scholarly journals Joint Optimized CPU and Networking Control Scheme for Improved Energy Efficiency in Video Streaming on Mobile Devices

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Sung-Woong Jo ◽  
Jong-Moon Chung
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Video Streaming ◽  
Joint Optimization ◽  
Battery Life ◽  
Processing Unit ◽  
Central Processing ◽  
Control Scheme ◽  
Streaming Service

Video streaming service is one of the most popular applications for mobile users. However, mobile video streaming services consume a lot of energy, resulting in a reduced battery life. This is a critical problem that results in a degraded user’s quality of experience (QoE). Therefore, in this paper, a joint optimization scheme that controls both the central processing unit (CPU) and wireless networking of the video streaming process for improved energy efficiency on mobile devices is proposed. For this purpose, the energy consumption of the network interface and CPU is analyzed, and based on the energy consumption profile a joint optimization problem is formulated to maximize the energy efficiency of the mobile device. The proposed algorithm adaptively adjusts the number of chunks to be downloaded and decoded in each packet. Simulation results show that the proposed algorithm can effectively improve the energy efficiency when compared with the existing algorithms.

A Novel Concept for Energy-Optimal, Independent-Phase Control of Brushless Motor Drivers

2021 ◽  
pp. 1-11
Author(s):  
Amin Ghorbanpour ◽  
Hanz Richter
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Robot Motion ◽  
Control Law ◽  
Outer Loop ◽  
Energy Regeneration ◽  
Brushless Motor ◽  
Control Scheme ◽  
Novel Concept ◽  
Constrained Quadratic Optimization

Abstract In this work, a new drive concept for brushless direct current (BLDC) motors is introduced. Energy regeneration is optimally managed with the aim of improving the energy efficiency of robot motion controls. The proposed scheme has three independent regenerative drives interconnected in a wye configuration. An augmented model of the robot, joint mechanisms, and BLDC motors is formed, and then a voltage-based control scheme is developed. The control law is obtained by specifying an outer-loop torque controller followed by minimization of power consumption via online constrained quadratic optimization. An experiment is conducted to assess the performance of the proposed concept against an off-the-shelf driver. It is shown that, in terms of energy regeneration and consumption, the developed driver has better performance. Furthermore, the proposed concept showed a reduction of 15% energy consumption for the conditions of the study.

Joint Optimization Offloading Strategy of Execution Time and Energy Consumption of Mobile Edge Computing

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Qingzhu Wang ◽  
Xiaoyun Cui
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Execution Time ◽  
Optimal Solution ◽  
Computation Offloading ◽  
Joint Optimization ◽  
Random Strategy ◽  
Fitness Value ◽  
Cloud Server ◽  
Time And Energy

As mobile devices become more and more powerful, applications generate a large number of computing tasks, and mobile devices themselves cannot meet the needs of users. This article proposes a computation offloading model in which execution units including mobile devices, edge server, and cloud server. Previous studies on joint optimization only considered tasks execution time and the energy consumption of mobile devices, and ignored the energy consumption of edge and cloud server. However, edge server and cloud server energy consumption have a significant impact on the final offloading decision. This paper comprehensively considers execution time and energy consumption of three execution units, and formulates task offloading decision as a single-objective optimization problem. Genetic algorithm with elitism preservation and random strategy is adopted to obtain optimal solution of the problem. At last, simulation experiments show that the proposed computation offloading model has lower fitness value compared with other computation offloading models.

scholarly journals LTTng CLUST: A System-Wide Unified CPU and GPU Tracing Tool for OpenCL Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
David Couturier ◽  
Michel R. Dagenais
Keyword(s):  
Application Programming Interface ◽  
Third Party ◽  
Battery Life ◽  
Portable Devices ◽  
Processing Unit ◽  
Central Processing ◽  
Application Programming ◽  
Graphical Processing ◽  
Performance Analysis Tools ◽  
Programming Interface

As computation schemes evolve and many new tools become available to programmers to enhance the performance of their applications, many programmers started to look towards highly parallel platforms such as Graphical Processing Unit (GPU). Offloading computations that can take advantage of the architecture of the GPU is a technique that has proven fruitful in recent years. This technology enhances the speed and responsiveness of applications. Also, as a side effect, it reduces the power requirements for those applications and therefore extends portable devices battery life and helps computing clusters to run more power efficiently. Many performance analysis tools such as LTTng, strace and SystemTap already allow Central Processing Unit (CPU) tracing and help programmers to use CPU resources more efficiently. On the GPU side, different tools such as Nvidia’s Nsight, AMD’s CodeXL, and third party TAU and VampirTrace allow tracing Application Programming Interface (API) calls and OpenCL kernel execution. These tools are useful but are completely separate, and none of them allow a unified CPU-GPU tracing experience. We propose an extension to the existing scalable and highly efficient LTTng tracing platform to allow unified tracing of GPU along with CPU’s full tracing capabilities.

scholarly journals Mobile Cloud Computing: Offloading Mobile Processing to the Cloud

2016 ◽  
Vol 9 (1) ◽  
pp. 90
Author(s):  
Sanjay P. Ahuja ◽  
Jesus Zambrano
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Response Times ◽  
Mobile Apps ◽  
Mobile Systems ◽  
Battery Life ◽  
Data Intensive ◽  
Computing Platforms ◽  
Computational Resources

<p class="zhengwen">The current proliferation of mobile systems, such as smart phones and tablets, has let to their adoption as the primary computing platforms for many users. This trend suggests that designers will continue to aim towards the convergence of functionality on a single mobile device (such as phone + mp3 player + camera + Web browser + GPS + mobile apps + sensors). However, this conjunction penalizes the mobile system both with respect to computational resources such as processor speed, memory consumption, disk capacity, and in weight, size, ergonomics and the component most important to users, battery life. Therefore, energy consumption and response time are major concerns when executing complex algorithms on mobile devices because they require significant resources to solve intricate problems.</p><p>Offloading mobile processing is an excellent solution to augment mobile capabilities by migrating computation to powerful infrastructures. Current cloud computing environments for performing complex and data intensive computation remotely are likely to be an excellent solution for offloading computation and data processing from mobile devices restricted by reduced resources. This research uses cloud computing as processing platform for intensive-computation workloads while measuring energy consumption and response times on a Samsung Galaxy S5 Android mobile phone running Android 4.1OS.</p>

On the Optimal Energy Efficiency and Spectral Efficiency Trade-Off of CF Massive MIMO SWIPT System

2021 ◽  
Author(s):  
Na Li ◽  
Yuan Yuan Gao ◽  
Kui Xu
Keyword(s):  
Energy Efficiency ◽  
Energy Harvesting ◽  
Information Transmission ◽  
Spectral Efficiency ◽  
Performance Optimization ◽  
Processing Unit ◽  
Processing Scheme ◽  
Selection Scheme ◽  
Trade Off ◽  
Central Processing

Abstract This paper studies a cell-free (CF) massive multi-input multi-output (MIMO) simultaneous wireless information and power transmission (SWIPT) system and proposes a user-centric (UC) access point (AP) selection method and a trade-off performance optimization scheme for spectral efficiency and energy efficiency. In this system, users have both energy harvesting and information transmission functions, and according to the difference between energy harvesting and information transmission, a flexible AP selection scheme is designed. This paper analyses the trade-off between energy efficiency and spectral efficiency, proposes an evaluation index that takes into account both energy efficiency and spectral efficiency, and jointly optimizes the AP selection scheme and the uplink (UL) and downlink (DL) time switching ratio to maximize the trade-off performance. Then, the non-convex problem is converted to a geometric planning (GP) problem to solve. The simulation results show that by implementing a suitable AP selection scheme and UL and DL time allocation, the information processing scheme on the AP side has a slight loss in spectral efficiency, but the energy efficiency is close to the performance of global processing on the central processing unit (CPU).

Hot Spot Cooling and Harvesting Central Processing Unit Waste Heat Using Thermoelectric Modules

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
Soochan Lee ◽  
Patrick E. Phelan ◽  
Carole-Jean Wu
Keyword(s):  
Energy Consumption ◽  
Hot Spots ◽  
Waste Heat ◽  
Heat Energy ◽  
Central Processing Unit ◽  
High Heat Flux ◽  
Processing Unit ◽  
Thermoelectric Coolers ◽  
Thermoelectric Modules ◽  
Central Processing

The increasing integration of high performance processors and dense circuits in current computing devices has produced high heat flux in localized areas (hot spots), which limits their performance and reliability. To control the hot spots on a central processing unit (CPU), many researchers have focused on active cooling methods such as thermoelectric coolers (TECs) to avoid thermal emergencies. This paper presents optimized thermoelectric modules on top of the CPU combined with a conventional air-cooling device to reduce the core temperature and at the same time harvest waste heat energy generated by the CPU. To control the temperature of the cores, we attach small-sized TECs to the CPU and use thermoelectric generators (TEGs) placed on the rest of the CPU to convert waste heat energy into electricity. This study investigates design alternatives with an analytical model considering the nonuniform temperature distribution based on two-node thermal networks. The results indicate that we are able to attain more energy from the TEGs than energy consumption for running the TECs. In other words, we can allow the harvested heat energy to be reused to power other components and reduce cores temperature simultaneously. Overall, the idea of simultaneous core cooling and waste heat harvesting using thermoelectric modules on a CPU is a promising method to control the problem of heat generation and to reduce energy consumption in a computing device.

scholarly journals Optimization of mobile device energy consumption in a fog-based mobile computing offloading mechanism

2021 ◽  
Vol 29 (1) ◽  
pp. 53-62
Author(s):  
Anastasia V. Daraseliya ◽  
Eduard S. Sopin
Keyword(s):  
Energy Consumption ◽  
Response Time ◽  
Mobile Devices ◽  
Mobile Device ◽  
Mobile Applications ◽  
Optimization Problem ◽  
Fog Computing ◽  
Computing System ◽  
Battery Life ◽  
Average Response Time

The offloading of computing tasks to the fog computing system is a promising approach to reduce the response time of resource-greedy real-time mobile applications. Besides the decreasing of the response time, the offloading mechanisms may reduce the energy consumption of mobile devices. In the paper, we focused on the analysis of the energy consumption of mobile devices that use fog computing infrastructure to increase the overall system performance and to improve the battery life. We consider a three-layer computing architecture, which consists of the mobile device itself, a fog node, and a remote cloud. The tasks are processed locally or offloaded according to the threshold-based offloading criterion. We have formulated an optimization problem that minimizes the energy consumption under the constraints on the average response time and the probability that the response time is lower than a certain threshold. We also provide the numerical solution to the optimization problem and discuss the numerical results.

scholarly journals Is HTTP/2 more energy efficient than HTTP/1.1 for mobile users?

2015 ◽  
Author(s):  
Shaiful Alam Chowdhury ◽  
Varun Sapra ◽  
Abram Hindle
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Point Of View ◽  
Transport Layer ◽  
Mobile Users ◽  
Battery Life ◽  
Trip Time ◽  
Synthetic Test ◽  
Test Scenarios ◽  
Critical Resources

Recent technological advancements have enabled mobile devices to provide mobile users with substantial capability and accessibility. Energy is evidently one of the most critical resources for such devices; in spite of the substantial gain in popularity of mobile devices, such as smartphones, their utility is severely constrained by battery life. Mobile users are very interested in accessing the Internet while it is one of the most expensive operations in terms of energy and cost. HTTP/2 has been proposed and accepted as the new standard for supporting the World Wide Web. HTTP/2 is expected to offer better performance, such as reduced page load time. Consequently, from the mobile users point of view, question arises: Does HTTP/2 offer improved energy consumption performance achieving longer battery life?In this paper, we compare the energy consumption of HTTP/2 with its predecessor (i.e., HTTP/1.1) using a variety of real world and synthetic test scenarios. We also investigate how Transport Layer Security (TLS) impacts the energy consumption of the mobile devices. Our study suggests that Round Trip Time (RTT) is one of the biggest factors in deciding how advantageous is HTTP/2 compared to HTTP/1.1. We conclude that for networks with higher RTTs, HTTP/2 has better energy consumption performance than HTTP/1.1.

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