SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions

Cloud computing systems promise to offer subscription-oriented, enterprise-quality computing services to users worldwide. With the increased demand for delivering services to a large number of users, they need to offer differentiated services to users and meet their quality expectations. Existing resource management systems in data centers are yet to support Service Level Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to realize cloud computing and utility computing. In addition, no work has been done to collectively incorporate customer-driven service management, computational risk management, and autonomic resource management into a market-based resource management system to target the rapidly changing enterprise requirements of Cloud computing. This paper presents vision, challenges, and architectural elements of SLA-oriented resource management. The proposed architecture supports integration of marketbased provisioning policies and virtualisation technologies for flexible allocation of resources to applications. The performance results obtained from our working prototype system shows the feasibility and effectiveness of SLA-based resource provisioning in Clouds.Comment: 10 pages, 7 figures, Conference Keynote Paper: 2011 IEEE International Conference on Cloud and Service Computing (CSC 2011, IEEE Press, USA), Hong Kong, China, December 12-14, 201

Dynamic pricing for 3G networks using admission control and traffic differentiation

Published in Networks, 2005. Jointly held with the 2005 IEEE 7th Malaysia International Conference on Communication., 2005 13th IEEE International Conference on (Volume:2 )In the pricing of network resources, network operators and service providers aim at facilitating the use of the limited network resources in a manner that would encourage responsibility among the end-users and lead to the maximisation of profits. The optimum tariff rates used for charging the mobile services are affected by factors like the market forces affecting the industry. However, the tariff rates generally increase with the achieved QoS level. Next generation networks will offer higher QoS, hence users need incentives to utilise the enhanced capacity. In this paper, we propose a pricing approach that introduces service profiles into a DiffServ-enabled network, whose prices and QoS levels depend on the degree of congestion in the network. The use of the UMTS connection admission control to support the proposed pricing scheme is explored. An emulation testbed is used to evaluate the scheme.In the pricing of network resources, network operators and service providers aim at facilitating the use of the limited network resources in a manner that would encourage responsibility among the end-users and lead to the maximisation of profits. The optimum tariff rates used for charging the mobile services are affected by factors like the market forces affecting the industry. However, the tariff rates generally increase with the achieved QoS level. Next generation networks will offer higher QoS, hence users need incentives to utilise the enhanced capacity. In this paper, we propose a pricing approach that introduces service profiles into a DiffServ-enabled network, whose prices and QoS levels depend on the degree of congestion in the network. The use of the UMTS connection admission control to support the proposed pricing scheme is explored. An emulation testbed is used to evaluate the scheme

Pricing differentiated brokered internet services

Price war, as an important factor in undercutting competitors and attracting customers, has spurred considerable work that analyzes such conflict situation. However, in most of these studies, quality of service (QoS), as an important decision-making criterion, has been neglected. Furthermore, with the rise of service-oriented architectures, where players may offer different levels of QoS for different prices, more studies are needed to examine the interaction among players within the service hierarchy. In this paper, we present a new approach to modeling price competition in service-oriented architectures, where there are multiple service levels. In our model, brokers, as the intermediaries between end-users and service providers, offer different QoS by adapting the service that they obtain from lower-level providers so as to match the demands of their clients to the services of providers. To maximize profit, players at each level, compete in a Bertrand game, while they offer different QoS. To maintain an oligopoly market, we then describe underlying dynamics which lead to a Bertrand game with price constraints at the providers' level. Numerical examples demonstrate the behavior of brokers and providers and the effect of price competition on their market shares.http://www.cs.bu.edu/fac/matta/Papers/sdp2016.pdfAccepted manuscrip

Cloudbus Toolkit for Market-Oriented Cloud Computing

This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.Comment: 21 pages, 6 figures, 2 tables, Conference pape