With Inter-Cloud, distributed cloud and open cloud exchange (OCX) emerging, a comprehensive resource allocation approach is fundamental to highly competitive cloud market. Oriented to infrastructure as a service (IaaS), an intelligent economic approach for dynamic resource allocation (IEDA) is proposed with the improved combinatorial double auction protocol devised to enable various kinds of resources traded among multiple consumers and multiple providers at the same time enable task partitioning among multiple providers. To make bidding and asking reasonable in each round of the auction and determine eligible transaction relationship among providers and consumers, a price formation mechanism is proposed, which is consisted of a back propagation neural network (BPNN) based price prediction algorithm and a price matching algorithm. A reputation system is proposed and integrated to exclude dishonest participants from the cloud market. The winner determination problem (WDP) is solved by the improved paddy field algorithm (PFA). Simulation results have shown that IEDA can not only help maximize market surplus and surplus strength but also encourage participants to be honest.

An Intelligent Economic Approach for Dynamic Resource Allocation in Cloud Services

Cloud computing is a new computing paradigm which features renting the computation devices instead of buying them. In a typical cloud computing environment, there will always be different kinds of cloud resources and a number of cloud services making use of cloud resources to run on. As we can see, these cloud services usually have different performance traits. Some may be I/O-intensive, like those data querying services, while others might demand more CPU cycles, like 3D image processing services. Meanwhile, cloud resources also have different kinds of capabilities such as data processing, I/O throughput, 3D image rendering, etc. A simple fact is that allocating a suitable resource will greatly improve the performance of the cloud service, and make the cloud resource itself more efficient as well. In this paper, a new cloud resource allocating algorithm via fitness-enabled auction is proposed to guarantee the fitness of performance traits between cloud resources (sellers) and cloud services (buyers). We study the allocating algorithm in terms of economic efficiency and system performance, and experiments show that the allocation is far more efficient in comparison with the continuous double auction in which the idea of fitness is not introduced.

Performance-Aware Cloud Resource Allocation via Fitness-Enabled Auction

Cloud computing is one of the fastest emerging technology. As we all know, cloud is a wide pool of resource which provides resources based on the users request. Any service can be provided as a service through cloud. As the cloud contains many resources, there may also wastage of resources. To reduce this wastage, cloud providers enter into auctioning of resources when the demand is high. Cloud computing includes distinct resources. Because of the complementary and supplementary effects between distinct assets, bidders have preferences not for just a single resource but also for a set of resources. Auctioning for a bundle of resources is called combinatorial auction. Dynamic resource allocation in on demand for a bundle of resource is proposed by using CA — PROVISION algorithm and the scenarios are simulated using Cloudsim, a simulator meant for cloud computing analysis.

Auction based dynamic resource allocation in cloud

As a new model of distributed computing, all kinds of distributed resources are virtualized to establish a shared resource pool through cloud computing. The target of cloud computing is to provide convenient and configurable resource for users with pay-per-usage charging model. Therefore, the reasonable and efficient mechanism for resource allocating is becoming a hot spot in research. According to the features of cloud resource allocation, methods of auction model, neural network and intelligent optimization are comprehensively applied in this paper for proposing a double multi-attribute auction based cloud resource allocation mechanism. In the mechanism, non-price attributes like quality of experience, level of delivery, level of payment and level of spiteful quote are described for better satisfying the requirements of users, and these attributes are transferred to an index of quality through BP neural network. Based on the history information of auction, support vector machine algorithm is utilized to predict the price in advance. In the end, using satisfaction extracted from index of quality and price as optimization goal, wind driven optimization algorithm is adopted to get the optimized allocation scheme. Simulation results have shown that the mechanism is feasible and effective.

A Cloud Resource Allocation Scheme Based on Microeconomics and Wind Driven Optimization

Double auction-based mechanisms have gained considerable attention as autonomous cloud service negotiation because it has been proven to be economically efficient and possesses the ability to accommodate multiple buyers and sellers. The main objective of this paper was to outline the limitations of the practically tested double auction mechanisms in cloud service negotiations; and subsequently suggest future vision. Ten practically proven double auction-based mechanisms have been surveyed from the research database, whereby most of them were found to employ the double auction protocol and two variations of the double auction protocol, namely continuous double auction and combinatorial double auction. Comparisons on the types, objectives, parameters, and results between the selected mechanisms are presented in this paper. Moreover, it was identified that the surveyed double auction mechanisms were unable to address multi-attributes heterogeneous cloud services because it required a long execution time. Nevertheless, future vision managed to be suggested to solve these issues.

A Survey and Future Vision of Double Auctions-Based Autonomous Cloud Service Negotiations

Cloud computing resource is distributed, heterogeneous and indeterminate. For these resources, how to reasonably manage these complicated resources and allocate these resources for users quickly will influence the efficiency of cloud computing. In this paper, we propose a resource allocation method based on multi-unit continuous double auction mechanism and corresponding pricing strategy. Firstly, we create a continuous double auction based resource allocation model which contains three agents. Then we improve the existing double auction pricing algorithm, and introduce new parameters in order to obtain the pricing algorithm that is suitable for cloud environment. In each time period, user agents and cloud node agents determine their bid values and request values respectively. This method can take into account both the requirement of users and cloud nodes, and provide fair chance for the users to achieve resources and the cloud nodes to supply resources.

Multi-unit continuous double auction based resource allocation method

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Multi-unit continuous double auction based resource allocation method