TOP 10 Cloud Computing research articles

TOP CLOUD COMPUTING: RECOMMENDED READING – NETWORK RESEARCH

  International Journal of Computer Networks & Communications (IJCNC)

(Scopus, ERA Listed)

ISSN 0974 – 9322 (Online); 0975 – 2293 (Print)

http://airccse.org/journal/ijcnc.html

 Citation Count – 22

Reducing Total Power Consumption Method in Cloud Computing Environments

Shin-ichi Kuribayashi

Department of Computer and Information Science, Seikei University, Japan

ABSTRACT

The widespread use of cloud computing services is expected to increase the power consumed by ICT equipment in cloud computing environments rapidly. This paper first identifies the need of the collaboration among servers, the communication network and the power network, in order to reduce the total power consumption by the entire ICT equipment in cloud computing environments. Five fundamental policies for the collaboration are proposed and the algorithm to realize each collaboration policy is outlined. Next, this paper proposes possible signaling sequences to exchange information on power consumption between network and servers, in order to realize the proposed collaboration policy. Then, in order to reduce the power consumption by the network, this paper proposes a method of estimating the volume of power consumption by all network devices simply and assigning it to an individual user.

KEYWORDS

Reducing power consumption, collaboration, cloud computing environments

For More Details: http://airccse.org/journal/cnc/0312cnc05.pdf

Volume Link:  http://airccse.org/journal/ijc2012.html

REFERENCES

[1] ITU Symposium on “ICTs and Climate Change” Summary Report, London, June 17&18,2008 http://www.itu.int/dms_pub/itu-t/oth/06/0F/T060F0060090001PDFE.pdf

[2] “Green IT Initiative in Japan”, METI, Japan Oct. 2008

http://www.meti.go.jp/english/policy/GreenITInitiativeInJapan.pdf

[3] J.W.Rittinghouse and J.F.Ransone: “Cloud computing: Imprementation, management, and security”, CRC Press LLC, Aug. 2009.

[4] P.Mell and T.Grance, “Effectively and securely using the cloud computing paradigm”, NIST, Information Technology Lab., July 2009.

[5] P.Mell and T.Grance: “The NIST definition of cloud computing” Version 15, 2009.

[6] S.Kuribayashi, “Optimal Joint Multiple Resource Allocation Method for Cloud Computing Environments”, International Journal of Research and Reviews in Computer Science (IJRRCS), Vol.2, No.1, pp.1-8, Feb. 2011

[7] S.Kuribayashi, “Proposed congestion control method for cloud computing environments”, International journal of Computer Networks & Communications (IJCNC), Vol.3, No.5, pp.161-176, Sep. 2011.

[8] M.Blackburn, “Five ways to reduce data center server power consumption”, “Five ways to save server power”, the green grid. http://www.thegreengrid.org/

[9] VMware Distributed power Management (DPM)

http://www.vmware.com/products/vi/vc/drs.html

[10] M.Gupta and S.Singh, “Greening of the Internet”, Proc.of ACM SIGCOMM’03, pp.19-26, Aug. 2003.

[11] C.Gunaratne, K.Christensen, S.Suen, and B. Nordman, “Reducing the Power Consumption of Ethernet with an Adaptive Link Rate (ALR),” IEEE Transactions on Computers, Vol. 57, No. 4, pp. 448-461, April 2008.

[12] F.Blanquicet, “An power efficient Internet: some ongoing work”, msrSeminar08 (June 2008).

[13] S.Nedevschi, L.Popa and G.Iannaccone, “Reducing network power consumption via sleeping and rate-adaptation”, Proc. 5th USENIX Symposium on Networked Systems Design and Implementation”, April 2008.

[14] U.Lee, I.Rimac and V.Hilt, “Greening the internet with content-centric networking”, Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking (2010).

[15] E.Jung and N.H.Vaidya, “An power efficient MAC protocol for wireless LANs”, Proc. IEEE INFOCOM, June 2002.

[16] X.Wu, A.Jaekel and A.Bari, “Optimal channel allocation with dynamic power control in cellular networks”, International Journal of Computer Networks & Communications (IJCNC) Vol.3, No.2, March 2011

[17] B.Hohlt, L.Dohertly and E.Brewer, “Flexible power scheduling for sensor networks”, IEEE and ACM Third International Symposium on Iformation Processing in Sensor Networks, April 2004.

[18] E.Jung and N.H.Vaidya, “An Energy efficient MAC protocol for wireless LANs”, In Proc. IEEE INFOCOM, June 2002.

[19] F.Blanquicet and K.Christensen, “Managing power use in a network with a new SNMP power state MIB”, IEEE Conference on Local Computer Networks (LCN) 2008, April 2008.

[20] Y.Matsumoto, S.Yanabu, “A vision of an electric power architecture for the next generation”, Electrical Engineering in Japan Vol. 150, Issue 1 , pp. 18 – 25, January 2005.

[21] “Power in Japan (2008)”,METI, Japan http://www.enecho.meti.go.jp/topics/power-injapan/english2008.pdf

 [22] K.Hatakeyama, Y.Osana and S.Kuribayashi, “Reducing total power consumption with collaboration between network and servers”, Proceeding of the 12-th International Conference on Network-Based Information Systems (NBiS-2009), Aug. 2009.

[23] S.Kuribayashi, “Reducing total ICT power consumption with collaboration among end systems, communication network and power network”, Proceeding of the 25th IEEE International Conference on Advanced Information Networking and Applications (AINA-2011), Mar. 2011

AUTHOR

Capture

Shin- ichi Kuribayashi received the B.E., M.E., and D.E. degrees from Tohoku University, Japan, in 1978, 1980, and 1988 respectively. He joined NTT Electrical Communications Labs in 1980. He has been engaged in the design and development of DDX and ISDN packet switching, ATM, PHS, and IMT 2000 and IP-VPN systems. He researched distributed communication systems at Stanford University from December 1988 through December 1989. He participated in international standardization on ATM signaling and IMT2000 signaling protocols at ITU-T SG11 from 1990 through 2000. Since April 2004, he has been a Professor in the Department of Computer and Information Science, Faculty of Science and Technology, Seikei University. His research interests include optimal resource management, QoS control, traffic control for cloud computing environments and green network. He is a member of IEEE, IEICE and IPSJ.

 Citation Count – 9

The Influence of Information Security on the Adoption of Cloud Computing: An Exploratory Analysis 

Omondi John Opala1, Shawon Rahman2, and Abdul hameed Alelaiwi3

 1Cisco Networks System, USA, 2University of Hawaii-Hilo, USA and 3King Saud University, Saudi Arabia

ABSTRACT

Cloud computing is the current IT buzzword synonymous with outsourced data center management and agile solution architecture. It has the potential to improve scalability of large enterprise network delivery of services and the capability to revolutionize how data is delivered as a service. At its core, cloud computing is not a new technology but rather a new approach to distributed shared pooling of IT infrastructure linked together to offer centralized IT services on demand. The study results determined that management’s perception of security, cost-effectiveness and IT compliance factors significantly influence their decisions to adopt cloud computing. The results of multiple linear regression analysis testing in this study showed that managements’ perception of cost-effectiveness is more significantly correlated to their decision to adopt cloud computing than it is to security.

KEYWORDS

Cloud computing, distributed computing, software as a service, infrastructure as a service, cloud security, and cloud compliance.

For More Details:  http://airccse.org/journal/cnc/7415cnc04.pdf

Volume Link:  http://airccse.org/journal/ijc2015.html

REFERENCES

[1] F. Lombardi and R. Di Pierto, “Secure virtualization for cloud computing,” J. of Network and Comput. Applicat., vol. 34, no. 4, pp.1113-1122, Mar. 2011.

[2] M. Armbrust et al., “A View of Cloud Computing,” Commun. of the ACM, vol. 53, no. 4, Nov. 2010.

[3] J.E. Anderson and H.P. Schwager, “SME adoption of wireless LAN technology: Applying the UTAUT model,” in7th Annu. Conf. of the Southern Assoc. for Inform. Syst., New York, NY 2004.

[4] S. Subashini and V. Kavitha, “A survey on security issues in service delivery models of cloud computing,” J. of Network and Comput. Applic., vol. 34, pp.1-11, Jan. 2011.

[5] C. Davidson, “Cloud control ,” Risk, vol. 23, no. 10, pp. 70-78, Mar. 2010.

[6] H. Katzan, “Identity and privacy services,” J. of Service Sci., vol. 3, no. 2, pp. 1-13, Feb. 2010.

[7] W. Jansen and T. Grance, “Guidelines on security and privacy in public cloud computing,. NIST, vol. 800, no. 144, pp. 1-60, Nov. 2011.

[8] H. Du and Y. Cong, “Cloud computing, accounting, auditing, and beyond certified public accountant,” The CPA J., vol.8, no. 10, pp. 66-70, Jan. 2010.

[9] A. Ahmed, “Using COBIT to manage the benefits, risks and security of outsourcing cloud computing,”COBIT Focus, vol. 2011, no. 2, pp.1-9, Jun. 2011.

[10] H. Miller and J. Veiga, (2009). « Cloud computing: Will commodity services benefit users long term? » IEE Comput. Soc., vol.1520-9202, no. 9, pp. 57-64, Apr. 2009.

[11] K. Kushida, J. Murray and J. Zysman, “Diffusing the cloud: Cloud computing and implications for public policy,”J. Ind. Compet. Trade, vol 5, no. 5, 1-30. Jul. 2010.

[12] Y. Dwivedi and N. Mustafee, “It’s unwritten in the cloud: The technology enablers for realising the promise of cloud computing,”J. of Enterprise Inform. Manage., vol. 23, no. 6, pp. 673-679, Jan. 2010.

[13] A.R. Swanson and F.E. Holton, Research in organizations: Foundations and methods of inquiry. San Francisco, CA: Berrett-Koehler Publications, Inc., 2005.

[14] P.W. Vogt, Quantitative research methods for professionals. Boston, MA: Pearson Education, Inc., 2010.

[15] J.C. De Winter, D. Dodou and A.P. Wieringa, “Exploratory factor analysis with small sample sizes,”Multivariate Behavioral Research, vol. 44, pp.147-181, Mar. 2009.

[16] D.F. Davis, P.R. Bagozzi and R.P. Warshaw, “User acceptance of information technology, system characteristics, user perceptions and behavioral impacts,”Int. J. of Man–Machine Stud., vol. 38, no. 3, pp. 475–487, Aug. 1993.

[17] V. Venkatesh and H. Bala, “Technology acceptance model 3 and a research agenda on interventions,”Decision Sciences, vol. 39, no. 2, pp. 273-315, Sep. 2008.

[18] F. Faul et al., “ G*Power 3: A flexible statistical power analysis for the social, behavioral, and biomedical sciences ,”Behavior Research Methods, vol. 39, no. 1, pp.175-191, Nov. 2007.

[19] L. Cronbach, “ Coefficient alpha and the internal structure of tests,”Psychometrika, vol.16, no. 3, pp. 297-334, May 1957.

[20]  A. Wikman, “Reliability, validity and true values in surveys,”Social Indicators Research,vol. 78, no.1, pp. 85-110, May 2006.

[21] M.D. Williams et al., “Contemporary trends and issues in IT adoption and diffusion research,”J. of Inform. Tech., vol. 2009, no.24, pp. 1-10, Jan. 2009.

[22] Tram Truong-Huu; Chen-Khong Tham, “A Novel Model for Competition and Cooperation among Cloud Providers,” Cloud Computing, IEEE Transactions on , vol.2, no.3, pp.251,265, July-Sept. 1 2014

[23] Jamshidi, P.; Ahmad, A.; Pahl, C., “Cloud Migration Research: A Systematic Review,” Cloud Computing, IEEE Transactions on , vol.1, no.2, pp.142,157, July-December 2013

[24] Kailasam, S.; Dhawalia, P.; Balaji, S.J.; Iyer, G.; Dharanipragada, J., “Extending MapReduce across Clouds with BStream,” Cloud Computing, IEEE Transactions on , vol.2, no.3, pp.362,376, July-Sept. 1 2014

[25] D.F Davis, P.R. Bagozzi and R.P. Warshaw, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,”MIS Quart.,vol.13, no. 3, pp. 319-339, Dec. 1989.

[26] M.E. Rogers, Diffusion of Innovation,5th ed. New York, Free Press, 2003.

[27] H.T. Nguyen, “Information technology adoption in SMEs: an integrated framework,”Int. J. of Entrepreneurial Behaviour & Research Tech. Manage.,vol. 15, no. 2, 162-186, Jan. 2009.

[28] M.A. Sharif, “It’s written in the cloud: The hype and promise of cloud computing,”J. of Enterprise Inform. Manage., vol. 23, no. 2, pp.131-134, May 2010. Available: doi: 10.1108/17410391011019732

[29] H. Katzan, “Identity and privacy services,”J. of Service Sci., vol. 3, no. 2, 1-13, May 2010.

[30] S. Keller et al., “Information security threats and practices in small businesses,” Inform. Syst. Manage.,vol. 22, no. 2, pp.7-19, Jul. 2005.

[31] S. Edson et al.” Ontologies for information security management and governance,”Inform. Manage.& Comput. Security, vol. 16, no. 2, pp. 150-165, Jul. 2008.

[32] F. Lombardi and R. Di Pierto, R. (2011). Secure virtualization for cloud computing. J. of Network and Comput. Applicat., vol. 34, no. 4, pp. 1113-1122, Oct. 2011.

[33] S. Mustafee, “Exploiting grid computing, desktop grids and cloud computing for escience,”Transforming Government: People, Process and Policy, vol. 4, no. 4, pp. 288-298, May 2010.

[34] E. Straub, “Understanding technology adoption: Theory and future directions for informal learning,”Review of Educational Research, vol. 79, no. 2, pp. 625-730, Feb. 2009.

[35] C. Drugescu and R. Etges, “Maximizing the return on innvestment of information security programs: Program governance and Metrics,”Inform. Syst. Security,vol. 15, no. 6, pp. 30-40, Mar. 2009.

[36] B. Furht and A. Escalante, “Handbook of cloud computing,” J. of Inform. Syst., vol. 3, Nov. 2010.

[37] A. Letaifa et al., “State of the art and research challenges of new services architecture technologies: Virtualization, SOA and cloud computing,”Int. J. of Grid & Distributed Computing, vol. 3, no. 4,pp. 69-87, May 2010.

[38] R. Gill, “Why cloud computing matters to finance,”Strategic Finance, vol. 92, no. 7, pp. 43-67, Jun. 2011.

[39] H. Demirkan, R.R. Harmon and M. Goul, “A service-oriented web application framework,”IT Professional Mag., vol. 13, no. 5, pp. 15-21, Jul. 2011.

[40] D. Owunwanne and R. Goel,“Radio frequency identification (RFID) technology: Gaining a competitive value through cloud computing,”Int. J. of Manage. and Inform. Syst., vol. 14, no. 5, pp.157-164, Apr. 2010.

[41] Opala, John, Omondi; Rahman, Shawon and Alelaiwi, Abdulhameed ; “An Analysis on the Factors Influencing Managers’ Decision to Adopt of Cloud Computing”; Invited book chapters in titled ” Handbook of Research on Architectural Trends in Service-Driven Computing” IGI Global, 2014.

[42] Opala, John, Omondi and Rahman, Syed (Shawon);“Corporate Role in Protecting Consumers from the Risk of Identify theft ”; International Journal of Computer Networks & Communications (IJCNC), Vol.5, No.5, September 2013.

AUTHORS

Dr. Omondi John Opala is an Associate Professor at the Department of Information Technology at Devry University’s Keller Graduate School and Technical Lead at Cisco Systems, North Carolina, USA. Omondi’s research interests include Systems Architecture, Cloud computing, Information Assurance, Security Governance, Big Data, and Software-defined Networks (SDN).

 Dr. Shawon (Syed) M. Rahman is an Associate Professor in the Department of Computer Science and Engineering at the University of Hawaii-Hilo, Hawaii, USA, and a visiting professor at the King Saud University, Riyadh, KSA. Shawon’s research interests include Software Engineering education, Software Testing & QA, Information Assurance and Security, Cloud Computing, Mobile Application Development, and Web Accessibility. He has published over 100 peer-reviewed articles and is a member of many professional organizations including IEEE, ACM, ASEE, ASQ, ISACA, and UPE.

Dr. Abdulhameed Alelaiwi is a vice dean for technical affairs, Scientific Research Deanship, King Saud University (KSU) and a faculty member in Software Engineering Department, College of Computer and Information Sciences, KSU. He holds a Ph.D. in the field of Software Engineering from the department of software engineering, Florida Tech Univ., USA, 2002. Before joining King Saud University, he worked in the industry around 7 years. He continues to consult with local corporations in the areas of Software Engineering, E- Government, and Information security.

Citation Count: 8

Resource Allocation Method for Cloud Computing Environments with Different Service Quality to Users at Multiple Access Points

Shin-ichi Kuribayashi

Seikei University, Japan

ABSTRACT

In a cloud computing environment with multiple data centers over a wide area, it is highly likely that each data center would provide the different service quality to users at different locations. It is also required to consider the nodes at the edge of the network (local cloud) which support applications such as IoTs that require low latency and location awareness. The authors proposed the joint multiple resource allocation method in a cloud computing environment that consists of multiple data centers and each data center provides the different network delay. However, the existing method does not take account of cases where requests that require a short network delay occur more than expected. Moreover, the existing method does not take account of service processing time in data centers and therefore cannot provide the optimal resource allocation when it is necessary to take the total processing time (both network delay and service processing time in a data center) into consideration in resource allocation. This paper proposes to enhance the existing joint multiple resource allocation method, so as to provide the following two functions: (1) a function to prevent the degradation in service quality of other request types when requests that require a short network delay occur more than expected, and (2) a function to take account of the total processing time of network delay and service processing time in allocating resources. It is demonstrated by simulation evaluations that the enhanced method can handle up to twice as many requests as the existing method with the same amount of resources, and can cope with the excessive generation of requests from the specific access point.

KEYWORDS

Cloud computing; joint multiple resource allocation, different service quality, multiple access points, and total processing time.

For More Details: http://aircconline.com/ijcnc/V7N6/7615cnc03.pdf

Volume Link:  http://airccse.org/journal/ijc2015.html

REFERENCES

[1]    G.Reese: “Cloud Application Architecture”, O’Reilly& Associates, Inc., Apr. 2009.

[2] J.W.Rittinghouse and J.F.Ransone: “Cloud Computing: Imprementation, Management, and Security”, CRC Press LLC, Aug. 2009.

[3]  P.Mell and T.Grance, “Effectively and securely Using the Cloud Computing Paradigm”, NIST, Information Technology Lab., July 2009.

[4] V.Vinothina, R.Sridaran, and P. Ganapathi, “A Survey on Resource Allocation Strategies in Cloud Computing”, International Journal of Advanced Computer Science and Applications,Vol. 3, No.6, 2012.

[5]  S.Kuribayashi,“Optimal Joint Multiple Resource Allocation Method for Cloud Computing Environments”, International Journal of Research and Reviews in Computer Science (IJRRCS), Vol. 2, No.1, Feb. 2011.

[6] M. Mazzucco, D. Dyachuk, and R. Deters, “Maximizing Cloud Providers’ Revenues via Energy Aware Allocation Policies,” in 2010 IEEE 3rd International Conference on Cloud Computing. IEEE, 2010.

[7]   K.Mochizuki and S.Kuribayashi, “Evaluation of optimal resource allocation method for cloud computing environments with limited electric power capacity”, Proceeding of the 14-th International Conference on Network-Based Information Systems (NBiS-2011), Sep. 2011.

[8]    F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog computing and its role in the internet of things,” in Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, ser. MCC’12. ACM,2012, pp. 13–16.

[9]  I.Stojmenovic and S.Wen, “The Fog Computing Paradigm: Scenarios and Security Issues“, Proceedings of the 2014 Federated Conference on Computer Science and Information Systems pp. 1– 8.

[10]  NTT press releases, “Announcing the Edge computing concept and the Edge accelerated Web platform prototype to improve response time of cloud applications,” Jan. 2014. http://www.ntt.co.jp/news2014/1401e/140123a.html

[11]  Y.Awano and S.Kuribayashi, “Proposed Joint Multiple Resource Allocation Method for Cloud Computing Services with Heterogeneous QoS”, Cloud Computing 2012, July 2012.

[12] S.Kuribayashi,“Joint Multiple Resource Allocation Method for Cloud Computing Services with different QoS to users at multiple locations”, International journal of Computer Networks & Communications (IJCNC), Vol.5, No.5, pp.1-18, Sep. 2013.

[13]  B. Soumya, M. Indrajit, and P. Mahanti, “Cloud computing initiative using modified ant colony framework,” in In the World Academy of Science, Engineering and Technology 56, 2009.

[14] R.Buyya, C.S. Yeo, and S.Venugopal, “Market-Oriented Cloud Computing:Vision, Hype, and Reality for Delivering IT Services as Computing Utilities”, Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications (HPCC-08), Sep. 2008

[15]   G.Wei, A.V. Vasilakos, Y.Zheng, and N.Xiong, “A game-theoretic method of fair resource allocation for cloud computing services”, The journal of supercomputing, Vol.54, No.2.

[16]  Yazir, Y.O., Matthews, C., Farahbod, R., Neville, S., Guitouni, A., Ganti, S., and Coady, Y., “Dynamic Resource Allocation in Computing Clouds through Distributed Multiple Criteria Decision Analysis”, 2010 IEEE 3rd Internatiuonal Conference on Cloud Computing (CLOUD 2010), July 2010.

[17]  B.Malet and P.Pietzuch, “Resource Allocation across Multiple Cloud Data Centres”, 8th International workshop on Middleware for Grids, Clouds and e-Science. (MGC’10), Nov. 2010.

[18]   G.Leey, B.G.Chunz, and R.H.Katz, “Heterogeneity-Aware Resource Allocation and Scheduling in the Cloud”, HotCloud ’11 June. 2011.

[19]   B. Rajkumar, B. Anton, and A. Jemal, “Energy efficient management of data center resources for computing: Vision, architectural elements and open challenges,” in International Conference on Parallel and Distributed Processing Techniques and Applications, Jul. 2010.

[20]  M. Mazzucco, D. Dyachuk, and R. Deters, “Maximizing Cloud Providers’ Revenues via Energy Aware Allocation Policies,” in 2010 IEEE 3rd International Conference on Cloud Computing. IEEE, 2010.

[21]  W.Y. Lin, G.Y. Lin, and H.Y.Wei, “Dynamic Auction Mechanism for Cloud Resource Allocation”, 10th IEEEACM International Conference on Cluster Cloud and Grid Computing (2010).

[22] Y.Magome and S.Kuribayashi, “Resource allocation method for cloud computing environments with different service quality to users at multiple access points”, Proceeding of the 17-th International Conference on Network-Based Information Systems (NBiS-2014), Sep. 2014.

[23]   M.Uriu and S.Kuribayashi, “Resource allocation method in cloud computing environments with multiple data centers over a wide area”, Proceeding of 2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (Pacrim2015), C1-1, Aug. 2015.

AUTHOR

CaptureShin-ichi Kuribayashi received the B.E., M.E., and D.E. degrees from Tohoku University, Japan, in 1978, 1980, and 1988 respectively. He joined NTT Electrical Communications Labs in 1980. He has been engaged in the design and development of DDX and ISDN packet switching, ATM, PHS, and IMT 2000 and IP-VPN systems. He researched distributed communication systems at Stanford University from December 1988 through December 1989. He participated in international standardization on ATM signaling and IMT2000 signaling protocols at ITU-T SG11 from 1990 through 2000. Since April 2004, he has been a Professor in the Department of Computer and Information Science, Faculty of Science and Technology, Seikei University. His research interests include optimal resource management, QoS control, traffic control for cloud computing environments and green network. He is a member of IEEE, IEICE and IPSJ.

Citation Count: 7

Study the Effect of Parameters to Load Balancing in Cloud Computing

Tran Cong Hung and Nguyen Xuan Phi

Posts and Telecommunications Institute of Technology, Vietnam

ABSTRACT

The rapid growth of users on the cloud service and number of services to the user increases the load on the servers at cloud datacenter. This issue is becoming a challenge for the researchers. And requires used effectively a load balancing technique not only to balance the resources for servers but also reduce the negative impact to the end-user service. The current, load balancing techniques have solved the various problems such as: (i) load balancing after a server was overloaded; (ii) load balancing and load forecast for the allocation of resources; iii) improving the parameters affecting to load balancing in cloud. The study of improving these parameters have great significance to improving system performance through load balancing. From there, we can propose more effective methods of load balancing, in order to increase system performance. Therefore, in this paper we researched some parameters affecting the performance load balancing on the cloud computing.

KEYWORDS

 Load balancing; Virtual Mmachines; Lload Balancing Parameters; Cloud Computing.

For More Details: http://aircconline.com/ijcnc/V8N3/8316cnc03.pdf

Volume Link:  http://airccse.org/journal/ijc2016.html

REFERENCES

[1] P. Srinivasa Rao, V.P.C Rao and A.Govardhan,“Dynamic Load Balancing With Central Monitoring of Distributed Job Processing System”, International Journal of Computer Applications (0975 – 8887) ,Volume 65– No.21, March 2013.

[2] Agraj Sharma, Sateesh K. Peddoju, “Response Time Based Load Balancing in Cloud Computing”, 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT).

[3] Huankai Chen, Frank Wang, Na Helian, Gbola Akanmu, “User-Priority Guided Min-Min Scheduling Algorithm For Load Balancing in Cloud Computing”, Parallel Computing Technologies, 2013 National Conference.

[4] Dhinesh Babu ,Venkata Krishna P, “Honey bee behavior inspired load balancing of tasks in cloud computing environments”, Elsevier- Journal of Applied Soft Computing, no-l3, 2013, pp-2292-2303

[5] Gaochao Xu, Junjie Pang and Xiaodong Fu, “A Load Balancing Model Based on Cloud Partitioning for the Public Cloud”, Tsinghua Science and Technology , ISSN 1007-0214 04/12, pp 34-39, Volume 18, Number I, Febuary 2013.

[6] Hiren H. Bhatt and Hitesh A. Bheda, “Enhance Load Balancing using Flexible Load Sharing in Cloud Computing”, 2015 1st International Conference on Next Generation Computing Technologies (NGCT-2015) Dehradun, India, 4-5 September 2015.

[7] Ritu Kapur, “A Workload Balanced Approach for Resource Scheduling in Cloud Computing”, 2015 Eighth International Conference on Contemporary Computing (IC3), 20-22 Aug. 2015

[8] Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A. F. De Rose and Rajkumar Buyya, “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms” , Softw. Pract. Exper. 2011; 41:23–50, Published online 24 August 2010 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/spe.995.

AUTHORS

Tran Cong Hung was born in Vietnam in 1961. He received the B.E in electronic and Telecommunication engineering with first class honors from HOCHIMINH University of technology in Vietnam, 1987. He received the B.E in informatics and computer engineering from HOCHIMINH University of technology in Vietnam, 1995. He received the master of engineering degree in telecommunications engineering course from postgraduate department Hanoi University of technology in Vietnam, 1998. He received Ph.D at Hanoi University of technology in Vietnam, 2004. His main research areas are B – ISDN performance parameters and measuring methods, QoS in high speed networks, MPLS. He is, currently, Associate Professor PhD. of Faculty of Information Technology II, Posts and Telecoms Institute of Technology in HOCHIMINH, Vietnam.

Nguyen Xuan Phi was born in Vietnam in 1980. He received Master in Posts & Telecommunications Institute of Technology in Ho Chi Minh, Vietnam, 2012, major in Networking and Data Transmission. Currently, he is a PhD candidate in Information System from Post & Telecommunications Institute of Technology, Vietnam. He is working at the Information Technology Center of AGRIBANK in Ho Chi Minh, Vietnam.

Citation Count: 4

Joint Multiple Resource Allocation Method for Cloud Computing Services with Different QOS to Users at Multiple Locations

Shin-ichi Kuribayashi

Seikei University, Japan

ABSTRACT

 In a cloud computing environment, it is necessary to simultaneously allocate both processing ability and network bandwidth needed to access it. The authors proposed the joint multiple resource allocation method in a cloud computing environment that consists of multiple data centers with different QoS (Quality of Service). This paper proposes to enhance the existing joint multiple resource allocation method, so that it can handle multiple heterogeneous resource-attributes. Resource-attributes of bandwidth, for example, are network delay time, packet loss probability, etc. The basic idea is to identify the key resource-attribute first which has the most impact on resource allocation and to select the resources which provide the lowest QoS for the key resource-attribute as it satisfies required Quality of Service. It is demonstrated by simulation evaluations that the enhanced method (Method A) can reduce the total amount of resources up to 30%, compared with the existing method. It is also highly likely that each data center provides the different network delay to users at multiple locations. This paper proposes the further enhancement of Method A in order to handle the case where each data center provides the different network delay to users at multiple locations.

KEYWORDS

Cloud computing, heterogeneous QoS, joint multiple resource allocation

For More Details: http://airccse.org/journal/cnc/5513cnc01.pdf

Volume Link: http://airccse.org/journal/ijc2013.html

REFERENCES

[1] G.Reese: “Cloud Application Architecture”, O’Reilly& Associates, Inc., Apr. 2009.

[2] J.W.Rittinghouse and J.F.Ransone: “Cloud Computing: Imprementation, Management, and Security”, CRC Press LLC, Aug. 2009.

[3] P.Mell and T.Grance, “Effectively and securely Using the Cloud Computing Paradigm”, NIST, Information Technology Lab., July 2009.

[4] P.Mell and T.Grance:“The NIST Definition of Cloud Computing” Version 15, 2009.

[5] Z.Hang, L.Cheng, and R.Boutaba, “Cloud compuing: state-of-the-art and research challenges”, J Internet Serv Apl, Jan. 2010.

[6] S.Kuribayashi, “Optimal Joint Multiple Resource Allocation Method for Cloud Computing Environments”, International Journal of Research and Reviews in Computer Science (IJRRCS), Vol.2, No.1, Feb. 2011.

[7] S.Tsumura and S.Kuribayashi: “Simultaneous allocation of multiple resources for computer communications networks”, In Proceeding of 12th Asia-Pacific Conference on Communications (APCC2006), 2F-4, Aug. 2006.

[8] K.Mochizuki and S.Kuribayashi, “Evaluation of optimal resource allocation method for cloud computing environments with limited electric power capacity”, Proceeding of the 14-th International Conference on Network-Based Information Systems (NBiS-2011), Sep. 2011.

[9] S.Kuribayashi, “Reducing Total Power Consumption Method in Cloud Computing Environments”, International journal of Computer Networks & Communications (IJCNC), Vol.4, No.2, pp.69-84, March 2012.

[10] H.Zhang, G.Jiang, K.Yoshihira, H.Chen, and A.Saxena, “Intelligent workload factoring for a hybrid cloud computing model”, Proceedings of the 2009 IEEE Congress on Services (Services’09), July 2009.

[11] B. Soumya, M. Indrajit, and P. Mahanti, “Cloud computing initiative using modified ant colony framework,” in In the World Academy of Science, Engineering and Technology 56, 2009.

[12] R.Buyya, C.S. Yeo, and S.Venugopal, “Market-Oriented Cloud Computing:Vision, Hype, and Reality for Delivering IT Services as Computing Utilities”, Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications (HPCC-08), Sep. 2008

[13] W.Y. Lin, G.Y. Lin, and H.Y.Wei, “Dynamic Auction Mechanism for Cloud Resource Allocation”, 10th IEEEACM International Conference on Cluster Cloud and Grid Computing (2010)

[14] G.Wei, A.V. Vasilakos, Y.Zheng, and N.Xiong, “A game-theoretic method of fair resource allocation for cloud computing services”, The journal of supercomputing, Vol.54, No.2.

[15] Yazir, Y.O., Matthews, C., Farahbod, R., Neville, S., Guitouni, A., Ganti, S., and Coady, Y., “Dynamic Resource Allocation in Computing Clouds through Distributed Multiple Criteria Decision Analysis”, 2010 IEEE 3rd Internatiuonal Conference on Cloud Computing (CLOUD 2010), July 2010.

[16] B.Malet and P.Pietzuch, “Resource Allocation across Multiple Cloud Data Centres”, 8th International workshop on Middleware for Grids, Clouds and e-Science. (MGC’10), Nov. 2010.

[17] G.Leey, B.G.Chunz, and R.H.Katz, “Heterogeneity-Aware Resource Allocation and Scheduling in the Cloud”, HotCloud ’11 June. 2011.

[18] B. Rajkumar, B. Anton, and A. Jemal, “Energy efficient management of data center resources for computing: Vision, architectural elements and open challenges,” in International Conference on Parallel and Distributed Processing Techniques and Applications, Jul. 2010.

[19] M. Mazzucco, D. Dyachuk, and R. Deters, “Maximizing Cloud Providers’ Revenues via Energy Aware Allocation Policies,” in 2010 IEEE 3rd International Conference on Cloud Computing. IEEE, 2010.

[20] K.Mochizuki and S.Kuribayashi, “Evaluation of optimal resource allocation method for cloud computing environments with limited electric power capacity”, Proceeding of the 14-th International Conference on Network-Based Information Systems (NBiS-2011), Sep. 2011.

[21] Yuuki Awano and Shin-ichi Kuribayashi, “Proposed Joint Multiple Resource Allocation Method for Cloud Computing Services with Heterogeneous QoS”, The Third International Conference on Cloud Computing, GRIDs,and Virtualization(CLOUD COMPUTING 2012) , July 2012.

[22] Yuuki Awano and Shin-ichi Kuribayashi, “A joint multiple resource allocation method for cloud computing environments with different QoS to users at multiple locations” Proceeding of 2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (Pacrim13), Aug. 2013.

AUTHORS

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Shin-ichi Kuribayashi received the B.E., M.E., and D.E. degrees from Tohoku University, Japan, in 1978, 1980, and 1988 respectively. He joined NTT Electrical Communications Labs in 1980. He has been engaged in the design and development of DDX and ISDN packet switching, ATM, PHS, and IMT 2000 and IP-VPN systems. He researched distributed communication systems at Stanford University from December 1988 through December 1989. He participated in international standardization on ATM signaling and IMT2000 signaling protocols at ITUT SG11 from 1990 through 2000. Since April 2004, he has been a Professor in the Department of Computer and Information Science, Faculty of Science and Technology, Seikei University. His research interests include optimal resource management, QoS control, traffic control for cloud computing environments and green network. He is a member of IEEE, IEICE and IPSJ.

Citation Count: 4

The Development and Study of the Methods and Algorithms for the Classification of Data Flows of Cloud Applications in the Network of the Virtual Data Center

Irina Bolodurina and Denis Parfenov

Orenburg State University, Russia

ABSTRACT

This paper represents the results of the research, which have allowed us to develop a hybrid approach to the processing, classification, and control of traffic routes. The approach enables to identify traffic flows in the virtual data center in real-time systems. Our solution is based on the methods of data mining and machine learning, which enable to classify traffic more accurately according to more criteria and parameters. As a practical result, the paper represents the algorithmic solution of the classification of the traffic flows of cloud applications and services embodied in a module for the controller of the software-defined network. This solution enables to increase the efficiency of handling user requests to cloud applications and reduce the response time, which has a positive effect on the quality of service in the network of the virtual data center.

KEYWORDS

Cloud applications; software-defined network; traffic flows; virtual data center; data mining; machine learning

For More Details: http://aircconline.com/ijcnc/V10N2/10218cnc02.pdf

Volume Link:  http://airccse.org/journal/ijc2018.html

REFERENCES

[1] Bein D., Bein W., Venigella S. “Cloud Storage and Online Bin Packing” Proc. of the 5th Intern. Symp. on Intelligent Distributed Computing, 2011, Delft: IDC, P. 63 -68.

[2] Nagendram S., Lakshmi J.V., Rao D.V., et al “Efficient Resource Scheduling in Data Centers using MRIS” Indian J. of Computer Science and Engineering, 2011, V. 2. Issue 5, P. 764-769.

[3] Arzuaga E., Kaeli D.R. “Quantifying load imbalance on virtualized enterprise servers” Proc. of the first joint WOSP/SIPEW international conference on Performance engineering, 2010, San Josa, CA: ACM, P. 235-242.

[4] Mishra M., Sahoo A. “On theory of VM placement: Anomalies in existing methodologies and their mitigation using a novel vector based approach” Cloud Computing (CLOUD), IEEE International Conference, 2011, Washington: IEEE Press, P.275-282.

[5] Korupolu M., Singh A., Bamba B. “Coupled placement in modern Data Centers” IEEE Intern. Symp. on Parallel & Distributed Processing. N. Y.: IPDPS, 2009. P. 1-12.

[6] Singh A., Korupolu M., Mohapatra D. “Server-storage virtualization: integration and load balancing in Data Centers” Proc. of the 2008 ACM/IEEE Conf. on   Supercomputing. Austin: IEEE Press, 2008. P.1- 12.

[7] Plakunov A., Kostenko V. “Data center resource mapping algorithm based on the ant colony optimization” Proc. of Science and Technology Conference (Modern  Networking Technologies) (MoNeTeC), Moscow: IEEE Press, 2014. P.1- 6.

[8] Darabseh, A., Al-Ayyoub, M., Jararweh, Y., Benkhelifa, E., Vouk, M., Rindos, A. “SDStorage: A Software Defined Storage Experimental Framework” Proc. of Cloud Engineering (IC2E), Tempe: IEEE Press, 2015. p.341- 346.

[9] Parfenov D., Bolodurina I. “Approaches to the effective use of limited computing resources in multimedia applications in the educational institutions” WCSE 2015- IPCE, 2015.

[10] Parfenov D., Bolodurina I. “Development and research of models of organization storages based on the software-defined infrastructure” 39th International Conference on Telecommunication and signal processing : materials of conference 27-29 June 2016, Vienna, Austria. 2016. – . – P. 1-6.

[11] Parfenov D., Bolodurina I. “Development and Research of Models of Organization Distributed Cloud Computing Based on the Software-defined Infrastructure” Procedia Computer Science, Volume 103, 2017. P. 569-576.

Citation Count: 2

Proposed Congestion Control Method for Cloud Computing Environments

Shin-ichi Kuribayashi

Seikei University, Japan

ABSTRACT

As cloud computing services rapidly expand their customer base, it has become important to share cloud resources, so as to provide them economically. In cloud computing services, multiple types of resources, such as processing ability, bandwidth and storage, need to be allocated simultaneously. If there is a surge of requests, a competition will arise between these requests for the use of cloud resources. This leads to the disruption of the service and it is necessary to consider a measure to avoid or relieve congestion of cloud computing environments. This paper proposes a new congestion control method for cloud computing environments which reduces the size of required resource for congested resource type instead of restricting all service requests as in the existing networks. Next, this paper proposes the user service specifications for the proposed congestion control method, and clarifies the algorithm to decide the optimal size of required resource to be reduced, based on the load offered to the system. It is demonstrated by simulation evaluations that the proposed method can handle more requests compared with the conventional methods and relieve the congestion. Then, this paper proposes to enhance the proposed method, so as to enable the fair resource allocation among users in congested situation.

KEYWORDS

Congestion control, cloud computing environments, fairness, joint multiple resource allocation

For More Details: http://airccse.org/journal/cnc/0911cnc12.pdf

Volume Link:  http://airccse.org/journal/ijc2011.html

REFERENCES

[1] G.Reese: “Cloud Application Architecture”, O’Reilly&Associates, Inc., Apr. 2009.

[2] J.W.Rittinghouse and J.F.Ransone: “Cloud Computing: Imprementation, Management, and Security”, CRC Press LLC, Aug. 2009.

[3] P.Mell and T.Grance, “Effectively and securely Using the Cloud Computing Paradigm”, NIST, Information Technology Lab., July 2009.

[4] P.Mell and T.Grance: “The NIST Definition of Cloud Computing” Version 15, 2009.

[5] S.Kuribayashi, “Optimal Joint Multiple Resource Allocation Method for Cloud Computing Environments”, International Journal of Research and Reviews in Computer Science (IJRRCS), Vol.2, No.1, pp.1-8, Feb. 2011

[6] G.Hasegawa and M.Murata, “Survey on Fairness Issues in TCP Congestion Control Mechanisms IEICE Trans. on Commun. Vol.E84-B No.6, pp.1461-1472, June 2001.

[7] H.Oda, H.Hisamatsu and H.Noborio, “Design, Implementation and Evaluation of Congestion Control Mechanism for Video Streaming,” International Journal of Computer Networks & Communication (IJCNC), Vol.3, No.3, May 2011

[8] I.A.Qazi, T.Znati and L.H.Andrew, “Congestion Control using Efficient Explicit Feedback,” INFOCOM2009.

[9] K.S.Reddy and L.C.Reddy, “A Survey on Congestion Control Mechanisms in High Speed Networks,” International Journal of Computer Science and Network Security (IJSNS), Vol.8, No.1, Jan. 2008.

[10] S.Ahmad, A.Mustafa, B.Ahmad,A.Bano, and A.S.Hosam, “ Comparative study of Congestion Control Techniques in High Speed Networks,” International Journal of Computer Science and Information Security (IJCSIS), Vol.6, No.2, 2009.

[11] R.Jain, “Congestion Control and Traffic Management in ATM networks: Recent advances and A Survey,“ Computer Networks and ISDN Systems, Vol.28, No.13, pp. 1723-1738, Oct. 1996.

[12] F.M.Holness,“Congestion Control Mechanisms within MPLS Networks”, Doctor paper, Queen Mary and West field College University of London, Sep.2000 .

[13] M.Welzl, “Network Congestion Control: Managing Internet Traffic,” Wiley Series on Communications Networking & Distributed Systems, John Wiley & Sons, Ltd, Sep. 2005.

[14] J.Wang, “Congestion Control in Computer Networks: Theory, Protocols and Applications (Distributed, Cluster and Grid Computing),” Nova Science Pub Inc, Oct. 2010.

[15] B.Raghavan, K.Vishwanath, S.Ramabhadran, K.Yocum, and A.C. Snoeren, “Cloud control with distributed rate limiting,” ACM SIGCOMM2007, Aug.2007.

[16] M.Gusat, R.Birke and C.Minkenberg, “Delay-based Cloud Congestion Control,” Globecom2009.

[17] K.Hatakeyama and S.Kuribayashi, “Proposed congestion control method for all-IP networks including NGN”, The 10th International Conference on Advanced Communication Technology (ICACT2008), 06C-02, pp.1082-1087, Feb. 2008 .

[18] K.Hatakeyama, M.Tanabe and S.Kuribayashi, “Proposed congestion control method reducing the size of required resource for all-IP”, Proceeding of 2009 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (Pacrim2009), Aug. 2009.

[19] T.Tomita and S.Kuribayashi, “Congestion control method with fair resource allocation for cloud computing environments,” Proceeding of 2011 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (Pacrim11), pp.1-6, Aug. 2011.

[20] M.Shreedhar and G.Varghese, “Efficient fair queuing using deficit round robin,” IEEE/ACM Transactions on Networking, vol.4, No.3, June 1996.

AUTHOR

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Shin-ichi Kuribayashi received the B.E., M.E., and D.E. degrees from Tohoku University, Japan, in 1978, 1980, and 1988 respectively. He joined NTT Electrical Communications Labs in 1980. He has been engaged in the design and development of DDX and ISDN packet switching, ATM, PHS, and IMT 2000 and IP-VPN systems. He researched distributed communication systems at Stanford University from December 1988 through December 1989. He participated in international standardization on ATM signaling and IMT2000 signaling protocols at ITU-T SG11 from 1990 through 2000. Since April 2004, he has been a Professor in the Department of Computer and Information Science, Faculty of Science and Technology, Seikei University. He is a member of IEEE, IEICE and IPSJ.

Citation Count: 2

Evaluation of Congestion Control Methods for Joint Multiple Resource Allocation in Cloud Computing Environments

Shin-ichiKuribayashi,

 Seikei University, Japan

ABSTRACT

As cloud computing provides not only services that have been traditionally provided on the Internet but also many other services, it has a dramatically higher risk than conventional networks that an occurrence of congestion in one service leads to congestion in other services. Unlike conventional networks, cloud computing environments should provide not only bandwidth but also processing ability simultaneously. First, this paper compares two congestion control methods (Methods A and B)in cloud computing environments, assuming that multiple types of resource are allocated simultaneously, and clarifies the effective areas of two congestion control methods with computer simulations. Method A postpones the service completion time by delaying resource allocation. Method B reduces the size of required resource and allocates to the request, extending in turn the duration of resource allocation so that the total amount of resource required by the request will be satisfied. The effective areas of two congestion control methods are clarified with computer simulations. Then, this paper compares three control methods (Methods 1, 2 and 3) to cope with the excessive generation of requests from a specific access point, which results in the degradation in service quality of requests from other access points, and clarifies the effective areas of three control methods with computer simulations. Method 1 allocates minimum resources dedicated to each access point in each center. Method 2reduces the size of required resources of requests from a specific access point, and Method 3 thins out some of requests from a specific access point.

KEYWORDS

Congestion control, Joint multiple resource allocation, Resource management, cloud computing environments

For More Details: http://airccse.org/journal/cnc/0312cnc05.pdf

Volume Link:  http://airccse.org/journal/ijc2012.html

REFERENCES

[1] G.Reese: “Cloud Application Architecture”, O’Reilly& Associates, Inc., Apr. 2009.

[2] P.Mell and T.Grance, “Effectively and securely Using the Cloud Computing Paradigm”, NIST, Information Technology Lab., July 2009.

[3] V. Vinothina, R.Sridaran, and P. Ganapathi, “A Survey on Resource Allocation Strategies in Cloud Computing”, International Journal of Advanced Computer Science and Applications,Vol. 3, No.6, 2012.

[4] S.Kuribayashi,“Optimal Joint Multiple Resource Allocation Method for Cloud Computing Environments”, International Journal of Research and Reviews in Computer Science (IJRRCS), Vol. 2, No.1, Feb. 2011.

[5] K.Hatakeyama and S.Kuribayashi, “Proposedcongestion control method for all-IP networks includingNGN”, ICACT2008 (2008.2)

[6] S.Kuribayashi,“Joint Multiple Resource Allocation Method for Cloud Computing Services with different QoS to users at multiple locations”, International journal of Computer Networks & Communications (IJCNC), Vol.5, No.5, pp.1-18, Sep. 2013.

[7] B. Soumya, M. Indrajit, and P. Mahanti, “Cloud computing initiative using modified ant colony framework,” in In the World Academy of Science, Engineering and Technology 56, 2009.

[8] R.Buyya, C.S. Yeo, and S.Venugopal, “Market-Oriented Cloud Computing:Vision, Hype, and Reality for Delivering IT Services as Computing Utilities”, Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications (HPCC-08), Sep. 2008.

[9] G.Wei, A.V. Vasilakos, Y.Zheng, and N.Xiong, “A game-theoretic method of fair resource allocation for cloud computing services”, The journal of supercomputing, Vol.54, Issue 2, Nov. 2010.

[10] Yazir, Y.O., Matthews, C., Farahbod, R., Neville, S., Guitouni, A., Ganti, S., and Coady, Y., “Dynamic Resource Allocation in Computing Clouds through Distributed Multiple Criteria Decision Analysis”, 2010 IEEE 3rd Internatiuonal Conference on Cloud Computing (CLOUD 2010), July 2010.

[11] B.Malet and P.Pietzuch, “Resource Allocation across Multiple Cloud Data Centres”, 8th International workshop on Middleware for Grids, Clouds and e-Science. (MGC’10), Nov. 2010.

[12] B. Rajkumar, B. Anton, and A. Jemal, “Energy efficient management of data center resources for computing: Vision, architectural elements and open challenges,” in International Conference on Parallel and Distributed Processing Techniques and Applications, July 2010.

[13] M. Mazzucco, D. Dyachuk, and R. Deters, “Maximizing Cloud Providers’ Revenues via Energy Aware Allocation Policies,” in 2010 IEEE 3rd International Conference on Cloud Computing. IEEE, 2010.

[14] M.Graiet, A.Mammar, S.Boubaker, and W.Gaaloul,”Towards Correct Cloud Resource Allocation in Business Processes,“IEEE Transactions on Services Computing, Vol.10, Issue1, pp.23-36, July 2016.

[15] P.S. Pillai and S.Rao, “Resource Allocation in Cloud Computing Using the Uncertainty Principle of Game Theory,” IEEE Systems Journal Vol. 10, Issue 2, June 2016.

[16] E.I.Nehru, J.I.S.Shyni, R.Balakrishnan, “Auction based dynamic resource allocation in cloud,”International Conference on Circuit, Power and Computing Technologies (ICCPCT 2016), Mar. 2016.

[17] Shin-ichi Kuribayashi, “Proposed congestion control method for cloud computing environments”, International Journal of Computer Networks & Communications (IJCNC) Vol.3, No.5, pp.161-176, Sep 2011.

[18] S.Tsumura and S.Kuribayashi, “Delayed resource allocation method for a joint multiple resource management system”, APCC2007, TPM2-3 (2007.10)

[19] Takahiro Yoshino and Shin-ichi Kuribayashi, “Evaluation of congestion control methods for joint multiple resource allocation”, Proceeding of the 13-th International Conference on Network-Based Information Systems (NBiS-2010), pp.94-97, Sep. 2010.

[20] S.Kuribayashi, “Resource Allocation Method for Cloud Computing Environments with Different Service Quality to Users at Multiple Access”, International Journal of Computer Networks & Communications (IJCNC) Vol.7, No.6, pp.33-51, Nov.2015.

AUTHOR

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Shin- ichi Kuribayashi received the B.E., M.E., and D.E. degrees from Tohoku University, Japan, in 1978, 1980, and 1988 respectively. He joined NTT Electrical Communications Labs in 1980. He has been engaged in the design and development of DDX and ISDN packet switching, ATM, PHS, and IMT 2000 and IP-VPN systems. He researched distributed communication systems at Stanford University from December 1988 through December 1989. He participated in international standardization on ATM signaling and IMT2000 signaling protocols at ITU-T SG11 from 1990 through 2000.Since April 2004, he has been a Professor in the Department of Computer and Information Science, Faculty of Science and Technology, Seikei University. His research interests include optimal resource management, QoS control, traffic control for cloud computing environments and green network. He is a member of IEEE, IEICE and IPSJ.

International Journal of Computer Networks & Communications (IJCNC)

(Scopus, ERA Listed)

ISSN 0974 – 9322 (Online); 0975 – 2293 (Print)

http://airccse.org/journal/ijcnc.html