Speaker Di Niu
Affiliation University of Alberta
Host Jin Li
Date recorded 29 March 2013
A fundamental tradeoff in cloud computing lies between workload consolidation and performance. While the cloud can reduce cost by serving clients using pooled resources, resource and network sharing among clients may lead to performance problems. Two economic issues are related to resource sharing : 1) How can the cloud reduce its operational cost through resource multiplexing while providing performance guarantees in face of varying demands? 2) How should the cloud competitively and fairly price its guaranteed services in the presence of resource sharing? To answer these questions, I will present two case studies. First, we discuss cloud network reservation and pricing for a large number of QoS-sensitive clients. We formulate the problem as network utility maximization (NUM) with a coupled objective function, and propose a new distributed solution that proves to be more efficient and scalable than the traditional method of dual decomposition in practice. We also show that the optimal prices form a Nash Equilibrium and depend not only on the usage but also on workload patterns. In the second case study, we propose a cloud brokerage service that books a large pool of reserved instances from IaaS clouds and serves clients with price discounts. The broker exploits both the pricing gap of reserved and on-demand instances and the gain from time-multiplexing partially used instance-hours. We propose strategies for the broker to make dynamic instance reservation decisions, which can efficiently handle a large volume of client demand estimates. Our studies are verified by 400 GB of traces collected from a commercial video delivery service called UUSee and 180 GB of Google cluster usage traces.
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