Efficient Resource Management for Sustainable Mobile Computing

Toolika Ghose Wichita State University, txghose@wichita.eduShubhangi Satras Argade Wichita State University, sdsatras@wichita.eduVinod Namboodiri Wichita State University, vinod.namboodiri@wichita.eduRavi Pendse Wichita State University, ravi.pendse@wichita.edu

Abstract. A conceivable and environmentally sustainable solution for simultaneous advancement on the energy consumption and durability issues in the mobile computing realm is the thin-client paradigm. In this technique, the resources at a remote server could be leveraged to carry out a majority of application tasks. In this paper, we develop a comprehensive life-cycle energy model based on energy consumption in three primary phases of the devices. The validation of our model was established by implementing it on a cloud based thin-client scenario in order to assess the local energy utilization on the devices. A thorough analysis of the benefits of reduction in resource utilization on the local smartphones under thin-client paradigm is also presented. It was observed through empirical testing of our gaming application that the smartphones working under thin-client paradigm can execute large and complex computations 33 times faster and could easily save 50% energy.

Introduction. The progressions in the mobile device technology brought in a great deal of mobility and global connectivity. Advancements in the mobile communication industry forecast that the smart mobile devices will grow exponentially in next five years. It is predicted that smartphones sales will increase at a compound rate of 30% by the year 2016. The exponentially increasing demand for mobile devices made the mobile communication sector as one of the fastest growing industries. The ANALYST foresees that smartphones would contribute to a $320 billion market by 2016 [1]. Since the first ever phone call over GSM in 1991 the market for mobile technologies expanded considerably and currently 77% of the global population uses cell phones. Nearly six billion smartphones were sold globally in 2011 and the sales are expected to reach up to 1600 million in next five years [1,2]. The features and functionality of these mobile devices have improved with time. For instance, 85% of the mobile users accessed the web via mobile devices in 2011 [2]. The rapid growth in the number of devices consequently resulted in many environmental challenges. A typically low life span of these mobile devices stimulated users to upgrade their devices every 18 months [3]. In the recent decade, majority of the users opted for newer devices as a result of the two-fold escalation in CPU processing power and mass storage every one and half years [4]. Millions of cell phones and other portable devices are discarded every year and only a small percentage of these discarded devices are recycled. A majority of the remaining devices subsequently contribute to the global electronic waste (e-waste). It was reported that approximately 75% of the discarded devices contribute towards e-waste or landfills. [5]. Furthermore, toxic materials like arsenic, lead, mercury, cadmium, nickel, copper, and zinc present in these mobile devices are capable of adversely affecting the global environment and human health [5,6]. The proliferation in feature and functionality of these smart mobile devices is also considered

Proceedings of the International Symposium on Sustainable Systems and Technologies (ISSN 2329-9169) is published annually by the Sustainable Conoscente Network. Melissa Bilec and Jun-ki Choi, co-editors. ISSSTNetwork@gmail.com.

Copyright 2013 by Toolika Ghose, Shubhangi Satras Argade, Vinod Namboodiri, Ravi Pendse. Licensed under CC-BY 3.0.

Cite As:Efficient Resource Management for Sustainable Mobile Computing. Proc. ISSST, Toolika Ghose, Shubhangi Satras Argade, Vinod Namboodiri, Ravi Pendse. http://dx.doi.org/10.6084/m9.figshare.862749. v1(2013)

Copyright 2013 by the Authors

accountable for increase in global energy consumption and emission of harmful greenhouse gases such as CO2. The global energy consumption was reported to be approximately 46.2 million megawatt hours (MWH) and 44.6 million MWH for smart phones and laptops, respectively [4]. A single smartphone could be responsible for emission of 9 Kg of CO2 during its life span of three years. This impact of such an emission is equivalent to that of driving a car for 54 km [7, 8]. The above discussed challenges are some of the key driving factors for researchers to explore the possible benefits of sustainability in mobile computing. Academic and industrial research for mobile computing and communication sector is consistently creating green and sustainable designs for mobile devices. Several green approaches like reusing, recycling, utilization of recyclable materials, virtualization etc. have been proposed to reduce the e-waste [10]. Many sustainable solutions such as using biodegradable material in mobile device design, recycling, refurbishing and take back programs for the disposed mobile devices have been implemented to reduce the impact of toxic materials in our environment [9,10,11,12]. The goals of sustainable mobile device design focused on the issues of increasing the life span, improving the power efficiency, usage of less toxic materials in the hardware, improved user satisfaction and simultaneous reduction in electronic waste. Accordingly, sustainability was aptly defined as fulfilling the existing needs by attaining proper equilibrium between economic, social and environmental priorities without compromising the future needs [7].

The primary focus of this work is to develop a life-cycle energy model for mobile devices and specifically comprehend the energy consumption for each of the life-cycle phases. In this study, the life-cycle model is based on three key phases, namely: manufacturing, usage and recycling. Furthermore, a green and sustainable cloud based thin-client-server solution for mobile devices is also presented in later sections. Preceding work on sustainable green cloud-computing was primarily focused on server side components of the data centers. Various approaches and algorithms were proposed to reduce the energy costs at the data center. Energy efficient and sustainable mobile computing has not been widely studied using cloud-computing [13,14]. As a result, characterization of the energy consumption in mobile devices was not a primary focus in these investigative studies. Hence, the goal of this paper is to understand the reduction in resource utilization using the cloud based thin-client approach. We present a comprehensive study of the reduction in energy consumption during the manufacturing and operational phase of these devices when used under thin-client paradigm. In this paper, we provide an insight in to the possible energy savings and reduction in e-waste by increasing the lifespan of these mobile devices. Our proposed cloud based thin-client approach could possibly lead to increase in life span of these devices.

Life-Cycle Energy Consumption for Mobile Devices. The total energy consumed by any mobile device throughout its life-cycle consists of the following components: manufacturing energy ( ), energy consumed during the usage phase ( ), and recycling energy ( ). Thus, the total life-cycle energy is shown in (1),

(1)

(2)

Presently users move beyond the first usage stage with very low probability [15, 16]. In our model, it was assumed that every device has more than one usage phase and each usage phase ranges between 18 and 20 months. Hence, the average life-cycle energy can be mathematically evaluated as shown in (2), where n number of usage phases. The results of our numerical analysis to evaluate the average energy consumed for various usage phases before the device exits to the recycling phase for smartphones are shown in Figure 1. Allowing for the technological progressions in the hardware of the devices, a hardware-upgrading-energy cost (Em) required to replace any components is included in our calculations, where is the fraction of Em required for a hardware upgrade.

The parameter typically varies between 0 and 1. The case where assumes a value of 1 implies that the energy required to extend the life of the device in to the next phase is equivalent to the original total manufacturing cost. To allow for easier hardware component replacements the devices are expected to be more modular. The data for the analysis shown in Figure 1 was obtained from Ref. [7] and a hardware upgrade cost of 5% was added after each usage stage of 18 months. It can be observed from Figure 1 that the average life-cycle energy consumption decreases linearly as the usage phase increases from 18 months to 48 months.

Approximately 18% reduction in the average LCE was observed as a result of a six month increase in the usage phase (18 to 24 months). This is due fact that the manufacturing energy is significantly higher in proportion compared to the usage phase energy due to short life span of these devices [7]. Thus, any increase in the number of usage years results in the manufacturing cost being evenly distributed among the total number of years of usage. As a result, the average life-cycle energy for any mobile communication device could therefore be lowered by, (i) Reducing the manufacturing energy of these devices (ii) Lowering the energy consumed during the use phase

A. Reduction in average LCE by reducing manufacturing energy. Thin-clients are a well-known technique for reduction in hardware utilization in desktop level configurations. However, the same technique has not been widely explored for mobile devices. Thin-client mobile devices use cloud based resources to enhance its functionality. In the thin-client paradigm most of the computation tasks associated with applications is sent to a remote server. The thin-client (or simply client) displays the only graphical output. For example, ChromeBooks by Google is a step in this direction where the local resources are limited and most of the functionality is supplied by remote cloud servers. Such an approach for mobile computing devices could also facilitate the increase in storage, memory, processing power and battery life of these devices. Thus these devices would eventually require less hardware directly helping cut the costs for manufacturing hardware components. Such a reduction in hardware components will result in lowering of the bill of materials for hardware components. Analytically, the total energy required to manufacture the hardware is directly proportional to the number of hardware components (n) as described by (3). It is assumed that energy cost for each component is equal to (ty2v[ Therefore, the manufacturing energy is the summation of the cost required for all the components as defined in (3).

.. (3)

, (p

hardware are shown in Figure 2. It can be observed from Figure 2 that mere 1-2% reductions in hardware components could possibly save energy in the magnitude of millions of Joules per year. Assuming a compound rate of 30% increment in the sales of smartphones every year, the thin-client approach for smart phones could possibly benefit in saving significant levels of manufacturing energy for these devices.

B. Lowering average LCE by reducing

usage phase energy consumption

(a) Empirical design and experimental setup. The thin-client-server model for smartphones was implemented using a socket program. For this experiment, a Dell laptop equipped with Intel Core i3 CPU (2.53 GHz) was used as the server while an Android HTC-Desire smartphone was implemented as the client. A certain dearth in the compatible open source applications for bench marking testing makes it challenging to test the proposed thin-client server architecture for the prevailing applications. Hence a gaming application named Lucky Numbers was developed for the benchmark testing. In this gaming application, the user wins if two numbers (input by the user) are found in a file consisting of a preset random numbers. Files with different quantities of random numbers (sample sizes) were considered to attain better accuracy of the results. These test files consisting of random numbers were stored in an asset folder separately on the local device as well as the remote server for access during application logic executions. In order to develop the gaming application, Eclipse IDE was utilized. ADT (Android Development Tool) plug-in was also installed on the Android 2.2 platform for development of these test applications. The apk file was installed on the client device (HTC Desire smartphone) to execute and test the applications [17].

Execution of programs - Traditional The application logic for local application execution on the client device was developed in order to execute the logic on the Smartphone. This program utilizes the CPU and other resources of the Smart phone for the computation. A simple graphical user interface (GUI) embedded with a button labeled Local and the required fields to enter the user input were also developed. An Event is generated for each click on the button followed by the extraction of the input file in the asset folder. The set of input numbers to be searched was entered by the user via the GUI.

Execution of programs - Thin-client. In thin-client-server scenario, the program is initiated on the client device i.e. the smartphone, but the computation logic is executed on the remote server. The client and server scenario for remote execution was implemented using socket programming as shown by the design in Figure 3. In this set-up, an Android HTC-Desire smartphone with a processor frequency of 1 GHz was chosen as the client. The remote server was a Dell laptop equipped with Intel Core i3 CPU The processor frequency of the server was 2.53 GHz.

Figure 2. Predicted manufacturing energy savings for thin-client mobile devices under various reduction rates in hardware components

Figure 3. Experimental set up for execution of application under thin-client-server paradigm

A GUI interface similar to that of the local execution scenario was developed and was incorporated with a button labeled Remote. The user is expected to enter the random numbers in the available fields. The Event is generated after the user enters the input for the application and a click is performed on the button labeled as Remote in the GUI. The input file extracted from the asset folder is directed to the remote server as a byte stream to the ObjectInputStream in the Java socket interface. After completion of the requested task by the client, the sever sends the results back to the client. The final result is displayed on the client device (smartphone).

(b) Characterization and Analysis of Resource Utilization The primary focus of the empirical testing was to identify the benefits of cloud based thin-client approach for mobile devices over traditional mobile devices where all the computation is executed locally on the device. Therefore, the power consumption for each hardware component was measured and energy savings for each sample was analyzed as will be discussed later in this section.

(b.i) End-to-end CPU Time measurement Androids Traceview tool was used to log the end-to-end time consumed by the applications. The Trace file includes the time spent in milliseconds by the parent and the child methods. The total time logged for our experiment is the time required to execute the parent method and all the child methods that are called by the parent method during the complete cycle of the execution of application. In the case of application execution on the local device, the time was logged from the instant of user input (event generation for computation) up to the display of the final result. However, in the case of thin-client paradigm where the application logic is executed on a remote server, the time was logged from the instant of the user input (Event generation for computation) until the result is generated and is sent back to a calling program on the client (smartphone). The intermediate step in this process includes the time taken to send the File as object stream to remote server (laptop). A trace log file named .trace is generated and saved on the storage (SD) card of the Android phone after completion of the application execution.

Figure 4. CPU time consumption versus quantity of random numbers in each file for local and remote application execution in smartphones

Figure 5. Energy saved (%) on smartphones as a function of size of test file for application execution under thin-client-server paradigm.

By means of empirical testing it was found that the utilization of CPU time for computation increases exponentially for larger computations on the local smartphone, however, under the thin-client paradigm (Remote Computation) the CPU utilization was observed to remain approximately constant as shown in Figure 4. In the thin-client scenario, a low CPU time utilization is the consequence of a comparatively less number of computations executed by the local smartphone. The CPU, in this case, is only utilized for sending the input data to remote server and for subsequently displaying the final results. Approximately 108 times increase in

1

10

100

1000

10000

10 100 1000 12000 20000

T CPU(m

s)

(lo

g sc

ale

)

Quantity of random numbers

Local computationRemote Computation

-500

-400

-300

-200

-100

0

100

200

10 100 1000 12000 20000

Esavings(

%)

Quantity of random numbers in each sample file

the computation time was observed when the number of random numbers increased from 10 to 20000 during local computations.

(b.ii) Energy Consumption Analysis. A commercially available application called PowerTutor was utilized to measure the power consumed by the applications. Power consumed by our gaming application was mapped with unique user identification (UID) number [19]. To calculate the total power consumption, an automated program was developed in Java. This program calculates the total power consumption by means of the power log from Power Tutor using regular expression matching pattern. For the local execution of application, the power consumption was calculated by combining the CPU power consumption as shown in (5). It was assumed (for a fair comparison) that for the computations performed locally (on the device) the communication interface does not need to remain active. In this scenario, the applications can be pre-installed on the mobile device. Thus, no communication between the client and the server is required. Hence, all the computations required by the applications will be executed by the processor on the local device.

.(5) 6)

It was observed that the power consumption by the display was constant for both the cases and therefore it was not included in our analysis. In the case of thin-client-server paradigm or remote computations, the access to the application is provided through remote cloud servers via a Wi-Fi interface on the device. In such test cases, the power consumption was calculated by summation of the total power consumed by the CPU and the Wi-Fi interface as defined in (6). The total energy consumption, , was determined by (7).

(7) (

..(8)

The percentage of energy savings for execution of applications under thin-client-server

paradigm is defined in (8). The for the first test case with 10 random numbers was found to be negative (excess energy consumption). The excess consumption is indicative of the energy required to process and send the file for remote computation ( being higher than

the energy required for local computation ( . It was analyzed using our power logs that the

power consumption by the wireless interface of the device ( contributes towards a larger portion of the total power ( . It can be observed from Figure 5 that energy could be saved on mobile devices under thin-client scenario if the processing energy consumed on the local device is greater than the energy consumed by the wireless interface to process and send the data for remote processing. It was also observed from our energy logs that for the test case with 20K random numbers, the computation energy for local computation was approximately 71 times more than that of the thin-client-server paradigm. This suggests that if the application requires less computation, it is energy-efficient to handle such a task locally. Similarly, for applications that require more complex computation remote processing would be energy efficient for small form factor devices.

Conclusion. In this work, it is conclusively shown that an environmentally sustainable solution for simultaneously addressing the energy consumption and durability issues in the mobile

computing realm could be accomplished via thin-client devices. It was comprehensively shown via empirical results that during the usage phase of the mobile devices resource benefits such as significant energy savings and reduction in CPU utilization could be achieved for larger and

complex computations under the thin-client paradigm.

Copyright 2013 by the Authors

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