Resource management for bursty streams on multi-tenancy cloud environments

Rafael Tolosana-Calasanz, José Ángel Bañares, Congduc Pham, Omer F. Rana
2016 Future generations computer systems  
h i g h l i g h t s • We provide a system for simultaneous bursty data streams on shared Clouds. • We enforce QoS based on a profit-based resource management model. • We provide real experiments within an OpenNebula based data centre. a b s t r a c t The number of applications that need to process data continuously over long periods of time has increased significantly over recent years. The emerging Internet of Things and Smart Cities scenarios also confirm the requirement for real time, large
more » ... cale data processing. When data from multiple sources are processed over a shared distributed computing infrastructure, it is necessary to provide some Quality of Service (QoS) guarantees for each data stream, specified in a Service Level Agreement (SLA). SLAs identify the price that a user must pay to achieve the required QoS, and the penalty that the provider will pay the user in case of QoS violation. Assuming maximization of revenue as a Cloud provider's objective, then it must decide which streams to accept for storage and analysis; and how many resources to allocate for each stream. When the real-time requirements demand a rapid reaction, dynamic resource provisioning policies and mechanisms may not be useful, since the delays and overheads incurred might be too high. Alternatively, idle resources that were initially allocated for other streams could be re-allocated, avoiding subsequent penalties. In this paper, we propose a system architecture for supporting QoS for concurrent data streams to be composed of self-regulating nodes. Each node features an envelope process for regulating and controlling data access and a resource manager to enable resource allocation, and selective SLA violations, while maximizing revenue. Our resource manager, based on a shared token bucket, enables: (i) the redistribution of unused resources amongst data streams; and (ii) a dynamic re-allocation of resources to streams likely to generate greater profit for the provider. We extend previous work by providing a Petrinet based model of system components, and we evaluate our approach on an OpenNebula-based Cloud infrastructure.
doi:10.1016/j.future.2015.03.012 fatcat:pqa6xm2c3bafllmw7r3s3hd2j4