Truthful Resourse Sheduling in Cloud Environment
Abstract
on the management of allotment of virtual machines to the
physical machines. Earlier systems resource scheduler is executed without taking a consideration of a user specification,
infrastructure property and cloud resources, so that system
results in a security issues and confidentiality. In This System
we propose a cloud Scheduler which is taking a consideration
of user requirement and infrastructure properties. This system
target to assure a user to allocate virtual resources to physical
machines as per the users demand but exclusive of disclosure
of information of cloud infrastructure and concerning of a user.
Virtualization Technology is used to allocates a data center assets
with dynamism and its purely depends upon the application
requirements and supports green computing by optimizing the
number of servers in use. We are forming a resource allocation
system that can avoid overload in the system effectively while
minimizing the number of servers used. In this paper we are
going to introduce the idea to measure the odd utilization of
server that can able to improve the global utilization of servers
in the face of multidimensional resource constraints.
Full Text:
PDFReferences
Y. Li, W. Dai, Z. Ming, and M. Qiu, Privacy protection for preventing
data over-collection in smart city, IEEE Transactions on Computers vol.
, no. 5, pp. 1339 1350, 2015.
M. Qiu, L. Chen, Y. Zhu, J. Hu, and X. Qin, Online data allocation for
hybrid memories on embedded tele-health systems, in 2014 IEEE 11th
Intl Conf on Embedded Software and Syst (ICESS), Aug 2014, pp. 574
M. Qiu and E. H.-M. Sha, Cost minimization while satisfying hard/soft
timing constraints for heterogeneous embedded systems, ACM Transactions on Design Automation of Electronic Systems, vol. 14, no. 2, p. 25,
J. Niu, C. Liu, Y. Gao, and M. Qiu, Energy efficient task assignment
with guaranteed probability satisfying timing constraints for embedded
systems, IEEE Transactions on Parallel and Distributed Systems, vol. 25,
no. 8, pp. 2043 2052, 2014.
J. Li, Z. Ming, M. Qiu, G. Quan, X. Qin, and T. Chen, Resource allocation
robustness in multi-core embedded systems with inaccurate information,
Journal of Systems Architecture, vol. 57, no. 9, pp. 840 849, 2011.
A. Beloglazov and R. Buyya, Optimal online deterministic algorithms
and adaptive heuristics for energy and performance efficient dynamic
consolidation of virtual machines in cloud data centers, Concurrency and
Computation: Practice and Experience, vol. 24, no. 13, pp. 1397 1420,
T. Sandholm, J. Ward, F. Balestrieri, and B. A. Huberman, Qos-based
pricing and scheduling of batch jobs in openstack clouds, arXiv preprint
arXiv:1504.07283, 2015..
W. Dai, H. Chen, and W. Wang, Rahec: A mechanism of resource
management for heterogeneous clouds, in 2015 IEEE 17th International Conference on High Performance Computing and Communications
(HPCC), Aug 2015, pp. 40 45.
M. Qiu, Y. Jiang, and W. Dai, Cost minimization for heterogeneous
systems with gaussian distribution execution time, in 2015 IEEE 17th
International Conference on High Performance Computing and Communications. IEEE, 2015, pp. 547 552..
B. Sotomayor, R. S. Montero, I. M. Llorente, and I. Foster, Virtual
infrastructure management in private and hybrid clouds, IEEE Internet
computing, vol. 13, no. 5, pp. 14 22, 2009.
X. Xu, W. Dou, X. Zhang, and J. Chen, Enreal: An energy-aware
resource allocation method for scientific workflow executions in cloud
environment.
K. Deb, M. Abouhawwash, and J. Dutta, An optimality theory based
proximity measure for evolutionary multi-objective and many-objective
optimization, in Evolutionary Multi-Criterion Optimization. Springer,
, pp. 18 33.
M. Marinaki, Y. Marinakis, and G. E. Stavroulakis, Fuzzy control optimized by a multi-objective differential evolution algorithm for vibration
suppression of smart structures, Computers Structures, vol. 147, pp. 126
, 2015.
D. Villegas, N. Bobroff, I. Rodero, J. Delgado, Y. Liu, A. Devarakonda,
L. Fong, S. Masoud Sadjadi, and M. Parashar, Cloud federation in a
layered service model, Journal of Computer and System Sciences, vol.
, no. 5, pp. 1330 1344, Sep. 2012.
C. C. Tutum and K. Deb, A multimodal approach for evolutionary multiobjective optimization (memo): Proof-of-principle results, in Evolutionary
Multi-Criterion Optimization. Springer, 2015, pp. 318.
K. Deb, M. Abouhawwash, and J. Dutta, An optimality theory based
proximity measure for evolutionary multi-objective and many-objective
optimization,in Evolutionary Multi-Criterion Optimization. Springer,
, pp. 18 33.
M. Marinaki, Y. Marinakis, and G. E. Stavroulakis, Fuzzy control optimized by a multi-objective differential evolution algorithm for vibration
suppression of smart structures, Computers Structures, vol. 147, pp. 126
, 2015.
J. Li, M. Qiu, J.-W. Niu, L. T. Yang, Y. Zhu, and Z. Ming, Thermalaware task scheduling in 3d chip multiprocessor with real-time constrained workloads, ACM Transactions on Embedded Computing Systems
(TECS), vol. 12, no. 2, p. 24, 2013.
W. Wang, D. Peng, H. Wang, H. Sharif, and H.-H. Chen, A multimedia
quality-driven network resource management architecture for wireless
sensor networks with stream authentication, IEEE Transactions on Multimedia, vol. 12, no. 5, pp. 439 447, 2010.
S. Frey, F. Fittkau, and W. Hasselbring, Search-based genetic optimization for deployment and reconfiguration of software in the cloud, in 2013
International Conference on Software Engineering. IEEE Press, 2013, pp.
521.
M. A. Rodriguez and R. Buyya, Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds,
Transactions on Cloud Computing, vol. 2, no. 2, pp. 222 235, 2014.
J. Li, M. Woodside, J. Chinneck, and M. Litoiu, Cloudopt: Multi-goal
optimization of application deployments across a cloud, in 2011 7th
International Conference on Network and Service Management, Paris,
France, Oct 2011, pp. 19.
R. Buyya, R. Ranjan, and R. N. Calheiros, Intercloud: Utility-oriented
federation of cloud computing environments for scaling of application
services, in Algorithms and architectures for parallel processing. Springer,
, pp. 13 31.
J. Li, M. Qiu, Z. Ming, G. Quan, X. Qin, and Z. Gu, Online optimization
for scheduling preemptable tasks on iaas cloud systems, Journal of
Parallel and Distributed Computing, vol. 72, no. 5, pp. 666 677, 2012.
S. Khatua, A. Ghosh, and N. Mukherjee, Optimizing the utilization of
virtual resources in cloud environment, in IEEE International Conference
on Virtual Environments Human-Computer Interfaces and Measurement
Systems, Taranto, Italy, Sept 2010, pp. 82 87.
W. Dai, H. Chen, W. Wang, and X. Chen, RMORM: A framework
of multi-objective optimization resource management in clouds, in IEEE
Ninth World Congress on Services, 2013, pp. 488 494.
S. A. Baset, Cloud SLAs: present and future, ACM SIGOPS Operating
Systems Review, vol. 46, no. 2, pp. 57 66, 2012.
H. Goudarzi, M. Ghasemazar, and M. Pedram, SLA-based optimization
of power and migration cost in cloud computing, in 2012 12th IEEE/ACM
International Symposium on Cluster, Cloud and Grid Computing. IEEE,
, pp. 172 179.
A. Undheim, A. Chilwan, and P. Heegaard, Differentiated availability in
cloud computing SLAs, in 2011 12th IEEE/ACM International Conference on Grid Computing. IEEE, 2011, pp. 129 136.
A. Beloglazov, J. Abawajy, and R. Buyya, Energy-aware resource
allocation heuristics for efficient management of data centers for cloud
computing, Future generation computer systems, vol. 28, no. 5, pp. 7 768,
Y. C. Lee and A. Y. Zomaya, Energy efficient utilization of resources in
cloud computing systems, The Journal of Supercomputing, vol. 60, no.
, pp. 268 280, 2012.
K. Gai, M. Qiu, H. Zhao, L. Tao, and Z. Zong, Dynamic energy-aware
cloudlet-based mobile cloud computing model for green computing,
Journal of Network and Computer Applications, vol. 59, pp. 46 54, 2015.
Refbacks
- There are currently no refbacks.
Copyright © IJETT, International Journal on Emerging Trends in Technology