Pham Phuoc Hung

Smart gateway based communication for cloud of things

Integration of Internet of Things with Cloud Computing is gaining importance, with the way the trend is going on in ubiquitous computing world. Literally, everything is going to be connected to the Internet and its data will be used for various progressive purposes, creating not only information from it, but also, knowledge and even wisdom. Internet of Things (IoT) becoming so pervasive that it is becoming important to integrate it with cloud computing because of the amount of data IoTs could generate and their requirement to have the privilege of virtual resource utilization and storage capacity, but also, to make it possible to create more usefulness from the data generated by IoTs and develop smart applications for the users. Integration of IoT with Cloud Computing, referred here as Cloud of Things, requires smart gateway to perform the rich tasks and preprocessing, which sensors and light IoTs are not capable of doing. This paper focuses on some of the key challenges involved in CoT and the proposal of smart gateway based communication.

Optimal collaboration of thin---thick clients and resource allocation in cloud computing

Cloud computing (CC) has recently become a rising paradigm in the information and communications technology industry, drawing a lot of attentions to professionals and researchers. During the last decade, the dominance of smart phones or tablet computers (known as thin clients) over traditional desktop or laptop computers (known as thick clients) has become more and more evident, reflecting a great change in the way people access the Internet. Despite the recent technology advancements that manufacture a new generation of mobile devices with generous resources, the fact that they can offer only limited processing capacity still remains a painful experience. This problem, fortunately, has been made less severe thanks to the recent adoption of CC platform. CC enables offloading heavy processing tasks up to the “cloud”, leaving only simple jobs to the user-end capacity-limited thin clients. So far, a number of research studies have been carried out, trying to eliminate problems arising from shortcomings in the connection between thin clients and cloud networks, yet little have been found efficient. In this paper, we present a novel architecture, taking advantage of collaboration of thin and thick clients, particularly aiming at optimizing data distribution and utilizing CC resources so that expected Quality-of-Service requirements can be met. We also propose an algorithm to select an optimal resource allocation strategy to satisfy various Service Level Agreements. In order to justify our proposal, we have used both numerical analysis and programming approaches. Simulation result shows that our proposed schemes can improve resource allocation efficiency and achieve better performance than the existing ones.

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.

A Thin-Thick Client Collaboration for Optimizing Task Scheduling in Mobile Cloud Computing

Although the convergence of the two emerging trends: cloud computing and mobile computing can somehow compensate the shortage of hardware capability in mobile devices by offloading requested work to the cloud, most of the research studies are not yet efficient in making cloud usage experience as painless as possible, especially when access to cloud becomes expensive. We propose in this paper an architecture based on a collaboration of thin-thick clients and clouds that focuses on providing an effective task scheduling which can boost up the processing time in the mobile cloud platform while considering the network bandwidth and cost for cloud service usage. Experimental results show the significantly improved efficiency of the task scheduling in bringing desired processing time corresponding to the money paid by customers.

M2M Emergency Help Alert Mobile Cloud Architecture

Emergency situations are unfortunately part of our lives. Todays smart computing allow us handle such situations and fulfill our requirements more efficiently and effectively. This paper presents architecture to handle various kinds of emergency situations more efficiently and effectively, by allowing the user (victim or witness) easy and quick way to alert the concerned department (s) with just a single button press. The service automatically sends the location of incident and contacts the appropriate emergency dealing department automatically through already stored contact numbers. The emergency related information is then synchronized automatically to the mobile cloud, allowing further analysis and improvement in safety of people and creates further services for the concerned authorities and users. Performance in most certain scenarios is also evaluated and presented in this study.

An Adaptive Procedure for Task Scheduling Optimization in Mobile Cloud Computing

Nowadays, mobile cloud computing (MCC) has emerged as a new paradigm which enables offloading computation-intensive, resource-consuming tasks up to a powerful computing platform in cloud, leaving only simple jobs to the capacity-limited thin client devices such as smartphones, tablets, Apple’s iWatch, and Google Glass. However, it still faces many challenges due to inherent problems of thin clients, especially the slow processing and low network connectivity. So far, a number of research studies have been carried out, trying to eliminate these problems, yet few have been found efficient. In this paper, we present an enhanced architecture, taking advantage of collaboration of thin clients and conventional desktop or laptop computers, known as thick clients, particularly aiming at improving cloud access. Additionally, we introduce an innovative genetic approach for task scheduling such that the processing time is minimized, while considering network contention and cloud cost. Our simulation shows that the proposed approach is more cost-effective and achieves better performance compared with others.

A solution for optimizing recovery time in cloud computing

Nowadays, thousands of servers in a cloud datacenter coordinate tasks to provide more reliable and highly available cloud computing services, especially in multi-task processing, as a crucial step to achieve high performance. Therefore, we need effective mechanisms to prepare for a failure of computing nodes. So far, a number of research studies have been carried out, trying to eliminate these problems, yet a little has been found efficient. In this paper, we present a cost-bandwidth based on scheduling algorithm that makes recovery from a saved state faster on heterogeneous computing environments. This algorithm not only considers the network bandwidth but also looks carefully at the monetary cost, which is paid by cloud customers (CCs) for utilizing cloud resources. In order to justify our proposal, we conducted numerous simulations and compared our method with existing ones. The results show that our approach can achieve higher performance, including recovery time in case of failure, while overhead in the case of no failure is a little in typical scenarios.

A New Approach for Task Scheduling Optimization in Mobile Cloud Computing

Mobile cloud computing is growing rapidly because its device (i.e., smart phone) is becoming one of the main processing devices for users nowadays. However, there are still some negative impacts that affect cloud access, especially when access to cloud becomes expensive but recent studies are not yet efficient in eliminating these. In this paper, we present an effective task scheduling by collaborating thick–thin clients and cloud to guarantee a better accessibility to cloud network and boost up the processing time in the mobile cloud platform while considering the network bandwidth and cost for cloud service usage. Intensive simulation proves that our method can improve the task scheduling efficiency and is better cost-effective than other works.

Collaboration of Thin-Thick Clients for Optimizing Data Distribution and Resource Allocation in Cloud Computing

Mobile cloud computing is growing rapidly because its device (i.e., smart phone) is becoming one of the main processing devices for users nowadays. Due to the limitation of resources such as battery life time, CPU and memory capacity, etc., a mobile device cannot satisfy some applications which usually demand more resources than it can afford. To alleviate this, the mobile device should collaborate with external resources to increase its capacity. In order to address these problems, we introduce a collaboration of thin-thick clients which enhances thin client capacities. We further propose a strategy to optimize the data distribution, especially big data in cloud computing. Moreover, we present an algorithm to allocate resources to meet service level agreement (SLA) and conduct simulations to evaluate our approach. Our results evaluation shows that our approach can improve resource allocation efficiency and has better performance than existing approaches.

A Novel Efficient Approach for Screen Image Classification in Remote Display Protocol

In remote display protocols, screen image compression plays an important role to improve quality of experience (QoE) of users and reduce the bandwidth consumption. Not all image elements on the display have the same type, so it is wise to apply a screen image classification for compression decision. In this paper, we propose a novel efficient approach for screen image classification that separate the captured screen into 2 types of blocks: text and non-text block. Our method is a 2-stage process that is different from other works because of the appearance of text localization in the screen image as the first stage. This text localization is mainly based on edge feature and morphological operation in which we experiment with many kinds of edge detection methods. Then block-based classification categorizes the screen image blocks based on the positions of the detected text regions. The experimental results of high accuracy rate and low time consumption state our method is efficient in remote display protocol.