Resource pricing game in geo-distributed clouds

Abstract-In the era of Big Data, with data growing massively in scale and velocity, cloud computing and its pay-as-you-go model continues to provide significant cost benefits and a seamless service delivery model for cloud consumers. The evolution of small-scale and large-scale geo-distributed datacenters operated and managed by individual Cloud Service Providers (CSPs) raises new challenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resources towards a globally efficient resource allocation model. Earlier solutions for geo-distributed clouds have focused primarily on achieving global efficiency in resource sharing, that although tries to maximize the global resource allocation, results in significant inefficiencies in local resource allocation for individual datacenters and individual cloud provi ders leading to unfairness in their revenue and profit earned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows CSPs to establish resource sharing contracts with individual datacenters apriori for defined time intervals during a 24 hour time period. Based on the established contracts, individual CSPs employ a contracts cost and duration aware job scheduling and provisioning algorithm that enables jobs to complete and meet their response time requirements while achieving both global resource allocation efficiency and local fairness in the profit earned. The proposed techniques are evaluated through extensive experiments using realistic workloads generated using the SHARCNET cluster trace. The experiments demonstrate the effectiveness, scalability and resource sharing fairness of the proposed model.

Section: Related Workmentioning

confidence: 99%

Optimized Contract-Based Model for Resource Allocation in Federated Geo-Distributed Clouds

Palanisamy

2021

“…Dynamic resource pricing of multiple geo-distributed Cloud providers interacting with multiple competing application providers is formulated in [55] as a Stackelberg game. The cost minimization of dynamic service placement in a geographically distributed Cloud, while fulfilling at the same time certain Quality of Service (QoS) requirements, is faced in [70].…”

Section: Related Workmentioning

confidence: 99%

Generalized Nash Equilibria for the Service Provisioning Problem in Multi-Cloud Systems

Ardagna

Ciavotta

Passacantando

2017

Abstract-The adoption of Cloud technologies is steadily increasing. In such systems, applications can benefit from nearly infinite virtual resources on a pay-per-use basis. However, being the Cloud massively multi-tenant and characterized by highly variable workloads the development of more and more effective provisioning policies assumes paramount importance. Boosted by the success of the Cloud, the application of Game Theory models and methodologies has also become popular, since they have been demonstrated to suit perfectly to Cloud social, economic, and strategic structures. This paper aims to study, model and efficiently solve the cost minimization problem associated with the service provisioning of SaaS virtual machines in multiple IaaSs. We propose a game-theoretic approach for the runtime management of resources from multiple IaaS providers to be allocated to multiple competing SaaSs, along with a cost model including revenues and penalties for requests execution failures. A distributed algorithm for identifying Generalized Nash Equilibria has been developed and analysed in detail. The effectiveness of our approach has been assessed by performing a wide set of analyses under multiple workload conditions. Results show that our algorithm is scalable and provides significant cost savings with respect to alternative methods (80% on average). Furthermore, increasing the number of IaaS providers SaaSs can achieve 9-15% cost savings from the workload distribution on multiple IaaSs.

“…Few works focus on dynamic resource pricing within a prespecified time-limit and fixed resource budget ensuring QoS [29]- [32]. Qin et al [33] proposed a dynamic pricing model, which is flexible to the change in the demand of the end-users and accordingly, it adjusts the pricing of the cloud resources.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Optimal Pricing for Heterogeneous Service-Oriented Architecture of Sensor-Cloud Infrastructure

Chatterjee

Ladia

Misra

2017

This paper proposes a dynamic and optimal pricing scheme for provisioning Sensors-as-a-Service (Se-aaS) [1] within the sensor-cloud infrastructure. Existing cloud pricing models are limited in terms of the homogeneity in service-types, and hence, are not compliant for the heterogeneous service oriented architecture of Se-aaS. We propose a new pricing model comprising of two components, applicable for Se-aaS architecture: pricing attributed to Hardware (pH) and pricing attributed to Infrastructure (pI). pH addresses the problem of pricing the physical sensor nodes subject to variable demand and utility of the end-users. It maximizes the profit incurred by every sensor owner, while keeping in mind the end-users' utility. pI mainly focuses on the pricing incurred due to the virtualization of resources. It takes into account the cost for the usage of the infrastructural resources, inclusive of the cost for maintaining virtualization within sensor-cloud. pI maximizes the profit of the sensor-cloud service provider (SCSP) by considering the user satisfaction. Simulation results depict improved performance of pH in comparison to the traditional hardware pricing algorithms, viz. PPM and Sprite, in terms of the residual energy, proximity to the Base Station (BS), Received Signal Strength (RSS), overhead, and cumulative energy consumption. The results also show the tendency of the sensor-owners to converge to the end-user utility, but not exceed it. We also analyze the performance of pI. The results show the optimality in the profit incurred by SCSP and the user satisfaction.