A dynamic binary translation system in a client/server environment

With rapid advances in mobile computing, multi-core processors and expanded memory resources are being made available in new mobile devices. This trend will allow a wider range of existing applications to be migrated to mobile devices, for example, running desktop applications in IA-32 (x86) binaries on ARM-based mobile devices transparently using dynamic binary translation (DBT). However, the overall performance could significantly affect the energy consumption of the mobile devices because it is directly linked to the number of instructions executed and the overall execution time of the translated code. Hence, even though the capability of today’s mobile devices will continue to grow, the concern over translation efficiency and energy consumption will put more constraints on a DBT for mobile devices, in particular, for thin mobile clients than that for severs. With increasing network accessibility and bandwidth in various environments, it makes many network servers highly accessible to thin mobile clients. Those network servers are usually equipped with a substantial amount of resources. This provides an opportunity for DBT on thin clients to leverage such powerful servers. However, designing such a DBT for a client/server environment requires many critical considerations.In this work, we looked at those design issues and developed a distributed DBT system based on a client/server model. It consists of two dynamic binary translators. An aggressive dynamic binary translator/optimizer on the server to service the translation/optimization requests from thin clients, and a thin DBT on each thin client to perform lightweight binary translation and basic emulation functions for its own. With such a two-translator client/server approach, we successfully off-load the DBT overhead of the thin client to the server and achieve a significant performance improvement over the non-client/server model. Experimental results show that the DBT of the client/server model could achieve 37% and 17% improvement over that of non-client/server model for x86/32-to-ARM emulation using MiBench and SPEC CINT2006 benchmarks with test inputs, respectively, and 84% improvement using SPLASH-2 benchmarks running two emulation threads.     

Distributed computing paradigms for collaborative signal and information processing in sensor networks

In this paper, we report the development of an energy-efficient, high-performance distributed computing paradigm to carry out Collaborative Signal and Information Processing (CSIP) in sensor networks using mobile agents. In this paradigm, the processing code is moved to the sensor nodes through mobile agents, in contrast to the client/server-based computing, where local data are transferred to a processing center. Although the client/server paradigm has been widely used in distributed computing, the many advantages of the mobile agent paradigm make it more suitable for sensor networks. The paper first presents simulation models for both the client/server paradigm and the mobile agent paradigm. We use the execution time, energy and energy*delay as metrics to measure the performance. Several experiments are designed to show the effect of different parameters on the performance of the paradigms. Experimental results show that the mobile agent paradigm performs much better when the number of nodes is large while the client/server paradigm is advantageous when the number of nodes is small. Based on this observation, we then propose a cluster-based hybrid computing paradigm to combine the advantages of these two paradigms. There are two schemes in this paradigm and simulation results show that there is always one scheme which performs better than either the client/server or the mobile agent paradigms. Thus, the cluster-based hybrid computing provides an energy-efficient and high-performance solution to CSIP.     

基于Android 的移动视频监控系统的设计

本文将采用全新的信息技术设计出满足用户需求的移动视频监控系统。该移动视频监控系统集中运用了 AI2 编程技术、H.264 视频压缩编码技术、网络传输技术、SAE云计算技术等。该系统具有移动视频在线点播和移动视频在线 采集功能,管理员可应用Windows 客户端管理网络服务器的用户信息和视频数据。     

Video personalization in heterogeneous and resource-constrained environments

Access to multimedia data and multimedia services is becoming increasingly common in networked mobile environments. In such environments, both the mobile client devices and multimedia servers are typically resource constrained. Moreover, the mobile client population is often heterogeneous in terms of the clients’ preferences with regard to multimedia content, the clients’ quality of service requirements and system-level resource constraints. In order to provide a resource-constrained mobile client with its desired video content, it is often necessary to personalize the requested multimedia content in a manner that satisfies simultaneously the various client-specified content preferences and the system-level resource constraints. Also, in order to simultaneously reduce the client-experienced latency, provide optimal quality of service to the clients and ensure efficient utilization of server and network resources, it is necessary to perform client request aggregation on the server end. To this end, a video personalization strategy is proposed to provide mobile, resource-constrained clients with personalized video content that is most relevant to the clients’ requests while satisfying simultaneously multiple client-side system-level resource constraints. A client request aggregation strategy is also proposed to cluster client requests with similar video content preferences and similar client-side resource constraints such that the number of requests the server needs to process and service, and the client-experienced latency are both reduced simultaneously. The primary contributions of the paper are: (1) the formulation and implementation of a Multiple-choice Multi-dimensional Knapsack Problem (MMKP)-based video personalization strategy; and (2) the design and implementation of a client request aggregation strategy based on a multi-stage clustering algorithm. Experimental results comparing the proposed MMKP-based video personalization strategy to existing 0/1 Knapsack Problem (0/1KP)-based and the Fractional Knapsack Problem (FKP)-based video personalization strategies are presented. It is observed that: (1) the proposed MMKP-based personalization strategy includes more relevant video content in response to the client’s request compared to the existing 0/1KP-based and FKP-based personalization strategies; and (2) in contrast to the 0/1KP-based and FKP-based personalization strategies which can satisfy only a single client-side resource constraint at a time, the proposed MMKP-based personalization strategy is capable of satisfying multiple client-side resource constraints simultaneously. Experimental results comparing the client-experienced latency with and without the proposed server-side client request aggregation strategy are also presented. It is shown that the proposed client request aggregation strategy reduces the mean client-experienced latency without significant reduction in the average relevance of the delivered video content and without significant deviation in the client-side resources actually consumed by the delivered video content from the client-specified resource constraints.     

基于B/S结构的智能家居远程巡警监控系统的设计

针对现代办公、室内家居监控多目标的问题,结合ZigBee无线传输技术、智能车控制技术,构建了以B/S模式为基础的Web服务器,设计了一种新的远程巡警监控系统。采用KB-204数字视频监控录像采集卡,前端视频服务器配以花生壳的动态域名,实现系统的远程视频监视;以OURS-IOTV2物联网创新实验套件为开发平台,运用Html、JDBC技术构建Web服务器,实现客户端用户只需使用IE浏览器便能完成对家居移动小车的远程运动控制,同时在移动小车上装有BH9402热释电红外传感模块,当模块检测出异常信号时,会在客户端浏览网页界面上即时显示"室内有人"等信息。     

Efficient Selective Frame Discard Algorithms for Stored Video Delivery Across Resource Constrained Networks

Video delivery from a server to a client across a network is an important component of many multimedia applications. While delivering a video stream across a resource constrained network, loss of frames may be unavoidable. Under such circumstances, it is desirable to find a server transmission schedule that can efficiently utilize the network resources while maximizing the perceived quality-of-service (QoS) at the client. To address this issue, in this paper we introduce the notion ofselective frame discard at the server and formulate the optimal selective frame discard problem using a QoS-based cost function. Given network bandwidth and client buffer constraints, we develop an O (N log N) algorithm to find the minimum number of frames that must be discarded in order to meet these constraints. The correctness of the algorithm is also formally established. We present a dynamic programming based algorithm for solving the problem of optimal selective frame discard. Since the computational complexity of the optimal algorithm is prohibitively high in general, we also develop several efficient heuristic algorithms for selective frame discard. These algorithms are evaluated using JPEG and MPEG video traces.     

移动远程视频监控系统

为了扩大单个摄像头的视频监控范围及灵活性, 设计了一种可远程操控的移动视频监控系统. 该系统由四个模块组成, 基于Arduino系统的智能车搭载有摄像头, 接收用户指令, 用于移动视频的采集; 嵌入式Linux系统通过V4L2接口实现对视频数据的实时采集, 一方面将数据通过网络发送至转发服务器, 另一方面将来自用户的控制指令转发至智能车; 服务器则用于转发视频至客户端以及转发用户控制指令至Linux系统; 基于Android的移动端呈现监控视频并提供用户控制界面. 与现有系统相比, 该系统可使用单摄像头实现无死角监控.     

Real time video frames allocation in mobile networks using cooperative pre-fetching

In this paper, we study the bandwidth allocation problem for serving video requests in a mobile real-time video player system. One of the main issues to maintain the quality of services (QoS) in mobile video playback is to ensure sufficient number of video frames available at the client side while a video is being played. However, due to the limited bandwidth of a mobile network and variable workload streaming video, this is not easy to achieve in practice. In addition, the communication link between mobile clients and a video server is subject to frequent errors and temporary disconnection. In this paper, we propose to use the notion of buffered bandwidth in addition to the network bandwidth to support real-time playback of videos at mobile clients. Based on this, we designed a bandwidth allocation scheme called Cooperative Pre-Fetching (CP) in which the amount of bandwidth to be allocated to serve a video request depends on the current buffer level of the video at the client relative to the target buffer level of the client. In determining the target buffer level, we consider the errors in communication to the client as well as the other clients who are concurrently served by the system. The buffered video frames at the clients are then used to minimize the impact of error in communications on the overall QoS of video playbacks in the whole system.

An enhanced client-centric approach for efficient video broadcast

Periodic broadcast is a cost-effective solution for large-scale distribution of popular videos. Regardless of the number of video requests, this strategy guarantees a constant worst service latency to all clients, making it possible to serve a large community with a minimal amount of broadcast bandwidth. Although many efficient periodic broadcast techniques have been proposed, most of them impose rigid requirements on client receiving bandwidth. They either demand clients to have the same bandwidth as the video server, or limit them to receive no more than two video streams at any one time. In our previous work, we addressed this problem with a Client-Centric Approach (CCA). This scheme takes into consideration both server broadcast bandwidth and client receiving bandwidth and allows clients to use all their receiving capability for prefetching broadcast data. As a result, given a fixed broadcast bandwidth, a shorter broadcast period can be achieved with an improved client communication capability. In this paper, we present an enhanced version of CCA to further leverage client bandwidth for more efficient video broadcast. The new scheme reduces the broadcast latency up to 50% as compared to CCA. We prove the correctness of this new technique and provide an analytical evaluation to show its performance advantage as compared with some existing techniques.

Periodic broadcast with dynamic server selection

Service replication is an effective way to address resource requirements and resource availability problem. Dynamic service selection enables clients to choose a server offering the best performance. Proper server selection is especially important for video streaming over the Internet due to its high bandwidth requirements. However, given the length of a typical video transmission, the server priorly selected may no longer be an optimal one for the duration of the entire transmission. More importantly, a server may fail during the transmission of a video. In this paper we examine the possibility of switching to another server during an on-going transmission for Periodic Broadcast schemes. Due to the timing requirements typical for Periodic Broadcast the server switch may cause playback disruptions. We analyze the magnitude of the problem and propose an easy to implement solution. We define the criteria, additional to the bandwidth availability for example, according to which a new server should be selected. The client is also required to delay its playback by the amount of time bounded by the server transmission offset. In addition, we propose an alternative method to ensure uninterrupted playback that relies on proxy caching. Simulation results show that our approach can significantly reduce the likelihood of playback disruptions.

ZipfAllocation: an algorithm for static allocation of movies in a cluster of video servers

When a single video server provides a streaming service, there are many limitations. Thus, clusters of multiple video servers are widely used today to provide better service. How movies are allocated to each server in a cluster is an important and ongoing research topic because the movies are large, and there are so many of them. We have developed a movie allocation algorithm based on the observation that client accesses follow a Zipf distribution. We have developed an algorithm in which servers use their storage to their full capacities, movies are assigned to servers as evenly as possible, servers receive requests as evenly as possible, and all movies are allocated to at least one server. Copyright © 2010 John Wiley & Sons, Ltd.     

基于MX27的嵌入式RTSP手机流媒体服务器的设计与实现

通信技术和数字视频技术的发展,为手机视频监控的实现创造了条件。如何实现视频数据的快速、低码率的编码以及构建高效、稳定的流媒体服务器是实现手机视频监控的关键。本文介绍一种嵌入式手机流媒体服务器方案。该方案使用飞思卡尔新推出的MX27作为主控芯片,选用稳定、开源的Linux作为嵌入式系统,并在此基础上给出一个RTSP流媒体服务器设计方案。实验证明本方案具有可行性和较好的实用性。     

基于Android的教学录像在线预约系统

为了改善高校教师课堂教学录像预约的传统人工预约与管理工作模式,提高工作效率,节约人力成本,根据提高高校教师教学实践能力及课堂教学效果的要求,研究并设计了一种基于Android平台的教学录像在线预约系统,该系统采用C/S架构, PC作为服务器提供底层服务, Android手机作为客户端为用户提供人机交互久界面.客户端不能直接访问数据库服务器,而是引入了业务逻辑层的Web Service接口,开发方法具有一定的通用性.测试和运行结果表明,该系统能够有效改善传统预约模式带来的不便,提高工作效率,方便教师和录像工作人员工作的进行,提高了高校教师教学发展工作的信息化水平.     

Dynamic Scheme Transition Adaptable to Variable Video Popularity in a Digital Broadcast Network

To provide on-demand video streaming services through the network, video objects with both high and low client request rates must be served efficiently. In this paper, we propose a dynamic scheme transition to provide on-demand streaming services efficiently regardless of video popularity. This approach can maintain quality-of-service (QoS) by transitioning the service scheme according to the request rate. The server provides services by heuristically broadcasting video segments when the video popularity is low and by a Periodic Broadcast when the video popularity is high. The server identifies the variations in client request rates from the number of service channels and determines transitions to more efficient service schemes autonomously. We evaluated this scheme transition for various parameters and traces from a company providing streaming services. The results show that the performance of scheme transition is very efficient.      

Adaptive interface selection over cloud-based split-layer video streaming via multi-wireless networks

As mobile devices such as tablet PCs and smartphones proliferate, the online video consumption over a wireless network has been accelerated. From this phenomenon, there are several challenges to provide the video streaming service more efficiently and stably in the heterogeneous mobile environment. In order to guarantee the QoS of real-time HD video services, the steady and reliable wireless mesh is necessary. Furthermore, the video service providers have to maintain the QoS by provisioning streaming servers to respond the clients’ request of different video resolution. In this paper, we propose a reliable cloud-based video delivery scheme with the split-layer SVC encoding and real-time adaptive multi-interface selection over LTE and WiFi links. A split-layer video streaming can effectively scale to manage the required channels on each layer of various client connections. Moreover, split-layer SVC model brings streaming service providers a remarkable opportunity to stream video over multiple interfaces (e.g. WiFi, LTE, etc.) with a separate controlling based on their network status. Through the adaptive interface selection, the proposed system aims to ensure the maximizing video quality which the bandwidth of LTE/WiFi accommodates. In addition, the system offers cost-effective streaming to mobile clients by saving the LTE data consumption. In our system, an adaptive interface selection is developed with two different algorithms, such as INSTANT and EWMA methods. We implemented a prototype of mobile client based on iOS particularly by using iPhone5S. Moreover, we also employ the split-layer SVC encodes in streaming server-side as the add-on module to SVC reference encoding tool in a virtualized environment of KVM hypervisor. We evaluated the proposed system in an emulated and a real-world heterogeneous wireless network environments. The results show that the proposed system not only achieves to guarantee the highest quality of video frames via WiFi and LTE simultaneous connection, but also efficiently saves LTE bandwidth consumption for cost-effectiveness to client-side. Our proposed method provides the highest video quality without deadline misses, while it consumes 50.6% LTE bandwidth of ‘LTE-only’ method and 72.8% of the conventional (non-split) SVC streaming over a real-world mobile environment.     

Server side,play buffer based quality control for adaptive media streaming

Existing media streaming protocols provide bandwidth adaptation features in order to deliver seamless video streams in an abrupt bandwidth shortage on the networks. For instance, popular HTTP streaming protocols such as HTTP Live Streaming (HLS) and MPEG-DASH are designed to select the most appropriate streaming quality based on client side bandwidth estimation. Unfortunately, controlling the quality at the client side means the effectiveness of the adaptive streaming is not controlled by service providers, and it harms the consistency in quality-of-service. In addition, recent studies show that selecting media quality based on bandwidth estimation may exhibit unstable behavior in certain network conditions. In this paper, we demonstrate that the drawbacks of existing protocols can be overcome with a server side, buffer based quality control scheme. Server side quality control solves the service quality problem by eliminating client assistance. Buffer based control scheme eliminates the side effects of bandwidth based stream selection. We achieve this without client assistance by designing a play buffer estimation algorithm. We prototyped the proposed scheme in our streaming service testbed which supports pre-transcoding and live-transcoding of the source media file. Our evaluation results show that the proposed quality control performs very well both in simulated and real environments.     

Analysis of Practical Backoff Protocols for Contention Resolution with Multiple Servers

Backoff protocols are probably the most widely used protocols for contention resolution in multiple access channels. In this paper, we analyze the stochastic behavior of backoff protocols for contention resolution among a set of clients and servers. each server being a multiple access channel that deals with contention like an ethernet channel. We use the standard model in which each client generates requests for a given server according to a Bernoulli distribution with a specified mean. Theclient–server request rateof a system is the maximum over all client–server pairs (i, j) of the sum of all request rates associated with either clientior serverj. (Having a subunit client–server request rate is a necessary condition for stability for single-server systems.) Our main result is that any superlinear polynomial backoff protocol is stable for any multiple-server system with a subunit client–server request rate. Our result is the first proof of stability for any backoff protocol for contention resolution with multiple servers. (The multiple-server problem does not reduce to the single-server problem, because each client can only send a single message at any step.) Our result is also the first proof thatanyweakly acknowledgment based protocol is stable for contention resolution with multiple servers and such high request rates. Two special cases of our result are of interest. Hastad, Leighton, and Rogoff have shown that for a single-server system with a subunit client–server request rate anymodifiedsuperlinear polynomial backoff protocol is stable. These modified backoff protocols are similar to standard backoff protocols but require more random bits to implement. The special case of our result in which there is only one server extends the result of Hastad, Leighton, and Rogoff to standard (practical) backoff protocols. Finally, our result applies to dynamic routing in optical networks. Specifically, a special case of our result demonstrates that superlinear polynomial backoff protocols are stable for dynamic routing in optical networks.     

The Analysis and Performance of Multi-Rate Service in Distributed Video-on-Demand Systems

In this paper, we develop an end-to-end analysis of a distributed Video-on-Demand (VoD) system that includes an integrated model of the server and the network subsystems with analysis of its impact on client operations. The VoD system provides service to a heterogeneous client base at multiple playback rates. A class-based service model is developed where an incoming video request can specify a playback rate at which the data is consumed on the client. Using an analytical model, admission control conditions at the server and the network are derived for multi-rate service. We also develop client buffer requirements in presence of network delay bounds and delay jitter bounds using the same integrated framework of server and network subsystems. Results from an extensive simulation show that request handling policies based on limited redirection of blocked requests to other resources perform better than load sharing policies. The results also show that downgrading the service for blocked requests to a lower bitrate improves VoD system performance considerably. Combining the downgrade option with restrictions on access to high bitrate request classes is a powerful tool for manipulating an incoming request mix into a workload that the VoD system can handle.     

Video distribution under multiple constraints

We consider the optimization problem of providing a set of video streams to a set of clients, where each stream has costs in m possible measures (such as communication bandwidth, processing bandwidth, etc.), and each client has its own utility function for each stream. We assume that the server has a budget cap on each of the m cost measures; each client has an upper bound on the utility that can be derived from it, and potentially also upper bounds in each of the m cost measures. The task is to choose which streams the server will provide, and out of this set, which streams each client will receive. The goal is to maximize the overall utility subject to the budget constraints. We give an efficient approximation algorithm with approximation factor of O(m) with respect to the optimal possible utility for any input, assuming that clients have only a bound on their maximal utility. If, in addition, each client has at most m_c capacity constraints, then the approximation factor increases by another factor of O(m_clogn), where n is the input length. We also consider the special case of “small” streams, namely where each stream has cost of at most O(1/logn) fraction of the budget cap, in each measure. For this case we present an algorithm whose approximation ratio is O(logn).