Context-aware computing is a mobile computing paradigm in which applications can discover and take advantage of contextual information (such as user location, time of day, nearby people and devices, and user activity) Since it was proposed about a decade ago, many researchers have studied this topic and built several context-aware applications to demonstrate the usefulness of this new technology Context-aware applications (or the system infrastructure to support them), however, have never been widely available to everyday users In this survey of research on context-aware systems and applications, we looked in depth at the types of context used and models of context information, at systems that support collecting and disseminating context, and at applications that adapt to the changing context Through this survey, it is clear that context-aware research is an old but rich area for research The difficulties and possible solutions we outline serve as guidance for researchers hoping to make context-aware computing a reality

/pdf/a-survey-of-context-aware-mobile-computing-research-1c5wp0noju.pdf

A Survey of Context-Aware Mobile Computing Research

Although there is tremendous interest in designing improved networks for data centers, very little is known about the network-level traffic characteristics of data centers today. In this paper, we conduct an empirical study of the network traffic in 10 data centers belonging to three different categories, including university, enterprise campus, and cloud data centers. Our definition of cloud data centers includes not only data centers employed by large online service providers offering Internet-facing applications but also data centers used to host data-intensive (MapReduce style) applications). We collect and analyze SNMP statistics, topology and packet-level traces. We examine the range of applications deployed in these data centers and their placement, the flow-level and packet-level transmission properties of these applications, and their impact on network and link utilizations, congestion and packet drops. We describe the implications of the observed traffic patterns for data center internal traffic engineering as well as for recently proposed architectures for data center networks.

/pdf/network-traffic-characteristics-of-data-centers-in-the-wild-43qhxtldqh.pdf

Network traffic characteristics of data centers in the wild

Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method-level computation offloading. Advancing on previous work, it focuses on the elasticity and scalability of the cloud and enhances the power of mobile cloud computing by parallelizing method execution using multiple virtual machine (VM) images. We implement ThinkAir and evaluate it with a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for a N-queens puzzle application and one order of magnitude for a face detection and a virus scan application. We then show that a parallelizable application can invoke multiple VMs to execute in the cloud in a seamless and on-demand manner such as to achieve greater reduction on execution time and energy consumption. We finally use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements.

/pdf/thinkair-dynamic-resource-allocation-and-parallel-execution-5di1n63wfi.pdf

ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading

The basic building block of ever larger data centers has shifted from a rack to a modular container with hundreds or even thousands of servers. Delivering scalable bandwidth among such containers is a challenge. A number of recent efforts promise full bisection bandwidth between all servers, though with significant cost, complexity, and power consumption. We present Helios, a hybrid electrical/optical switch architecture that can deliver significant reductions in the number of switching elements, cabling, cost, and power consumption relative to recently proposed data center network architectures. We explore architectural trade offs and challenges associated with realizing these benefits through the evaluation of a fully functional Helios prototype.

/pdf/helios-a-hybrid-electrical-optical-switch-architecture-for-3pk00vbyfq.pdf

Helios: a hybrid electrical/optical switch architecture for modular data centers

The computation for today's intelligent personal assistants such as Apple Siri, Google Now, and Microsoft Cortana, is performed in the cloud. This cloud-only approach requires significant amounts of data to be sent to the cloud over the wireless network and puts significant computational pressure on the datacenter. However, as the computational resources in mobile devices become more powerful and energy efficient, questions arise as to whether this cloud-only processing is desirable moving forward, and what are the implications of pushing some or all of this compute to the mobile devices on the edge.In this paper, we examine the status quo approach of cloud-only processing and investigate computation partitioning strategies that effectively leverage both the cycles in the cloud and on the mobile device to achieve low latency, low energy consumption, and high datacenter throughput for this class of intelligent applications. Our study uses 8 intelligent applications spanning computer vision, speech, and natural language domains, all employing state-of-the-art Deep Neural Networks (DNNs) as the core machine learning technique. We find that given the characteristics of DNN algorithms, a fine-grained, layer-level computation partitioning strategy based on the data and computation variations of each layer within a DNN has significant latency and energy advantages over the status quo approach.Using this insight, we design Neurosurgeon, a lightweight scheduler to automatically partition DNN computation between mobile devices and datacenters at the granularity of neural network layers. Neurosurgeon does not require per-application profiling. It adapts to various DNN architectures, hardware platforms, wireless networks, and server load levels, intelligently partitioning computation for best latency or best mobile energy. We evaluate Neurosurgeon on a state-of-the-art mobile development platform and show that it improves end-to-end latency by 3.1X on average and up to 40.7X, reduces mobile energy consumption by 59.5% on average and up to 94.7%, and improves datacenter throughput by 1.5X on average and up to 6.7X.

https://dl.acm.org/doi/pdf/10.1145/3037697.3037698

Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge

Resource constrained mobile devices need to leverage computation on nearby servers to run responsive applications that recognize objects, people, or gestures from real-time video. The two key questions that impact performance are what computation to offload, and how to structure the parallelism across the mobile device and server. To answer these questions, we develop and evaluate three interactive perceptual applications. We find that offloading and parallelism choices should be dynamic, even for a given application, as performance depends on scene complexity as well as environmental factors such as the network and device capabilities. To this end we develop Odessa, a novel, lightweight, runtime that automatically and adaptively makes offloading and parallelism decisions for mobile interactive perception applications. Our evaluation shows that the incremental greedy strategy of Odessa converges to an operating point that is close to an ideal offline partitioning. It provides more than a 3x improvement in application performance over partitioning suggested by domain experts. Odessa works well across a variety of execution environments, and is agile to changes in the network, device and application inputs.

/pdf/odessa-enabling-interactive-perception-applications-on-skfe0yrvfv.pdf

Odessa: enabling interactive perception applications on mobile devices

Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, ldquosimilarityrdquo can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one goal without consideration of the other. This is problematic for medical image retrieval where the goal is to assist doctors in decision making. In these applications, given a query image, the goal is to retrieve similar images from a reference library whose semantic annotations could provide the medical professional with greater insight into the possible interpretations of the query image. If the system were to retrieve images that did not look like the query, then users would be less likely to trust the system; on the other hand, retrieving images that appear superficially similar to the query but are semantically unrelated is undesirable because that could lead users toward an incorrect diagnosis. Hence, learning a distance metric that preserves both visual resemblance and semantic similarity is important. We emphasize that, although our study is focused on medical image retrieval, the problem addressed in this work is critical to many image retrieval systems. We present a boosting framework for distance metric learning that aims to preserve both visual and semantic similarities. The boosting framework first learns a binary representation using side information, in the form of labeled pairs, and then computes the distance as a weighted Hamming distance using the learned binary representation. A boosting algorithm is presented to efficiently learn the distance function. We evaluate the proposed algorithm on a mammographic image reference library with an interactive search-assisted decision support (ISADS) system and on the medical image data set from ImageCLEF. Our results show that the boosting framework compares favorably to state-of-the-art approaches for distance metric learning in retrieval accuracy, with much lower computational cost. Additional evaluation with the COREL collection shows that our algorithm works well for regular image data sets.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3315&context=sis_research

A Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval

In this paper we present qualitative and quantitative data on file access in a mobile computing environment. This information is based on actual usage experience with the Coda File System over a period of about two years. Our experience confirms the viability and effectiveness of disconnected operation. It also exposes certain deficiencies of the current implementation of Coda, and identifies new functionality that would enhance its usefulness for mobile computing. The paper concludes with a description of what we are doing to address these issues.

/pdf/experience-with-disconnected-operation-in-a-mobile-computing-41nfkkztib.pdf

Experience with disconnected operation in a mobile computing environment

This paper demonstrates the feasibility of recovering fine-scale plant structure in 3D point clouds by leveraging recent advances in structure from motion and 3D point cloud segmentation techniques. The proposed pipeline is designed to be applicable to a broad variety of agricultural crops. A particular agricultural application is described, motivated by the need to estimate crop yield during the growing season. The structure of grapevines is classified into leaves, branches, and fruit using a combination of shape and color features, smoothed using a conditional random field (CRF). Our experiments show a classification accuracy (AUC) of 0.98 for grapes prior to ripening (while still green) and 0.96 for grapes during ripening (changing color), significantly improving over the baseline performance achieved using established methods.

/pdf/classification-of-plant-structures-from-uncalibrated-image-flz3e14jti.pdf

Classification of plant structures from uncalibrated image sequences

The rising tide of data-intensive, massive scale cluster
computing is creating new challenges for traditional,
hierarchical data center networks. In response to this
challenge, the research community has begun exploring
novel interconnect topologies to provide high bisection
bandwidth—examples include Fat trees [2, 12, 8],
DCell [9], and BCube [10], among a rapidly growing set
of alternatives, many adapted from earlier solutions from
the telecom and supercomputing areas.

We argue that these solutions may provide too much—
full bisection bandwidth on packet timescales—at too
high a cost—literally tons of wiring and thousands of
switches. In this work, we suggest that research should
take a look back not only at historical topologies, but also
historical technologies. More specifically, we suggest
that a hybrid packet-switched/circuit-switched network
can provide the functions and ease-of-use of today’s allpacket
networks, while providing high bandwidth for a
large class of applications at lower cost and lower network
complexity. Taking advantage of this network requires,
however, a philosophical change to the design of
data center networks. We propose to augment the electrical
switch architecture with an optical circuit-switched
network. Implementing this approach requires a network
re-design to provide substantial pre-optical queueing at
the nodes, treating the entire data center as one large virtually
output-queued router. We explain this argument
briefly, and expand upon our proposed solution in the
sections that follow.

/pdf/your-data-center-is-a-router-the-case-for-reconfigurable-oqhq9gcndf.pdf

Lily Mummert

Papers

Odessa: enabling interactive perception applications on mobile devices

A Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval

Experience with disconnected operation in a mobile computing environment

Classification of plant structures from uncalibrated image sequences

Your Data Center Is a Router: The Case for Reconfigurable Optical Circuit Switched Paths