130,231 research outputs found

    ADAPTIVE POWER MANAGEMENT FOR COMPUTERS AND MOBILE DEVICES

    Get PDF
    Power consumption has become a major concern in the design of computing systems today. High power consumption increases cooling cost, degrades the system reliability and also reduces the battery life in portable devices. Modern computing/communication devices support multiple power modes which enable power and performance tradeoff. Dynamic power management (DPM), dynamic voltage and frequency scaling (DVFS), and dynamic task migration for workload consolidation are system level power reduction techniques widely used during runtime. In the first part of the dissertation, we concentrate on the dynamic power management of the personal computer and server platform where the DPM, DVFS and task migrations techniques are proved to be highly effective. A hierarchical energy management framework is assumed, where task migration is applied at the upper level to improve server utilization and energy efficiency, and DPM/DVFS is applied at the lower level to manage the power mode of individual processor. This work focuses on estimating the performance impact of workload consolidation and searching for optimal DPM/DVFS that adapts to the changing workload. Machine learning based modeling and reinforcement learning based policy optimization techniques are investigated. Mobile computing has been weaved into everyday lives to a great extend in recent years. Compared to traditional personal computer and server environment, the mobile computing environment is obviously more context-rich and the usage of mobile computing device is clearly imprinted with user\u27s personal signature. The ability to learn such signature enables immense potential in workload prediction and energy or battery life management. In the second part of the dissertation, we present two mobile device power management techniques which take advantage of the context-rich characteristics of mobile platform and make adaptive energy management decisions based on different user behavior. We firstly investigate the user battery usage behavior modeling and apply the model directly for battery energy management. The first technique aims at maximizing the quality of service (QoS) while keeping the risk of battery depletion below a given threshold. The second technique is an user-aware streaming strategies for energy efficient smartphone video playback applications (e.g. YouTube) that minimizes the sleep and wake penalty of cellular module and at the same time avoid the energy waste from excessive downloading. Runtime power and thermal management has attracted substantial interests in multi-core distributed embedded systems. Fast performance evaluation is an essential step in the research of distributed power and thermal management. In last part of the dissertation, we present an FPGA based emulator of multi-core distributed embedded system designed to support the research in runtime power/thermal management. Hardware and software supports are provided to carry out basic power/thermal management actions including inter-core or inter-FPGA communications, runtime temperature monitoring and dynamic frequency scaling

    System Support For Energy Efficient Mobile Computing

    Get PDF
    Mobile devices are developed rapidly and they have been an integrated part of our daily life. With the blooming of Internet of Things, mobile computing will become more and more important. However, the battery drain problem is a critical issue that hurts user experience. High performance devices require more power support, while the battery capacity only increases 5% per year on average. Researchers are working on kinds of energy saving approaches. For examples, hardware components provide different power state to save idle power; operating systems provide power management APIs to better control power dissipation. However, the system energy efficiency is still low that cannot reach users’ expectation. To improve energy efficiency, we studied how to provide system support for mobile computing in four different aspects. First, we focused on the influence of user behavior on system energy consumption. We monitored and analyzed users’ application usages information. From the results, we built battery prediction model to estimate the battery time based on user behavior and hardware components’ usage. By adjusting user behavior, we can at most double the battery time. To understand why different applications can cause such huge energy difference, we built a power profiler Bugu to figure out where does the power go. Bugu analyzes power and event information for applications, it has high accuracy and low overhead. We analyzed almost 100 mobile applications’ power behavior and several implications are derived to save energy of applications and systems. In addition, to understand the energy behavior of modern hardware architectures, we analyzed the energy consumption and performance of heterogeneous platforms and compared them with homogeneous platforms. The results show that heterogeneous platforms indeed have great potential for energy saving which mostly comes from idle and low workload situations. However, a wrong scheduling decision may cause up to 30% more energy consumption. Scheduling becomes the key point for energy efficient computing. At last, as the increased power density leads to high device temperature, we investigated the thermal management system and developed an ambient temperature aware thermal control policy Falcon. It can save 4.85% total system power and more adaptive in various environments compared with the default approach. Finally, we discussed several potential directions for future research in this field

    Investigation into a best practice model for providing an integrated user experience with mobile cloud applications

    Get PDF
    Mobile Cloud Computing promises to overcome the physical limitations of mobile devices by executing demanding mobile applications on cloud infrastructure. In practice, implementing this paradigm is difficult; network disconnection often occurs, bandwidth may be limited, and a large power draw is required from the battery, resulting in a poor user experience. This thesis presents a mobile cloud middleware solution, Context Aware Mobile Cloud Services (CAMCS), which provides cloudbased services to mobile devices, in a disconnected fashion. An integrated user experience is delivered by designing for anticipated network disconnection, and low data transfer requirements. CAMCS achieves this by means of the Cloud Personal Assistant (CPA); each user of CAMCS is assigned their own CPA, which can complete user-assigned tasks, received as descriptions from the mobile device, by using existing cloud services. Service execution is personalised to the user's situation with contextual data, and task execution results are stored with the CPA until the user can connect with his/her mobile device to obtain the results. Requirements for an integrated user experience are outlined, along with the design and implementation of CAMCS. The operation of CAMCS and CPAs with cloud-based services is presented, specifically in terms of service description, discovery, and task execution. The use of contextual awareness to personalise service discovery and service consumption to the user's situation is also presented. Resource management by CAMCS is also studied, and compared with existing solutions. Additional application models that can be provided by CAMCS are also presented. Evaluation is performed with CAMCS deployed on the Amazon EC2 cloud. The resource usage of the CAMCS Client, running on Android-based mobile devices, is also evaluated. A user study with volunteers using CAMCS on their own mobile devices is also presented. Results show that CAMCS meets the requirements outlined for an integrated user experience

    Efficient runtime management for enabling sustainable performance in real-world mobile applications

    Full text link
    Mobile devices have become integral parts of our society. They handle our diverse computing needs from simple daily tasks (i.e., text messaging, e-mail) to complex graphics and media processing under a limited battery budget. Mobile system-on-chip (SoC) designs have become increasingly sophisticated to handle performance needs of diverse workloads and to improve user experience. Unfortunately, power and thermal constraints have also emerged as major concerns. Increased power densities and temperatures substantially impair user experience due to frequent throttling as well as diminishing device reliability and battery life. Addressing these concerns becomes increasingly challenging due to increased complexities at both hardware (e.g., heterogeneous CPUs, accelerators) and software (e.g., vast number of applications, multi-threading). Enabling sustained user experience in face of these challenges requires (1) practical runtime management solutions that can reason about the performance needs of users and applications while optimizing power and temperature; (2) tools for analyzing real-world mobile application behavior and performance. This thesis aims at improving sustained user experience under thermal limitations by incorporating insights from real-world mobile applications into runtime management. This thesis first proposes thermally-efficient and Quality-of-Service (QoS) aware runtime management techniques to enable sustained performance. Our work leverages inherent QoS tolerance of users in real-world applications and introduces QoS-temperature tradeoff as a viable control knob to improve user experience under thermal constraints. We present a runtime control framework, QScale, which manages CPU power and scheduling decisions to optimize temperature while strictly adhering to given QoS targets. We also design a framework, Maestro, which provides autonomous and application-aware management of QoS-temperature tradeoffs. Maestro uses our thermally-efficient QoS control framework, QScale, as its foundation. This thesis also presents tools to facilitate studies of real-world mobile applications. We design a practical record and replay system, RandR, to generate repeatable executions of mobile applications. RandR provides this capability by automatically reproducing non-deterministic input sources in mobile applications such as user inputs and network events. Finally, we focus on the non-deterministic executions in Android malware which seek to evade analysis environments. We propose the Proteus system to identify the instruction-level inputs that reveal analysis environments

    Towards Secure, Power-Efficient and Location-Aware Mobile Computing

    Get PDF
    In the post-PC era, mobile devices will replace desktops and become the main personal computer for many people. People rely on mobile devices such as smartphones and tablets for everything in their daily lives. A common requirement for mobile computing is wireless communication. It allows mobile devices to fetch remote resources easily. Unfortunately, the increasing demand of the mobility brings many new wireless management challenges such as security, energy-saving and location-awareness. These challenges have already impeded the advancement of mobile systems. In this dissertation we attempt to discover the guidelines of how to mitigate these problems through three general communication patterns in 802.11 wireless networks. We propose a cross-section of a few interesting and important enhancements to manage wireless connectivity. These enhancements provide useful primitives for the design of next-generation mobile systems in the future.;Specifically, we improve the association mechanism for wireless clients to defend against rogue wireless Access Points (APs) in Wireless LANs (WLANs) and vehicular networks. Real-world prototype systems confirm that our scheme can achieve high accuracy to detect even sophisticated rogue APs under various network conditions. We also develop a power-efficient system to reduce the energy consumption for mobile devices working as software-defined APs. Experimental results show that our system allows the Wi-Fi interface to sleep for up to 88% of the total time in several different applications and reduce the system energy by up to 33%. We achieve this while retaining comparable user experiences. Finally, we design a fine-grained scalable group localization algorithm to enable location-aware wireless communication. Our prototype implemented on commercial smartphones proves that our algorithm can quickly locate a group of mobile devices with centimeter-level accuracy

    User-Centric Power Management For Mobile Operating Systems

    Get PDF
    The power consumption of mobile devices must be carefully managed to provide a satisfied battery life to users. This target, however, recently has become more and more difficult to complete. We still cannot expect the battery life problem be solved economically shortly, even though researchers already addressed many aspects of this problem. Principally, that\u27s because existing power management systems, which concentrate on controlling hardware power states, cannot effectively make these hardware components work in low-power mode. Why is this the case? Based on our analysis of 14 users\u27 device usage trace, we found that background applications generate too many activities when the device is either idle or active. These activities are either unimportant or unnecessary for the user. However, a significant amount of CPU time was consumed by them. Moreover, these application activities cause many system services to consume a considerable quantity of battery energy. When we install more applications on our mobile devices, this situation will become even worse. Most application developers rarely consider the power consumption of applications. How to control application state and eliminate redundant application activities become more and more important. Existing power management systems, apparently, cannot handle this situation. Some publications already tried to solve the problem several years ago. For example, EcoSystem and Cinder operating systems try to allocate battery energy precisely to applications based on their requirements. However, the problem with their solution is that the estimated application power consumption cannot accurately represent its reasonable demand. Energy-aware adaptation is another solution to decrease application power consumption. In our previous research, we implemented the {\em Anole} framework to supply energy adaptation APIs to applications. To use this framework, application developers have to implement power-saving strategies in their program. In the operating system, we need to change application behavior automatically in energy adaptation mode. We noticed the latest iOS operating system implemented the idea; the system notifies users to turn off background application update when the battery level is lower than 20%20\%. However, this kind of uniformity in power management can hardly be accepted by most users, because user habits are different from each other. We need to customize the power management strategy for each user. Otherwise, the user experience may be significantly impacted. To solve this problem, we propose user-centric power management, which utilizes the usage pattern of the individual user to distinguish important application from regular applications. Energy-saving strategies will not influence important applications to the user. From the analysis of 14 users\u27 device usage traces, we found that most users\u27 user behavior follows their pattern, which is both time-dependent and location-dependent. Based on this observation, we propose the UPS power management, which collects user behaviors and analyzes the usage pattern of users. We can easily use it to bridge usage behavior to energy-saving strategies. We also proposed three energy-saving strategies, UCASS, LocalLite and WakeFilter, to optimize the redundancy in background application activities and location service usage, and the abuse of in wakelock usage. Our simulation result based on real device usage traces shows that these three strategies can effectively save battery energy consumed background application activities, location requests, and wakelock requests

    Effect of oil palm empty fruit bunches (OPEFB) fibers to the compressive strength and water absorption of concrete

    Get PDF
    Growing popularity based on environmentally-friendly, low cost and lightweight building materials in the construction industry has led to a need to examine how these characteristics can be achieved and at the same time giving the benefit to the environment and maintain the material requirements based on the standards required. Recycling of waste generated from industrial and agricultural activities as measures of building materials is not only a viable solution to the problem of pollution but also to produce an economic design of building
    corecore