PERFORMANCE EVALUATION OF MOBILE MULTIMEDIA APPLICATIONS IN CLOUD COMPUTING ENVIRONMENTS

The surge in global internet traffic, primarily fueled by the rise of streaming platforms, cloud computing, and data-intensive applications, has posed significant challenges in effectively managing network resources. Ensuring consistent Quality of Service (QoS) across various types of traffic, such as video streaming, online gaming, and real-time communications, is becoming an increasingly challenging task. Traditional routing techniques, such as best-effort and shortest-path methods, are increasingly falling short in meeting these demands due to their inability to account for different traffic requirements. To address these limitations, this paper introduces QSroute, a novel QoS-based routing (QBR) scheme specifically designed for Software Defined Networking (SDN) environments. QSroute leverages the global view provided by SDN to dynamically optimize routing decisions based on critical QoS metrics, including available bandwidth, packet delay, and packet loss ratio. Our approach computes the optimal path between source and destination nodes by minimizing a composite link cost derived from these metrics. Extensive simulation results show that QSroute significantly outperforms existing QBR schemes, particularly in terms of reducing end-to-end delays, minimizing jitter, and improving overall throughput. These performance gains underscore QSroute’s potential as a highly effective solution for addressing the complex demands of modern network environments, offering enhanced scalability and network efficiency. Future research will investigate the integration of additional QoS metrics, as well as the scheme's scalability in more complex, multi-domain SDN environments, to further enhance its applicability.

Offloading tasks to cloud is one of the proposed solutions for extending battery life of mobile devices. Most prior research focuses on offloading computation, leaving communication-related tasks out of scope. However, most popular applications today involve intensive communication that consumes a significant part of the overall energy. Hence, we currently do not know how feasible it is to use offloading for saving energy in such apps. In this paper, we first show that it is possible to save energy by offloading communication-related tasks of the app to the cloud. We use an open source Twitter client, AndTweet, as a case study. However, using a set of popular open source applications, we also show that existing apps contain constraints that have to be released with code modifications before offloading can be profitable, and that the potential energy savings depend on many communication parameters. We therefore develop two tools: the first to identify the constraints and the other for fine-grained communication energy estimation. We exemplify the tools and explain how they could be used to help offloading parts of popular apps successfully.

The major preoccupation regarding the actual research is to visualize a theoretical model of the natural forces interaction between a multimedia mobile application deployed upon an educational activity over a group of students and an automatic process of evaluation regarding the final academic results. We implemented a new metric indicator, named here with effectiveness of a multimedia mobile application, correlated with the educational resultant force emerged from a mobile environment learning experiment. The variables involved in our model are connected to the cross-disciplinary curricula, the educational time coverage disclosed by a multimedia mobile application, the multimedia concentration, and the academic performance measured just in time at the interface between formal and informal evaluation. We are proposing another educational force couple, a resultant force, which is represented by the quality attributes allocated to the mobile devices and, furthermore, to the multimedia mobile applications. We are using the assumption that a multimedia mobile application generates an educational field in front of a learner taking into consideration not only the easiness of the process by which the learner’s attention is retaining toward a mobile device. The fact that between a mobile user and a multimedia mobile application could be established a quantitative relation of physical attraction or rejection, disclosed by surveys and questionnaires including qualities attributes, was an argument for our hypothesis that educational forces encountered could be explained by a cloaking educational field where a touch screen electric impulse is only the beginning of the educational magnetism encountered at the learners.KeywordsMobile applicationsMobile learningAndroidImpact on academic performance

Wireless networks are becoming a part of everyday life for many people. When a mobile device has wireless LAN capability, multimedia content can be streamed over a wireless network to that device. However, a major disadvantage of all mobile devices is their limited battery lifetime. Multimedia streaming puts extra pressure on the battery, causing it to discharge faster. In some cases, streaming tasks cannot be completed purely because the battery of the device becomes fully discharged, which causes significant user dissatisfaction. Consequently, it is necessary to devise mechanisms to enable longer battery lifetime in order to support complex applications such as those involving mobile multimedia streaming. This paper proposes an adaptive-buffer power save mechanism (AB-PSM) for increasing the battery life of mobile devices during multimedia streaming. This increase is achieved by controlling how and when data is sent over a wireless LAN. AB-PSM introduces an additional buffer which hides data from the station it is intended for, allowing it to return to sleep and consequently saving power. Data is eventually delivered in one of the station's following attempts to receive it. Tests involving AB-PSM have been performed and show good results in terms of significant increases in battery lifetime. The comparison between AB-PSM and the IEEE 802.11 legacy power save mechanism shows important increases in battery lifetime of more than 100%.

Real-time and embedded systems have traditionally been designed for closed environments where operating conditions, input workloads, and resource availability are known a priori and are subject to little or no change at runtime. There is an increasing demand, however, for autonomous capabilities in open distributed real-time and embedded (DRE) systems that execute in environments where input workload and resource availability cannot be accurately characterized a priori. These systems can benefit from autonomic computing capabilities, such as self-(re)configuration and self-optimization, that enable autonomous adaptation under varying-even unpredictable-operational conditions. A challenging problem faced by researchers and developers in enabling autonomic computing capabilities to open DRE systems involves devising adaptive planning and resource management strategies that can meet mission objectives and end-to-end quality of service (QoS) requirements of applications. To address this challenge, this paper presents the integrated planning, allocation, and control (IPAC) framework, which provides decision-theoretic planning, dynamic resource allocation, and runtime system control to provide coordinated system adaptation and enable the autonomous operation of open DRE systems. This paper presents two contributions to research on autonomic computing for open DRE systems. First, we describe the design of IPAC and show how IPAC resolves the challenges associated with the autonomous operation of a representative open DRE system case study. Second, we empirically evaluate the planning and adaptive resource management capabilities of IPAC in the context of our case study. Our experimental results demonstrate that IPAC enables the autonomous operation of open DRE systems by performing adaptive planning and management of system resources.

Cloud computation offloading is a promising method that sending heavy computation to resourceful servers on cloud and then receiving the results from them. In this paper, we study the offloading techniques and further explore the tradeoff between shortening execution time and extending battery life of mobile devices. A novel adaptive offloading scheme is proposed and analyzed based on the tradeoff analysis. And it can be realized thanks to the elasticity of cloud computing that the resources can be bought on demand. We have tried to find a server on cloud with a critical value of speedup F for a specified mobile device. When satisfying the requirement such as performance improvement by the system, it is worth sacrificing large F when taking economic factor into consideration.

SummaryMobile data offloading has already appeared to offer the means of addressing the challenges of limited computing capability and battery life of mobile devices. Most existing code offloading frameworks only consider migrating an application within a single network at a time and failed to fully utilize the energy efficiency mechanism of the latest CPU chips. To tackle these issues, we propose an offloading approach under both a multitasking environment and a heterogeneous network to increase energy and execution efficiency. The offloading problem was formulated as a biobjective optimization problem that aims to save energy and keep a good performance by combining mobile cloud computing with big.LITTLE technology under a heterogeneous network equipped with cellular and WiFi connectivity. By varying the applications and network scenarios, the experimental results show that with offloading a single application using our proposed framework, the data‐intensive application can obviously benefit when the access point density of WiFi reaches 0.0002 unit/m2. Under multiple application scenarios, our proposed framework can increase processing speed by an average of 0.3× over the single application and can save an average of 25% power over the single application.

Mobile applications require software reconfiguration to improve resource usage and availability. We propose a power-aware reconfiguration scheme that (1) moves energy-demanding applications to proxy servers, and (2) adjusts the fidelity of mobile applications as resources diminish. We formulate a cooperative reconfiguration plan which determines when, where, and which components should be deployed and have their fidelity controlled, so as to minimize the power consumption of mobile devices and to utilize the system resources of servers efficiently. We then construct a graph-theoretic model of the cost of migrating components to one proxy server or to a cluster of servers. In this model, changes to the residual energy of mobile devices, available server resources, and the wireless network bandwidth can all accelerate or decelerate the migration and fidelity control of applications. We suggest an approximation algorithm that achieves a near-optimal solution in terms of energy consumption. Our proposal will support mobile applications which require large amount of computation and need to maintain their services for an extended time such as video conferencing, multimedia e-mail, and real-time navigation. Simulation-based experiments verify that our scheme is an efficient way to extend the battery life of mobile devices and to improve the response time of mobile applications.

liquid crystal displays(LCDs) are the dominant display technology in today's mobile devices. For the battery life of mobile devices, it is very demanding to lower the power consumption of mobile displays. Operating active‐matrix liquid‐crystal displays (AMLCDs) with an ultra‐wide refresh‐rate range, down to ∼1 Hz, can lower the power consumption and offer video rate as well. It has been shown that a SiOx‐based liquid crystal (LC) alignment technique can overcome the ion problems typically encountered with low‐refresh‐rate devices because of its adsorption ability. In this paper, a further study is taken on the adsorption property of SiOx alignment film. To estimate the structure of ionic impurities in LC, a new model given constant ion concentration is implemented to simulate the voltage holding profiles, which shows that the ionic impurities adsorbed by the SiOx film are probably associated with water. Besides, it is found that adding salt into LC can help the adsorption of SiOx film, and thus lower the ion concentration in LC more effectively.

Virtual YouTuber (VTuber) live streaming, which renders and streams a virtual avatar of the actual streamer on top of the live camera view, has gained significant popularity recently. Despite the engaging user experience, the intensive and power-consuming computations required by VTuber applications, such as facial feature extraction and avatar rendering, pose significant challenges to the constrained battery life of mobile devices. We develop a power-efficient VTuber live streaming system by offloading the camera view and the computation-intensive operations from the mobile device to an edge server. Our approach not only reduces the power consumption of the mobile device but also enables larger-scale rendering of multiple avatars, which is infeasible in existing mobile VTuber systems. Furthermore, to reduce the bandwidth overhead caused by the camera view offloading, we develop an adaptive framerate control mechanism to dynamically adjust the framerate of the offloaded camera view based on the variations of inter-frame luminance, as well as resolution control to dynamically adjust the resolution of the offloaded camera view based on the number and size of the faces. Our evaluations on the end-to-end VTuber live streaming system demonstrate 26%-29% power savings with limited latency, bandwidth, and quality overhead.

Mobile edge computing (MEC) is considered a promising technique that prolongs battery life and enhances the computation capacity of mobile devices (MDs) by offloading computation-intensive tasks to the resource-rich cloud located at the edges of mobile networks. In this study, the problem of energy-efficient computation offloading with guaranteed performance in multiuser MEC systems was investigated. Given that MDs typically seek lower energy consumption and improve the performance of computing tasks, we provide an energy-efficient computation offloading and transmit power allocation scheme that reduces energy consumption and completion time. We formulate the energy efficiency cost minimization problem, which satisfies the completion time deadline constraint of MDs in an MEC system. In addition, the corresponding Karush–Kuhn–Tucker conditions are applied to solve the optimization problem, and a new algorithm comprising the computation offloading policy and transmission power allocation is presented. Numerical results demonstrate that our proposed scheme, with the optimal computation offloading policy and adapted transmission power for MDs, outperforms local computing and full offloading methods in terms of energy consumption and completion delay. Consequently, our proposed system could help overcome the restrictions on computation resources and battery life of mobile devices to meet the requirements of new applications.

ENOrMOUS: ENergy Optimization for MObile plateform using User needS

With the growing appeal of real-time interactions between physical views and virtual objects in augmented reality (AR) applications among contemporary users, optimizing power consumption is crucial for extending the battery life of mobile devices. This paper delves into methods for reducing power consumption specifically in mobile OLED devices when running AR applications, depending on user behaviors. Initially, we introduce a detection algorithm designed to accurately identify user status using cost-effective sensors. Subsequently, we present two dynamic configurations aimed at adjusting the display of physical views and virtual objects based on user visual attention of AR applications. The results of extensive experiments conducted on a commercial smartphone using an open-source AR application to assess the performance of the proposed power-saving design are highly promising.

Time-of-flight (TOF) cameras are becoming increasingly popular for many mobile applications. To obtain accurate depth maps, TOF cameras must emit many pulses of light, which consumes a lot of power and lowers the battery life of mobile devices. However, lowering the number of emitted pulses results in noisy depth maps. To obtain accurate depth maps while reducing the overall number of emitted pulses, we propose an algorithm that adaptively varies the number of pulses to infrequently obtain high power depth maps and uses them to help estimate subsequent low power ones. To estimate these depth maps, our technique uses the previous frame by accounting for the 3D motion in the scene. We assume that the scene contains independently moving rigid objects and show that we can efficiently estimate the motions using just the data from a TOF camera. The resulting algorithm estimates 640 × 480 depth maps at 30 frames per second on an embedded processor. We evaluate our approach on data collected with a pulsed TOF camera and show that we can reduce the mean relative error of the low power depth maps by up to 64% and the number of emitted pulses by up to 81%.

Mobile cloud computing has introduced offloading to save the battery life of mobile devices. In mobile cloud computing optimization of power and delay for offloading has become a vital research scope. However, migration of the storage and computation from the mobile device to the remote cloud server enhances the delay and power consumption. To overcome this difficulty, cloudlet comes which is located nearby the mobile device. Since the cloudlet may not be able to fulfill all the offloading requests, sometimes remote public cloud server is used for the same. As a result the power and delay consumptions are increased. For solving this difficulty, private cloud server is used in our scheme along with the cloudlet and public cloud server. In this paper multilevel full and partial offloading strategies are proposed based on cloudlet, private and public cloud servers. The power and delay consumption in the proposed methods are determined and compared with the existing offloading methods. The theoretical and experimental analyses demonstrate that the proposed multilevel offloading methods are power and delay efficient. The simulation results show that the proposed multilevel full and partial offloading strategies reduce the power consumption by approximately 8–9% and 20% respectively than the existing methods.