Negotiation for resource allocation on infrastructure as a service cloud

Abstract

The Cloud is a computing platform that provides dynamic resource pools, virtualization, and high availability. Cloud computing infrastructures can allow enterprises to achieve more efficient use of their IT hardware and software investments. Infrastructure As A Service (IAAS) cloud providers manage a large set of computing resources. These resources can be provided to cloud consumers on demand in the form of virtual machines. Cloud consumers do not need to manage resources and be worried about the performance issues because they are handled by cloud providers. Open Nebula is an open source cloud toolkit which can be used to setup an IAAS cloud. It has three components: Open Nebula Core, Virtual Machine Scheduler and Cloud Drivers. Haizea is an open-source resource lease manager, and can act as a virtual machine scheduler for Open Nebula or used on its own as a simulator to evaluate the performance of different scheduling strategies. Haizea supports four kinds of resource allocation policies: immediate, best-effort, advance reservation and deadline sensitive. To reserve resources in advance using Haizea, consumer submits parameters like amount of resources, start time and duration of a reservation as a request. If one or more parameters can not be satisfied, then Haizea will reject the request. This method is very rigid method because it does not allow negotiation of any parameter. Consumer can resubmit new requests by modifying previously submitted request parameters. Consumer will not be aware of the current resource allocation on provider side so, the chances of new requests getting rejected are more. Thus, it will increase communication overhead between cloud provider and consumer as well as it will decrease resource utilization on provider’s side. It will also degrade the performance of a provider in managing many incoming requests due to previously rejected ones. To overcome the above problems, negotiation can be provided. Negotiation process consists of three components which are negotiation protocol, negotiation objectives and agents’ decision making algorithm. The proposed algorithm to generate set of counter offers is a part of decision making model at provider side. It provides set of counter offers to consumer when his advance reservation request gets rejected. It provides set of counter offers considering parameters’ flexibilities to maximize the chances of their acceptance. The proposed algorithm for User selection policy is a part of decision making model at consumer side. Consumer can get best suitable offer from set of counter offers using the algorithm of user selection policy. Ranking algorithm is a partof algorithm for user selection policy. Using this ranking algorithm, consumers will get suitable offers sorted according to their needs. It will reduce consumers’ efforts to go through all the provided counter offers and choose best suitable one. These algorithms are implemented in Haizea. Experiments are performed to demonstrate the effectiveness of algorithms. The results show that the proposed algorithm to generate counter offers maximizes resource utilization and acceptance of requests compared to rigid and exact methods.