Performance Guarantees for Web Server End-Systems: A Control-Theoretical Approach
IEEE Transactions on Parallel and Distributed Systems
Homeostatic and Tendency-Based CPU Load Predictions
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Time Series Prediction using Adaptive Association Rules
DFMA '05 Proceedings of the First International Conference on Distributed Frameworks for Multimedia Applications
Resource Allocation for Autonomic Data Centers using Analytic Performance Models
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Adaptive control of virtualized resources in utility computing environments
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Heuristic for resources allocation on utility computing infrastructures
Proceedings of the 6th international workshop on Middleware for grid computing
Automated control in cloud computing: challenges and opportunities
ACDC '09 Proceedings of the 1st workshop on Automated control for datacenters and clouds
Autonomic virtual resource management for service hosting platforms
CLOUD '09 Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing
Dynamic resource allocation for shared data centers using online measurements
IWQoS'03 Proceedings of the 11th international conference on Quality of service
Forecasting for Grid and Cloud Computing On-Demand Resources Based on Pattern Matching
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
Pattern Matching Based Forecast of Non-periodic Repetitive Behavior for Cloud Clients
Journal of Grid Computing
A new model for allocating resources to scheduled lightpath demands
Computer Networks: The International Journal of Computer and Telecommunications Networking
Resource management framework for collaborative computing systems over multiple virtual machines
Service Oriented Computing and Applications
Utilization and SLO-Based control for dynamic sizing of resource partitions
DSOM'05 Proceedings of the 16th IFIP/IEEE Ambient Networks international conference on Distributed Systems: operations and Management
Optimal resource provisioning for cloud computing environment
The Journal of Supercomputing
Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds
Future Generation Computer Systems
An adaptive resource management scheme in cloud computing
Engineering Applications of Artificial Intelligence
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Cloud computing has become an innovative computing paradigm, which aims at providing reliable, customized, Quality of Service QoS and guaranteed computing infrastructures for users. Efficient resource provisioning is required in cloud for effective resource utilization. For resource provisioning, cloud provides virtualized computing resources that are dynamically scalable. This property of cloud differentiates it from the traditional computing paradigm. But the initialization of a new virtual instance causes a several minutes delay in the hardware resource allocation. Furthermore, cloud provides a fault tolerant service to its clients using the virtualization. But, in order to attain higher resource utilization over this technology, a technique or a strategy is needed using which virtual machines can be deployed over physical machines by predicting its need in advance so that the delay can be avoided. To address these issues, a value based prediction model in this paper is proposed for resource provisioning in which a resource manager is used for dynamically allocating or releasing a virtual machine depending upon the resource usage rate. In order to know the recent resource usage rate, the resource manager uses sliding window to analyze the resource usage rate and to predict the system behavior in advance. By predicting the resource requirements in advance, a lot of processing time can be saved. Earlier, a server has to perform all the calculations regarding the resource usage that in turn wastes a lot of processing power thus decreasing its overall capacity to handle the incoming request. The main feature of the proposed model is that a lot of load is being shifted from the individual server to the resource manager as it performs all the calculations and therefore the server is free to handle the incoming requests to its full capacity.