HP Labs Singapore


HP Labs Singapore

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Liang Q.,HP Labs Singapore | Wu X.,Hefei University of Technology | Wu X.,University of Vermont | Park E.K.,University of Missouri - Kansas City | And 2 more authors.
IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans | Year: 2011

A key goal of the Semantic Web is to shift social interaction patterns from a producer-centric paradigm to a consumer-centric one. Treating customers as the most valuable assets and making the business models work better for them are at the core of building successful consumer-centric business models. It follows that customizing business processes constitutes a major concern in the realm of a knowledge-pull-based human semantic Web. This paper conceptualizes the customization of service-based business processes leveraging the existing knowledge of Web services and business processes. We represent this conceptualization as a new Extensible Markup Language (XML) markup language Web Ontology Language-Business Process Customization (OWL-BPC), based on the de facto semantic markup language for Web-based information [Web Ontology Language (OWL)]. Furthermore, we report a framework, built on OWL-BPC, for customizing service-based business processes, which supports customization detection and enactment. Customization detection is enabled by a business-goal analysis, and customization enactment is enabled via event-condition-action rule inference. Our solution and framework have the following capabilities in dealing with inconsistencies and misalignments in business process interactions: 1) resolve semantic mismatch of process parameters; 2) handle behavioral mismatches which may or may not be compatible; and 3) process misaligned rendezvous requirements. Such capabilities are applicable to business processes with heterogeneous domain ontology. We present an architectural description of the implementation and a walk-through of an example of solving a customization problem as a validation of the proposed approach. © 2011 IEEE.

Yan S.,Hp Labs Singapore | Lee B.S.,Hp Labs Singapore | Lee B.S.,Nanyang Technological University | Singhal S.,Hewlett - Packard
Proceedings of the 2010 4th International DMTF Academic Alliance Workshop on Systems and Virtualization Management, SVM 2010, Co-located with CNSM 2010 | Year: 2010

The use of Infrastructure-as-a-Service (IaaS) has become more and more prevalent over the past few years. Many IaaS users face the challenge of managing different services and applications running on different IaaS providers. This requires them to interact with different APIs offered by the different providers, and increases the complexity of managing their services. In this paper, we address this problem by modeling IaaS using the DMTF Common Information Model (CIM) meta-model. Based on this model, a generic IaaS proxy was developed using Web2Exchange to enable users to easily manage services provided by variant IaaS providers in a heterogeneous environment. As an initial case study we have prototyped the unified IaaS proxy with capability to support management of Amazon Elastic Compute Cloud (EC2) service. © 2010 IEEE.

Chaisiri S.,Nanyang Technological University | Lee B.-S.,Nanyang Technological University | Lee B.-S.,Hp Labs Singapore | Niyato D.,Nanyang Technological University
Proceedings - 2010 IEEE International Conference on Service-Oriented Computing and Applications, SOCA 2010 | Year: 2010

Cloud providers can offer cloud consumers two plans to provision resources, namely reservation and on-demand plans. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, this resource provisioning is challenging due to the uncertainty. For example, consumers' demand and providers' resource prices can be fluctuated. Moreover, inefficiency of resource provisioning leads to either overprovisioning or underprovisioning problem. In this paper, we propose a robust cloud resource provisioning (RCRP) algorithm to minimize the total resource provisioning cost (i.e., overprovisioning and underprovisioning costs). Various types of uncertainty are considered in the algorithm. To obtain the optimal solution, a robust optimization model is formulated and solved. Numerical studies are extensively performed in which the results show that the solution obtained from the RCRP algorithm achieves both solution-and model-robustness. That is, the total resource provisioning cost is close to the optimality (i.e., solution-robustness), and the overprovisioning and underprovisioning costs are significantly reduced (i.e., model-robustness).

Whitney C.,HP Labs Singapore | Lee B.S.,HP Labs Singapore | Lee B.S.,Nanyang Technological University | Yan S.,HP Labs Singapore
Proceedings - 2011 Annual SRII Global Conference, SRII 2011 | Year: 2011

Cloud computing has become more and more prevalent over the past few years. New cloud service providers intensify competition in the fast-growing market. While competition not always makes customers better off, cloud service providers can adopt a more collaborative model for satisfying its user need very much like the telecommunication industry that supports mobile roaming. We call this collaborative model Cloud Peering which is a possible way for service providers to collaborate with each other to accomplish a win-win solution. © 2011 IEEE.

Yan S.,HP Labs Singapore | Lee B.S.,HP Labs Singapore | Lee B.S.,Nanyang Technological University | Zhao G.,HP Labs Singapore | And 2 more authors.
2011 5th International DMTF Academic Alliance Workshop on Systems and Virtualization Management: Standards and the Cloud, SVM 2011 | Year: 2011

Cloud Computing has become more and more prevalent over the past few years, and we have seen the emergence of Infrastructure-as-a-Service (IaaS) which is the most acceptable Cloud Computing service model. However, coupled with the opportunities and benefits brought by IaaS, the adoption of IaaS also faces management complexity in the hybrid cloud environment which enterprise users are mostly building up. A cloud management system, Monsoon proposed in this paper provides enterprise users an interface and portal to manage the cloud infrastructures from multiple public and private cloud service providers. To meet the requirements of the enterprise users, Monsoon has key components such as user management, access control, reporting and analytic tools, corporate account/role management, and policy implementation engine. Corporate Account module supports enterprise users' subscription and management of multi-level accounts in a hybrid cloud which may consist of multiple public cloud service providers and private cloud. Policy Implementation Engine module in Monsoon will allow users to define the geography-based requirements, security level, government regulations and corporate policies and enforce these policies to all the subscription and deployment of user's cloud infrastructure. © 2011 IEEE.

Chaisiri S.,Nanyang Technological University | Lee B.-S.,Nanyang Technological University | Lee B.-S.,Hp Labs Singapore | Niyato D.,Nanyang Technological University
2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2012 | Year: 2012

This paper studies a cloud computing market where a cloud provider rents a set of computing resources from Windows Azure operated by Microsoft. The cloud provider can integrate value-added services to the resources. Then, the services can be sold to customers, and the cloud provider can earn a profit. Moreover, the cloud provider could save much cost and increase higher profit with the 6-month subscription plan offered by Windows Azure. However, the maximization of profit is not trivial to be achieved since the amount of the customers' demand cannot be perfectly known in advance. Consequently, the subscription plan could not be optimally purchased. To deal with such a maximization problem, the paper proposes a stochastic programming model with two-stage recourse. The numerical studies show that the model can maximize the profit under the customers' demand uncertainty. © 2012 IEEE.

Chaisiri S.,Nanyang Technological University | Kaewpuang R.,Nanyang Technological University | Lee B.-S.,Nanyang Technological University | Lee B.-S.,Hp Labs Singapore | Niyato D.,Nanyang Technological University
IEEE International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems - Proceedings | Year: 2011

Amazon Elastic Compute Cloud (EC2) provides a cloud computing service by renting out computational resources to customers (i.e., cloud users). The customers can dynamically provision virtual servers (i.e., computing instances) in EC2, and then the customers are charged by Amazon on a pay-per-use basis. EC2 offers three options to provision virtual servers, i.e., on-demand, reservation, and spot options. Each option has different price and yields different benefit to the customers. Spot price (i.e., price of spot option) could be the cheapest, however, the spot price is fluctuated and even more expensive than the prices of on-demand and reservation options due to supply-and-demand of available resources in EC2. Although the reservation and on-demand options have stable prices, their costs are mostly more expensive than that of spot option. The challenge is how the customers efficiently purchase the provisioning options under uncertainty of price and demand. To address this issue, two virtual server provisioning algorithms are proposed to minimize the provisioning cost for long- and short-term planning. Stochastic programming, robust optimization, and sample-average approximation are applied to obtain the optimal solutions of the algorithms. To evaluate the performance of the algorithms, numerical studies are extensively performed. The results show that the algorithms can significantly reduce the total provisioning cost incurred to customers. © 2011 IEEE.

Kaewpuang R.,Nanyang Technological University | Chaisiri S.,Nanyang Technological University | Niyato D.,Nanyang Technological University | Lee B.-S.,Nanyang Technological University | And 2 more authors.
Proceedings of the 2012 10th IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2012 | Year: 2012

We propose an adaptive power management (APM) algorithm for a data center with an objective to minimize the total cost of power bought from an electrical grid. This APM algorithm is developed for a smart grid environment which is envisioned to be a cooperative, responsive, and economical power system. In particular, APM algorithm takes the spot power price from an electrical grid, the power supply from a renewable power source, and users' demand in terms of application workload processing into account when managing the power consumption. Therefore, an APM algorithm is considered to be the demand side management in a smart grid. To obtain an optimal decision of the APM algorithm, an optimization model based on stochastic programming with multi-stage recourse is developed. This optimization model considers various uncertainties and is able to determine the optimal solution for the APM algorithm. The APM algorithm is evaluated by numerical studies. The numerical results clearly show that the APM algorithm can minimize the power cost of a data center. © 2012 IEEE.

Yan S.,Hp Labs Singapore | Chen C.,Hp Labs Singapore | Zhao G.,Hp Labs Singapore | Lee B.S.,Hp Labs Singapore | Lee B.S.,Nanyang Technological University
Proceedings of the 2012 8th International Conference on Network and Service Management, CNSM 2012 | Year: 2012

The standardization of cloud services makes it possible to have one cloud service management platform for customers to take advantage of their subscriptions from various cloud providers. One immediate benefit lies in the way that enterprise customers may have plenty of choices of available providers, when seeking cloud services to fulfill their criteria, such as price or Service Level Agreement (SLA). However, how to select appropriate cloud offerings in terms of business requirements and company policies becomes challenging and non-trivial, especially when a composition of multiple services are to be chosen for achieving business goals. In this paper, a systematic framework on top of a hybrid cloud management platform is proposed for enterprises to automatically recommend and select cloud services according to business requirements, company policies and standards, and the specifications of cloud offerings. © 2012 IFIP.

Lee B.S.,HP Labs Singapore | Lee B.S.,Nanyang Technological University | Yan S.,HP Labs Singapore | Ma D.,HP Labs Singapore | Zhao G.,Nanyang Technological University
Proceedings - 2011 Annual SRII Global Conference, SRII 2011 | Year: 2011

Infrastructure-as-a-Service (IaaS) is the most acceptable Cloud Computing delivery model that CIO and IT managers are exploring as they relook at their IT infrastructure. However, the adoption of IaaS faces challenge/concerns such as provider lock-in, reliability, and regulatory compliance for data locality. Our project, Aggregated IaaS Service, addresses this issue by providing a common interface and description of IaaS services across multiple IaaS service provider. We had developed an abstraction model for Iaas services and then provide the interface to the commercial IaaS providers: AWS and GoGrid. The Aggregated IaaS services are exposed to the users through a portal which allow users to subscribe, monitor, and manage the life-cycle of IaaS services from multiple providers. to meet the requirements of the enterprise users the IaaS Aggregator portal have modules such as corporate account management and policy enforcement modules which transform it from a single client user to corporate/company usage of IaaS service from multiple providers. © 2011 IEEE.

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