Srikumar Venugopal

Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities

This keynote paper: presents a 21st century vision of computing; identifies various computing paradigms promising to deliver the vision of computing utilities; defines Cloud computing and provides the architecture for creating market-oriented Clouds by leveraging technologies such as VMs; 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; presents some representative Cloud platforms especially those developed in industries along with our current work towards realising market-oriented resource allocation of Clouds by leveraging the 3rd generation Aneka enterprise Grid technology; reveals our early thoughts on interconnecting Clouds for dynamically creating an atmospheric computing environment along with pointers to future community research; and concludes with the need for convergence of competing IT paradigms for delivering our 21st century vision.

The Grid Economy

This work identifies challenges in managing resources in a Grid computing environment and proposes computational economy as a metaphor for effective management of resources and application scheduling. It identifies distributed resource management challenges and requirements of economy-based Grid systems, and discusses various representative economy-based systems, both historical and emerging, for cooperative and competitive trading of resources such as CPU cycles, storage, and network bandwidth. It presents an extensive, service-oriented Grid architecture driven by Grid economy and an approach for its realization by leveraging various existing Grid technologies. It also presents commodity and auction models for resource allocation. The use of commodity economy model for resource management and application scheduling in both computational and data grids is also presented.

A taxonomy of Data Grids for distributed data sharing, management, and processing

Data Grids have been adopted as the next generation platform by many scientific communities that need to share, access, transport, process, and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this article, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks, and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation, and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration.

A grid service broker for scheduling distributed data-oriented applications on global grids

Large communities of researchers distributed around the world are engaged in analyzing huge collections of data generated by scientific instruments and replicated on distributed resources. In such an environment, scientists need to have the ability to carry out their studies by transparently accessing distributed data and computational resources. In this paper, we propose and develop a Grid broker that mediates access to distributed resources by (a) discovering suitable data sources for a given analysis scenario, (b) suitable computational resources, (c) optimally mapping analysis jobs to resources, (d) deploying and monitoring job execution on selected resources, (e) accessing data from local or remote data source during job execution and (f) collating and presenting results. The broker supports a declarative and dynamic parametric programming model for creating grid applications. We have used this model in grid-enabling a high energy physics analysis application (Belle Analysis Software Framework) on a grid testbed having resources distributed across Australia.

Market-oriented Grids and Utility Computing: The State-of-the-art and Future Directions

Traditional resource management techniques (resource allocation, admission control and scheduling) have been found to be inadequate for many shared Grid and distributed systems, that consist of autonomous and dynamic distributed resources contributed by multiple organisations. They provide no incentive for users to request resources judiciously and appropriately, and do not accurately capture the true value, importance and deadline (the utility) of a user’s job. Furthermore, they provide no compensation for resource providers to contribute their computing resources to shared Grids, as traditional approaches have a user-centric focus on maximising throughput and minimising waiting time rather than maximising a providers own benefit. Consequently, researchers and practitioners have been examining the appropriateness of ‘market-inspired’ resource management techniques to address these limitations. Such techniques aim to smooth out access patterns and reduce the chance of transient overload, by providing a framework for users to be truthful about their resource requirements and job deadlines, and offering incentives for service providers to prioritise urgent, high utility jobs over low utility jobs. We examine the recent innovations in these systems (from 2000–2007), looking at the state-of-the-art in price setting and negotiation, Grid economy management and utility-driven scheduling and resource allocation, and identify the advantages and limitations of these systems. We then look to the future of these systems, examining the emerging ‘Catallaxy’ market paradigm. Finally we consider the future directions that need to be pursued to address the limitations of the current generation of market oriented Grids and Utility Computing systems.

A Grid service broker for scheduling e‐Science applications on global data Grids

The next generation of scientific experiments and studies, popularly called e‐Science, is carried out by large collaborations of researchers distributed around the world engaged in the analysis of huge collections of data generated by scientific instruments. Grid computing has emerged as an enabler for e‐Science as it permits the creation of virtual organizations that bring together communities with common objectives. Within a community, data collections are stored or replicated on distributed resources to enhance storage capability or the efficiency of access. In such an environment, scientists need to have the ability to carry out their studies by transparently accessing distributed data and computational resources. In this paper, we propose and develop a Grid broker that mediates access to distributed resources by: (a) discovering suitable data and computational resources sources for a given analysis scenario; (b) optimally mapping analysis jobs to resources; (c) deploying and monitoring job execution on selected resources; (d) accessing data from local or remote data sources during job execution; and (e) collating and presenting results. The broker supports a declarative and dynamic parametric programming model for creating Grid applications. We have used this model in Grid‐enabling a high‐energy physics analysis application (the Belle Analysis Software Framework). The broker has been used in deploying Belle experimental data analysis jobs on a Grid testbed, called the Belle Analysis Data Grid, having resources distributed across Australia interconnected through GrangeNet. Copyright

Aneka: Next-Generation Enterprise Grid Platform for e-Science and e-Business Applications

In this paper, we present the design of Aneka, a .NET based service-oriented platform for desktop grid computing that provides: (i) a configurable service container hosting pluggable services for discovering, scheduling and balancing various types of workloads and (ii) a flexible and extensible framework/API supporting various programming models including threading, batch processing, MPI and dataflow. Users and developers can easily use different programming models and the services provided by the container to run their applications over desktop Grids managed by Aneka. We present the implementation of both the essential and advanced services within the platform. We evaluate the system with applications using the grid task and dataflow models on top of the infrastructure and conclude with some future directions of the current system.

A Dynamic Critical Path Algorithm for Scheduling Scientific Workflow Applications on Global Grids

Effective scheduling is a key concern for the execution of performance driven grid applications. In this paper, we propose a dynamic critical path (DCP) based workflow scheduling algorithm that determines efficient mapping of tasks by calculating the critical path in the workflow task graph at every step. It assigns priority to a task in the critical path which is estimated to complete earlier. Using simulation, we have compared the performance of our proposed approach with other existing heuristic and meta-heuristic based scheduling strategies for different type and size of workflows. Our results demonstrate that DCP based approach can generate better schedule for most of the type of workflows irrespective of their size particularly when resource availability changes frequently.

Autonomic metered pricing for a utility computing service

An increasing number of providers are offering utility computing services which require users to pay only when they use them. Most of these providers currently charge users for metered usage based on fixed prices. In this paper, we analyze the pros and cons of charging fixed prices as compared to variable prices. In particular, charging fixed prices do not differentiate pricing based on different user requirements. Hence, we highlight the importance of deploying an autonomic pricing mechanism that self-adjusts pricing parameters to consider both application and service requirements of users. Performance results observed in the actual implementation of an enterprise Cloud show that the autonomic pricing mechanism is able to achieve higher revenue than various other common fixed and variable pricing mechanisms.

A Market-Oriented Grid Directory Service for Publication and Discovery of Grid Service Providers and their Services

The emergence of Grids as a platform for sharing and aggregation of distributed resources increases the need for mechanisms that allow an efficient management of resources. The Grid economy has been identified as one of the potential solutions as it helps in managing the supply and demand for resources and enables sustained sharing of resources by providing economic incentive for Grid resource providers. An economy based Grid computing environment needs to support an infrastructure that enables the creation of a marketplace for meeting of providers and consumers. This paper presents the Grid Market Directory (GMD) that serves as a registry for publication and discovery of Grid service providers and their services.