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.

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.

Energy Consumption Optimization by Increasing The Processor Speed of Moving Communication Devices in Transport Layer Protocol

2019 ◽  
Vol 16 (2) ◽  
pp. 30
Author(s):  
Fakhrur Razi ◽  
Ipan Suandi ◽  
Fahmi Fahmi
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Device ◽  
Packet Loss ◽  
Video Data ◽  
Transmission Delay ◽  
Transport Layer ◽  
Tcp Protocol ◽  
Transport Layer Protocol ◽  
Processor Speed

The energy efficiency of mobile devices becomes very important, considering the development of mobile device technology starting to lead to smaller dimensions and with the higher processor speed of these mobile devices. Various studies have been conducted to grow energy-aware in hardware, middleware and application software. The step of optimizing energy consumption can be done at various layers of mobile communication network architecture. This study focuses on examining the energy consumption of mobile devices in the transport layer protocol, where the processor speed of the mobile devices used in this experiment is higher than the processor speed used in similar studies. The mobile device processor in this study has a speed of 1.5 GHz with 1 GHz RAM capacity. While in similar studies that have been carried out, mobile device processors have a speed of 369 MHz with a RAM capacity of less than 0.5 GHz. This study conducted an experiment in transmitting mobile data using TCP and UDP protocols. Because the video requires intensive delivery, so the video is the traffic that is being reviewed. Energy consumption is measured based on the amount of energy per transmission and the amount of energy per package. To complete the analysis, it can be seen the strengths and weaknesses of each protocol in the transport layer protocol, in this case the TCP and UDP protocols, also evaluated the network performance parameters such as delay and packet loss. The results showed that the UDP protocol consumes less energy and transmission delay compared to the TCP protocol. However, only about 22% of data packages can be transmitted. Therefore, the UDP protocol is only effective if the bit rate of data transmitted is close to the network speed. Conversely, despite consuming more energy and delay, the TCP protocol is able to transmit nearly 96% of data packets. On the other hand, when compared to mobile devices that have lower processor speeds, the mobile devices in this study consume more energy to transmit video data. However, transmission delay and packet loss can be suppressed. Thus, mobile devices that have higher processor speeds are able to optimize the energy consumed to improve transmission quality.Key words: energy consumption, processor, delay, packet loss, transport layer protocol

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>

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 Offloading of Mobile Apps in Hand-Held Devices using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 10536-10543
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Execution Time ◽  
Mobile Apps ◽  
Poor Performance ◽  
Cloud Service ◽  
Mobile App ◽  
Cloud Services ◽  
Mobile Users

The usage of hand-held and mobile devices has been increased rapidly in recentyears. The execution of sophisticated softwares and Apps on mobile phonescan lead to poor performance with respect to energy consumption and responsetime. With the emergence of the offloading concept of App workloads, an attempt has been made toimprove the performance of the hand-held devices by exploiting cloud service. The computation offloadingin hand-held devices consumes energy as well as time for transferring the datafrom hand-held devices to cloud. For the effective use of cloud services, there is a need to optimize the execution time of mobile App and energy consumedby the respective App. Many research endeavors have been made in recentyears to reduce the execution time and energy consumption during offloadingprocess. However, the usage of offloading has been evolved to quench the thirstof mobile users who execute multiple Apps simultaneously and are in dire needof seamless connectivity but some dynamic algorithms are needed to decidewhether offloading is favorable or not for a mobile App. If the mobile Apptakes more time and consumes more battery if executed on cloud then it isrecommended to use mobile platform rather than using cloud services. In thispaper, we are presenting machine learning based techniques which would help the mobile users in decision making to execute the App on mobile devices or on cloud using cloud services.

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.

Spatial-temporal data collection process models using mobile sinks

2019 ◽  
Vol 942 (12) ◽  
pp. 22-28
Author(s):  
A.V. Materuhin ◽  
V.V. Shakhov ◽  
O.D. Sokolova
Keyword(s):  
Energy Consumption ◽  
Operation Time ◽  
Point Of View ◽  
Process Models ◽  
Temporal Data ◽  
Network Nodes ◽  
Mobile Sinks ◽  
Autonomous Operation ◽  
Data Collection Process ◽  
Spatio Temporal

Optimization of energy consumption in geosensor networks is a very important factor in ensuring stability, since geosensors used for environmental monitoring have limited possibilities for recharging batteries. The article is a concise presentation of the research results in the area of increasing the energy consumption efficiency for the process of collecting spatio-temporal data with wireless geosensor networks. It is shown that in the currently used configurations of geosensor networks there is a predominant direction of the transmitted traffic, which leads to the fact that through the routing nodes that are close to the sinks, a much more traffic passes than through other network nodes. Thus, an imbalance of energy consumption arises in the network, which leads to a decrease in the autonomous operation time of the entire wireless geosensor networks. It is proposed to use the possible mobility of sinks as an optimization resource. A mathematical model for the analysis of the lifetime of a wireless geosensor network using mobile sinks is proposed. The model is analyzed from the point of view of optimization energy consumption by sensors. The proposed approach allows increasing the lifetime of wireless geosensor networks by optimizing the relocation of mobile sinks.

scholarly journals Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 229
Author(s):  
Xianzhong Tian ◽  
Juan Zhu ◽  
Ting Xu ◽  
Yanjun Li
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Wireless Network ◽  
Deep Neural Networks ◽  
Mobile User ◽  
Computation Offloading ◽  
Long Latency ◽  
Total Latency ◽  
And Performance

The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions.

scholarly journals A Longitudinal Study of Pervasive Display Personalisation

2021 ◽  
Vol 28 (1) ◽  
pp. 1-45
Author(s):  
Mateusz Mikusz ◽  
Peter Shaw ◽  
Nigel Davies ◽  
Petteri Nurmi ◽  
Sarah Clinch ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Mobile Devices ◽  
Potential User ◽  
Mobile Users ◽  
Prior Work ◽  
Physical Spaces

Widespread sensing devices enable a world in which physical spaces become personalised in the presence of mobile users. An important example of such personalisation is the use of pervasive displays to show content that matches the requirements of proximate viewers. Despite prior work on prototype systems that use mobile devices to personalise displays, no significant attempts to trial such systems have been carried out. In this article, we report on our experiences of designing, developing and operating the world’s first comprehensive display personalisation service for mobile users. Through a set of rigorous quantitative measures and 11 potential user/stakeholder interviews, we demonstrate the success of the platform in realising display personalisation, and offer a series of reflections to inform the design of future systems.

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