Abstract. This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3 rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.

Existing Grid meta-schedulers such as GridWay either target system-centric metrics, (such as utilization or throughput), or prioritize applications based on utility functions provided by the users. The system centric approach gives less importance to users ’ individual utility, while the user centric may have adverse effects such as poor system performance and unfair treatment of users. Therefore, this paper proposes a novel Continuous Double Auction (CDA)-based meta-scheduler mechanism that schedules parallel applications providing improvements that benefit both users and resources in terms of their effective utilization. We have designed valuation metrics for both user applications and resources that commodify the complex resource requirements of the users and the capabilities of available computational resources. We analytically model the meta-scheduling problem in Grid using queuing theory and compare our heuristic with the analytical model results to ensure its correctness. Then, through simulation, we compare our scheduling mechanism with other common mechanisms widely used by both existing market-based and traditional meta-schedulers. The results show that our meta-scheduling mechanism not only satisfies more user requirements than others, but also improves system utilization through load balancing.