publication . Article . Other literature type . 2015

User Profiling for Energy Optimisation in Mobile Cloud Computing

Anas Basalamah; Yaser Jararweh; Lo'ai Tawalbeh; Elhadj Benkhelifa; Thomas Welsh;
Open Access English
  • Published: 01 Jan 2015 Journal: Procedia Computer Science (issn: 18770509, Copyright policy)
  • Publisher: The Authors. Published by Elsevier B.V.
  • Country: United Kingdom
Abstract
AbstractBoth mobile and cloud computing are two areas which are rapidly expanding in terms of use case and functionality.Both mobile and cloud computing are two areas which are rapidly expanding in terms of use case and functionality. This paper reviews current work in energy consumption of mobile cloud computing and then proposes a system whereby user applications may be profiled for their resource consumption locally and then if augmentation is required, they may negotiate with an external cloud for optimum energy consumption. Such a system is particularly useful for cloudlets which contain constrained resources so may need to choose between a number of client...
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doi: 10.1016/j.procs.2015.05.151
Subjects
free text keywords: Mobile-Cloud-Computing, Energy, Profiling, Optimisation, Smartphone, Networks., G400, G500, H100, Cloud computing security, Mobile computing, Mobile cloud computing, Distributed computing, Cloud computing, business.industry, business, Mobile search, Computer science, Cloud testing, Mobile technology, Utility computing
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23 references, page 1 of 2

1. Yang, L., Cao, J., Tang, S., Li, T., Chan, A.. A framework for partitioning and execution of data stream applications in mobile cloud computing. In: Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on. 2012, p. 794-802. doi:10.1109/CLOUD.2012.97.

2. Kotwal, P., Singh, A.. Evolution and effects of mobile cloud computing, middleware services on cloud, future prospects: A peek into the mobile cloud operating systems. In: Computational Intelligence Computing Research (ICCIC), 2012 IEEE International Conference on. 2012, p. 1-5. doi:10.1109/ICCIC.2012.6510270.

3. Sanaei, Z., Abolfazli, S., Gani, A., Buyya, R.. Heterogeneity in mobile cloud computing: Taxonomy and open challenges. Communications Surveys Tutorials, IEEE 2014;16(1):369-392. doi:10.1109/SURV.2013.050113.00090. [OpenAIRE]

4. Quwaider, M., Jararweh, Y.. Cloudlet-based for big data collection in body area networks. In: Internet Technology and Secured Transactions (ICITST), 2013 8th International Conference for. 2013, p. 137-141.

5. Quwaider, M., Jararweh, Y.. Cloudlet-based efficient data collection in wireless body area networks. Simulation Modelling Practice and Theory 2015;50(0):57 - 71. doi:http://dx.doi.org/10.1016/j.simpat.2014.06.015. [OpenAIRE]

6. Fernando, N., Loke, S., Rahayu, W.. Dynamic mobile cloud computing: Ad hoc and opportunistic job sharing. In: Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on. 2011, p. 281-286. doi:10.1109/UCC.2011.45.

7. Jararweh, Y., Tawalbeh, L., Ababneh, F., Khreishah, A., Dosari, F.. Scalable cloudlet-based mobile computing model. Procedia Computer Science 2014;34(0):434 - 441. doi:http://dx.doi.org/10.1016/j.procs.2014.07.051; the 11th International Conference on Mobile Systems and Pervasive Computing (MobiSPC'14). [OpenAIRE]

8. Jararweh, Y., Tawalbeh, L., Ababneh, F., Dosari, F.. Resource efficient mobile computing using cloudlet infrastructure. In: 2013 IEEE Ninth International Conference on Mobile Ad-hoc and Sensor Networks (MSN). 2013, p. 373-377. doi:10.1109/MSN.2013.75.

9. Tawalbeh, L., Jararweh, Y., ababneh, F., Dosari, F.. Large scale cloudlets deployment for efficient mobile cloud computing. Journal of Networks 2015;10(01). [OpenAIRE]

10. Tilevich, E., Kwon, Y.W.. Cloud-based execution to improve mobile application energy efficiency. Computer 2014;47(1):75-77. doi:10.1109/MC.2014.6.

11. Kumar, K., Lu, Y.H.. Cloud computing for mobile users: Can offloading computation save energy? Computer 2010;43(4):51-56. doi:10.1109/MC.2010.98.

12. Zhang, W., Wen, Y., Guan, K., Kilper, D., Luo, H., Wu, D.. Energy-optimal mobile cloud computing under stochastic wireless channel. Wireless Communications, IEEE Transactions on 2013;12(9):4569-4581. doi:10.1109/TWC.2013.072513.121842.

13. Fekete, K., Csorba, K., Forstner, B., Vajk, T., Feher, M., Albert, I.. Analyzing computation offloading energy-efficiency measurements. In: Communications Workshops (ICC), 2013 IEEE International Conference on. 2013, p. 301-305. doi:10.1109/ICCW.2013.6649248.

14. Fekete, K., Csorba, K., Vajk, T., Forstner, B., Pandi, K.. Towards an energy efficient code generator for mobile phones. In: Cognitive Infocommunications (CogInfoCom), 2013 IEEE 4th International Conference on. 2013, p. 647-652. doi:10.1109/CogInfoCom.2013.6719182.

15. Altamimi, M., Palit, R., Naik, K., Nayak, A.. Energy-as-a-service (eaas): On the efficacy of multimedia cloud computing to save smartphone energy. In: Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on. 2012, p. 764-771. doi:10.1109/CLOUD.2012.72.

23 references, page 1 of 2
Related research
Abstract
AbstractBoth mobile and cloud computing are two areas which are rapidly expanding in terms of use case and functionality.Both mobile and cloud computing are two areas which are rapidly expanding in terms of use case and functionality. This paper reviews current work in energy consumption of mobile cloud computing and then proposes a system whereby user applications may be profiled for their resource consumption locally and then if augmentation is required, they may negotiate with an external cloud for optimum energy consumption. Such a system is particularly useful for cloudlets which contain constrained resources so may need to choose between a number of client...
Read more
doi: 10.1016/j.procs.2015.05.151
Subjects
free text keywords: Mobile-Cloud-Computing, Energy, Profiling, Optimisation, Smartphone, Networks., G400, G500, H100, Cloud computing security, Mobile computing, Mobile cloud computing, Distributed computing, Cloud computing, business.industry, business, Mobile search, Computer science, Cloud testing, Mobile technology, Utility computing
Related Organizations
Download fromView all 5 versions
Elsevier
Article
Provider: CORE (RIOXX-UK Aggregator)
Procedia Computer Science
Article
Provider: UnpayWall
STORE - Staffordshire Online Repository
Article
Provider: CORE (RIOXX-UK Aggregator)
BASE (Open Access Aggregator)
Article . 2015
Provider: BASE (Open Access Aggregator)
View more
23 references, page 1 of 2

1. Yang, L., Cao, J., Tang, S., Li, T., Chan, A.. A framework for partitioning and execution of data stream applications in mobile cloud computing. In: Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on. 2012, p. 794-802. doi:10.1109/CLOUD.2012.97.

2. Kotwal, P., Singh, A.. Evolution and effects of mobile cloud computing, middleware services on cloud, future prospects: A peek into the mobile cloud operating systems. In: Computational Intelligence Computing Research (ICCIC), 2012 IEEE International Conference on. 2012, p. 1-5. doi:10.1109/ICCIC.2012.6510270.

3. Sanaei, Z., Abolfazli, S., Gani, A., Buyya, R.. Heterogeneity in mobile cloud computing: Taxonomy and open challenges. Communications Surveys Tutorials, IEEE 2014;16(1):369-392. doi:10.1109/SURV.2013.050113.00090. [OpenAIRE]

4. Quwaider, M., Jararweh, Y.. Cloudlet-based for big data collection in body area networks. In: Internet Technology and Secured Transactions (ICITST), 2013 8th International Conference for. 2013, p. 137-141.

5. Quwaider, M., Jararweh, Y.. Cloudlet-based efficient data collection in wireless body area networks. Simulation Modelling Practice and Theory 2015;50(0):57 - 71. doi:http://dx.doi.org/10.1016/j.simpat.2014.06.015. [OpenAIRE]

6. Fernando, N., Loke, S., Rahayu, W.. Dynamic mobile cloud computing: Ad hoc and opportunistic job sharing. In: Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on. 2011, p. 281-286. doi:10.1109/UCC.2011.45.

7. Jararweh, Y., Tawalbeh, L., Ababneh, F., Khreishah, A., Dosari, F.. Scalable cloudlet-based mobile computing model. Procedia Computer Science 2014;34(0):434 - 441. doi:http://dx.doi.org/10.1016/j.procs.2014.07.051; the 11th International Conference on Mobile Systems and Pervasive Computing (MobiSPC'14). [OpenAIRE]

8. Jararweh, Y., Tawalbeh, L., Ababneh, F., Dosari, F.. Resource efficient mobile computing using cloudlet infrastructure. In: 2013 IEEE Ninth International Conference on Mobile Ad-hoc and Sensor Networks (MSN). 2013, p. 373-377. doi:10.1109/MSN.2013.75.

9. Tawalbeh, L., Jararweh, Y., ababneh, F., Dosari, F.. Large scale cloudlets deployment for efficient mobile cloud computing. Journal of Networks 2015;10(01). [OpenAIRE]

10. Tilevich, E., Kwon, Y.W.. Cloud-based execution to improve mobile application energy efficiency. Computer 2014;47(1):75-77. doi:10.1109/MC.2014.6.

11. Kumar, K., Lu, Y.H.. Cloud computing for mobile users: Can offloading computation save energy? Computer 2010;43(4):51-56. doi:10.1109/MC.2010.98.

12. Zhang, W., Wen, Y., Guan, K., Kilper, D., Luo, H., Wu, D.. Energy-optimal mobile cloud computing under stochastic wireless channel. Wireless Communications, IEEE Transactions on 2013;12(9):4569-4581. doi:10.1109/TWC.2013.072513.121842.

13. Fekete, K., Csorba, K., Forstner, B., Vajk, T., Feher, M., Albert, I.. Analyzing computation offloading energy-efficiency measurements. In: Communications Workshops (ICC), 2013 IEEE International Conference on. 2013, p. 301-305. doi:10.1109/ICCW.2013.6649248.

14. Fekete, K., Csorba, K., Vajk, T., Forstner, B., Pandi, K.. Towards an energy efficient code generator for mobile phones. In: Cognitive Infocommunications (CogInfoCom), 2013 IEEE 4th International Conference on. 2013, p. 647-652. doi:10.1109/CogInfoCom.2013.6719182.

15. Altamimi, M., Palit, R., Naik, K., Nayak, A.. Energy-as-a-service (eaas): On the efficacy of multimedia cloud computing to save smartphone energy. In: Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on. 2012, p. 764-771. doi:10.1109/CLOUD.2012.72.

23 references, page 1 of 2
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