is performed in batches of coded packets, and unfairness prevails in terms of delay increases between receivers with worse channel conditions and those with better channel conditions. In this paper, we focus on optimizing the delay performance in reliably multicasting a data stream to a set of one-hop receivers from the receiver perspective. We analyze the system based on queueing theory using semi-Markov chains from both the system-wide and receiver perspectives. We find that the average delay per received packet at the receivers' end can be minimized by appropriate scheduling of data packets and appropriate size of the coding buffer, which depends on the rate of incoming data stream and capacities of the receivers. To circumvent unduly computational complexities, we design a heuristic scheme which can achieve significant performance gain when compared to an existing method. Our scheme readily adapts the coding size to the dynamics of the system, and schedules data packets to be coded via some strict priority measure for optimized delay performance. We show through extensive simulations that our scheme gives low average delay at high streaming rates and narrows the performance gap between receivers with bad and good channel conditions.

It is well-known that throughput of wired multi-hop, multi-commodity networks can be maximized by employing back-pressure scheduling. With this approach, packets belonging to different destinations are dynamically routed/scheduled in a network based on buffer occupancies and link quality. There has been a considerable amount of research on applying back-pressure scheduling in the wireless environment; usually by abstracting each wireless channel as a point-to-point link and therefore, ignoring the fundamental wireless broadcast property. In this paper, we consider a two-hop wireless cooperative network and characterize the minimum throughput gain obtained by exploiting broadcast property in back-pressure scheduling. Numerical results are provided to support our analysis.

10.1109/INFOCOM.2006.118

Classical tracking methods are not concerned with energy efficiency and require precise localisation. We addressed these in our previous work through HMTT (hierarchical Markov decision process for target tracking) that tracks single targets at location granularity. HMTT conserves energy by reducing the rate of sensing but preserves acceptable tracking accuracy through trajectory prediction. In this paper, HMTT is extended for the multiple targets case where the state of clusters could be affected by multiple incoming targets and where multiple updates are required at the lower level. The theoretical performance of HMTT in the multiple targets case is derived and simulations demonstrate its effectiveness against 2 other predictive tracking algorithms with up to 200% improvement.