Predictive Dynamic Algorithm: An Approach toward QoS-Aware Service for IoT-Cloud Environment

This paper presents an approach to provide quality of service for IoT-Cloud users through a dynamic virtual machine allocation algorithm on a Metascheduler. Although there are others reactive approach to handle with general cases of virtual machine allocation and placement, they may have not optimal performance for IoT workload, because it aggregates a new characteristic of large bursts of events, but with short service time. For this, in this paper, a predictive dynamic algorithm from our Metascheduler is outlined which can provide QoS. Our approach combines several informations to build a operating virtual machines to perform the services from IoT gateways devices deployed in the field, that conform to the constraints imposed by QoS with regards to costs and deadlines. As a part of the results obtained from the planning design, our experiments on evaluation performance show the value of this algorithm in meeting the requirements time e cost to the IoT-users, while also saving money and computer resources.

[1]  D. Bernstein,et al.  An Intercloud Cloud Computing Economy - Technology, Governance, and Market Blueprints , 2011, 2011 Annual SRII Global Conference.

[2]  Young-Sik Jeong,et al.  Achieving Quality of Service (QoS) Using Resource Allocation and Adaptive Scheduling in Cloud Computing with Grid Support , 2014, Comput. J..

[3]  Deepesh Kumar,et al.  A survey on resource allocation techniques in cloud computing , 2015, International Conference on Computing, Communication & Automation.

[4]  Qian Zhu,et al.  Resource Provisioning with Budget Constraints for Adaptive Applications in Cloud Environments , 2010, IEEE Transactions on Services Computing.

[5]  Mahmoud Al-Ayyoub,et al.  Towards improving resource management in cloud systems using a multi-agent framework , 2016, Int. J. Cloud Comput..

[6]  Antonio Pescapè,et al.  On the Integration of Cloud Computing and Internet of Things , 2014, 2014 International Conference on Future Internet of Things and Cloud.

[7]  Moustafa Ghanem,et al.  Future Generation Computer Systems ( ) – Future Generation Computer Systems Enabling Cost-aware and Adaptive Elasticity of Multi-tier Cloud Applications , 2022 .

[8]  Mohammad Fairus Khalid,et al.  A survey on SLA-based brokering for inter-cloud computing , 2015, 2015 Second International Conference on Computing Technology and Information Management (ICCTIM).

[9]  Antonio Pescapè,et al.  Integration of Cloud computing and Internet of Things: A survey , 2016, Future Gener. Comput. Syst..

[10]  Marcos José Santana,et al.  A P2P Hierarchical Metascheduler to Obtain QoS in a Grid Economy Services , 2009, 2009 International Conference on Computational Science and Engineering.

[11]  Klaus Wehrle,et al.  A comprehensive approach to privacy in the cloud-based Internet of Things , 2016, Future Gener. Comput. Syst..

[12]  Weidong Hu,et al.  Sleep–wake up scheduling with probabilistic coverage model in sensor networks1 , 2014, Int. J. Parallel Emergent Distributed Syst..

[13]  Marcos José Santana,et al.  P2P routing in the metascheduler architecture to provide QoS in cloud computing , 2015 .

[14]  Walid Osamy,et al.  Efficient Compressive Sensing based Technique for Routing in Wireless Sensor Networks , 2015 .

[15]  Yahya Slimani,et al.  Load Balancing Approach for QoS Management of Multi-instance Applications in Clouds , 2013, 2013 International Conference on Cloud Computing and Big Data.

[16]  Jie Wu,et al.  Virtual machine placement in cloud systems through migration process , 2015, Int. J. Parallel Emergent Distributed Syst..

[17]  Laurent Lefèvre,et al.  The Green Grid’5000: Instrumenting and Using a Grid with Energy Sensors , 2012 .

[18]  Peter J. Varman,et al.  Defragmenting the cloud using demand-based resource allocation , 2013, SIGMETRICS '13.

[19]  Zhoujun Li,et al.  An Integrated Approach to Automatic Management of Virtualized Resources in Cloud Environments , 2011, Comput. J..

[20]  Marcos José Santana,et al.  A Metascheduler architecture to provide QoS on the cloud computing , 2010, 2010 17th International Conference on Telecommunications.

[21]  Marcos José Santana,et al.  Performance Evaluation of Resource Management in Cloud Computing Environments , 2015, PloS one.

[22]  Raj Jain,et al.  The art of computer systems performance analysis - techniques for experimental design, measurement, simulation, and modeling , 1991, Wiley professional computing.

[23]  Jing Zhang,et al.  The placement method of resources and applications based on request prediction in cloud data center , 2014, Inf. Sci..

[24]  Raouf Boutaba,et al.  Dynamic workload management in heterogeneous Cloud computing environments , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).

[25]  Xiaoyun Zhu,et al.  Utilization and SLO-Based Control for Dynamic Sizing of Resource Partitions , 2005, DSOM.

[26]  Farookh Khadeer Hussain,et al.  An online fuzzy Decision Support System for Resource Management in cloud environments , 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).

[27]  Raouf Boutaba,et al.  Cloud Architectures, Networks, Services, and Management , 2015 .

[28]  Timo Ojala,et al.  CloudThings: A common architecture for integrating the Internet of Things with Cloud Computing , 2013, Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[29]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[30]  Qunying Huang,et al.  A Service Brokering and Recommendation Mechanism for Better Selecting Cloud Services , 2014, PloS one.

[31]  Helen D. Karatza,et al.  Towards scheduling for Internet‐of‐Things applications on clouds: a simulated annealing approach , 2015, Concurr. Comput. Pract. Exp..

[32]  Gabor Kecskemeti,et al.  An interoperable and self-adaptive approach for SLA-based service virtualization in heterogeneous Cloud environments , 2014, Future Gener. Comput. Syst..

[33]  Liana L. Fong,et al.  Grid broker selection strategies using aggregated resource information , 2010, Future Gener. Comput. Syst..

[34]  Sunilkumar S. Manvi,et al.  Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey , 2014, J. Netw. Comput. Appl..