Fernando Cores

Cooperative scheduling mechanism for large-scale peer-to-peer computing systems

Over recent years, peer-to-peer (P2P) systems have become an important part of Internet. Millions of users have been attracted to their structures and services. P2P computing is a distributed computing paradigm that uses Internet to connect thousands, or even millions, of users into a single large virtual computer based on the sharing of computational resources. One of the most critical aspects to the design of P2P computing systems is the development of scheduling techniques to manage the computational resources efficiently and in a scalable way. This paper proposes a cooperative scheduling mechanism with a two-level topology designed to work on large-scale distributed computing P2P systems. Our main contribution is proposing three criteria that only use local information to schedule tasks thus providing scalability to the overall scheduling system. By setting up these three criteria, the system can be easily adapted to work efficiently with very different kinds of distributed applications. The extensive experimentation carried out justifies the importance of good scheduling in such heterogeneous systems, but also emphasizes the importance of having a scheduling algorithm capable of being adapted to the requirements of different kinds of application.

Designing a Video-on-Demand System for a Brazilian High Speed Network

Despite the availability of video-on-demand (VoD) services in a number of cities around the world, the large-scale deployment of VoD services has not been widely explored due to economic constraints. The high complexity founded in these systems makes the design process very difficult. This study presents a VoD system design for a highspeed metropolitan network, based on ATM technology, located in Brazil. To accomplish this goal, we have utilized our own simulation-based tool for VoD design called VODSim. This tool allows VoD research on any VoD platform, simulating the complete flow path followed by requests from clients to servers passing through the network infrastructure, allowing the configuration and tuning of multiple system parameters. Using VODSim, we analyse possible VoD architectures for the proposal environment (i.e. Centralized-server, Independent-servers, One-level-proxies and Distributedservers), studying key-parameters that have influence in system design. Experimental results show that Distributed-server architecture is the one that adjusts better to the analyzed infrastructure, improving streaming capacity by 16%, 25% and 28% more than One-level-proxies, Centralized and Independent-servers respectively. Taking in account future growth in VoD system, the Distributed-server even provides an unlimited scalability through new local network incorporation and using P2P techniques.

High Performance computing improvements on bioinformatics consistency-based multiple sequence alignment tools

BGT method improves the execution time of progressive alignment by 62%.OLM decreases the memory requirements by 75%, aligning up twice more sequences than previous method.MTA improves the alignments accuracy, letting it to regain the quality lost due to the performance improvements. Multiple Sequence Alignment (MSA) is essential for a wide range of applications in Bioinformatics. Traditionally, the alignment accuracy was the main metric used to evaluate the goodness of MSA tools. However, with the growth of sequencing data, other features, such as performance and the capacity to align larger datasets, are gaining strength. To achieve these new requirements, without affecting accuracy, the use of high-performance computing (HPC) resources and techniques is crucial. In this paper, we apply HPC techniques in T-Coffee, one of the more accurate but less scalable MSA tools. We integrate three innovative solutions into T-Coffee: the Balanced Guide Tree to increase the parallelism/performance, the Optimized Library Method with the aim of enhancing the scalability and the Multiple Tree Alignment, which explores different alignments in parallel to improve the accuracy. The results obtained show that the resulting tool, MTA-TCoffee, is able to improve the scalability in both the execution time and also the number of sequences to be aligned. Furthermore, not only is the alignment accuracy not affected by these improvements, as would be expected, but it improves significantly. Finally, we emphasize that the presented methods are not just restricted to T-Coffee, but may be implemented in any other alignment tools that use similar algorithms (progressive alignment, consistency or guide trees).

Exploiting parallelism on progressive alignment methods

Multiple Sequence Alignment (MSA) constitutes an extremely powerful tool for important biological applications such as phylogenetic analysis, identification of conserved motifs and domains and structure prediction. In spite of the improvement in speed and accuracy introduced by MSA programs, the computational requirements for large-scale alignments requires high-performance computing and parallel applications. In this paper we present an improvement to a parallel implementation of T-Coffee, a widely used MSA package. Our approximation resolves the bottleneck of the progressive alignment stage on MSA. This is achieved by increasing the degree of parallelism by balancing the guide tree that drives the progressive alignment process. The experimental results show improvements in execution time of over 68% while maintaining the biological accuracy.

Multiple job co-allocation strategy for heterogeneous multi-cluster systems based on linear programming

Multi-cluster environments are composed of multiple clusters of computers that act collaboratively, and thus allowing computational problems to be treated that require more resources than those available in a single cluster. However, the degree of complexity of the scheduling process is greatly increased by the heterogeneity of resources and co-allocation process, which distributes the tasks of parallel jobs across cluster boundaries.This work presents a new scheduling strategy that allocates multiple jobs from the system queue simultaneously on a heterogeneous multicluster, by applying co-allocation when is necessary. Our strategy is composed by a job selection function and a linear programming model to find the best allocation for multiple jobs. The proposed scheduling technique is shown to reduce the execution times of the parallel jobs and the overall response times by 38% compared with other scheduling techniques in the literature.

Distributed P2P merging policy to decentralize the multicasting delivery

Advances in network technology make multicast one of the most feasible video streaming delivery techniques for the near future. However, the scalability of a multicast VoD system is limited by the server bandwidth. In this paper, we propose a new multicast delivery scheme that allows every active client to collaborate with the server in order to scale the VoD system performance beyond the servers physical limitations. The solution combined the multicast delivery scheme and peer-2-peer paradigm in order to decentralize the delivery process. The new video delivery scheme is able to merge two or more multicast channels using distributed collaborations between a group of clients. We compared the new policy with chaining and patching schemes and the experimental results showed that our policy is better than previous schemes in terms of reduction of resource requirements and local network load. Compared with multicast patching policy, the new scheme reduced the resource requirement up to 77.5% while the local network load was 66.9% lower than a peer-2-peer chaining policy.

Dynamic distributed collaborative merging policy to optimize the multicasting delivery scheme

The advance of Internet 2 and the proliferation of switches and routers with level three functionalities made the multicast one of the most feasible video streaming delivering techniques for the near future. Assuming this to be true, this study addressed the over-load situation that a streaming server could suffer due to client requests. As a solution, we proposed new multicast delivery scheme that allows every active client to collaborate with the server regardless of the video that they are watching, alleviating server loads, and therefore server resource requirements. The solution combined the multicast delivery scheme and client-side buffer collaboration in order to decentralize the delivery process. The new video delivering scheme was designed as two separate policies: the first policy used client collaboration to deliver first part of videos and the second policy could merge two or more multicast channels using distributed collaboration between a group of clients. Experimental results show that this scheme is better than previous schemes in terms of resource requirements and scalability.

Improving multiple sequence alignment biological accuracy through genetic algorithms

Accuracy on multiple sequence alignments (MSA) is of great significance for such important biological applications as evolution and phylogenetic analysis, homology and domain structure prediction. In such analyses, alignment accuracy is crucial. In this paper, we investigate a combined scoring function capable of obtaining a good approximation to the biological quality of the alignment. The algorithm uses the information obtained by the different quality scores in order to improve the accuracy. The results show that the combined score is able to evaluate alignments better than the isolated scores.

Incentive mechanism for scheduling jobs in a peer-to-peer computing system

Abstract Peer-to-Peer (P2P) computing, the harnessing of idle CPU cycles through the Internet, offers new research challenges in distributed computing. One of the most critical aspects in the design of P2P systems is the development of incentive techniques to encourage cooperation and resource sharing among participants. To tackle this problem, in this paper we propose an incentive and scheduling mechanism based on credits with a two-level topology which is designed to operate on different types of shared computing networks, such as P2P, P2P Grid, Opportunistic Grid, Desktop Grid, volunteer computing platforms, among others. The low level association of peers is made up of one super-peer and its constituent peers. The main contribution at this level is a new reinvestment policy, called “Weighted”, that increases peer participation significantly. This mechanism reflects P2P user dynamics, penalizes free-riders efficiently and encourages peer participation. Scalability limitations on the first level are avoided by providing the mechanism with an upper level, made up of super-peers interconnected through an upper level overlay. Our main contribution at this level is the proposal of a task scheduling policy based on three criteria, computing capacity with neighbors, distance and reputation that only use local information for mapping tasks. Simulation results show our proposal performs well and is functional.

Double P-Tree: A Distributed Architecture for Large-Scale Video-on-Demand

In order to ensure a more widespread implementation of video-on-demand (VoD) services, it is essential that the design of cost-effective large-scale VoD (LVoD) architectures be able to support hundreds of thousands of concurrent users. The main keys for the designing of such architectures are high streaming capacity, low costs, scalability, fault tolerance, load balance, low complexity and resource sharing among user requests. To achieve these objectives, we propose a distributed architecture, called double P-Tree, which is based on a tree topology of independent local networks with proxies. The proxy functionality has been modified in such a way that it works at the same time as cache for the most-watched videos, and as a distributed mirror for the remaining videos. In this way, we manage to distribute main server functionality (as a repository of all system videos, server of proxy-misses and system manager) among all local proxies. The evaluation of this new architecture, through an analytical model, shows that double P-Tree architecture is a good approach for the building of scalable and fault-tolerant LVoD systems. Experimental results show that this architecture achieves a good tradeoff between effective bandwidth and storage requirements.