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Computing

, Volume 96, Issue 2, pp 87–117 | Cite as

Energy-efficient job stealing for CPU-intensive processing in mobile devices

  • Juan Manuel Rodriguez
  • Cristian Mateos
  • Alejandro Zunino
Article

Abstract

Mobile devices have evolved from simple electronic agendas and mobile phones to small computers with great computational capabilities. In addition, there are more than 2 billion mobile devices around the world. Taking these facts into account, mobile devices are a potential source of computational resources for clusters and computational Grids. In this work, we present an analysis of different schedulers based on job stealing for mobile computational Grids. These job stealing techniques have been designed to consider energy consumption and battery status. As a result of this work, we present empirical evidence showing that energy-aware job stealing is more efficient than traditional random stealing in this context. In particular, our results show that mobile Grids using energy-aware job stealing might finish up to 11 % more jobs than when using random stealing, and up to 24 % more jobs than when not using any job stealing technique. This means that using energy-aware job stealing increases the energy efficiency of mobile computational Grids because it increases the number of jobs that can be executed using the same amount of energy.

Keywords

Mobile Grid Mobile devices Job stealing CPU intensive application Job scheduling 

Mathematics Subject Classification

68M14 Distributed systems 68M20 Performance evaluation; queueing; scheduling  68U99 None of the above, but in this section 

References

  1. 1.
    Aron J (2012) Harness unused smartphone power for a computing boost. New Scientist 215(2880):18. doi: 10.1016/S0262-4079(12)62255-6. http://www.sciencedirect.com/science/article/pii/S0262407912622556
  2. 2.
    Blom S, Book M, Gruhn V, Hrushchak R, Köhler A (2008) Write once, run anywhere: a survey of mobile runtime environments. In: International conference on grid and pervasive computing, pp 132–137. doi: 10.1109/GPC.WORKSHOPS.2008.19
  3. 3.
    Blumofe R, Leiserson C (1994) Scheduling multithreaded computations by work stealing. In: Annual IEEE symposium on foundations of computer science. IEEE Computer Society, Los Alamitos, pp 356–368. doi: 10.1109/SFCS.1994.365680
  4. 4.
    Boovaragavan V, Harinipriya S, Subramanian VR (2008) Towards real-time (milliseconds) parameter estimation of lithium-ion batteries using reformulated physics-based models. J Power Sources 183(1):361–365. doi: 10.1016/j.jpowsour.2008.04.077. http://www.sciencedirect.com/science/article/B6TH1-4SFS0MD-4/2/e4746e187e06b4aebb77c9a930f56b7f
  5. 5.
    Buyya R, Murshed M (2002) GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurrency Comput Pract Exp 14(13):1175–1220CrossRefzbMATHGoogle Scholar
  6. 6.
    Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2011) Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50CrossRefGoogle Scholar
  7. 7.
    Callou G, Maciel P, Tavares E, Andrade E, Nogueira B, Araujo C, Cunha P (2011) Energy consumption and execution time estimation of embedded system applications. Microprocess Microsyst 35(4):426–440. doi: 10.1016/j.micpro.2010.08.006. http://www.sciencedirect.com/science/article/pii/S0141933110000529
  8. 8.
    Choi S, Lee J, Yu H, Lee H (2011) Replication and checkpoint schemes for task-fault tolerance in campus-wide mobile grid. In: Kim Th, Adeli H, Cho Hs, Gervasi O, Yau SS, Kang BH, Villalba JG (eds) Grid and distributed computing. Communications in computer and information science, vol 261. Springer, Berlin, pp 455–467. http://dx.doi.org/10.1007/978-3-642-27180-9_56
  9. 9.
    Chu DC, Humphrey M (2004) Mobile ogsi.net: Grid computing on mobile devices. In: Proceedings of the 5th IEEE/ACM international workshop on Grid computing, GRID ’04. IEEE Computer Society, Washington, pp 182–191. doi: 10.1109/GRID.2004.44
  10. 10.
    Duan L, Kubo T, Sugiyama K, Huang J, Hasegawa T, Walrand J (2012) Incentive mechanisms for smartphone collaboration in data acquisition and distributed computing. In: INFOCOM, 2012 Proceedings IEEE, pp 1701–1709. doi: 10.1109/INFCOM.2012.6195541
  11. 11.
    Fernando N, Loke SW, Rahayu W (2013) Mobile cloud computing: a survey. Future Gen Comput Syst 29(1):84–106. doi: 10.1016/j.future.2012.05.023. http://www.sciencedirect.com/science/article/pii/S0167739X12001318
  12. 12.
    Ghosh P, Das SK (2010) Mobility-aware cost-efficient job scheduling for single-class grid jobs in a generic mobile grid architecture. Future Gen Comput Syst 26(8):1356–1367. doi: 10.1016/j.future.2009.05.003. http://www.sciencedirect.com/science/article/pii/S0167739X09000648
  13. 13.
    González-Castaño FJ, Vales-Alonso J, Livny M, Costa-Montenegro E, Anido-Rifón L (2003) Condor grid computing from mobile handheld devices. SIGMOBILE Mobile Comput Commun Rev 7(1): 117–126. doi: 10.1145/881978.882005
  14. 14.
    Gray J (2008) Distributed computing economics. Queue 6(3):63–68. doi: 10.1145/1394127.1394131 CrossRefGoogle Scholar
  15. 15.
    Ham HK, Park YB (2011) Mobile application compatibility test system design for android fragmentation. In: Kim Th, Adeli H, Kim HK, Kang Kj, Kim KJ, Kiumi A, Kang BH (eds) Software engineering, business continuity, and education. Communications in computer and information science, vol 257. Springer, Berlin, pp 314–320Google Scholar
  16. 16.
    Hu Y, Yurkovich S (2012) Battery cell state-of-charge estimation using linear parameter varying system techniques. J Power Sources 198(0):338–350. doi: 10.1016/j.jpowsour.2011.09.058. http://www.sciencedirect.com/science/article/pii/S0378775311018295
  17. 17.
    Huang Y, Venkatasubramanian N (2007) Supporting mobile multimedia applications in mapgrid. In: Proceedings of the 2007 international conference on wireless communications and mobile computing, IWCMC ’07. ACM, New York, pp 176–181. doi: 10.1145/1280940.1280978
  18. 18.
    Huang Y, Venkatasubramanian N, Wang Y (2007) MAPGrid: a new architecture for empowering mobile data placement in Grid environments. In: Seventh IEEE international symposium on cluster computing and the grid, 2007. CCGRID 2007, pp 725–730. doi: 10.1109/CCGRID.2007.69
  19. 19.
    Huynh D, Knezevic D, Peterson J, Patera A (2011) High-fidelity real-time simulation on deployed platforms. Comput Fluids 43(1):74–81. doi: 10.1016/j.compfluid.2010.07.007. http://www.sciencedirect.com/science/article/pii/S0045793010001829
  20. 20.
    Ibrohimovna M, Groot S (2008) Proxy-based fednets for sharing personal services in distributed environments. In: The fourth international conference on wireless and mobile communications, 2008, ICWMC ’08, pp 150–157. doi: 10.1109/ICWMC.2008.25
  21. 21.
    Kaushik A, Vidyarthi DP (2012) A cooperative cell model in computational mobile grid. Int J Bus Data Commun Networking 8:19–36. doi: 10.4018/jbdcn.2012010102 CrossRefGoogle Scholar
  22. 22.
    Kelenyi I, Nurminen J (2008) Energy aspects of peer cooperation measurements with a mobile dht system. In: IEEE international conference on communications workshops, 2008. ICC Workshops ’08, pp 164–168. doi: 10.1109/ICCW.2008.36
  23. 23.
    Khalaj A, Lutfiyya H, Perry M (2010) The proxy-based mobile grid. In: Cai Y, Magedanz T, Li M, Xia J, Giannelli C, Akan O, Bellavista P, Cao J, Dressler F, Ferrari D, Gerla M, Kobayashi H, Palazzo S, Sahni S, Shen XS, Stan M, Xiaohua J, Zomaya A, Coulson G (eds) Mobile wireless middleware, operating systems, and applications. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 48. Springer, Berlin, pp 59–69. http://dx.doi.org/10.1007/978-3-642-17758-3_5
  24. 24.
    Kumar K, Lu YH (2010) Cloud computing for mobile users: Can offloading computation save energy? Computer 43(4):51–56. doi: 10.1109/MC.2010.98 CrossRefGoogle Scholar
  25. 25.
    Lehr W, McKnight LW (2003) Wireless Internet access: 3G vs. WiFi? Telecommun Policy 27:351–370Google Scholar
  26. 26.
    Li C, Li L (2009) Utility-based scheduling for grid computing under constraints of energy budget and deadline. Comput Stand Interf 31(6):1131–1142. doi: 10.1016/j.csi.2008.12.004. http://www.sciencedirect.com/science/article/B6TYV-4V70RB2-4/2/65554f30c6e3068ba1697c540f160003
  27. 27.
    Li C, Li L (2010) Energy constrained resource allocation optimization for mobile grids. J Parallel Distrib Comput 70(3):245–258. doi: 10.1016/j.jpdc.2009.06.003. http://www.sciencedirect.com/science/article/B6WKJ-4WKTWWB-1/2/7f0834c24e7b7dd44adae8e22ce49ad5 Google Scholar
  28. 28.
    Li C, Li L (2010) Energy efficient resource management in mobile Grid. Mobile Inf Syst 6:193–211. doi: 10.3233/MIS-2010-0099. http://iospress.metapress.com/content/3R214MM142481741
  29. 29.
    Li C, Li L (2011) A multi-agent-based model for service-oriented interaction in a mobile grid computing environment. Pervas Mobile Comput 7(2):270–284. doi: 10.1016/j.pmcj.2010.10.006. http://www.sciencedirect.com/science/article/pii/S1574119210001173
  30. 30.
    Li C, Li L (2011) Tradeoffs between energy consumption and qos in mobile grid. J Supercomput 55:367–399. doi: 10.1007/s11227-009-0330-5 CrossRefGoogle Scholar
  31. 31.
    Li Z, Shen H (2012) Game-theoretic analysis of cooperation incentive strategies in mobile ad hoc networks. IEEE Trans Mobile Comput 11(8):1287–1303. doi: 10.1109/TMC.2011.151 CrossRefGoogle Scholar
  32. 32.
    Lin CM, Lin JH, Dow CR, Wen CM (2011) Benchmark dalvik and native code for android system. In: 2011 Second international conference on innovations in bio-inspired computing and applications (IBICA), pp 320–323. doi: 10.1109/IBICA.2011.85
  33. 33.
    Litke A, Skoutas D, Tserpes K, Varvarigou T (2007) Efficient task replication and management for adaptive fault tolerance in mobile grid environments. Future Gen Comput Syst 23(2):163–178. doi: 10.1016/j.future.2006.04.014. http://www.sciencedirect.com/science/article/pii/S0167739X0600080X
  34. 34.
    Ludwig S, Moallem A (2011) Swarm intelligence approaches for grid load balancing. J Grid Comput 9(3):279–301CrossRefGoogle Scholar
  35. 35.
    Mateos C, Zunino A, Campo M (2010) On the evaluation of gridification effort and runtime aspects of JGRIM applications. Future Gen Comput Syst 26(6):797–819CrossRefGoogle Scholar
  36. 36.
    Mateos C, Zunino A, Hirsch M, Fernández M, Campo M (2011) A software tool for semi-automatic gridification of resource-intensive java bytecodes and its application to ray tracing and sequence alignment. Adv Eng Softw 42(4):172–186CrossRefGoogle Scholar
  37. 37.
    Mateos C, Zunino A, Trachsel R, Campo M (2011) A novel mechanism for gridification of compiled java applications. Comput Inf 30(6):1259–1285Google Scholar
  38. 38.
    Neary MO, Cappello P (2005) Advanced eager scheduling for java-based adaptive parallel computing. Concurrency Comput Practice Exp 17(7–8):797–819. doi: 10.1002/cpe.v17:7/8 CrossRefGoogle Scholar
  39. 39.
    Pacini E, Mateos C, García Garino, C (2012) Schedulers based on ant colony optimization for parameter sweep experiments in distributed environments. In: Bhattacharyya S, Dutta P (eds) Research on computational intelligence for engineering, science and business. IGI Global (in Press)Google Scholar
  40. 40.
    Palmer N, Kemp R, Kielmann T, Bal H (2009) Ibis for mobility: solving challenges of mobile computing using grid techniques. In: HotMobile ’09: Proceedings of the 10th workshop on mobile computing systems and applications. ACM, New York, pp 1–6. doi: 10.1145/1514411.1514426
  41. 41.
    Paradiso JA, Starner T (2005) Energy scavenging for mobile and wireless electronics. IEEE Pervas Comput 4(1):18–27. doi: 10.1109/MPRV.2005.9 CrossRefGoogle Scholar
  42. 42.
    Rice A, Hay S (2010) Measuring mobile phone energy consumption for 802.11 wireless networking. Pervas Mob Comput 6(6):593–606. doi: 10.1016/j.pmcj.2010.07.005. http://www.sciencedirect.com/science/article/pii/S1574119210000593
  43. 43.
    Rodriguez J, Mateos C, Zunino A (2012) Are smartphones really useful for scientific computing? Lecture notes in computer science, vol 7547, pp 38–47Google Scholar
  44. 44.
    Rodriguez JM, Zunino A, Campo M (2010) Mobile grid seas: simple energy-aware scheduler. In: 39th JAIIO 3rd high-performance computing symposiumGoogle Scholar
  45. 45.
    Rodriguez JM, Zunino A, Campo M (2011) Introducing mobile devices into grid systems: a survey. Int J Web Grid Services 7(1):1–40CrossRefGoogle Scholar
  46. 46.
    Rosado DG, Fernández-Medina E, López J, Piattini M (2011) Systematic design of secure mobile grid systems. J Network Comput Appl 34(4):1168–1183. doi: 10.1016/j.jnca.2011.01.001. http://www.sciencedirect.com/science/article/pii/S1084804511000026
  47. 47.
    Rosinha RB, Geyer CFR, Vargas PK (2009) WSPE: a peer-to-peer grid programming environment. Concurrency Comput Practice Exp 21(13):1709–1724. doi: 10.1002/cpe.v21:13 CrossRefGoogle Scholar
  48. 48.
    Shen WX, Chan CC, Lo EWC, Chau KT (2002) Estimation of battery available capacity under variable discharge currents. J Power Sources 103(2):180–187. doi: 10.1016/S0378-7753(01)00840-0. http://www.sciencedirect.com/science/article/B6TH1-44V3JXV-2/2/77bf800f9c9901c16d550f67a4a31e6b Google Scholar
  49. 49.
    Van Nieuwpoort R, Wrzesińska G, Jacobs C, Bal H (2010) Satin: a high-level and efficient Grid programming model. ACM Trans Programm Lang Syst 32(3):9:1–9:39Google Scholar
  50. 50.
    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 (2008) The worst-case execution-time problem: overview of methods and survey of tools. ACM Trans Embed Comput Syst 7(3):36:1–36:53. doi: 10.1145/1347375.1347389
  51. 51.
    Zhang BY, Yang GW, Zheng WM (2006) Jcluster: an efficient Java parallel environment on a large-scale heterogeneous cluster. Concurrency Comput Practice Exp 18(12):1541–1557. doi: 10.1002/cpe.v18:12 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2012

Authors and Affiliations

  • Juan Manuel Rodriguez
    • 1
    • 2
  • Cristian Mateos
    • 1
    • 2
  • Alejandro Zunino
    • 1
    • 2
  1. 1.ISISTAN Research Institute, UNICEN UniversityBuenos AiresArgentina
  2. 2.Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)Buenos AiresArgentina

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