Minkyong Kim

Extracting a Mobility Model from Real User Traces

Understanding user mobility is critical for simula- tions of mobile devices in a wireless network, but current mobility models often do not reflect real user movements. In this paper, we provide a foundation for such work by exploring mobility characteristics in traces of mobile users. We present a method to estimate the physical location of users from a large trace of mobile devices associating with access points in a wireless network. Using this method, we extracted tracks of always-on Wi-Fi devices from a 13-month trace. We discovered that the speed and pause time each follow a log-normal distribution and that the direction of movements closely reflects the direction of roads and walkways. Based on the extracted mobility characteristics, we developed a mobility model, focusing on movements among popular regions. Our validation shows that synthetic tracks match real tracks with a median relative error of 17%.

Mobile network estimation

Mobile systems must adapt their behavior to changing network conditions. To do this, they must accurately estimate available network capacity. Producing quality estimates is challenging because network observations are noisy, particularly in mobile, ad hoc networks. Current systems depend on simple, exponentially-weighted moving average (EWMA) filters. These filters are either able to detect true changes quickly or to mask observed noise and transients, but cannot do both. In this paper, we present four filters designed to react quickly to persistent changes while tolerating transient noise. Such filters are agile when possible, but stable when necessary, adapting their behavior to prevailing conditions. These filters are evaluated in a variety of networking situations, including persistent and transient change, congestion, and topology changes. We find that one filter, based on techniques from statistical process control provides performance superior to the other three. Compared to two EWMA filters, one agile and the other stable, it is able to offer the agility of the former in four of five scenarios and the stability of the latter in three of four scenarios.

Building realistic mobility models from coarse-grained traces

In this paper we present a trace-driven framework capable of building realistic mobility models for the simulation studies of mobile systems. With the goal of realism, this framework combines coarse-grained wireless traces, i.e., association data between WiFi users and access points, with an actual map of the space over which the traces were collected. Through a sequence of data processing steps, including filtering the data trace and converting the map to a graph representation, this framework generates a probabilistic mobility model that produces user movement patterns that are representative of real movement. This is done by adopting a set of heuristics that help us infer the paths users take between access points. We describe our experience applying this approach to a college campus, and study a number of properties of the trace data using our framework.

VDN: Virtual machine image distribution network for cloud data centers

Cloud computing centers face the key challenge of provisioning diverse virtual machine instances in an elastic and scalable manner. To address this challenge, we have performed an analysis of VM instance traces collected at six production data centers during four months. One key finding is that the number of instances created from the same VM image is relatively small at a given time and thus conventional file-based p2p sharing approaches may not be effective. Based on the understanding that different VM image files often have many common chunks of data, we propose a chunk-level Virtual machine image Distribution Network (VDN). Our distribution scheme takes advantage of the hierarchical network topology of data centers to reduce the VM instance provisioning time and also to minimize the overhead of maintaining chunk location information. Evaluation shows that VDN achieves as much as 30-80× speed up for large VM images under heavy traffic.

Periodic properties of user mobility and access-point popularity

Understanding user mobility and its effect on access points (APs) is important in designing location-aware systems and wireless networks. Although various studies of wireless networks have provided useful insights, it is hard to apply them to other situations. Here we present a general methodology for extracting mobility information from wireless network traces, and for classifying mobile users and APs. We used the Fourier transform to reveal important periods and chose the two strongest periods to serve as parameters to a classification system based on Bayes’ theory. Analysis of 1-month traces shows that while a daily pattern is common among both users and APs, a weekly pattern is common only for APs. Analysis of 1-year traces revealed that both user mobility and AP popularity depend on the academic calendar. By plotting the classes of APs on our campus map, we discovered that their periodic behavior depends on their proximity to other APs.

A Scalable and Elastic Publish/Subscribe Service

The rapid growth of sense-and-respond applications and the emerging cloud computing model present a new challenge: providing publish/subscribe as a scalable and elastic cloud service. This paper presents the Blue Dove attribute based publish/subscribe service that seeks to address such a challenge. Blue Dove uses a gossip-based one-hop overlay to organize servers into a scalable cluster. It proactively exploits skew ness in data distribution to achieve high performance. By assigning each subscription to multiple servers through a multidimensional subscription space partitioning technique, it provides multiple candidate servers for each publication message. A message can be matched on any of its candidate servers with one hop forwarding. The performance-aware forwarding in Blue Dove ensures that the message is sent to the least loaded candidate server for processing, leading to low latency and high throughput. The evaluation shows that Blue Dove has a linear capacity increase as the system scales up, adapts to sudden workload changes within tens of seconds, and achieves multifold higher throughput than the techniques used in the existing enterprise and peer-to-peer pub/sub systems.

A Hybrid Approach to High Availability in Stream Processing Systems

Stream processing is widely used by today’s applications such as financial data analysis and disaster response. In distributed stream processing systems, machine fail-stop events are handled by either active standby or passive standby. However, existing high availability (HA) schemes have not sufficiently addressed the situation when a machine becomes temporarily unavailable due to data rate spikes, intensive analysis or job sharing, which happens frequently but lasts for short time. It is not clear how well active and passive standby fare against such transient unavailability. In this paper, we first critically examine the suitability of active and passive standby against transient unavailability in a real testbed environment. We find that both approaches have advantages and drawbacks, but neither is ideal to provide fast recovery at low overhead as required to handle transient unavailability. Based on the insights gained, we propose a novel hybrid HA method that switches between active and passive standby modes depending on the occurrence of failure events. It presents a desirable tradeoff that is different from existing HA approaches: low overhead during normal conditions and fast recovery upon transient or permanent failure events. We have implemented our hybrid method and compared it with existing HA designs with comprehensive evaluation. The results show that our hybrid method can reduce two-thirds of the recovery time compared to passive standby and 80% message overhead compared to active standby, allowing applications to enjoy uninterrupted processing without paying a high premium.

EMS: Encoded Multipath Streaming for real-time live streaming applications

Multipath streaming protocols have recently attracted much attention because they provide an effective means to provide high-quality streaming over the Internet. However, many existing schemes require a long start-up delay and thus are not suitable for interactive applications such as video conferencing and tele-presence. In this paper, we focus on real-time live streaming applications with stringent end-to-end latency requirement, say several hundreds of milliseconds. To address these challenges, we take a joint multipath and FEC approach that intelligently splits the FEC-encoded stream among multiple available paths. We develop an analytical model and use asymptotic analysis to derive closed-form, optimal load splitting solutions, which are surprisingly simple yet insightful. To our best knowledge, this is the first work that provides such closed-form optimal solutions. Based on the analytical insights, we have designed and implemented a novel Encoded Multipath Streaming (EMS) scheme for real-time live streaming. EMS strives to continuously satisfy the applications QoS requirements by dynamically adjusting the load splitting decisions and the FEC settings. Our simulation results have shown that EMS can not only outperform the existing multipath streaming schemes, but also adapt to the dynamic loss and delay characteristics of the network with minimal overhead.

Association Control in Mobile Wireless Networks

As mobile nodes roam in a wireless network, they continuously associate with different access points and perform handoff operations. However, frequent handoffs can potentially incur unacceptable delays and even interruptions for interactive applications. To alleviate these negative impacts, we present novel association control algorithms that can minimize the frequency of handoffs occurred to mobile devices. Specifically, we show that a greedy LookAhead algorithm is optimal in the offline setting, where the users future mobility is known. Inspired by such optimality, we further propose two online algorithms, namely LookBack and Track, that operate without any future mobility information. Instead, they seek to predict the lifetime of an association using randomization and statistical approaches, respectively. We evaluate the performance of these algorithms using both analysis and trace-driven simulations. The results show that the simple LookBack algorithm has surprisingly a competitive ratio .of (log k + 2), where k is the maximum number of APs that a user can hear at any time, and the Track algorithm can achieve near-optimal performance in practical scenarios.

Efficacy of techniques for responsiveness in a wide-area publish/subscribe system

As the multiplicity of organizational domains often span across nations, or even continents, the need for federated communications across domains becomes paramount. Consequently, messaging middleware has become critical towards enabling cross-domain, wide-area federations. Cross-domain federation has placed increased emphasis on the need for the messaging system to provide Quality of Service (QoS), particularly with respect to responsive delivery of messages. Responsiveness, or timely delivery of messages, is critical in real-world services, such as a smart utility grid system. This study explores the efficacy of providing responsiveness in wide-area publish/subscribe messaging by evaluating several key techniques for managing latency. Specifically, this paper evaluates the following techniques: proactive best-path routing, reactive QoS-aware routing, and multipath routing. We present Harmony, a QoS-aware publish/subscribe middleware system, that adapts these techniques in order to provide responsive and high availability messaging. This study seeks to provide an in-depth understanding of how different techniques to manage responsiveness affect the end-to-end performance under various network conditions.