Abstract
The profusion of mobile devices over the world and their evolved computational capabilities promote their inclusion as resource providers in traditional Grid environments. However, their efficient exploitation requires adapting current schedulers to operate with computing capabilities limited by energy supply and mobile devices that cannot be assumed to be dedicated, among other concerns. We propose a two-phase scheduling approach for running CPU-intensive jobs on mobile devices that combines novel energy-aware criteria with job stealing techniques. The approach was evaluated through an event-based simulator that uses battery consumption profiles extracted from real mobile devices. CPU usage derived from non-Grid processes was also modelled. For evaluating the first phase we compared the number of finalized jobs by all energy-aware criteria, while for the second phase we analyzed the performance boost introduced by job stealing. While the best first phase criteria finalized up to 90 % of submitted jobs, job stealing increased this percentage by up to 9 %.
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Huynh, D., Knezevic, D., Peterson, J., Patera, A.: High-fidelity real-time simulation on deployed platforms. Comput. Fluids 43(1), 74–81 (2011)
Ryabinin, K., Chuprina, S.: Adaptive scientific visualization system for desktop computers and mobile devices. Procedia Computer Science 18(0), 722–731 (2013)
Shiraz, M., Gani, A., Shamim, A., Khan, S., Ahmad, R.: Energy efficient computational offloading framework for mobile cloud computing. Journal of Grid Computing 13(1), 1–18 (2015)
Khan, A.u.R., Othman, M., Khan, A., Abid, S., Madani, S.: Mobibyte: An application development model for mobile cloud computing. Journal of Grid Computing, 1–24 (2015)
Rodriguez, J.M., Mateos, C., Zunino, A.: Are smartphones really useful for scientific computing?. Lect. Notes Comput. Sci 7547, 38–47 (2012)
Karan, O., Bayraktar, C., Gümüşkaya, H., Karlik, B.: Diagnosing diabetes using neural networks on small mobile devices. Expert Syst. Appl. 39(1), 54–60 (2012)
Rodriguez, J.M., Mateos, C., Zunino, A.: Energy-efficient job stealing for cpu-intensive processing in mobile devices. Computing 96(2), 87–117 (2014)
Rodriguez, J.M., Zunino, A., Campo, M.: Mobile Grid Seas: Simple Energy-Aware Scheduler. In: 3Rd High-Performance Computing Symposium. 39Th JAIIO (2010)
Ghosh, P., Das, S.K.: Mobility-aware cost-efficient job scheduling for single-class grid jobs in a generic mobile grid architecture. Futur. Gener. Comput. Syst. 26(8), 1356–1367 (2010)
Rodriguez, J.M., Zunino, A., Campo, M.: Introducing mobile devices into grid systems: a survey. International Journal of Web and Grid Services 7(1), 1–40 (2011)
Li, C., Li, L.: Tradeoffs between energy consumption and qos in mobile grid. J. Supercomput. 55, 367–399 (2011)
Aron, J.: Harness unused smartphone power for a computing boost. New Scientist, 215 (2880)
Li, W., Wu, J., Zhang, Q., Hu, K., Li, J.: Trust-driven and qos demand clustering analysis based cloud workflow scheduling strategies. Clust. Comput., 1–18 (2014)
Callou, G., Maciel, P., Tavares, E., Andrade, E., Nogueira, B., Araujo, C., Cunha, P.: Energy consumption and execution time estimation of embedded system applications. Microprocess. Microsyst. 35(4), 426–440 (2011)
Wilhelm, R., Engblom, J., Ermedahl, A., Holsti, N., Thesing, S., Whalley, D., Bernat, G., Ferdinand, C., Heckmann, R., Mitra, T., Mueller, F., Puaut, I., Puschner, P., Staschulat, J., Stenström, P.: The worst-case execution-time problem – overview of methods and survey of tools. ACM Trans. Embed. Comput. Syst. 7(3), 36:1–36:53 (2008)
Serrano, P., de la Oliva, A., Patras, P., Mancuso, V., Banchs, A.: Greening wireless communications: Status and future directions. Comput. Commun. 35(14), 1651–1661 (2012)
Arroqui, M., Mateos, C., Machado, C., Zunino, A.: RESTful web services improve the efficiency of data transfer of a whole-farm simulator accessed by android smartphones. Comput. Electron. Agric. 87(0), 14–18 (2012)
Thiagarajan, N., Aggarwal, G., Nicoara, A., Boneh, D., Singh, J.P.. In: Proceedings of the 21St International Conference on World Wide Web, WWW’12. Who Killed My Battery?: Analyzing Mobile Browser Energy Consumption, pp. 41–50. ACM, New York (2012)
Mahapatra, R., Domenico, A.D., Gupta, R., Strinati, E.C.: Green framework for future heterogeneous wireless networks. Comput. Netw. 57(6), 1518–1528 (2013)
Nicolaos, A., Vasileios, K., George, A., Harris, M., Angeliki, K., Costas, G.: A data locality methodology for matrix-matrix multiplication algorithm. J. Supercomput. 59, 830–851 (2012)
Hermelin, D., Rawitz, D., Rizzi, R., Vialette, S.: The minimum substring cover problem. Information and Computation/information and Control - IANDC 206, 1303–1312 (2008)
Baron, R., Lioubashevski, O., Katz, E., Niazov, T., Willner, I.: Elementary arithmetic operations by enzymes: a model for metabolic pathway based computing. Angew. Chem. Int. Ed. 45, 1572–1576 (2006)
Falaki, H., Mahajan, R., Kandula, S., Lymberopoulos, D., Govindan, R., Estrin, D.: Diversity in smartphone usage. In: Proceedings of the 8th international conference on Mobile systems, applications, and services, ACM, pp. 179–194 (2010)
Busching, F., Schildt, S., Wolf, L.: Droidcluster: Towards smartphone cluster computing – the streets are paved with potential computer clusters. In: 2012 32nd International Conference on Distributed Computing Systems Workshops (ICDCSW), pp. 114–117 (2012)
Rodriguez, A.V., Mateos, C., Zunino, A.: Mobile Devices-Aware Refactorings for Scientific Computational Kernels. In: 13Th Argentine Symposium on Technology, AST 2012 (2012). 41Th JAIIO
Li, C., Li, L.: A multi-agent-based model for service-oriented interaction in a mobile grid computing environment. Pervasive and Mobile Computing 7(2), 270–284 (2011)
Chunlin, L., Layuan, L.: Exploiting composition of mobile devices for maximizing user qos under energy constraints in mobile grid. Inf. Sci. 279(0), 654–670 (2014)
Shah, S.C.: Energy efficient and robust allocation of interdependent tasks on mobile ad hoc computational grid, Concurrency and Computation: Practice and Experience
Wei, X., Fan, J., Lu, Z., Ding, K.: Application scheduling in mobile cloud computing with load balancing. J. Appl. Math (2013)
Shah, S., Park, M.S.: An energy-efficient resource allocation scheme for mobile ad hoc computational grids. Journal of Grid Computing 9(3), 303–323 (2011)
Loke, S.W., Napier, K., Alali, A., Fernando, N., Rahayu, W.: Mobile computations with surrounding devices: Proximity sensing and multilayered work stealing. ACM Trans. Embed. Comput. Syst. 14(2), 22:1–22:25 (2015)
Li, B., Pei, Y., Wu, H., Shen, B.: Heuristics to allocate high-performance cloudlets for computation offloading in mobile ad hoc clouds. J. Supercomput., 1–28 (2015)
Shi, T., Yang, M., Jiang, Y., Li, X., Lei, Q.: An Adaptive Probabilistic Scheduler for Offloading Time-Constrained Tasks in Local Mobile Clouds. In: Ubiquitous and Future Networks (ICUFN), vol. 2014 Sixth International Conf on, pp. 243–248. IEEE (2014)
Castro, M.C., Kassler, A.J., Chiasserini, C.-F., Casetti, C., Korpeoglu, I.: Peer-to-peer overlay in mobile ad-hoc networks, pp. 1045–1080. Springer (2010)
Macone, D., Oddi, G., Pietrabissa, A.: Mq-routing: Mobility-, gps- and energy-aware routing protocol in MANETs for disaster relief scenarios. Ad Hoc Networks 11(3), 861–878 (2013)
Torres, R., Mengual, L., Marban, O., Eibe, S., Menasalvas, E., Maza, B.: A management ad hoc networks model for rescue and emergency scenarios. Expert Syst. Appl. 39(10), 9554–9563 (2012)
van Nieuwpoort, R., Wrzesinska, G., Jacobs, C.J.H., Bal, H.E.: Satin: A high-level and efficient grid programming model. ACM Trans. Program. Lang. Syst. 32(3)
Xu, H., Yang, B.: An incentive-based heuristic job scheduling algorithm for utility grids. Futur. Gener. Comput. Syst. 49(0), 1–7 (2015)
Hu, Y., Yurkovich, S.: Battery cell state-of-charge estimation using linear parameter varying system techniques. J. Power. Sources 198(0), 338–350 (2012)
Mednieks, Z., Dornin, L., Meike, G.B., Nakamura, M.: Programming Android, 2nd Edn. Java Programming for the New Generation of Mobile Devices, O’Reilly Media (2012)
Shen, W.X., Chan, C.C., Lo, E.W.C., Chau, K.T.: Estimation of battery available capacity under variable discharge currents. J. Power Sources 103(2), 180–187 (2002)
Khalaj, A., Lutfiyya, H., Perry, M.: The Proxy-Based Mobile Grid. In: Cai, Y., Magedanz, T., Li, M., Xia, J., Giannelli, C. (eds.) Mobile Wireless Middleware, Operating Systems, and Applications, Vol. 48 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pp. 59–69. Springer, Berlin (2010)
Calheiros, R.N., Ranjan, R., Beloglazov, A., de Rose, C.A.F., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience 41(1), 23–50 (2011)
Rice, A., Hay, S.: Measuring Mobile Phone Energy Consumption for 802.11 Wireless Networking. Pervasive and Mobile Computing 6(6), 593–606 (2010)
Takeno, K., Ichimura, M., Takano, K., Yamaki, J.: Influence of cycle capacity deterioration and storage capacity deterioration on li-ion batteries used in mobile phones. J. Power. Sources 142(1-2), 298–305 (2005)
Pacini, E., Mateos, C., García Garino, C.: Distributed job scheduling based on swarm intelligence: A survey. Comput. Electr. Eng. 40(1), 252–269 (2014). 40th-year commemorative issue
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Hirsch, M., Rodríguez, J.M., Mateos, C. et al. A Two-Phase Energy-Aware Scheduling Approach for CPU-Intensive Jobs in Mobile Grids. J Grid Computing 15, 55–80 (2017). https://doi.org/10.1007/s10723-016-9387-6
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DOI: https://doi.org/10.1007/s10723-016-9387-6