Xiaoming Nan

Optimal resource allocation for multimedia cloud based on queuing model

Multimedia cloud, as a specific cloud paradigm, addresses how cloud can effectively process multimedia services and provide QoS provisioning for multimedia applications. There are two major challenges in multimedia cloud. The first challenge is the service response time in multimedia cloud, and the second challenge is the cost of cloud resources. In this paper, we optimize resource allocation for multimedia cloud based on queuing model. Specifically, we optimize the resource allocation in both single-class service case and multiple-class service case. In each case, we formulate and solve the response time minimization problem and resource cost minimization problem, respectively. Simulation results demonstrate that the proposed optimal allocation scheme can optimally utilize the cloud resources to achieve a minimal mean response time or a minimal resource cost.

Optimal resource allocation for multimedia cloud in priority service scheme

Multimedia cloud is an emerging computing paradigm that can effectively process multimedia applications and provide multi-QoS provisions for customers. Two major challenges exist in multimedia cloud: the resource cost and the service response time. In this paper, we employ the proposed queuing model to optimize the resource allocation for multimedia cloud in priority service scheme. Specifically, we formulate and solve the resource cost minimization problem and the service response time minimization problem respectively. The simulation results demonstrate that the proposed optimal resource allocation method can greatly enhance the performance of multimedia cloud data center in terms of resource cost and service response time.

Optimal allocation of virtual machines for cloud-based multimedia applications

With the emergence of cloud computing, cloud-based multimedia applications have been increasingly adopted in recent years. There are two major challenges for multimedia application providers: the round trip time (RTT) requirement and the resource cost. In this paper, we study the virtual machine (VM) allocation problem for multimedia application providers to minimize the resource cost under RTT requirements. Specifically, we propose the optimal VM allocation schemes for single-site cloud and multi-site cloud, respectively. Moreover, we propose the greedy algorithms to efficiently allocate VMs in each case. Simulation results demonstrate that the proposed optimal VM allocation schemes can optimally allocate VMs to achieve a minimal resource cost.

Queueing model based resource optimization for multimedia cloud

Abstract Multimedia cloud is a specific cloud computing paradigm, focusing on how cloud can effectively support multimedia services. For multimedia service providers (MSP), there are two fundamental concerns: the quality of service (QoS) and the resource cost. In this paper, we investigate these two fundamental concerns with queueing theory and optimization methods. We introduce a queueing model to characterize the service process in multimedia cloud. Based on the proposed queueing model, we study resource allocation problems in three different scenarios: single-service scenario, multi-service scenario, and priority-service scenario. In each scenario, we formulate and solve the response time minimization problem and the resource cost minimization problem, respectively. We conduct extensive simulations with practical parameters of Windows Azure. Simulation results demonstrate that the proposed resource allocation schemes can optimally allocate cloud resources for each service to achieve the minimal response time under a certain budget or guarantee the QoS provisioning at the minimal resource cost.

Optimization of workload scheduling for multimedia cloud computing

The cloud based multimedia applications have been widely adopted in recent years. Due to the large-scale and time-varying workload, an effective workload scheduling scheme is becoming a challenge faced by multimedia application providers. In this paper, we study the workload scheduling schemes for multimedia cloud. Specifically, we examine and solve the response time minimization problem and the resource cost minimization problem, respectively. Moreover, we propose a greedy algorithm to efficiently schedule workload for practical multimedia cloud. Simulation results demonstrate that the proposed workload scheduling schemes can optimally balance workload to achieve the minimal response time or the minimal resource cost for multimedia application providers.

TOWARDS OPTIMAL RESOURCE ALLOCATION FOR DIFFERENTIATED MULTIMEDIA SERVICES IN CLOUD COMPUTING ENVIRONMENT

Cloud-based multimedia services have been widely used in recent years. As the growing scale, users often have quite diverse quality of service (QoS) expectations. A key challenge for differentiated services is how to optimally allocate cloud resources to satisfy different users. In this paper, we study resource allocation problems for differentiated multimedia services. We first propose a queueing model to characterize differentiated services in cloud. Based on the model, we optimize cloud resources in the first-come first-served (FCFS) scenario and priority scenario. In each scenario, we formulate and solve the optimal resource allocation problem to minimize resource cost under response time constraints. We conduct extensive simulations with practical parameters of Amazon EC2. Simulation results demonstrate that the proposed resource allocation schemes can optimally configure resources to provide satisfactory services at the minimal resource cost.

vDesign: a CAVE-based virtual design environment using hand interactions

The cave automatic virtual environment (CAVE) system is one of the most fully immersive systems for virtual reality environments. By providing users with realistic perception and immersive experience, CAVE systems have been widely used in many fields, including military, education, health care, entertainment, design, and others. In this paper, we focus on the design applications in the CAVE. The design applications involve many interactions between the user and the CAVE. However, the conventional interaction tool, the wand, cannot provide fast and convenient interactions. In this paper, we propose vDesign, a CAVE-based virtual design environment using hand interactions. The hand interactions in vDesign are classified into menu navigation and object manipulations. For menu navigation, we define two interactions: activating the main menu and selecting a menu item. For object manipulations, we define three interactions: moving, rotating, and scaling an object. By using the proposed hand interactions, we develop the functions of image segmentation and image composition in vDesign. With the image segmentation function, the designer can select and cut the interested objects from different images. With the image composition function, the designer can manipulate the segmented objects and combine them as a composite image. We implemented the vDesign prototype in CAVE and conducted experiments to evaluate the interaction performance in terms of manipulation time and distortion. The experimental results demonstrated that the proposed hand interactions can provide faster and more accurate interactions compared to the traditional wand interactions.

A novel cloud gaming framework using joint video and graphics streaming

As the popularity of smart phones and tablets, users have an increasing desire to enjoy ubiquitous game playing. The emerging cloud gaming turns this desire into reality, enabling users to play games at anywhere on any devices. However, due to the huge amount of data transmission, it is challenging to provide a high quality game experience under the limited bandwidth capacity. In this paper, we propose a novel cloud gaming framework, in which we introduce two synchronized graphics buffers at both the server and the client sides. The server not only streams the compressed frames captured from game scenes, but also progressively transmits graphics data. The received graphics data is used to generate reference frames. When compressing the next frame, the cloud server will choose the reference frame with a lower residual error, from the previous frame and the current frame rendered from the graphics buffer. With the accumulation of graphics data, the frame rendered from the graphics buffer is close to the captured frame, which greatly reduces the transmission bit rates. Based on the proposed framework, we study the rate allocation problem, in which we optimize the allocated bit rates between the compressed frame and the graphics data to minimize the total distortion under the bandwidth constraint. Experimental results demonstrate that the proposed framework can optimally allocate bit rates to achieve a minimal distortion for cloud gaming compared to the traditional video streaming and graphics streaming approaches.

Optimal task-level scheduling for cloud based multimedia applications

As an emerging computing paradigm, cloud computing has been increasingly used in multimedia applications. One fundamental challenge for application providers is how to effectively schedule multimedia tasks to multiple virtual machines for distributed processing. In this paper, we study task-level scheduling problem for cloud based multimedia applications. Specifically, we introduce a directed acyclic graph to model precedence constraints among tasks. Based on the model, we study the optimal task scheduling problem for the sequential, the parallel, and the mixed structures, respectively. Moreover, we propose a heuristic to perform the near optimal task scheduling in a practical way. Experimental results demonstrate that the proposed scheduling scheme can optimally assign tasks to virtual machines to minimize the execution time.

Monocular Human Motion Tracking by Using DE-MC Particle Filter

Tracking human motion from monocular video sequences has attracted significantly increased interests in recent years. A key to accomplishing this task is to efficiently explore a high-dimensional state space. However, the traditional particle filter method and many of its variants have not been able to meet expectations as they lack a strategy to do efficiently sampling or stochastic search. We present a novel approach, namely differential evolution-Markov chain (DE-MC) particle filtering. By taking the advantage of the DE-MC algorithms ability to approximate complicated distributions, substantial improvement can be made to the traditional structure of the particle filter. As a result, an efficient stochastic search can be performed to locate the modes of likelihoods. Furthermore, we apply the proposed algorithm to solve the 3D articulated model-based human motion tracking problem. A reliable image likelihood function is built for visual tracker design. Based on the proposed DE-MC particle filter and the image likelihood function, we perform a variety of monocular human motion tracking experiments. Experimental results, including the comparison with the performance of other particle filtering methods demonstrate the reliable tracking performance of the proposed approach.