Squeezing Out the Cloud via Profit-Maximizing Resource Allocation Policies (original) (raw)
Related papers
Future Generation Computer Systems, 2013
Cloud computing provides on-demand access to computational resources which together with pay-peruse business models, enable application providers seamlessly scaling their services. Cloud computing infrastructures allow creating a variable number of virtual machine instances depending on the application demands. An attractive capability for Software-as-a-Service (SaaS) providers is having the potential to scale up or down application resources to only consume and pay for the resources that are really required at some point in time; if done correctly, it will be less expensive than running on regular hardware by traditional hosting. However, even when large-scale applications are deployed over payper-use cloud high-performance infrastructures, cost-effective scalability is not achieved because idle processes and resources (CPU, memory) are unused but charged to application providers. Over and under provisioning of cloud resources are still unsolved issues. Even if peak loads can be successfully predicted, without an effective elasticity model, costly resources are wasted during nonpeak times (underutilization) or revenues from potential customers are lost after experiencing poor service (saturation). This work attempts to establish formal measurements for under and over provisioning of virtualized resources in cloud infrastructures, specifically for SaaS platform deployments and proposes a resource allocation model to deploy SaaS applications over cloud computing platforms by taking into account their multitenancy, thus creating a cost-effective scalable environment. (J. Espadas).
Reserved or On-Demand Instances? A Revenue Maximization Model for Cloud Providers
2011 IEEE 4th International Conference on Cloud Computing, 2011
We examine the problem of managing a server farm in a way that attempts to maximize the net revenue earned by a cloud provider by renting servers to customers according to a typical Platform-as-a-Service model. The Cloud provider offers its resources to two classes of customers: 'premium' and 'basic'. Premium customers pay upfront fees to reserve servers for a specified period of time (e.g. a year). Premium customers can submit jobs for their reserved servers at any time and pay a fee for the server-hours they use. The provider is liable to pay a penalty every time a 'premium' job can not be executed due to lack of resources. On the other hand, 'basic' customers are served on a best-effort basis, and pay a server-hour fee that may be higher than the one paid by premium customers. The provider incurs energy costs when running servers. Hence, it has an incentive to turn off idle servers. The question of how to choose the number of servers to allocate to each pool (basic and premium) is answered by analyzing a suitable queuing model and maximizing a revenue function. Experimental results show that the proposed scheme adapts to different traffic conditions, penalty levels, energy costs and usage fees.
Optimizing Cloud providers revenues via energy efficient server allocation
Sustainable Computing: Informatics and Systems, 2012
Cloud providers, like Amazon, offer their data centers' computational and storage capacities for lease to paying customers. High electricity consumption, not only reflects on the data center's carbon footprint but also increases the costs of running the data center itself. We examine the problem of managing a server farm in a way that attempts to maximize the net revenue earned by a Cloud provider renting servers to customers according to a typical Platform-as-a-Service model. As a solution allocation policies which are based on the dynamic powering servers on and off are introduced and evaluated. The policies aim at satisfying the conflicting goals of maximizing the users' experience while minimizing the amount of consumed electricity. Special emphasis is given to cases where user demand is time-varying and cannot be predicted with absolute accuracy. In order to deal with that, allocation policies resilient to errors in the forecasting, as well as a method for finding the parameters leading to the highest revenues are introduced. The results of several experiments are described, showing that the proposed scheme performs well under different traffic conditions.
Generalized Nash Equilibria for the Service Provisioning Problem in Cloud Systems
IEEE Transactions on Services Computing, 2000
In recent years the evolution and the widespread adoption of virtualization, service-oriented architectures, autonomic, and utility computing have converged letting a new paradigm to emerge: The Cloud Computing. Clouds allow the on-demand delivering of software, hardware, and data as services. Currently the Cloud offer is becoming day by day wider since all the major IT Companies and Service providers, like Microsoft, Google, Amazon, HP, IBM, and VMWare have started providing solutions involving this new technological paradigm.
Exploiting Task Elasticity and Price Heterogeneity for Maximizing Cloud Computing Profits
IEEE Transactions on Emerging Topics in Computing, 2015
This paper exploits cloud task elasticity and price heterogeneity to propose an online resource management framework that maximizes cloud profits while minimizing energy expenses. This is done by reducing the duration during which servers need to be left ON and maximizing the monetary revenues when the charging cost for some of the elastic tasks depends on how fast these tasks complete, while meeting all resource requirements. Comparative studies conducted using Google data traces show the effectiveness of our proposed framework in terms of improving resource utilization, reducing energy expenses, and increasing cloud profits.
Revenue maximization approaches in IaaS clouds: Research challenges and opportunities
Transactions on Emerging Telecommunications Technologies, 2022
Revenue generation is the main concern of any business, particularly in the cloud, where there is no direct interaction between the provider and the consumer. Cloud computing is an emerging core for today's businesses, however, Its complications (e.g, installation and migration) with traditional markets are the main challenges. It earns more but needs exemplary performance and marketing skills. In recent years, cloud computing has become a successful paradigm for providing desktop services. It is expected that more than $ 331 billion will be invested by 2023, likewise, 51 billion devices are expected to be connected to the cloud. Infrastructure as a Service (IaaS) provides physical resources (e.g, computing, memory, storage and network) as VM instances. In this article, the main revenue factors are categorized as SLA and penalty management, resource scalability, customer satisfaction and management, resource utilization and provision, cost and price management, and advertising and auction. These parameters are investigated in detail and new dynamics for researchers in the field of the cloud are discovered.
Profit Maximization for Service Providers using Hybrid Pricing in Cloud Computing
Cloud computing has recently emerged as one of the buzzwords in the IT industry. Several IT vendors are promising to offer computation, data/storage, and application hosting services, offering Service-Level Agreements (SLA) backed performance and uptime promises for their services. While these "clouds" are the natural evolution of traditional clusters and data centers, they are distinguished by following a pricing model where customers are charged based on their utilization of computational resources, storage and transfer of data. They offer subscription-based access to infrastructure, platforms, and applications that are popularly termed as IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service). In order to improve the profit of service providers we implement a technique called hybrid pricing , where this hybrid pricing model is a pooled with fixed and spot pricing techniques.
buyya.com
Cloud computing has been considered as a solution for solving enterprise application distribution and configuration challenges in the traditional software sales model. Migrating from traditional software to Cloud enables on-going revenue for software providers. However, in order to deliver hosted services to customers, SaaS companies have to either maintain their own hardware or rent it from infrastructure providers. This requirement means that SaaS providers will incur extra costs. In order to minimize the cost of resources, it is also important to satisfy a minimum service level to customers. Therefore, this paper proposes resource allocation algorithms for SaaS providers who want to minimize infrastructure cost and SLA violations. Our proposed algorithms are designed in a way to ensure that Saas providers are able to manage the dynamic change of customers, mapping customer requests to infrastructure level parameters and handling heterogeneity of Virtual Machines. We take into account the customers' Quality of Service parameters such as response time, and infrastructure level parameters such as service initiation time. This paper also presents an extensive evaluation study to analyze and demonstrate that our proposed algorithms minimize the SaaS provider's cost and the number of SLA violations in a dynamic resource sharing Cloud environment.
Market-based Autonomous Application and Resource Management in the Cloud
Organizations owning HPC infrastructures are facing difficulties in managing their infrastructures. These difficulties come from the need to provide concurrent resource access to applications with different resource requirements while considering that users might have different performance objectives, or Service Level Objectives (SLOs) for executing them. To address these challenges this paper proposes a market-based SLO-driven cloud platform. This platform relies on a market-based model to allocate resources to applications while taking advantage of cloud flexibility to maximize resource utilization. The combination of currency distribution and dynamic resource pricing ensures fair resource distribution. In the same time, autonomous controllers apply adaptation policies to scale the application resource demand according to user SLOs. The adaptation policies can: (i) dynamically tune the amount of CPU and memory provisioned for the virtual machines in contention periods; (ii) dynamically change the number of virtual machines. We evaluated this proposed platform on the Grid'5000 testbed. Results show that: (i) the platform provides flexible support for different application types and different SLOs; (ii) the platform is capable to provide good user satisfaction achieving acceptable performance degradation compared to existing centralized solutions.
Triangulation Resource Provisioning for Web Applications in Cloud Computing: A Profit-Aware Approach
Scalable Computing: Practice and Experience, 2019
The elasticity feature of cloud attracts the application providers to host the application in a cloud environment. The dynamic resource provisioning arranges the resources on-demand according to the application workload. The over-utilization and under-utilization of resources can be prevented with autonomic resource provisioning. In literature, the Service Level Agreement (SLA) based, load-aware, resource-aware and user-behavior aware solutions have been proposed. The solutions are rigid for a particular metric which provides benefit either to end users or to the application providers. In this article, we proposed a Triangulation Resource Provisioning (TRP) technique with a profit-aware surplus VM selection policy. This policy ensures the fair resource utilization in hourly billing cycle while giving the Quality of Service (QoS) to the end-users. The proposed technique used time series workload forecasting, CPU utilization and response time in the analysis phase. The experiment resu...