On Mas-Based, Scalable Resource Allocation In Large-Scale, Dynamic Environments

The cloud landscape is becoming increasingly diverse, with more businesses and E-science moving their services to the cloud and more resource providers entering the market to meet these growing demands. Resource allocation is a central component of this landscape. Commonly used fixed-price allocation is inefficient due to its inflexible nature. Novel approaches, using market mechanisms such as auctions, can be employed for more efficient allocation and pricing. However, combinatorial auctions are inherently NP-hard and centralised. Scaling the resource allocation problem is therefore a real challenge.In this paper, we investigate the scalability and efficiency of resource allocation in large-scale, dynamic environments by using a decentralised, market-driven approach. We design and implement a generic multi-agent simulator for trading resources. We then extensively evaluate a greedy approach for solving two-sided combinatorial auctions. We show how this approach scales when distributing the allocation task over multiple agents and propose different methods to improve scalability. Moreover, we analyse the scalability of different use cases and present a use case where the centralised algorithm performs poorly. Finally, we show that a deep understanding of centralised algorithms for resource allocation allows us to mix-and-match different algorithms to increase efficiency in the decentralised approach.