The siloed state of IoT ecosystem is a well-recognized issue. Companies that operate IoT ecosystems have participated in standardization bodies to address this concern. One such organization that has attracted many industry members is the Open Connectivity Foundation (OCF), which recently published a new standard, Universal Cloud Interface, to facilitate the integration of IoT clouds. As one of the companies who have been actively involved in developing this standard and the first to implement it, we share our experiences in hope of creating IoT ecosystems that are seamlessly integrated and providing new multi-device experiences to users without imposing the burden of extensive development efforts on device manufacturers.

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.

The number of real-world applications that require QoS guarantees is constantly increasing and they often follow the publish/subscribe (pub/sub)messaging paradigm, which provides loosely coupled many-to-many communication. Many QoS-aware systems use overlay networks as they allow flexible routing. To provide QoS-aware pub/sub messaging in overlay networks, the messaging system should be adaptive to the changes in network conditions (such as delay and failures). However, many pub/sub systems depend on a flexed routing topology and it is costly to rebuild this topology in case of failures. This study seeks to address this challenge with Delay-Cognizant Reliable Delivery (DCRD), a novel and delay-aware dynamic routing algorithm to provide reliable message delivery for pub/sub overlay networks. For reliable message delivery, DCRD no longer uses a flxed routing topology. Instead, it dynamically switches among different links to bypass link failures and increase the chance to meet QoS requirement. Each node tries different neighboring nodes in an order that is mathematically proven to minimize the expected delay of packet delivery. With all possible neighboring nodes sorted this way, DCRD guarantees that packets are delivered as long as there exists a path between the publisher and subscriber and that the expected delay is minimized. DCRD is extensively evaluated in simulation with comparison to existing tree-based routing approaches as well as a multi path approach using different network topologies, delay constraints, and loss probabilities. Simulation results show that DCRD performs better than all the baselines, providing reliable message delivery and satisfying the delay requirement for more than 98% of messages when the link failure probability is 4% or less.

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 skewness 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.

Queuing is commonly used to connect loosely coupled components to form large-scale, highly-distributed, and fault-tolerant applications. As cloud computing continues to gain popularity, a number of vendors have started offering cloud-hosted, multi-tenant queuing service. They provide high availability at the cost of reduced consistency. Although they offer at-least-once delivery guarantee, that is, no message loss, they do not make any effort in maintaining FIFO order, which is an important aspect of the queuing semantics. Thus they are not adequate for some applications. This paper presents the design and implementation of a scalable cloud-based queuing service, called Blue Dove Queuing Service (BDQS). It provides improved queuing consistency - at-least-once and best-effort in-order message delivery - while preserving high availability and reliability. It also offers clients a flexible trade-off between duplication and message order. Comprehensive evaluation is carried out on an Infrastructure-as-a-Service cloud computing platform with up to 70 server nodes and 1000 queues. It shows that BDQS achieves excellent performance scalability. Meanwhile, it offers an order-of-magnitude improvement in out-of-order measurement compared to existing no-order systems. Results also indicate that BDQS is highly reliable and available.

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.

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 application's 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.

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 user's 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.

Abstract — 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%. I.

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.

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.