Ranieri Baraglia

A hybrid heuristic for the traveling salesman problem

The combination of genetic and local search heuristics has been shown to be an effective approach to solving the traveling salesman problem (TSP). This paper describes a new hybrid algorithm that exploits a compact genetic algorithm in order to generate high-quality tours, which are then refined by means of the Lin-Kernighan (LK) local search. The local optima found by the LK local search are in turn exploited by the evolutionary part of the algorithm in order to improve the quality of its simulated population. The results of several experiments conducted on different TSP instances with up to 13,509 cities show the efficacy of the symbiosis between the two heuristics.

Dynamic personalization of web sites without user intervention

A novel online recommender system builds profiling models and offers suggestions without the user taking the lead.

A grid information service based on peer-to-peer

Information Services are fundamental blocks of the Grid infrastructure. They are responsible for collecting and distributing information about resource availability and status to users: the quality of these data may have a strong impact on scheduling algorithms and overall performance. Many popular information services have a centralized structure. This clearly introduces problems related to information updating and fault tolerance. Also, in very large configurations, scalability may be an issue. In this work, we present a Grid Information Service based on the peer-to-peer technology. Our system offers a fast propagation of information and has high scalability and reliability. We implemented our system complying to the OGSA standard using the Globus Toolkit 3. Our system can run on Linux and Windows systems, with different network configurations, so to trade off between redundancy (reliability) and cost.

Document Similarity Self-Join with MapReduce

iven a collection of objects, the Similarity Self-Join problem requires to discover all those pairs of objects whose similarity is above a user defined threshold. In this paper we focus on document collections, which are characterized by a sparseness that allows effective pruning strategies. Our contribution is a new parallel algorithm within the MapReduce framework. This work borrows from the state of the art in serial algorithms for similarity join and MapReduce-based techniques for set-similarity join. The proposed algorithm shows that it is possible to leverage a distributed file system to support communication patterns that do not naturally fit the MapReduce framework. Scalability is achieved by introducing a partitioning strategy able to overcome memory bottlenecks. Experimental evidence on real world data shows that our algorithm outperforms the state of the art by a factor 4.5.

The MOL project: an open, extensible metacomputer

Distributed high-performance computing (so-called metacomputing) refers to the coordinated use of a pool of geographically distributed high-performance computers. The users view of an ideal metacomputer is that of a powerful monolithic virtual machine. The implementors view, on the other hand, is that of a variety of interacting services implemented in a scalable and extensible manner. We present MOL, the Metacomputer Online environment. In contrast to other metacomputing environments, MOL is not based on specific programming models or tools. It has rather been designed as an open, extensible software system comprising a variety of software modules, each of them specialized in serving one specific task such as resource scheduling, job control, task communication, task migration, user interface, and much more. All of these modules exist and are working. The main challenge in the design of MOL lies in the specification of suitable, generic interfaces for the effective interaction between the modules.

Comparison of Multi-Criteria Scheduling Techniques

We propose a novel schedule-based approach for scheduling a continuous stream of batch jobs on the machines of a computational Grid. Our new solutions represented by dispatching rule Earliest Gap-Earliest Deadline First (EG-EDF) and Tabu search are based on the idea of filling gaps in the existing schedule. EG-EDF rule is able to build the schedule for all jobs incrementally by applying technique which fills earliest existing gaps in the schedule with newly arriving jobs. If no gap for a coming job is available EG-EDF rule uses Earliest Deadline First (EDF) strategy for including new job into the existing schedule. Such schedule is then optimized using the Tabu search algorithm moving jobs into earliest gaps again. Scheduling choices are taken to meet the Quality of Service (QoS) requested by the submitted jobs, and to optimize the usage of hardware resources. Proposed solution is compared with FCFS, EASY backfilling, and Flexible backfilling. Experiments shows that EG-EDF rule is able to compute good assignments, often with shorter algorithm runtime w.r.t. the other queue-based algorithms. Further Tabu search optimization results in higher QoS and machine usage.

SUGGEST: a Web usage mining system

During their navigation, Web users leave many records of their activity. This huge amount of data can be a useful source of knowledge. Sophisticated data mining processes are needed for this knowledge to be extracted, understood and used. In this paper, we propose a Web usage mining (WUM) system, called SUGGEST, which dynamically generates suggested links to Web pages of potential interest for a user. SUGGEST is designed to efficiently integrate the WUM process with the ordinary Web server functionalities. It can provide useful information to make the users Web navigation easier and to optimize the Web servers performance. Two quantities are introduced in order to give a measure of the quality of our WUM system.

A peer-to-peer recommender system for self-emerging user communities based on gossip overlays

Gossip-based peer-to-peer protocols proved to be very efficient for supporting dynamic and complex information exchange among distributed peers. They are useful for building and maintaining the network topology itself as well as to support a pervasive diffusion of the information injected into the network. This is very useful in a world where there is a growing need to access and be aware of many types of distributed resources like Internet pages, shared files, online products, news and information. Finding flexible, scalable and efficient mechanisms addressing this topic is a key issue, even with relevant social and economic aspects. In this paper, we propose the general architecture of a system whose aim is to exploit the collaborative exchange of information between peers in order to build a system able to gather similar users and spread useful suggestions among them.

Backfilling Strategies for Scheduling Streams of Jobs On Computational Farms

This paper presents a set of strategies for scheduling a stream of batch jobs on the machines of a heterogeneous computational farm. Our proposal is based on a flexible backfilling, which schedules jobs according to a priority assigned to each job submitted for execution. Priority values are computed as a result of a set of heuristics whose main goal is to improve resources utilization and to meet the job QoS requirements. The heuristics consider job deadlines, estimated execution time and aging of the jobs in the scheduling queue. Furthermore, the set of software licenses required by a job is also considered. The different proposals have been compared through simulations. Performance figures show the applicability of our approach.

AssistConf: a Grid configuration tool for the ASSIST parallel programming environment

The paper presents AssistConf, a graphical user interface designed to configure an ASSIST program and to run it on a Grid platform. ASSIST (a software development system based upon integrated skeleton technology) is a new programming environment for the development of parallel and distributed high-performance applications. The main goals of ASSIST are allowing high-level programmability and software productivity for complex multidisciplinary applications, and performance portability across different platforms, including homogenous parallel machines and cluster/Beowulf systems, heterogeneous clusters, and computational Grids. AssistConf is used to configure the ASSIST program and establish a mapping between the program modules and the most suitable machines in the Grid candidate to execute them. It simplifies the creation of the XML ASSIST configuration file, giving users a graphical view of the XML file produced by the ASSIST compilation phase, and permitting an easy identification of the machines to be used for execution. Finally, the configuration file produced by AssistConf is used as input to the assistrun command, which drives the execution of the ASSIST program over the Grid.