Middleware: a model for distributed system services
Communications of the ACM
Utility computing SLA management based upon business objectives
IBM Systems Journal
Managing clouds: a case for a fresh look at large unreliable dynamic networks
ACM SIGOPS Operating Systems Review
On the Use of Fuzzy Modeling in Virtualized Data Center Management
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Future Generation Computer Systems
Exploiting semantics and virtualization for SLA-driven resource allocation in service providers
Concurrency and Computation: Practice & Experience
SLA-driven Elastic Cloud Hosting Provider
PDP '10 Proceedings of the 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing
Profit-Driven Service Request Scheduling in Clouds
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Resource-level QoS metric for CPU-based guarantees in cloud providers
GECON'10 Proceedings of the 7th international conference on Economics of grids, clouds, systems, and services
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
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With the advent of gaining popularity in cloud computing, there is an increase in demand of resources among the heterogeneous workload types on cloud. Resource management is a key challenging problem that is faced by the cloud service providers, by achieving business goals and agreed level of service with the subscribers. This paper focuses on dynamic resource allocation with risk analysis by meeting service level agreements. Further proposed framework handles heterogeneous workload types by dynamic capacity planning with risk assessment to maximise the profit and resource utilisation on clouds. In addition to advanced resource reservation, SLA-based scheduling/rescheduling with risks involved in resource allocation is considered in the proposed model. The experimental results demonstrate that proposed framework maximises the resource utilisation and profit gain of the cloud service provider when evaluated against widely used static configuration strategy.