Fixed and market pricing for cloud services

Abhishek, Vineet; Kash, Ian A.; Key, Peter

doi:10.1109/infcomw.2012.6193479

Cited by 74 publications

(128 citation statements)

References 17 publications

(48 reference statements)

Supporting

Mentioning

124

Contrasting

Order By: Relevance

“…In most cases these are fixed prices, with the notable exception of Amazon's spot instances that use dynamically changing prices. 2 Spot-instance offerings, however, do not provide guaranteed service, as the instances might be evicted if the user bid is too low. Hence, utilizing spot instances requires special attention from the user when determining his bid, and might not be suitable for high-priority production jobs [21,28,2].…”

Section: The Economic Challenge and Era's Approachmentioning

confidence: 99%

“…2 Spot-instance offerings, however, do not provide guaranteed service, as the instances might be evicted if the user bid is too low. Hence, utilizing spot instances requires special attention from the user when determining his bid, and might not be suitable for high-priority production jobs [21,28,2]. The fundamental problem is finding a pricing and a scheduling scheme that will result in highly desired outcome, that of high efficiency.…”

Section: The Economic Challenge and Era's Approachmentioning

confidence: 99%

See 1 more Smart Citation

Era

Babaioff

Mansour

Nisan

et al. 2017

Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion

View full text Add to dashboard Cite

Cloud computing has reached significant maturity from a systems perspective, but currently deployed solutions rely on rather basic economics mechanisms that yield suboptimal allocation of the costly hardware resources. In this paper we present Economic Resource Allocation (ERA), a complete framework for scheduling and pricing cloud resources, aimed at increasing the efficiency of cloud resources usage by allocating resources according to economic principles. The ERA architecture carefully abstracts the underlying cloud infrastructure, enabling the development of scheduling and pricing algorithms independently of the concrete lowerlevel cloud infrastructure and independently of its concerns. Specifically, ERA is designed as a flexible layer that can sit on top of any cloud system and interfaces with both the cloud resource manager and with the users who reserve resources to run their jobs. The jobs are scheduled based on prices that are dynamically calculated according to the predicted demand. Additionally, ERA provides a key internal API to pluggable algorithmic modules that include scheduling, pricing and demand prediction. We provide a proof-of-concept software and demonstrate the effectiveness of the architecture by testing ERA over both public and private cloud systems -Azure Batch of Microsoft and Hadoop/YARN. A broader intent of our work is to foster collaborations between economics and system communities. To that end, we have developed a simulation platform via which economics and system experts can test their algorithmic implementations. * Microsoft Research, moshe,ishai@microsoft.com † TAU and Microsoft Research, mansour@microsoft.com ‡ HUJI and Microsoft Research, noam

show abstract

Section: The Economic Challenge and Era's Approachmentioning

confidence: 99%

Section: The Economic Challenge and Era's Approachmentioning

confidence: 99%

Era

Babaioff

Mansour

Nisan

et al. 2017

Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion

View full text Add to dashboard Cite

show abstract

“…Hybrid Market Kasbekar et al (2010), Abhishek et al (2012), Muthusamy et al (2011), Gao et al (2012a this sense, their market models are closely related to the ideal competitive market. We assume that the market price is endogenously determined by the associated seller and buyer (through, for example, an VCG mechanism).…”

Section: Spot Marketmentioning

confidence: 99%

“…Thus, we essentially consider the monopoly market. Abhishek et al (2012) also considered a hybrid market, in which a cloud service provider sells its service to users via two different pricing schemes: pay-as-you-go (PAYG) and spot pricing. Under the PAYG, users are charged a fixed price per unit time.…”

Section: Spot Marketmentioning

confidence: 99%

Combining Spot and Futures Markets: A Hybrid Market Approach to Dynamic Spectrum Access

Gao

Shou

Chen

et al. 2016

Operations Research

View full text Add to dashboard Cite

Dynamic spectrum access is a new paradigm of secondary spectrum utilization and sharing. It allows unlicensed secondary users (SUs) to exploit opportunistically the under-utilized licensed spectrum. Market mechanism is a widely-used promising means to regulate the consuming behaviours of users and, hence, achieve the efficient allocation and consumption of limited resources. In this paper, we propose and study a hybrid secondary spectrum market consisting of both the futures market and the spot market, in which SUs (buyers) purchase under-utilized licensed spectrum from a spectrum regulator, either through predefined contracts via the futures market, or through spot transactions via the spot market. We focus on the optimal spectrum allocation among SUs in an exogenous hybrid market that maximizes the secondary spectrum utilization efficiency. The problem is challenging due to the stochasticity and asymmetry of network information. To solve this problem, we first derive an off-line optimal allocation policy that maximizes the ex-ante expected spectrum utilization efficiency based on the stochastic distribution of network information. We then propose an on-line VickreyCClarkeCGroves (VCG) auction that determines the real-time allocation and pricing of every spectrum based on the realized network information and the pre-derived off-line policy. We further show that with the spatial frequency reuse, the proposed VCG auction is NP-hard; hence, it is not suitable for on-line implementation, especially in a large-scale market. To this end, we propose a heuristics approach based on an on-line VCG-like mechanism with polynomial-time complexity, and further characterize the corresponding performance loss bound analytically. We finally provide extensive numerical results to evaluate the performance of the proposed solutions.

show abstract

“…A similar discussion can be found in (Wei et al 2010) where a QoS constrained parallel tasks resource allocation problem is considered. In (Abhishek et al 2012) the authors consider two simple pricing schemes for selling Cloud instances and study the trade-off between them. Exploiting Bayesian Nash equilibrium the authors provide theoretical and simulation based evidence suggesting that fixed prices generate a higher expected revenue than hybrid systems.…”

Section: Introductionmentioning

confidence: 99%

Service Provisioning Problem in Cloud and Multi-Cloud Systems

Passacantando

Ardagna

Savi

2016

INFORMS Journal on Computing

View full text Add to dashboard Cite

Cloud Computing is a new emerging paradigm that aims at streamlining the on-demand provisioning of resources as services, providing end-user with flexible and scalable services accessible through the Internet on a pay-per-use basis. Since modern Cloud systems operate in an open and dynamic world characterized by continuous changes, the development of efficient resource provisioning policies for Cloud-based services becomes increasingly challenging. This paper aims to study the hourly basis service provisioning problem through a generalized Nash game model. We take the perspective of SaaS (Software as a Service) providers which want to minimize the costs associated with the virtual machine instances allocated in a multi-IaaSs (Infrastructure as a Service) scenario, while avoiding incurring in penalties for requests execution failures and providing quality of service guarantees. SaaS providers compete and bid for the use of infrastructural resources, while the IaaSs want to maximize their revenues obtained providing virtualized resources.We propose a solution algorithm based on the best-reply dynamics, which is suitable for a distributed implementation. We demonstrate the effectiveness of our approach by performing numerical tests, considering multiple workloads and system configurations. Results show that our algorithm is scalable and provides significant cost savings with respect to alternative methods (5% on average but up to 260% for individual SaaS providers). Furthermore, varying the number of IaaS providers 8-15% cost savings can be achieved from the workload distribution on multiple IaaSs.

show abstract

Fixed and market pricing for cloud services

Cited by 74 publications

References 17 publications

Era

Era

Combining Spot and Futures Markets: A Hybrid Market Approach to Dynamic Spectrum Access

Service Provisioning Problem in Cloud and Multi-Cloud Systems

Contact Info

Product

Resources

About