A Comparative Study of Time Management and Energy ... - IEEE Xplore

2014 IEEE 5th Control and System Graduate Research Colloquium, Aug. 11 - 12, UiTM, Shah Alam, Malaysia

A Comparative Study of Time Management and Energy Consumption in Mobile Cloud Computing 1

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Mohammad Ahmadi

Faculty of Computing Asia Pacific University Kuala Lumpur, Malaysia [email protected]

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Nasrin Khanezaei

Sina Manavi

Faculty of Computer Science and IT U.P.M. University Kuala Lumpur, Malaysia [email protected]

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Faculty of Computer Science and IT U.P.M. University Kuala Lumpur, Malaysia [email protected] 5

Faraz Fatemi Moghaddam

Touraj Khodadadi

Faculty of Computer Science and IT U.P.M. University Kuala Lumpur, Malaysia [email protected]

Malaysia-Japan International Institute of Technology (MJIIT) Universiti Teknologi Malaysia 54100, Kuala Lumpur, Malaysia [email protected]

Abstract— The effectiveness and influence rate of cloud computing services in devices with limited power and computation resources (e.g. mobile devices) has been considered by many researchers and has led to many performed researches and IT products for these devices. One of the most challenging issues during migration of applications and processes to clouds is the rate energy consumption and time management in comparison with local applications. In this paper, a comparative experimental study has been presented to compare the rate of energy consumption and total execution time in mobile cloud computing and local devices. Hence, three cloud-based environments and two mobile devices with various computation resources were selected to host a cryptography application. Furthermore, cloud-based servers were selected in several places with different distance from the experiment site to investigate the effect of distance and number of hops in the performance of application. The application was run 25 times with different size of plain text to determine the performance of each environment during various workloads. The results showed that the rate of energy consumption and execution time were reduced significantly in cloud-based environments regarding to limited computation resources of mobile devices. This decrease was more considerable when the number of workloads had been increased.

devices) has been considered by many researchers and has led to many performed researches and IT products for mobile devices.

Index Terms — Cloud Computing, Energy Consumption, Execution Time, Mobile Cloud, Workloads.

Flinn et al. [3] proposed a history based profiling mobile cloud model to decrease energy consumption and total execution time, and enhance fidelity. In this model, resources (e.g. CPU, battery, network, and memory usage) are monitored. Furthermore, resource usage of an operation was similar to the amount used by recent operations of similar type. Moreover, a user specifiable model with similar specifications was presented in 2003 by Balan et al. [4]. In Balan’s model, system performance was improved and the rate of saving energy was enhanced by tactics-based remote execution process.

The most challenging issue in this area is the rate of efficiency for migrating a process from mobile device to cloud server in comparison with carrying out this process in the mobile device. This efficiency involves various parameters such as time and energy consumption. Thus, a comparative study of performing an application process in cloud computing server and local mobile device is performed in this paper to investigate the rate of efficiency for migrating to cloud computing environments.

II. PROBLEM BACKGROUND AND RELATED WORKS As was explained, there are several performed researches that have investigated the effectiveness and influence rate of cloud-based services in mobile devices. Energy consumption and total execution time are the most important parameters that have been considered in these researches.

I. INTRODUCTION Cloud computing is a newfound technology that uses the concept of virtualization and connectivity for store and share resources with the lowest rate of energy consumption, resource utilization and power usage [1]. The rapid growth of cloud-based services for storing data and deploying ondemand services, is an undeniable fact that have attracted attentions of IT service providers, enterprises and users to this emerging technology [2]. Furthermore, the effectiveness and influence rate of cloud computing services in devices with limited power and computation resources (e.g. mobile

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In 2010, Cuervo et al. [5] proposed a model named Maui to make smartphones last longer with code offload. The establishment of Maui was based on saving energy. In

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addition, total execution time was reduced in this model by using a predictor of future invocations concerning energy usage. Maui supported fine-grained code offload to enhance saving energy by using advantages of a managed code environment and a run-time decision for selecting a method to execute.

III. METHODOLOGY A. Parameters As was explained in previous section, there are two main parameters that have been considered in this experimental study: Total Execution Time (Ttotal), and Energy Consumption (Etotal).

In 2011, Chun et al. [6] presented a system for automate transforming mobile application to benefit from the cloud by enabling un-modified mobile applications to run in an application level of virtual machine to seamlessly off-load part of their executions fro a mobile device into device clones operating in a computational cloud. Regarding to the evaluation results, the proposed model can adapt application partitioning to different cloud-based environments and contribute other applications to reduce total execution time and energy consumption at least 20-fold on mobile devices.

Total execution time in a local mobile device is limited to computation time during a process. However, in mobile cloud computing this time is calculated according to three main processes: • • •

In 2011, Perrucci et al. [7] provided a detailed survey on energy consumption of mobile devices by classification of heterogeneous communication technologies of mobile devices regarding to different usability and performance. They proved that the most energy consumption parts of a smart-phone are the wireless technologies and not the display or the CPU. Hence, they noted to employ energy-aware communication technology selection for improving performance of mobile applications.

Local Computation Time (TLC): The required time for performing local computations in mobile device. Round Trip Time (TRT): The required time for sending request to cloud server and receiving response from that. Cloud-Based Computation Time (TCC): The required time for performing requested processes in cloud side. Hence, Ttotal is calculated as follows: Ttotal = TLC + TRT + TCC

TLC is completely based on capabilities of a mobile-device and TCC is based on capabilities of cloud computing environment (e.g. CPU, RAM, etc.). Moreover, the value of TRT is depended on transmission, propagations, processing, and queuing time during sending a request to cloud server and receiving a response from that. The way of determining TRT has been explained in [9].

Miettinen and Nurminen [8] presented an analysis of the critical parameters affecting the energy consumption of mobile clients in cloud computing. In their analysis a basic balance between remote and local computing was defined and energy efficiency of mobile devices when using distant cloud resources was investigated by focusing on communication technologies and computational efficiency of native device.

Energy Consumption is the second parameter that has been considered in this experimental study. The value of Etotal is calculated based on the value of energy consumption during performing a process in local device (EL), the value of energy consumption to prepare a request for transmission to cloud server such as cryptography (EP), the value of energy consumption to transmit a request to cloud server and receive a respond from that (ET), the value of energy consumption during transmission process in local device (EW), and the value of energy consumption for reintegration and synchronization received results with the client code (ES). Therefore, Etotal is calculated as follows:

Abolfazli et al. [9] presented an experimental analysis on cloud-based mobile augmentation in mobile cloud computing services. In this analysis two parameters (i.e. total execution time and energy consumption) were considered to compare three execution environments (i.e. local execution, Singapore cloud execution, and Sydney cloud execution) regarding to two mathematical workload scenarios (i.e. prime and matrix). The analysis showed that time and energy in resourceintensive mobile application was decreased considerably due to increase in distance and the performance on resourceconstraint devices was enhanced significantly when the mobile-cloud distance was reduced especially for dataintensive mobile applications.

Etotal = EL + EP + ET + EW + ES B. Environments Two local devices and three cloud virtual machine instances are selected for this experiment:

In overall, the main aim of all researches is to investigate possible way to reduce energy consumption and total execution time in mobile applications. Therefore, in this paper, a comparative experimental study has been considered to compare the rate of energy consumption and total execution time in local mobile device and mobile cloud-based environment.

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Mobile Device 1 (M1): A Dual-core 1 GHz Cortex-A9 with 1GB RAM. Mobile Device 2 (M2): A Quad-core 2.5 GHz Krait 400 with 2GB RAM. Cloud VM 1 (C1): AP Cloud Server in Singapore (340km Distance). Cloud VM 2 (C2): AP Cloud Server in Tokyo (5300km Distance).

2014 IEEE 5th Control and System Graduate Research Colloquium, Aug. 11 - 12, UiTM, Shah Alam, Malaysia M1

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Fig. 1. Total Execution Time for RSA Encryption Algorithm Regarding to Various Environments and Workloads

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and C3 more than C1 regarding to the distance between cloud server and experiment site and number of hops. Accordingly, C3 with approximately 10900km distance from the experiment site has the worst performance in the first eight workloads.

Cloud VM 3 (C3): EU Cloud Server in Dublin (10900km Distance).

C. Application RSA cryptography algorithm [10] has been chosen as experimental application for this study. RSA algorithm is an asymmetric key algorithm that was publicly described in 1977 by Rivest, Shamir, and Adleman. Public and private key size in the proposed application was 1024bits and previously had been available. The encryption algorithm was carried out by the application in 25 workloads. In the first workload, a message with 1000 characters was encrypted by the application, and 1000 characters was added to the plain text in next workloads. Thus, the message size was between 1000 to 25000 characters in these 25 workloads.

By increasing the size of plain text in workloads, the performance of M1 and M2 was affected considerably and the total execution time in these two local environments was enhanced significantly due to the limited computation resources in mobile devices. Thus, from the 8th workload, total execution time in Singapore cloud server was less than M2. Moreover, this time in Tokyo and Dublin cloud server were less than M2 from the 15th and 18th workloads respectively. In addition, the value of execution time in M1 was increased intensity from the 9th workload due to the limited computation resources in this environment in comparison with M2 and other cloud-based environments. One of the other findings in this experiment is the approximate constant performance of three cloud-based environments during the increase of workloads. In fact, the only difference between the performances of these cloud-based environments is related to the different distances and number of hops between cloud servers and experiment site.

IV. DATA ANALYSIS As was explained, the comparative study was done regarding to two local and three cloud-based environments and results are classified according to defined parameters as follows: A. Total Execution Time Figure1 shows the comparison of total execution time in the five defined environments according to 25 different workloads. As was expected, M2 has the best performance in comparison with other environments in the first workload. The high potential of hardware in M2 is quite enough for carrying out the first workload considerably faster than other environments. Moreover, round trip time for C1, C2, and C3 has increased the total execution time. This time increase can be observed in C2

In overall, experimented results in this study shows that the performance of cloud-based environments is faster than local mobile devices by increasing workloads due to the limited power and computation resources in mobile devices. However, local devices have a better performance in low or temporary workloads in comparison with cloud-based environments due to the round trip time and distances between cloud servers and local devices.

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2014 IEEE 5th Control and System Graduate Research Colloquium, Aug. 11 - 12, UiTM, Shah Alam, Malaysia M1

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Fig. 2. Energy Consumption for RSA Encryption Algorithm Regarding to Various Environments and Workloads

Energy Consumption The comparison of energy consumption in defined environments during 25 various workloads have been shown in figure 2. Apart from the results of total execution time, the difference between the rate of energy consumption in cloudbased and local-based environments was appeared sooner and more considerable. The difference was started from the 5th workload for M1 and from the 6th workload for M2. The rate of energy consumption in C1 was 11% less than M1 and 41% more than M2 in the first workload. However in the 25th workload, this rate in C1 was approximately 22-fold and 10fold less than M1 and M2 respectively. It shows that migration of processes to cloud-based environments has reduced the rate of energy consumption significantly due to established limitations in mobile devices such as CPU and RAM.

In overall, the results shows that mobile cloud computing can decrease execution time when the number of workloads are increased. Furthermore, cloud-based environments decrease the rate of energy consumption in mobile devices significantly due to limited computation resources in mobile devices.

The other issue is the value of energy consumption in different cloud servers according to their distance form the experiment site. Accordingly, the results shows that in the first workload, the rate of energy consumption in C1 is 27% and 40% less than C2 and C3 respectively. This difference was decreased to 7% and 11% respectively in the last workload. It shows that, the difference between the value of energy consumption in cloud-based environments is less during more workloads.

[1] A. Uchechukwu, L. Keqiu, and Y. Shen, “Improving Cloud Computing Energy Efficiency,” in Proc. of IEEE Asia Pacific Cloud Computing Congress (APCloudCC), 2012, pp. 53-58. [2] F. Fatemi Moghaddam, O. Karimi, and M. Hajivali, “A Survey for Effectiveness and Influence Rate of Cloud Computing Services in Malaysia,” in Proc. of IEEE Malaysia International Conference on Communications (MICC), 2013, pp. 283–286. [3] J. Flinn, S. Park, and M. Satyanarayanan, “Balancing Performance, Energy, and Quality in Pervasive Computing,” in Proc. of 22nd International Conference on Distributed Computing Systems, 2002, pp. 217–226. [4] R. Balan, M. Satyanarayanan, S. Park, and T. Okoshi, “TacticsBased Remote Execution for Mobile Computing,” in Proc. of ACM 1st International Conference on Mobile Systems, Applications and Services, 2003, pp. 273–286. [5] E. Cuervo, A. Balasubramanian, D.K. Cho, A. Wolman, S. Saroiu, R. Chandra, and P. Bahl, “Maui: Making Smart-Phones Last Longer with Code Offload,” in Proc. of ACM 8th International Conference on Mobile Systems, Applications, and Services, (MobiSys), 2010, pp. 49–62. [6] B.G. Chun, S. Ihm, P. Maniatis, M. Naik, and A. Patti, CloneCloud: Elastic Execution between Mobile Device and

ACKNOWLEDGMENT We acknowledge the assistance and logistical support provided by Meta Soft Co. (Medica Tak Sdn Bhd), Dr. Pardis Najafi, Ms. Fatemeh Afsahi, and the bright memory of Dr. Enayat Fatemi Moghaddam. REFERENCES

V. CONCLUSION In this paper a comparative experimental study was done to compare the rate of energy consumption and total execution time between local mobile devices and cloud-based environments. Hence, three cloud-based environments and two local devices were provided to host a RSA cryptography application. The application was run 25 five times in each environment regarding to different size of plain text and defined parameters were considered in during each execution.

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[7] [8] [9]

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Cloud,” in Proc. of ACM Sixth Conference on Computer Systems, (EuroSys), 2011, pp. 301-314. G.P. Perrucci, F.H. Fitzek, and J. Widmer, “Survey on Energy Consumption Entities on the Smartphone Platform,” in Proc. of IEEE Conference Vehicular Technology Society, 2011, p. 1-6. A.P. Miettinen and J.K. Nurminen, “Energy Efficiency of Mobile Clients in Cloud Computing,” in Proc. of 2nd USENIX Conference on Hot Topics in Cloud Computing, 2010, pp. 4–11. S. Abolfazli, Z. Sanaei, M. Alizadeh, A. Gani, and F. Xia, “An Experimental Analysis on Cloud-Based Mobile Augmentation in Mobile Cloud Computing,” IEEE Transactions on Consumer Electronics, vol. 60, no. 1, pp. 146-154, February 2014. R. Rivest, A. Shamir, and L. Adleman, “A Method for Obtaining Digital Signatures and Public-Key Cryptosystems,” ACM Trans. On Communications, vol. 21, no. 2, pp. 120-126, Feb 1978.

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