In utility-driven cluster computing, cluster systems need to know the specific needs of different users so as to allocate resources according to their needs. They are also vital in supporting service-oriented Grid computing that harness resources distributed worldwide based on users' objectives. Market-based resource management systems make use of real-world market concepts and behavior to assign resources to users. This paper outlines a taxonomy that describes how market-based resource management systems can support utility-driven cluster computing. The taxonomy is used to survey existing market-based resource management systems to better understand how they can be utilized.

Utility functions are used by clients of a service to communicate the value of a piece of work and other QoS aspects such as its timely completion. However, utility functions on individual work items do not capture how important it is to complete all or part of a batch of items; for this purpose, a higher-level construct is required. We propose a multi-job aggregate-utility function, and show how a service provider that executes jobs on rented resources can use it to drive admission control and job scheduling decisions. Using a profit-seeking approach to its policies, we find that the service provider can cope gracefully with client overload and varying resource availability. The result is significantly greater value delivered to clients, and higher profit (net value) generated for the service provider. 1

There has been a great deal of hype about Amazon’s simple storage service (S3). S3 provides infinite scalability and high availability at low cost. Currently, S3 is used mostly to store multi-media documents (videos, photos, audio) which are shared by a community of people and rarely updated. The purpose of this paper is to demonstrate the opportunities and limitations of using S3 as a storage system for general-purpose database applications which involve small objects and frequent updates. Read, write, and commit protocols are presented. Furthermore, the cost ($), performance, and consistency properties of such a storage system are studied.

Abstract. With the development of grids, distributed applications are spread across multiple computing resources and require efficient security mechanisms among the processes. Although protocols for authenticated group Diffie-Hellman key exchange protocols seem to be the natural mechanisms for supporting these applications, current solutions are either limited by the use of public key infrastructures or by their scalability, requiring a number of rounds linear in the number of group members. To overcome these shortcomings, we propose in this paper the first provably-secure password-based constant-round group key exchange protocol. It is based on the protocol of Burmester and Desmedt and is provably-secure in the random-oracle and ideal-cipher models, under the Decisional Diffie-Hellman assumption. The new protocol is very efficient and fully scalable since it only requires four rounds of communication and four multi-exponentiations per user. Moreover, the new protocol avoids intricate authentication infrastructures by relying on passwords for authentication. 1

The Grid is rapidly emerging as the dominant paradigm for wide area distributed application systems. As a result, there is a need for modeling and analyzing the characteristics and requirements of Grid systems and programming models. This paper adopts the well-established body of models for distributed computing systems, which are based upon carefully stated assumptions or axioms, as a basis for defining and characterizing Grids and their programming models and systems. The requirements of programming Grid applications and the resulting requirements on the underlying virtual organizations and virtual machines are investigated. The assumptions underlying some of the programming models and systems currently used for Grid applications are identified and their validity in Grid environments is discussed. A more in-depth analysis of two programming systems, the Imperial College E-Science Networked Infrastructure (ICENI) and Accord, using the proposed definitions’ structure is presented. Keywords—Distributed systems, Grid programming models, Grid programming systems, Grid system definition. I.

Current grid-aware applications are implemented on top of low-level libraries by developers who are experts on grid middleware architecture. This approach can hardly support the additional complexity of QoS control in real applications. We discuss a novel approach used in the ASSIST programming environment to implement/guarantee user provided QoS contracts in a transparent and effective way. Our approach is based on the implementation of automatic run-time reconfiguration of ASSIST application executions triggered by mismatch between the user provided QoS contract and the actual performance values achieved.

The Grid is a promising technology for providing access to distributed high-end computational capabilities. Thus, computational tasks can be performed spontaneously by other resources in the Grid that are not under the user’s control. However, one of the key problems in the Grid is deciding which jobs are to be allocated to which resources at what time. In this context, the use of market mechanisms for scheduling and allocating Grid resources is a promising approach toward solving these problems. This paper proposes an auction mechanism for allocating and scheduling computer resources such as processors or storage space which have multiple quality attributes. The mechanism is evaluated according to its economic and computational performance as well as its practical applicability by means of a simulation. 1

Optical networking may dramatically change high performance distributed computing. One reason is that optical networks can support provisioning dynamically configurable lightpaths, a form of circuit switching, through reservations. However, to use it (and all other network reservation mechanisms), the user or developer must modify the application. We present a system, VRESERVE, that automatically and dynamically creates network reservation requests based on the inferred network demands of running distributed and/or parallel applications with no modification to the application or operating system, and no input from the user or developer. Our execution model is a collection of virtual machines interconnected by an overlay network. The overlay network infers application demands, providing a dynamic run-time assessment of the application's topology and traffic load matrix. We then reserve lightpaths corresponding to the topology and use the overlay to forward virtual network traffic over them. We evaluate our system on the OMNInet network.

We present the NIMO system that automatically learns cost models for predicting the execution time of computationalscience applications running on large-scale networked utilities such as computational grids. Accurate cost models are important for selecting efficient plans for executing these applications on the utility. Computational-science applications are often scripts (written, e.g., in languages like Perl or Matlab) connected using a workflow-description language, and therefore, pose different challenges compared to modeling the execution of plans for declarative queries with wellunderstood semantics. NIMO generates appropriate training samples for these applications to learn fairly-accurate cost models quickly using statistical learning techniques. NIMO’s approach is active and noninvasive: it actively deploys and monitors the application under varying conditions, and obtains its training data from passive instrumentation streams that require no changes to the operating system or applications. Our experiments with real scientific applications demonstrate that NIMO significantly reduces the number of training samples and the time to learn fairly-accurate cost models. 1.

Abstract. The adequate location of wells in oil and environmental applications has a significant economic impact on reservoir management. However, the determination of optimal well locations is both challenging and computationally expensive. The overall goal of this research is to use the emerging Grid infrastructure to realize an autonomic self-optimizing reservoir framework. In this paper, we present a policy-driven peer-to-peer Grid middleware substrate to enable the use of the Simultaneous Perturbation Stochastic Approximation (SPSA) optimization algorithm, coupled with the Integrated Parallel Accurate Reservoir Simulator (IPARS) and an economic model to find the optimal solution for the well placement problem.