A Modified Method for Task Scheduling in Linux Kernel Based on Determining the Intensity of Memory Access

Studies related to smartphone power consumption have intensified in recent years motivated not only by the significant increase in the number of users, but also by the increase in daily usage time by an average user. Therefore, considering that the power supply of mobile devices is limited by the battery capacity, an accurate method to measure power consumption in mobile devices is necessary. However, the current hardware approach used by researchers and industry can cause damage in non-removable battery devices or it may require a long time to measure precisely the power consumption on smartphones. In this paper, we use a new non-invasive method to measure the energy consumed by smartphones. The evaluation results confirm that this method ensures support for all models of smartphones that use the Android operational system based with Linux kernel so far. The switch mode method has an accuracy of 98,18% when compared with the current method used by industry and avoids the high costs of the invasive approaches. The power consumption of important features of a smartphone were measured and divided in five classes. The results show that camera, display, speaker and 5G are the hardware components with highest power consumption compared to the other parts of the smartphone. As future work, the results obtained shall be used to describe a model to estimate and reduce the high power consumption from hardware consumption classes.

As mobile data consumption increases, several venues of research investigate energy efficiency and optimizations, oftentimes realized through on-device services; optimization approaches based around on-device optimizations in turn need to overcome the power consumption added by these services. We present a measurement framework for the power consumption of mobile devices based on the Pandaboard (mobile) development platform using the Android operating system. Utilizing the framework, we perform power consumption evaluations for basic web requests as well as HTML5 video streaming to a mobile browser in (i) direct connection and (ii) indirect connections through a mobile proxy server on the device, which represents a baseline for application layer optimization approaches. We find that using the local proxy server on the device for web requests results in very limited overheads, both in terms of power consumption and added delays which tend to remain request size independent. HTML5 video streaming through the proxy service incurs an average energy consumption penalty of approximately one percent. Our findings corroborate that even smaller realized optimization gains through services on mobile devices themselves are not usurped by the service enablers consuming mobile CPU cycles.

As the extension of cloud computing, multi-access edge computing (MEC) can better support applications to accomplish larger tasks. Existing works on energy optimization of MEC systems fail to utilize multi-relay diversity, which plays a vital role to reduce the computing power consumption of mobile devices by leveraging different offloading modes. In this paper, we propose a novel computing architecture with multi-source, multi-relay, and a single edge server in an orthogonal frequency division multiplexing access (OFDMA) wireless network. The application task on a mobile device can be calculated locally, offloaded to the relay device and completed on the relay device, offloaded to the edge server directly or via a relay device with cooperative communication and executed in the edge server. The two optimization targets are total energy consumption minimization problem, and maximum energy consumption per source-relay pair devices minimization problem. For the first sub-problem, we design an optimal total energy consumption algorithm (OTCA) based on bipartite matching. The optimal energy consumption assignment algorithm (OECAA) is designed for the second sub-problem with an approximation ratio of (1+e). Extensive simulation results show that OTCA outperforms random algorithm by 53.14% on the reduction of total energy consumption, and algorithm OECAA outperforms OTCA by 30.58% on the reduction of maximum energy consumption per source-relay pair devices.

Mobile edge computing (MEC) is a promising way to improve the application performance on mobile devices in recent years, especially in terms of energy saving. However, users have to pay additional service costs such as communication costs when using services provided by MEC. In this paper, we manage to minimize both the average energy consumption and the average communication cost of mobile devices in a mobile edge computing environment with multiple users. We consider a MEC system that consists of multiple cloudlets and a cloud. Users submit their applications consisting of tasks with dependencies to this MEC system. We combine computation offloading and dynamic voltage frequency scaling (DVFS) technologies to reduce the power consumption of mobile devices. We formulate this problem into a mixed-integer nonlinear programming model and propose an adaptive multiobjective evolutionary algorithm based on Non-dominated Sorting Genetic Algorithm III (NSGA-III) to solve it. Simulation experiments are conducted to compare the proposed algorithm with three baseline algorithms and two multi-objective optimization algorithms, which validates the superiority of the proposed algorithms.

We investigate the impact of how a cryptocurrency mining system can affect the power consumption of mobile devices. Specifically we look at CoinHive, a cryptocurrency miner and associated mining pool targeting the Monero (XMR) cryptocurrency. CoinHive distributes a JavaScript-based miner to webpages where visitors run the script and provide computing power to the web host’s CoinHive account. Ideally, hosting JavaScript-based cryptocurrency miners provides alternatives to using ad-networks as a source of website revenue. While some users may not oppose an energy for advertisement trade-off, it may be less palatable to mobile users concerned with battery life. Our initial studies have revealed that, at a minimum, running the normally distributed JavaScript-based miner increases the power draw of a Samsung Galaxy S4 by about 5 fold. Under certain circumstances, the power draw can increase to eleven-fold. We illustrate these findings by comparing them with various normal use benchmarks and with a variety of different ad-enabled webpages.

We propose a luminance scalable video coding scheme (LSC) that uses the scalable video coding (SVC) extension of H.264/AVC to reduce the power consumption of mobile devices. The insufficient battery life of mobile hand-held devices is the main obstacle for mobile video applications. In particular, liquid crystal displays consume a large portion of the system power. Building on previous power reduction schemes, such as dynamic backlight luminance scaling, we exploit SVC which is used to adapt a video stream to the preferences of users, different mobile capabilities, and varying network conditions. Our video coding scheme can reduce the power consumed by an LCD display by more than 20%, even though it has a lower overhead than previous schemes and does not require any modification of the decoders.

The active matrix organic light-emitting diode (AMOLED) is expected to serve as next generation flat panels display with the outstanding features of wide viewing angle, vivid images, and quick response. For practical use of full-color AMOLEDs in mobile devices, it is essential to reduce the power consumption, which is generally higher than that of liquid crystal displays (LCDs). For this aim, a red, green, blue, and white (RGBW) pixel format combined with an RGB color filter array (RGBW format) with a common white emission layer (EML) has been developed. We find that the RGBW format can successfully reduce the power consumption of a full-color AMOLED by nearly half that of a conventionally filtered RGB pixel format. This improved power consumption is almost equal to the power consumption of a same-sized LCD. The RGBW format is a promising technique for the further reduction of the power consumption of a full-color AMOLED.

The computer industry is moving towards two extremes: extremely high-performance high-throughput cloud computing, and low-power mobile computing. Cloud computing, while providing high performance, is very costly. Google and Microsoft Bing spend billions of dollars each year to maintain their server farms, mainly due to the high power bills. On the other hand, mobile computing is under a very tight energy budget, but yet the end users demand ever increasing performance on these devices. This trend indicates that conventional architectures are not able to deliver high-performance and low power consumption at the same time, and we need a new architecture model to address the needs of both extremes. In this paper, we thus introduce our Extremely Heterogeneous Architecture (EHA) project: EHA is a novel architecture that incorporates both general-purpose and specialized cores on the same chip. The general-purpose cores take care of generic control and computation. On the other hand, the specialized cores, including GPU, hard accelerators (ASIC accelerators), and soft accelerators (FPGAs), are designed for accelerating frequently used or heavy weight applications. When acceleration is not needed, the specialized cores are turned off to reduce power consumption. We demonstrate that EHA is able to improve performance through acceleration, and at the same time reduce power consumption. Since EHA is a heterogeneous architecture, it is suitable for accelerating heterogeneous workloads on the same chip. For example, data centers and clouds provide many services, including media streaming, searching, indexing, scientific computations. The ultimate goal of the EHA project is two-fold: first, to design a chip that is able to run different cloud services on it, and through this design, we would be able to greatly reduce the cost, both recurring and non-recurring, of data centers\clouds, second, to design a light-weight EHA that runs on mobile devices, providing end users with improved experience even under tight battery budget constraints.

Both the capacity and the complexity of modern FPGA devices increase rapidly. Also, it is common that battery-powered embedded systems are equipped with FPGA devices. Therefore, reducing the power consumption of FPGA devices has become a very crucial issue. Not only dynamic power consumption but also leakage power consumption increases as the feature size to manufacture FPGA devices shrinks. To reduce the power consumption of FPGA devices, carbon-nanotube field effect transistor (CNFET) has emerged as a promising candidate for replacing silicon metal oxide semiconductor field effect transistor (Si-MOSFET). In this paper, we propose an FPGA slice design based on CNFET technology, and compare the performance and the power consumption characteristics of the proposed design with the same design based on Si-MOSFET. From the performance evaluation, we learned that the proposed design showed up to 5000 times better power-delay product (PDP) than the Si-MOSFET design. Especially reduction in power consumption was truly significant.

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.

Public AI-as-a-Service (AIaaS) is a promising next-generation computing paradigm that attracts resource-limited mobile users to outsource their machine learning tasks. However, the time delay between cloud/edge servers and end users makes it hard for real-time mobile artificial intelligence applications. In this paper, we propose a real-time mobile facial expression tracking framework combining AIaaS and mobile local auxiliary computing, including facial expression tracking and the corresponding task offloading. Mobile facial expression tracking estimates the facial expression of the current frame with the help of sparse optical flow and neural network. Task offloading utilizes neural network to dynamically estimate the moment of task offloading. According to the experimental results in a real-world environment, the proposed framework successfully fulfills the mobile real-time facial expression tracking requirements with high accuracy. Moreover, compared to its benchmark, it effectively reduces the power consumption of mobile devices and the potential service cost for AIaaS.

In this paper, we present the novel scheduling algorithm of the multimedia data retrieval for the mobile disk drive. Our algorithm is focused on minimizing the power consumption in multimedia data retrieval. While the disk based storage devices, e.g. hard disk and optical disk becomes small enough to be used in mobile devices, the practical usage of which leaves much to be desired due to the stringent power consumption restriction of the mobile device. The playback of multimedia data requires that data blocks are delivered to the destination in periodic fashion. The major issue here has been how to guarantee the continuous flow of data. Most of preceding works assume that the disk drive always operates in the steady state. However, this does not hold in modern disk drive for the mobile device. Modern low power disk drive for mobile device goes into standby state when it is not in use. While this feature can significantly extend the battery life, it adds another dimension of complexity in scheduling of the multimedia data retrieval. We elaborately model the power consumption behavior of the low power mobile drive and develop an Adaptive Round Merge(ARM) scheduling algorithm which guarantees a certain disk bandwidth for the multimedia playback while minimizing the power consumption of the storage device. According to our simulation based experiment, the ARM algorithm reduces the power consumption by as much as 23%. It manifests itself when the video clip is relatively short, typically less than 30 sec.

New algorithms of coding the internal states of finite-state machine (FSM) have been (were) proposed. These algorithms make it possible to reduce the power consumption of sequential devices at the stage of their designing. The algorithms presented are based on solving the minimization problem of the switching activity of FSM memory elements that directly results in the reduced power consumption. The sequential and iterative algorithms of coding the FSM internal states have been proposed that are focused on the input power reduction. The experimental studies corroborated a substantial reduction of the power consumption in devices designed with the use of the described algorithms of coding as compared with the known approaches.

Spiking-PhysFormer: Camera-based remote photoplethysmography with parallel spike-driven transformer.

Recently, there is a widespread use of Web browser-based Web applications such as e-mails and chats. However, frequent communication between mobile wireless terminals and HTTP servers give rise to problematic increases of average power consumption for the mobile devices. Current attempts to tackle this problem focus on reducing power consumption at the network and data link layers used by the devices. In this study, we propose a different solution where certain functionalities of the mobile application are delegated to other devices with abundant power resources (either from large capacity batteries or power outlets). This method off-loads parts of the communication process normally done at wireless terminals to amply powered machines. With messages pushed from the machines only when the HTTP server responds with an update, the wireless terminal needs to transmit data less frequently, thus cutting down on power consumption. Our prototype implementing the proposed method succeeded in reducing the rise in average power consumption.