Abstract Maximizing resource utilization by performing an efficient resource provisioning is a key factor for any cloud provider: commercial actors can maximize their revenues, whereas scientific and non-commercial providers can maximize their infrastructure utilization. Traditionally, batch systems have allowed data centers to fill their resources as much as possible by using backfilling and similar techniques. However, in an IaaS cloud, where virtual machines are supposed to live indefinitely, or at least as long as the user is able to pay for them, these policies are not easily implementable. In this work we present a new scheduling algorithm for IaaS providers that is able to support preemptible instances, that can be stopped by higher priority requests without introducing large modifications in the current cloud schedulers. This scheduler enables the implementation of new cloud usage and payment models that allow more efficient usage of the resources and potential new revenue sources for commercial providers. We also study the correctness and the performance overhead of the proposed scheduler against existing solutions.
Nadjaran have developed a Spot Instances as a Service (SIPaaS) framework, a set of web services that makes possible to run a Spot market on top of an OpenStack cloud @cite_24 . However, even if this framework aims to deliver preemptible instances on OpenStack cloud, it is designed to utilize normal resources to provide this functionality. SIPaaS utilizes normal resources to create the Spot market that is provided to the users by means of a thin layer on top of a given OpenStack, providing a different API to interact with the resources. From the CMF point of view, all resources are of the same type, being SIPaaS the responsible of handling them, in different ways. In contrast, our work leverages two different kind of instances at the CMF level, performing different scheduling strategies depending on which kind of resource it is being requested. SIPaaS also delivers a price market similar to the Amazon EC2 Spot Instances market, therefore they also provide the Ex-CORE auction algorithm @cite_5 in order to govern the price fluctuations.
@cite_24 @cite_5
2483807727 2099393515 2065752649 1552783944
Designing dynamic pricing mechanisms that efficiently price resources in line with a provider's profit maximization goal is a key challenge in cloud computing environments. Despite the large volume of research published on this topic, there is no publicly available software system implementing dynamic pricing for Infrastructure as a Service cloud spot markets. This paper presents the implementation of a framework called Spot instance pricing as a Service SipaaS that supports an auction mechanism to price and allocate virtual machine instances. SipaaS is an open-source project offering a set of web services to price and sell virtual machine instances in a spot market resembling the Amazon EC2 spot instances. Cloud providers, who aim at utilizing SipaaS, should install add-ons in their existing platform to make use of the framework. As an instance, we provide an extension to the Horizon - the OpenStack dashboard project - to employ SipaaS web services and to add a spot market environment to OpenStack. To validate and evaluate the system, we conducted an experimental study with a group of 10 users utilizing the provided spot market in a real environment. Results show that the system performs reliably in a practical test environment. Copyright © 2015 John Wiley & Sons, Ltd. Amazon introduced Spot Instance Market to utilize the idle resources of Amazon Elastic Compute Cloud (EC2) more efficiently. The price of a spot instance changes dynamically according to the current supply and demand for cloud resources. Users can bid for a spot instance and the job request will be granted if the current spot price falls below the bid, whereas the job will be terminated if the spot price exceeds the bid. In this paper, we investigate the problem of designing a bidding strategy from a cloud service broker's perspective, where the cloud service broker accepts job requests from cloud users, and leverages the opportunistic yet less expensive spot instances for computation in order to maximize its own profit. In this context, we propose a profit aware dynamic bidding (PADB) algorithm, which observes the current spot price and selects the bid adaptively to maximize the time average profit of the cloud service broker. We show that our bidding strategy achieves a near-optimal solution, i.e., (1−∈) of the optimal solution to the profit maximization problem, where ∈ can be arbitrarily small. The proposed dynamic bidding algorithm is self-adaptive and requires no a priori statistical knowledge on the distribution of random job sizes from cloud users. Cloud resources are usually priced in multiple markets with different service guarantees. For example, Amazon EC2 prices virtual instances under three pricing schemes -- the subscription option (a.k.a., Reserved Instances), the pay-as-you-go offer (a.k.a., On-Demand Instances), and an auction-like spot market (a.k.a., Spot Instances) -- simultaneously. There arises a new problem of capacity segmentation: how can a provider allocate resources to different categories of pricing schemes, so that the total revenue is maximized? In this paper, we consider an EC2-like pricing scheme with traditional pay-as-you-go pricing augmented by an auction market, where bidders periodically bid for resources and can use the instances for as long as they wish, until the clearing price exceeds their bids. We show that optimal periodic auctions must follow the design of m+1-price auction with seller's reservation price. Theoretical analysis also suggests the connections between periodic auctions and EC2 spot market. Furthermore, we formulate the optimal capacity segmentation strategy as a Markov decision process over some demand prediction window. To mitigate the high computational complexity of the conventional dynamic programming solution, we develop a near-optimal solution that has significantly lower complexity and is shown to asymptotically approach the optimal revenue. In late 2009, Amazon introduced spot instances to offer their unused resources at lower cost with reduced reliability. Amazon's spot instances allow customers to bid on unused Amazon EC2 capacity and run those instances for as long as their bid exceeds the current spot price. The spot price changes periodically based on supply and demand of spot instances, and customers whose bid exceeds it gain access to the available spot instances. Customers may expect their services at lower cost with spot instances compared to on-demand or reserved. However the reliability is compromised since the instances (IaaS) providing the service (SaaS) may become unavailable at any time without any notice to the customer. In this paper, we study various checkpointing schemes to increase the reliability over spot instances. Also we devise a novel checkpointing scheme on top of application-centric resource provisioning framework that increases the reliability while reducing the cost significantly.