Eddy Caron

Diet: A Scalable Toolbox to Build Network Enabled Servers on the Grid

Among existing grid middleware approaches, one simple, powerful, and flexible approach consists of using servers available in different administrative domains through the classical client-server or Remote Procedure Call (RPC) paradigm. Network Enabled Servers implement this model also called GridRPC. Clients submit computation requests to a scheduler whose goal is to find a server available on the grid. The aim of this paper is to give an overview of a middleware developed by the GRAAL team called DIET (for Distributed Interactive Engineering Tool-box). DIET is a hierarchical set of components used for the development of applications based on computational servers on the grid.

Forecasting for Grid and Cloud Computing On-Demand Resources Based on Pattern Matching

The Cloud phenomenon brings along the cost-saving benefit of dynamic scaling. As a result, the question of efficient resource scaling arises. Prediction is necessary as the virtual resources that Cloud computing uses have a setup time that is not negligible. We propose an approach to the problem of workload prediction based on identifying similar past occurrences of the current short-term workload history. We present in detail the Cloud client resource auto-scaling algorithm that uses the above approach to help when scaling decisions are made, as well as experimental results by using real-world traces from Cloud and Grid platforms. We also present an overall evaluation of this approach, its potential and usefulness for enabling efficient auto-scaling of Cloud user resources.

Cloud Computing Resource Management through a Grid Middleware: A Case Study with DIET and Eucalyptus

The cloud phenomenon is quickly growing towards becoming the de facto standard of Internet computing, storage and hosting both in industry and academia. The large scalability possibilities offered by cloud platforms can be harnessed not only for services and applications hosting but also as a raw on-demand computing resource. This paper proposes the use of a cloud system as a raw computational on-demand resource for a grid middleware. We illustrate a proof of concept by considering the DIET-solve grid middleware and the EUCALYPTUS open-source cloud platform.

Pattern Matching Based Forecast of Non-periodic Repetitive Behavior for Cloud Clients

The Cloud phenomenon brings along the cost-saving benefit of dynamic scaling. As a result, the question of efficient resource scaling arises. Prediction is necessary as the virtual resources that Cloud computing uses have a setup time that is not negligible. We propose an approach to the problem of workload prediction based on identifying similar past occurrences of the current short-term workload history. We present in detail the Cloud client resource auto-scaling algorithm that uses the above approach to help when scaling decisions are made, as well as experimental results by using real-world Cloud client application traces. We also present an overall evaluation of this approach, its potential and usefulness for enabling efficient auto-scaling of Cloud user resources.

Definition, modelling and simulation of a grid computing scheduling system for high throughput computing

In this paper, we study and compare grid and global computing systems and outline the benefits of having a hybrid system called DIRAC. To evaluate the DIRAC scheduling for high throughput computing, a new model is presented and a simulator was developed for many clusters of heterogeneous nodes belonging to a local network. These clusters are assumed to be connected to each other through a global network and each cluster is managed via a local scheduler which is shared by many users. We validate our simulator by comparing the experimental and analytical results of a M/M/4 queuing system. Next, we do the comparison with a real batch system and we obtain an average error of 10.5% for the response time and 12% for the makespan. We conclude that the simulator is realistic and well describes the behaviour of a large-scale system. Thus we can study the scheduling of our system called DIRAC in a high throughput context. We justify our decentralized, adaptive and opportunistic approach in comparison to a centralized approach in such a context.

Relaxing Synchronization in a Parallel SystemC Kernel

SystemC has become a very popular standardized language for the modeling of system-on-chip (SoC) devices. However, due to the ever increasing complexity of SoC designs, the ever longer simulation times affect SoC exploration potential and time-to-market. In order to reduce these times, we have developed a parallel SystemC kernel. Because the SystemC semantics require a high level of synchronization which can dramatically affect the performance gains, we investigate in this paper some ways to reduce the synchronization overheads. We validate then our approaches against an academic design model and a real, industrial application.

SCILAB to SCILAB // : the Ouragan project

In this paper, we present the developments realized in the Ouragan project around the parallelization of a Matlab-like tool called Scilab. These developments use high-performance numerical libraries and different approaches based either on the duplication of Scilab processes or on computational servers. This tool, Scilab//, allows users to perform high-level operations on distributed matrices in a metacomputing environment. We also present performance results on different architectures.

A self-stabilizing k-clustering algorithm for weighted graphs

Mobile ad hoc networks as well as grid platforms are distributed, changing, and error prone environments. Communication costs within such infrastructure can be improved, or at least bounded, by using k-clustering. A k-clustering of a graph, is a partition of the nodes into disjoint sets, called clusters, in which every node is distance at most k from a designated node in its cluster, called the clusterhead. A self-stabilizing asynchronous distributed algorithm is given for constructing a k-clustering of a connected network of processes with unique IDs and weighted edges. The algorithm is comparison based, takes O(nk) time, and uses O(logn+logk) space per process, where n is the size of the network. To the best of our knowledge, this is the first solution to the k-clustering problem on weighted graphs.

Diet: new developments and recent results

Among existing grid middleware approaches, one simple, powerful, and flexible approach consists of using servers available in different administrative domains through the classic client-server or Remote Procedure Call (RPC) paradigm. Network Enabled Servers (NES) implement this model also called GridRPC. Clients submit computation requests to a scheduler whose goal is to find a server available on the grid. The aim of this paper is to give an overview of an NES middleware developed in the GRAAL team called DIET and to describe recent developments. DIET (Distributed Interactive Engineering Toolbox) is a hierarchical set of components used for the development of applications based on computational servers on the grid.

Budget constrained resource allocation for non-deterministic workflows on an iaas cloud

Many scientific applications are described through workflow structures. Due to the increasing level of parallelism offered by modern computing infrastructures, workflow applications now have to be composed not only of sequential programs, but also of parallel ones. Cloud platforms bring on-demand resource provisioning and pay-as-you-go billing model. Then the execution of a workflow corresponds to a certain budget. The current work addresses the problem of resource allocation for non-deterministic workflows under budget constraints. We present a way of transforming the initial problem into sub-problems that have been studied before. We propose two new allocation algorithms that are capable of determining resource allocations under budget constraints and we present ways of using them to address the problem at hand.