While tremendous growing mobile apps offer users rich services and functionalities, they also bring significant performance and energy issues. Code sharing is promising to address these issues, but existing application-level code sharing is rather restrictive. This paper develops the a transparent machine code sharing for mobile devices, and presents its design, implementation, and deployment. SnapCode enables machine code sharing across a wide variety of commercial off-the-shelf Android devices. By sharing and running machine code, SnapCode can offer significant speed-ups: an average speed-up of 9.9X for one-time trial apps, and up to 120X in apps’ regular uses. In addition, it can save more than 80 percent energy consumption.

Deep learning plays an increasingly important role in human life. However, resource-constrained IoT devices are still inefficient in performing deep neural network (DNN) inference. Existing works have attempted to improve the performance by leveraging edge computing that partitions DNN and offloads a part of workloads to the edge server. However, most of them focus on scheduling workloads among different devices, ignoring the network costs. Thus, user experience easily suffers from inferior conditions such as network congestion. To address this issue, we jointly consider network conditions and computing capabilities of the IoT device and edge server, and then propose FastCoDNN, a co-execution framework that enables high-performance DNN inference. Specifically, under inferior conditions, we conduct redundant calculation instead of data synchronization to reduce network costs. Then, we orchestrate redundant calculations and data synchronizations among the local and edge, and flexibly adjust them according to new network conditions. We propose a new algorithm based on dynamic programming to achieve this adjustment. Experimental results show that FastCoDNN achieves fewer network costs and much performance improvement compared with existing methods.

In cloud and edge networks, federated learning involves training statistical models over decentralized data, where servers aggregate models through intermediate updates trained from clients. By utilizing private and local data it improves quality of personalized services and reduces user's concern for privacy. However, federated learning still leaks multimedia features through trained intermediate updates and thereby is not privacy-preserving for multimedia. Existing techniques applied from secure community attempt to avoid multimedia features leakages for federated learning but yet cannot address issues of privacy. In this paper, we propose a privacy-preserving solution that avoids multimedia privacy leakages in federated learning. Firstly, we devise a novel encryption scheme called Non-Informative Transformation (NIT) for federated aggregation to eliminates residual multimedia features in intermediate updates. Based on the scheme, we then propose Just-Learn-over-Ciphertext (JLoC) mechanism for federated learning, which includes three stages in each model iteration. The Encrypt stage encrypts intermediate updates and makes it non-informative distribution at clients. The Aggregate stage performs model aggregation without decryption at servers. Specifically, this stage just computes over ciphertext, and its output of aggregation also keeps non-informative. The Decrypt stage converts non-informative outputs of aggregation to available parameters for the next iteration at clients. Moreover, we implement a prototype and conduct experiments to evaluate its privacy and performance on real devices. The experimental results demonstrate that our methods can defend against potential attacks for multimedia privacy leakages without accuracy loss in commercial off-the-shelf products.

Graphics processing units (GPUs) have of late enjoyed increased popularity as a general purpose computing accelerator in multiple application domains like artificial intelligence. However, there has been little exploration into the performance and energy optimization GPUs can deliver for increasingly popular but heavy burden on transparent computing for Internet-of-Things (IoT), such as deep-inference tasks under energy-constrained scenarios. This paper presents FuncShare, an extension of the capability of dynamic functional connectivity among IoT to cross-device hand over identified computation-intensive functional codes at runtime. As a kernel-level offloading solution, FuncShare now enables unmodified Android and Linux-based transparent computing applications (apps) to utilize not only application functionalities but also system functionalities across devices, as if they were to utilize them inside the same OpenCL or CUDA libraries. Toward secure network connection, FuncShare also allows performing of permission checks for remote apps in the same method as for local. Experimental results show that FuncShare enables transparent cross-device sharing for power-hungry functionalities and benefits significant performance enhancing than within-device processing even if a large amount of data is transferred.

Code offload has been a key technique that provides new opportunities to achieve high performance with edge devices of weak computation capability. However, the implementation of code offload system on edge devices today is largely depending on backbone routers or cloud servers, imposes a significant burden on network traffic while ignores the potentials of utilizing Internet-of-Thing (IoT) devices physically nearby. In this article, we seek to obviate such limitations in improper code offload on edge devices, preferably focusing on minimizing interdomain data transfer and cross-device I/O cost while maximizing the CPU computation capability. First, we conduct the most in-depth and efficient analysis of both performance optimization and extra system burden. Second, we identify a new research problem of flexibly selecting the computation-intensive codes to offload at OS runtime across heterogeneous hardwares. Third, our measurement findings lead us to design and implement a fast and distributed code offload principle for edge devices called codeSpec, that shifts the destined devices from interdomain servers to IoT devices nearby, and only offloads binary code of user-specified regions across different instruction set architectures, thus to not only improve the system performance (by up to 80%) but also reduce burdens on data transfer (by at least 61%) and I/O latency (by on average 84%) than existing. codeSpec also makes user to flexibly specify the codes for offloading by plugging-in features, and is friendly to the commercial-off-the-shelf devices.

Deep learning provides new opportunities for mobile applications to achieve higher performance than before. Rather, the deep learning implementation on mobile device today is largely demanding on expensive resource overheads, imposes a significant burden on the battery life and limited memory space. Existing methods either utilize cloud or edge infrastructure that require to upload user data, however, resulting in a risk of privacy leakage and large data transfers; or adopt compressed deep models, nevertheless, downgrading the algorithm accuracy. This paper provides DeepShark, a platform to enable mobile devices with the ability of flexible resource allocation in using commercial-off-the-shelf (COTS) deep learning systems. Compared to existing approaches, DeepShark seeks a balanced point between time and memory efficiency by user requirements, breaks down sophisticated deep model into code block stream and incrementally executes such blocks on system-on-chip (SoC). Thus, DeepShark requires significantly less memory space on mobile device and achieves the default accuracy. In addition, all referred user data of model processing is handled locally, thus to avoid unnecessary data transfer and network latency. DeepShark is now developed on two COTS deep learning systems, i.e., Caffe and TensorFlow. The experimental evaluations demonstrate its effectiveness in the aspects of memory space and energy cost.

The ever increasing of display resolution on mobile devices raises high demand for GPU rendering details. However, the challenge of poor hardware support but fine-grained rendering details often makes user unsatisfied especially in calling for high frame rate scenarios, e.g., game. To resolve such issue, we propose ButterFly, a novel system which collaboratively utilizes mobile GPUs to process high-quality rendering details for on-the-go mobile users. In particular, ButterFly achieves three technical contributions for the collaborative design: (1) a mobile device can migrate GPU workloads in buffer queue to peers, (2) the collaborative rendering mechanism benefits user high quality details while significant power saving performance, and (3) unnecessary 3D texture rendering can be clipped for further optimization. All the techniques are compatible with the OpenGL ES standards. Furthermore, a 40-person survey perceives that ButterFly can provide excellent user experience of both rendering details and frame rate over Wi-Fi network. In addition, our comprehensive trace-driven experiments on Android prototype reveal the benefits of Butterfly have more superior performance over state-of-the-art systems, which achieves more than 28.3 percent power saving.

The ever increasing of display resolution on mobile devices raises high demand for GPU rendering details. However, the challenge of poor hardware support but fine-grained rendering details often makes user unsatisfied especially in calling for high frame rate scenarios, e.g., game. To resolve such issue, we propose BUTTERFLY, a novel system which collaboratively utilizes mobile GPUs to process high-quality rendering details for on-the-go mobile users. In particular, ButterFly achieves two technical contributions for the collaborative design: (1) a mobile device can migrate GPU workloads in buffer queue to peers, and (2) the collaborative rendering mechanism benefits user high quality details while significant power saving performance. Both techniques are compatible with the OpenGL ES standards. Furthermore, a 40-person survey perceives that ButterFly can provide excellent user experience of both rendering details and frame rate over Wi-Fi network. In addition, our comprehensive trace-driven experiments on Android prototype reveal the benefits of Butterfly have more superior performance over state-of-the-art systems, which achieves more than 28.3% power saving.

Videos over HTTP adaptive streaming have been the most popular vehicle for delivering media content on mobile platform. Rather, today's mobile video streaming are excessively tailored for visual quality, imposing a heavy burden on user's data budget. In this paper, we aim to optimize mobile video streaming of low bitrate efficiency with considering human visual acuity, i.e., preferably avoid sacrificing viewing quality. First, we conduct to in-depth analysis of mobile HTTP adaptive video streaming with a focus not only on how it works, but also on the significance of bitrate saving. Second, we identify a novel research problem on excessive visual quality which leads to bitrate-inefficient video streaming, and propose a flexible system called EyeTube to address it. Specifically, we apply dynamic resolution scaling on mobile video streaming to trade off the bitrate efficiency and user viewing experience Third, we derive general principles for achieving bitrate-efficient mobile video streaming, and employ the principles to an open source web browser, i.e., Chromium, to verify its applicability. An end-to-end EyeTube system is implemented on Samsung smartphones, and the efficiency are evaluated against 10 popular YouTube videos. Experimental results show that all the bitrates of the 10 videos can be reduced by at least 54.2% on average and up to 90.9% at most when the resolution is quartered. A user study with 40 respondents has indicated that our system can achieve good performance on both bitrate saving and high viewing quality.

Exponential growth of media consumption in online social networks demands effective recommendation to improve the quality of experience especially for on-the-go mobile users. By means of large-scale trace-driven measurements over mobile Twitter traces from users, we reveal the significance of affective features in shaping users' social media behaviors. Existing recommender systems however, rarely support such psychological effect in real-life. To capture such effect, in this paper we propose Kaleido, a real mobile system that achieves an online social media recommendation solution by taking affective context into account. Specifically, we design a machine learning mechanism to infer the affective pulse of online social media. Furthermore, a cluster-based latent bias model (LBM) is provided for jointly training the affective pulse as well as user's behavior, location, and social contexts. Our comprehensive trace-driven experiments on Android prototype expose a superior prediction accuracy of 87 percent, which has 25 percent accuracy superior to existing mobile recommender systems. Moreover, by enabling users to offload their machine learning procedures to the deployed edge-cloud testbed, our system achieves speed-up of a factor of 1,000 against the local data training execution on smartphones.

Mobile online social networks (OSNs) are emerging as the popular mainstream platform for information and content sharing among people. In order to provide the quality of experience support for mobile OSN services, in this paper, we propose a socially-driven learning-based framework, namely Spice, for the media content prefetching to reduce the access delay and enhance mobile user's satisfaction. Through a large-scale data-driven analysis over real-life mobile Twitter traces from over 17 000 users during a period of five months, we reveal that the social friendship has a great impact on user's media content click behavior. To capture this effect, we conduct the social friendship clustering over the set of user's friends, and then develop a cluster-based Latent Bias Model for socially-driven learning-based prefetching prediction. We then propose a usage-adaptive prefetching scheduling scheme by taking into account that different users may possess heterogeneous patterns in the mobile OSN app usage. We comprehensively evaluate the performance of Spice framework using trace-driven emulations on smartphones. Evaluation results corroborate that the Spice can achieve superior performance, with an average 80.6% access delay reduction at the low cost of cellular data and energy consumption. Furthermore, by enabling users to offload their machine learning procedures to a cloud server, our design can achieve up to a factor of 1000 speed-up over the local data training execution on smartphones.

Exponential growth of media generated in online social networks demands effective recommendation to improve the efficiency of media access especially for mobile users. In particular, content, objective quality or general popularity are less decisive for the prediction of user-click behavior than friendship-conditioned patterns. Existing recommender systems however, rarely consider user behavior in real-life. By means of a large-scale data-driven analysis over real-life mobile Twitter traces from 15,144 users over a period of one year, we reveal the importance of social closeness related behavior features. This paper proposes CoCo, the first-ever behavior-inspired mobile social media recommender system to improve the media access experience. CoCo exploits representative behavior features via a latent bias based machine learning approach. Our comprehensive evaluation through trace-driven emulations on the Android app exposes a superior accuracy of 72.3%, with a small additional daily energy consumption of 1.3% and a monthly data overhead of 9.1MB.

Mobile online social networks (OSNs) are emerging as the popular mainstream platform for information and content sharing among people. In order to provide Quality of Experience (QoE) support for mobile OSN services, in this paper we propose a socially-driven learning-based framework, namely Spice, for media content prefetching to reduce the access delay and enhance mobile user's satisfaction. Through a large-scale data-driven analysis over real-life mobile Twitter traces from over 17,000 users during a period of five months, we reveal that the social friendship has a great impact on user's media content click behavior. To capture this effect, we conduct social friendship clustering over the set of user's friends, and then develop a cluster-based Latent Bias Model for socially-driven learning-based prefetching prediction. We then propose a usage-adaptive prefetching scheduling scheme by taking into account that different users may possess heterogeneous patterns in the mobile OSN app usage. We comprehensively evaluate the performance of Spice framework using trace-driven emulations on smartphones. Evaluation results corroborate that the Spice can achieve superior performance, with an average 67.2% access delay reduction at the low cost of cellular data and energy consumption. Furthermore, by enabling users to offload their machine learning procedures to a cloud server, our design can achieve speed-up of a factor of 1000 over the local data training execution on smartphones.

Exponential growth of media consumption in online social networks demands effective recommendation to improve the quality of experience especially for on-the-go mobile users. By means of large-scale trace-driven measurements over mobile Twitter traces from users, we reveal the significance of affective features in shaping users' social media behaviors. Existing recommender systems however, rarely support this psychological effect in real-life. To capture this effect, in this paper we propose Kaleido, a real mobile system to achieve an affect-aware learning-based social media recommendation.Specifically, we design a machine learning mechanism to infer the affective feature within media contents. Furthermore, a cluster-based latent bias model is provided for jointly training the affect, behavior and social contexts. Our comprehensive experiments on Android prototype expose a superior prediction accuracy of 82%, with more than 20% accuracy improvement over existing mobile recommender systems. Moreover, by enabling users to offload their machine learning procedures to the deployed edge-cloud testbed, our system achieves speed-up of a factor of 1,000 against the local data training execution on smartphones.