Yonghua Xiong

An energy-optimization-based method of task scheduling for a cloud video surveillance center

The number of cloud video surveillance (CVS) systems has been increasing rapidly over the last decade. Since CVS systems are big energy consumers, it is urgent to take the problem of optimizing the energy consumption of CVS systems into consideration. In this study, we build a task scheduling model, and present a method of scheduling that minimizes energy consumption by reducing the number of virtual machines. The optimization problem is first formulated as a multi-dimensional bin-packing problem due to the constrains on the resources (sizes of the bandwidth, the memory, the hard disk, the CPU utilization, etc.). We convert the problem into a one-dimensional bin-packing problem by making use of the relationships between the resources, and solve it using the greedy best-fit search algorithm. This method greatly reduces the computational expense and can be used in a real-time fashion. An experimental system is designed to evaluate the method, and four experiments are carried out to demonstrate the validity of the method. Experimental results show that the method not only largely improved the resource utilization and reduces energy consumption but also the scheduling time was significantly decreased when handling the same number of video tasks. And it is obviously superior to the common approach and First Fit Decreasing (FFD) algorithm. HighlightsWe build a model of power consumption of the cloud video surveillance center.We build a model of video task scheduling of the cloud video surveillance center.We formulate the model of task scheduling into multi-dimensional bin-packing problem.The multi-dimensional problem is converted into a one-dimensional bin-packing problem.A greedy best-fit algorithm is presented with better effective and lower expense.

Shared Resource and Service Management for Mobile Transparent Computing

The emerging technology of transparent computing has provided a new perspective of resource sharing and management due to the separation of computation and storage in this mode. In this paper, we introduce a framework of mobile transparent computing (MTC) by efficient integration of transparent computing and mobile computing. The MTC environment is a kind of decentralized networks that consist of mobile terminals and wireless links with the conspicuous characteristics: i.e., heterogeneous hardware architecture, diversified operating system (OS), closely coupling between hardware and software and unreliable network. However, these characters make it difficult to manage and allocate the resources and services in MTC. Therefore, we put forward a kind of shared resources and services management (SRSM) strategy for MTC with the ability of compatibility to multiple hardware architectures and mobile OSes. It is designed in a three layered architecture, i.e., the user layer, manage layer and resource layer. In the user layer, the virtualization technology is used to monitor heterogeneous mobile devices and decouple between hardware and software. After that, we run a user agent in each mobile device to collect resources information and requirements on it. The resource layer located in the server is responsible for central storage of resources and services including mobile OSes. The manage layer with several mobile agents on it acts as the core manager in SRSM. Through the interaction of user agent and mobile agents, the resources and services are scheduled and accounted dynamically on demand. Practical experiments on several development boards have testified that the proposed strategy is effective and stable under MTC circumstances.

A novel resource management method of providing operating system as a service for mobile transparent computing.

This paper presents a framework for mobile transparent computing. It extends the PC transparent computing to mobile terminals. Since resources contain different kinds of operating systems and user data that are stored in a remote server, how to manage the network resources is essential. In this paper, we apply the technologies of quick emulator (QEMU) virtualization and mobile agent for mobile transparent computing (MTC) to devise a method of managing shared resources and services management (SRSM). It has three layers: a user layer, a manage layer, and a resource layer. A mobile virtual terminal in the user layer and virtual resource management in the manage layer cooperate to maintain the SRSM function accurately according to the users requirements. An example of SRSM is used to validate this method. Experiment results show that the strategy is effective and stable.

A Cross Platform Computing Method and Its Application for Mobile Device in Transparent Computing

Recently, the fact that mobile devices with various kinds of hardware and software platforms constantly appear on the market has resulted in huge amount of mobile applications coming out based on different platforms. Thus a big challenge has emerged to design applications on the heterogeneous devices and operating systems (OSes). This paper proposes a completely cross platform mobile transparent computing (CPMTC) method to make it possible for mobile devices to support various operating systems and application programs from underlying platform to application perspective based on the concept of transparent computing on PC. On the underlying platform of mobile device, a mobile pre-boot firmware (MPBF) is designed to initialize the mobile device and stream the OS image data to device through network. Within the firmware program, multiple OSes image data are able to be loaded on demand at runtime. In order to provide the cross platform support on the application layer, we develop an application design method based on HTML5 using the transparent server to manage the application and user data, and apply it to the e-Learning system. We deploy the MPBF on the experimental tablet, and then load different OSes into the tablet using the MPBF. After that, we run and evaluate the e-learning system over every loaded OS. The practical experiment results confirm that the CPMTC can be taken as the effective method to decouple not only OSes from hardware but also application programs from OS in mobile devices.

A Low Energy Consumption Storage Method for Cloud Video Surveillance Data Based on SLA Classification

With the continuous expansion of the amount of data with time in mobile video applications such as cloud video surveillance (CVS), the increasing energy consumption in video data centers has drawn widespread attention for the past several years. Addressing the issue of reducing energy consumption, we propose a low energy consumption storage method specially designed for CVS systems based onthe service level agreement (SLA) classification. A novel SLA with an extra parameter of access time period is proposed and then utilized as a criterion for dividing virtual machines (VMs) and data storage nodes into different classifications. Tasks can be scheduled in real time for running on the homologous VMs and data storage nodes according to their access time periods. Any nodes whose access time periods do not encompass the current time will be placed into the energy saving state while others are in normal state with the capability of undertaking tasks. As a result, overall electric energy consumption in data centers is reduced while the SLA is fulfilled. To evaluate the performance, we compare the method with two related approaches using the Hadoop Distributed File System (HDFS). The results show the superiority and effectiveness of our method.

A Task Scheduling Method for Energy-Efficient Cloud Video Surveillance System Using a Time-Clustering-Based Genetic Algorithm

Demands for cloud video surveillance systems are growing rapidly. Addressing to the issue of low energy-efficiency in cloud video datacenters, a task scheduling method using a time-clustering-based genetic algorithm is proposed. Firstly, an off-line scheduling model with SLA (service level agreement) time constraint is proposed after the analysis of the constrain relationship between the SLA and surveillance tasks. Then, a time-clustering-based genetic algorithm (TCGA) is proposed to solve the model for an optimal energy-efficient solution. According to the solution, the service quality is guaranteed and the total operating time of virtual machines is minimized. Meanwhile, idle virtual machines are shut down to reduce energy consumption. Simulations of large scale tasks scheduling are conducted. Several comparison experiments verify that the proposed method can improve the resource utilization greatly and achieve energy saving extremely.

An Energy-Aware Workload Balancing Method for Cloud Video Data Storage Management

The data centers of cloud video surveillance (CVS) systems based on Hadoop have a couple of common problems such as large energy consumption, low power utilization, etc. Addressing to the issue of reducing energy consumption while guaranteeing quality of service, we propose an energy-aware workload balancing method for efficient data storage management in cloud video data centers. A dynamic adjusting algorithm is designed to control the running status of nodes in the data centers according to the access frequency of video data blocks for the purpose of reducing the energy consumption, in order to eliminate the potential influence on the service quality posed by the changes of running status of nodes, the workload balancing between nodes in ring network topology is executed, a nonlinear programming model is established to obtain the minimum number of data blocks transferred during the workload balancing. Experimental results in the GridSim simulation environment show that the proposed method can achieve more energy saving and better performance than the original Hadoop.

A Lightweight Approach to Access to Wireless Network without Operating System Support

AbstractWireless network is crucial for the Mobile Transparent Computing (MTC), in which a mobile device without any Operating System (OS) support needs to load the demanded OSes and applications through accessing the wireless network connection. In this paper, a lightweight approach based on the Boot Management System (BMS) was proposed to ensure the wireless network connection before booting OS. In BMS, the Virtual File System (VFS) technology was used to drive the wireless network card and establish a stable network connection. A prototype of the BMS was tested on ARM11 hardware platform and the results demonstrate the validity of the BMS.

A Johnson's-Rule-Based Genetic Algorithm for Two-Stage-Task Scheduling Problem in Data-Centers of Cloud Computing

One of the keys to making cloud data-centers (CDCs) proliferate impressively is the implementation of efficient task scheduling. Since all the resources of CDCs, even including operating systems (OSes) and application programs, can be stored and managed on remote data-centers, this study first analyzed the task scheduling problem for CDCs and established a mathematical model of the scheduling of two-stage tasks. The Johnsons rule was combined with the genetic algorithm to create a Johnsons-rule-based genetic algorithm (JRGA), which takes into account the characteristics of multiprocessor scheduling in CDCs. New crossover and mutation operations were devised to make the algorithm converge more quickly. In the decoding process, the Johnsons rule is used to optimize the makespan for each machine. Simulations were used to compare the performance of the JRGA with that of the list scheduling algorithm and an improved list scheduling algorithm. The results demonstrate the validity of the JRGA.

Cross platform method for ubiquitous computing and its application to mobile terminal

Commercially available mobile devices with various kinds of hardware and software platforms have resulted in a huge amount of mobile applications. This provides a big challenge to design applications that are compatible with heterogeneous devices and operating systems OSes. This paper presents a method of cross platform mobile transparent computing CPMTC for mobile devices. A mobile pre-boot firmware MPBF is designed to stream OS image data to the device through a network. Moreover, in application layer, we devise a method of designing applications based on HTML5, which ensures that applications are available for different OSes. We use the MPBF to load different OSes into the experimental tablet. After that, we use the CPMTC method to run and evaluate a knowledge collection e-learning KCE system for different OSes. Experiment results confirm that the CPMTC is effective in separating OSes both from hardware and application programs for mobile devices.