Tien-Dung Nguyen

Service Image Placement for Thin Client in Mobile Cloud Computing

Mobile Cloud Computing (MCC) is broadening the market for mobile devices with more and more variety and heavier services by using service images on cloud. With the limitation of battery life time, cpu and memory capacity, etc., mobile device can not adapt with those new services. And remote display solution becomes the key technology to remove the barrier of those limitations in MCC. Due to the scalability, mobility of mobile devices, changing in resource demand or network condition, service images must be managed in the most efficient way. In this paper, we formulate the service image placement problem as an optimization problem by minimizing the cost function that is the combination of composite cost and resource demand. We further propose the algorithms to address the service image placement issue and do simulation to evaluate our proposed algorithms. By the results of experiment, we show that our approach can improve the network traffic and the quality of service than others.

An Efficient Key Management for Secure Multicast in Sensor-Cloud

Nowadays, in Publish/Subscribe system, the number of subscriptions from customers can be large, the events happening in such a system are more frequent and the notification load is heavy. Combining with cloud computing that is becoming increasingly popular for scalability and availability it promises, called Sensor-Cloud, there are many approaches that improve data delivery issues in such scenario efficiently. However, there are few approaches that consider secure data delivery while security plays an important role in Sensor-Cloud. In this paper, we propose an efficient secure multicast approach by combining Group-key and Time-key (CoGKTK) to minimize number of updated key for such dynamic scenario in Sensor-Cloud. We analytically show the performance and scalability benefits of our approach over data delivery infrastructure in Sensor-Cloud.

An optimized hybrid remote display protocol using GPU-assisted M-JPEG encoding and novel high-motion detection algorithm

In this paper, we design a novel hybrid remote display for mobile thin-client system. The remote frame buffer (RFB) protocol and motion JPEG (M-JPEG) protocol are assigned to handle remote display tasks in the slow-motion region and high-motion region, respectively. Graphic processing units (GPU) are utilized to do a part of a real-time JPEG compression task. A novel quality of experience (QoE)-based high-motion detection algorithm is also proposed to reduce the network bandwidth consumption and the server-side computing resource consumption. The continuity of screen delivery remains whenever the JPEG compression is applied to different screen regions. The proposed hybrid remote display approach has many good features which have been justified by comprehensive simulation studies.

Prediction-based energy policy for mobile virtual desktop infrastructure in a cloud environment

Using cloud services from mobile devices has become a growing trend because of its mobility and convenience. However, mobile devices join and leave cloud services more frequently than traditional computers, which causes energy inefficiency in a cloud data center. Waste, in the form of energy and cooling requirements, particularly occurs when a mobile device disconnects from a service, but the cloud servers, known as virtual machines (VMs), continue running. The VMs should transition to lower-power states instead remaining active. However, transition to a lower-power state causes a service delay when users reconnect to the service because VMs in a lower-power state are not ready to serve. Therefore, an efficient energy policy must not only maximize energy savings but also minimize service delays. In this paper, we propose two approaches to energy efficiency: an Instant Energy Policy (IEP) that can quickly find an appropriate low-power state based on a predicted disconnection time and a Prediction-based Energy Policy (PrEP) that determines when to transition VMs to a low-power state and when to return them to the active state based on each users activity history. IEP predicts the unknown disconnection time using the multisize sliding windows workload estimation technique, which supports a non-stationary environment. This method can quickly obtain an energy policy, but it is limited when disconnection time fluctuates widely. PrEP presents an improved approach to achieve an optimal global result with respect to both energy consumption and service delay. Through simulations with a real-world dataset collected by the MIT Human Dynamics Lab, we show that PrEP provides approximately 20% power saving over the benchmark policies while guaranteeing minimal service delay.

An Improvement of Resource Allocation for Migration Process in Cloud Environment

Virtualmachine(VM)migrationhasrecentlyemergedasapotentialtooltoimprovetheperformance, cost and fault tolerance of data centers. However, migratingVMs requires more hardware resources to reserve target slots during the migration.A large number of simultaneous migrations can exhaust the physical resources of a data center. Consequently, the system may deny new service requests. In this paper, we introduce an efficient strategy to limit the number of target slots used for the migration process with the aim of maintaining high data center availability. In particular, the number of target slots is determined by the tradeoff between the waiting time of the queued migration processes and theprobabilitythatthesystemrejectsnewuserrequestsduetothelackofavailableresources.Wealso propose a method to reduce the waiting time of the migration process by adding a sufficient number of slots during the migration process. Accordingly, a data center can use fewer resources while still guaranteeing the quality of the service. The efficiency of our proposed method is demonstrated by the experimental results.

Cost efficient real-time applications scheduling in mobile cloud computing

Real-time applications require the system, which has enough processing capacity to respond the results, complete all the tasks in time. Cloud computing represents an attractive and cost-efficient of server-based computing and application service provider models. Virtualization technology enables Cloud Service Providers (CSP) to dynamically allocate their resources based on the workload fluctuations from Cloud Consumers. Cloud Service Providers have to tradeoff using different types of virtual machines in order to satisfy the Quality of Service (QoS) about response time also achieve cost efficient when hiring Virtual Machine (VM) from Cloud Hosting Providers (CHP). In this paper, we propose the Cost efficient Real-time Applications Scheduling (CERAS) algorithm in Cloud Computing to solve aforementioned issue. We first develop the scheduling algorithm to schedule tasks while finding the optimal number of VMs needed so that applications execution time is minimized. Based on that optimal number, we develop the cost efficient algorithm, that finds the minimum cost which CSP has to pay for CHP. The experiment results show that how efficient the CERAS algorithm can guarantee applications deadline while achieving the optimal resources needed and cost, compares to other traditional approaches.

A Survey Study on QoE Perspective of Mobile Cloud Computing

Recently the mobile cloud computing (MCC) are increasingly emerging as an efficient environment to provide lots of effective and convenient mobile services for our daily life. Different from cloud computing, MCC dominant the properties of mobile devices rather than PC such as mobile network 3G/4G, limited hardware resources, mobility, etc. Therefore, Quality of Experience (QoE) on mobile devices plays an important role in evaluating the quality of mobile service in MCC. In this paper, we surveyed lots of works on QoE perspective of MCC. The QoE of end-user affected by network QoS factors (i.e., network bandwidth and latency) and application QoS factors, which depend on specific type of MCC services. All of these factors converged to the responsiveness and content visual quality, which determined the QoE perceived by end-user. We further presented the optimization efforts at research literatures to improve the QoE of MCC. Finally, we proposed couple of promising trends which have not mentioned before to enhance the QoE. We believe that the paper is helpful to better understand the impacts of massive deployment and adoption of MCC services.

Improving the performance of data center with real-time service image placement in mobile cloud environment

Mobile Cloud Computing (MCC) is broadening the ubiquitous market for mobile devices. Because of the hardware limitation of mobile devices, the heavy computing tasks should be processed by service images (SIs) on the cloud. Due to the scalability and mobility of users and services, dynamic resource demands and time-varying network condition, SIs must be re-located to adapt the new circumstances. In this paper, we formulate the SI placement as an optimization problem which minimizes the communication cost subject to resource demand constraints. We then propose a real-time SI placement scheme which includes two sequent stages of clustering/filtering and condensed placement to solve the formulated problem. The former omits the infeasible slots prior to placement in order to improve computational complexity. The latter focuses on the SI placement through a novel condensed solution. The numerical results show that our solution converges to the global optimum with a negligible gap while performing much faster execution time compared with the exhaustive search method. This improvement leverages the real-time services especially in MCC environment.

An efficient mobile thin client technology supporting multi-sessions remote control over VNC

Nowadays, using mobile devices is enjoying a boom, that is required a tremendous services supported from mobile cloud. From movie and music entertainment to games, multimedia applications play an everyday role in mobile cloud. With mobile thin client technologies, limited resource mobile devices can utilize resources from powerful servers to run high performance applications. However, those remote control platforms have limited or not support quality of service or multi-connections at all multimedia applications (such as watching high quality movies, play 3D games). In this paper, we propose a remote control model that integrated audio/video (A/V) isolation technology aim to support separating multi-A/V sessions and enhance the quality of multimedia applications. The results of our implementation show that our approach has efficiencies in CPU load, memory and network bandwidth usage.

An efficient block classification for multimedia service in mobile cloud computing

Nowadays, according to the rapid development of mobile smart phone, mobile-based multimedia services are leveraged overwhelmingly. Among the recently efficient client-server protocols, Remote Desktop Protocol (RDP) is the typical method providing those kinds of multimedia services. However, in order to satisfy the quality of service (QoS) in real time multimedia services, RDP needs enhancements to adapt for those rigorous requirements. This paper presents an architecture framework with flexible coding screen image to improve quality of experience (QoE) of users and provides low bandwidth for multimedia services. It segments the captured screen into two types of blocks: text/graphics and picture/background. Then, wise compress algorithm is applied for each block in order to guarantee the best lossless visual quality and reduce amount redundant data transferred. Based on Sobel edge detection (strongly focuses through horizontal, vertical direction and low complexity computation), our proposed method presents the efficient classification for multimedia services.