Elsevier

Computer Networks

Volume 56, Issue 7, 3 May 2012, Pages 1920-1934
Computer Networks

Energy-aware resource sharing with mobile devices

https://doi.org/10.1016/j.comnet.2012.02.007Get rights and content

Abstract

Ad hoc sharing of resources by offering remote services through an appropriate infrastructure enables new applications for mobile devices. However, the willingness of device owners to contribute resources to such applications remains low as long as they cannot control the amount of energy spent in sharing. In this paper, we present a framework for energy-aware resource sharing among mobile devices of various kinds that comprises (1) energy-aware strategies for selecting remote service providers and (2) a generic energy estimator for forecasting and accounting the energy consumption of a remote service call. To illustrate the benefit of (1), we show by simulation that the battery lifetime of devices running the framework can be extended up to 40% by service selection strategies that take into account the energy cost of a requested service compared to energy-unaware (random) service selection. For providing the energy-related input for service selection, we present (2) a generic estimator that can be customized easily for different hardware-platforms by solving a linear equation system with coefficients derived from benchmark measurements. We present a prototype-based case study for three different platforms, the Nokia N810, the HTC Touch Cruise and the Samsung Galaxy S showing that for all of them the estimation error is below 10% for 90% of the service calls. Furthermore, measurements conducted with a prototype implementation of the resource sharing framework show that battery lifetime can in fact be extended by energy-aware service selection strategies.

Introduction

The number, pervasiveness, and capabilities of mobile devices like phones, PDAs, navigation devices, cameras, and mp3 players are growing fast and steadily. Nowadays we live surrounded by a multitude of smart appliances offering plenty of resources, e.g., communication capabilities like 3G, WiFi, or Bluetooth interfaces, sensors like GPS and acceleration, and data and information like digital maps for a navigation utility. Different devices may provide different subsets of these resources, and therefore, combining the resources of multiple devices can enable new types of applications. For example, a camera may add a location tag to a picture by obtaining a GPS reading from a nearby navigation device, or PDAs may pool wireless WAN links using local WiFi connectivity in order to speed up downloads [1]. Beyond offering remote access to a single resource, a device can offer remote services that combine multiple resources in a non-trivial way. For example, by exploiting its GPS and WAN links a PDA can offer an integrated service for location-tagging and gallery-upload of a picture.

To enable flexible resource sharing among mobile devices, one can use an approach inspired by Grid systems for the Internet [2]. Grid systems allow remote access to distributed resources and services in a standardized way. The potential of using Grid technology to implement mobile resource sharing systems has been widely discussed in recent years [3], [4]. If Internet access is available, e.g., by a cellular connection, mobile devices can be integrated into existing infrastructure-based Grids, as proposed, e.g., in [5]. But even if no Internet access is available, devices can share resources with other nearby devices in local mobile ad hoc Grids, as discussed, e.g., in [3]. Furthmüller and Waldhorst [4] gives an elaborate survey of the various approaches to establishing a Grid-like infrastructure among mobile devices.

Although creating an infrastructure for resource sharing among mobile devices is technically feasible, the question remains of whether device owners are willing to share at all. To answer this question, we conducted a user poll in several Internet forums1. We received 38 responses from random forum visitors as well as from students and colleagues we encouraged to participate. The exact results of the poll are shown in Table 1, Table 2, Table 3, Table 4, Table 5. We found that almost 23 of the participants were willing to share resources. Confirming the claim in [4], 53% of the participants stated that their biggest concern besides security was the limitation of the available energy budget. Thus, energy-awareness is a key driver for user acceptance of mobile resource sharing.

Motivated by the results of the poll, this paper presents an OSGi-based [6] framework for energy-aware resource sharing among mobile devices that comprises two important parts. On the one hand, to chose an appropriate service provider in the case that a remote service is offered by multiple devices, the framework provides multiple energy-aware service selection strategies (1). On the other hand, to gather energy-related information for service selection independently of the actual device-hardware, an energy estimator (2) for forecasting and accounting the energy required by a particular service call is incorporated.

Using simulation, we shed light on the impact of energy-aware service selection strategies (1) on the battery lifetime of the participating mobile devices and service availability in the system. We find that in the scenarios considered, the baseline energy consumption, i. e., the energy consumed for listening for remote service calls has a huge impact on the battery lifetime. This significantly reduces the gain in battery lifetime due to the service selection strategy. Nevertheless, using an appropriate strategy can extend the time until the first device failure by up to 40%, as compared to choosing the service provider at random, given a reasonable baseline energy consumption. Such strategy requires knowledge of both the energy available on a device and the energy required for a particular service call.

Since the energy consumption of a service call is on the one hand highly platform-specific, and on the other hand depends on the characteristics of the particular service, we present an approach for an energy estimator (2) that can be flexibly customized to different hardware platforms and services. For this purpose, we use a two step-approach. In a first step, a benchmark program is executed on the specific platform, which yields input values for deriving a device-specific energy model by solving a linear equation system. The energy model describes the energy consumption of key resources such as CPU, WiFi, GPS, and display. In combination with a resource demand vector that describes to what extent a service accesses each of the key resources, the energy consumption of a service call can be estimated using the energy model in a second step. The resource demand vector is iteratively refined by tracking the history of previous service calls at run-time. Note that the energy model must be derived only once per device and can be easily generated. Thus the burden of doing energy measurements for each service is eliminated.

For validation, we customize the energy model for three different platforms – Nokia N810 (Maemo Linux), HTC Touch Cruise (Windows Mobile) and Samsung Galaxy S (Android). Extensive measurements show that the energy estimator is very accurate. In fact, on all mentioned devices it provides estimates with an average error below 10% for more than 90% of the service usages. Furthermore, we show by measurements based on a prototype of the framework [7] that it in fact extends battery lifetime, as predicted by the simulation results.

The remainder of this paper is structured as follows. Section 2 presents the energy-aware resource sharing framework. Section 3 discloses by simulation the impact of different service selection strategies on battery lifetime and service availability. Customization of the framework to specific platforms by adjusting its energy estimation component is illustrated in Section 4. Evaluation results derived through extensive measurements are shown in Section 5. We discuss related work in Section 6. Finally, we give concluding remarks.

Section snippets

A framework for energy-aware resource sharing

In this paper we assume a scenario comprising a group of users equipped with mobile devices. Each of the mobile devices offers a set of resources, e.g., communication capabilities like 3G, WiFi, or Bluetooth interfaces, sensors like GPS and acceleration, and data and information like a digital map for a navigation utility. Since the devices are potentially heterogeneous, the resources may differ from device to device, with the proviso that all devices must be able to communicate with each other

Design-choices for service selection

In the remainder of this paper, we assume that all users are cooperative, i. e., each user follows the strategies of the resource sharing framework. In particular, a user does not cheat when providing data about energy consumption and available energy resources in order to prolong her own battery lifetime. Incentives, accounting, and security mechanisms that ensure the cooperativeness of all users, such as, e.g., a unified energy cost model [1] or an energy-efficient micro-payment system [13]

Estimating energy consumption

Recall that energy cost is a key factor in selecting a provider and in accounting for the expense of a service. Although some mobile devices provide means for energy accounting the energy cost for the execution of a given service is usually unknown. However this is required if the cheapest provider should be selected and if a provider should be compensated for the energy spent on service provision. We therefore provide a methodology for estimating the energy consumption of a certain service at

Evaluation

Two questions have been subjected to an evaluation process:

  • 1.

    How accurate are the energy estimations provided by our energy estimator?

  • 2.

    Can the qualitative simulation results presented in Section 3 be strengthened by experiments on real hardware?

Both questions are answered subsequently in Sections 5.1 Accuracy of energy estimator, 5.2 Extension of lifetime.

Related work

Resource sharing with mobile devices as a way to save energy and increase the performance of mobile devices has already been the topic of several research projects, e.g., [1], [24]. However, these approaches focus on one specific resource, the 3G WAN link of the devices. Also, they only consider the energy cost of this specific resource.

Using OSGi as a platform for service composition has been suggested before, e.g., by Bottaro et al. [25]. Bottaro et al. also mention the need for dynamic

Conclusion and outlook

In this paper we have explored the potential, limitations, and trade-offs of ad hoc resource sharing with mobile devices from an energy perspective. We presented a framework that, among other functionality, allows for energy-aware service selection and accurate estimation of the energy consumed by a service call. We identified the baseline consumption for listening for remote service calls and the service selection strategy as two important drivers for the energy consumption of a resource

Jochen Furthmüller is currently working as a researcher and Ph.D. student at the Institute of Telematics, Karlsruhe Institute of Technology. His research interests are in mobile computing and resource sharing systems. He received a Diplom-Informatiker degree (comparable to M.Sc. in computer science) from Universität Karlsruhe in 2008. The diploma thesis dealt with energy-efficient management of wireless sensor networks. During his studies he worked as a research intern in Sun Labs, Menlo Park

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  • Cited by (0)

    Jochen Furthmüller is currently working as a researcher and Ph.D. student at the Institute of Telematics, Karlsruhe Institute of Technology. His research interests are in mobile computing and resource sharing systems. He received a Diplom-Informatiker degree (comparable to M.Sc. in computer science) from Universität Karlsruhe in 2008. The diploma thesis dealt with energy-efficient management of wireless sensor networks. During his studies he worked as a research intern in Sun Labs, Menlo Park and contributed to the Sun SPOT project. At present, he is working on middleware for a mobile Grid, focusing on energy-awareness and creating incentives for users to contribute. As a staff member he assists in teaching a couple of courses and seminars.

    Oliver P. Waldhorst received a Diplom-Informatiker degree (comparable to M.Sc. in computer science) in 2000 and a Ph.D. in computer science in 2005, both from University of Dortmund, Germany. In February 2011 he completed a Habilitation (postdoctoral lecture qualification) at the Computer Science Department of Karlsruhe Institute of Technology (KIT), Germany. Dr. Waldhorst is currently a senior researcher and lecturer at the Institute of Telematics at KIT. From April 2011 to March 2012 he was a Visiting Professor at Technical University of Ilmenau, Germany. Before, he led a ‘Young Investigator Group’, an independent junior research group funded by the ‘Concept for the Future’ of KIT within the framework of the German Excellence Initiative, from 2007 to 2011. From September 2009 to February 2010 he has been a Visiting Researcher in the group of Prof. Liebeherr at University of Toronto, ON, Canada.

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