Corina Stratan

MonALISA: An agent based, dynamic service system to monitor, control and optimize distributed systems ☆

The MonALISA (Monitoring Agents in a Large Integrated Services Architecture) framework provides a set of distributed services for monitoring, control, management and global optimization for large scale distributed systems. It is based on an ensemble of autonomous, multi-threaded, agent-based subsystems which are registered as dynamic services. They can be automatically discovered and used by other services or clients. The distributed agents can collaborate and cooperate in performing a wide range of management, control and global optimization tasks using real time monitoring information.

A decentralized strategy for genetic scheduling in heterogeneous environments

The paper describes a solution to the key problem of ensuring high performance behavior of the Grid, namely the scheduling of activities It presents a distributed, fault-tolerant, scalable and efficient solution for optimizing task assignment The scheduler uses a combination of genetic algorithms and lookup services for obtaining a scalable and highly reliable optimization tool The experiments have been carried out on the MonALISA monitoring environment and its extensions The results demonstrate very good behavior in comparison with other scheduling approaches.

A performance study of grid workflow engines

To benefit from grids, scientists require grid workflow engines that automatically manage the execution of inter-related jobs on the grid infrastructure. So far, the workflows community has focused on scheduling algorithms and on interface tools. Thus, while several grid workflow engines have been deployed, little is known about their performance-related characteristics, and there are no commonly-used testing practices. This situation limits the adoption of the grid workflow engines, and hampers their tuning and their further development. In this work we propose a testing methodology for grid workflow engines that focuses on five characteristics: overhead, raw performance, stability, scalability, and reliability. Using this methodology, we evaluate in a real test environment several middleware stacks that include grid workflow engines, including two based on DAGMan/Condor and on Karajan/Globus.

Realistic Simulation of Large Scale Distributed Systems using Monitoring

In this paper we present an extension to MONARC, a generic simulator for large scale distributed systems, which allows realistic evaluation of various actual distributed system technologies based on real-world monitored data supplied by MonALISA. The field of modelling and simulation was long-time seen as a viable solution to develop new algorithms and technologies and to enable the development of large-scale distributed systems, where analytical validations are prohibited by the nature of the encountered problems. This paper presents a novel approach to combining two distributed systems domains, monitoring and simulation, highlighting a realistic solution to the problem of accurately evaluating various distributed systems technologies using simulation. We also present a simulation study which demonstrates the interoperability between the simulation framework and the monitoring instrument, demonstrating important properties of the US LHCNet research network in the context of the LHC experiments in CERN.

A dependability layer for large-scale distributed systems

Ensuring dependability in large-scale distributed systems represents an important research subject today. Despite the fact that many projects obtained valuable results in this domain, no acceptable solution was yet found that could integrate all the requirements for designing a dependable system and that could exploit all the capabilities of modern systems. We present a unitary and aggregate approach to ensuring reliability, availability, safety and security of distributed systems. Starting from the proposed architecture, we present implementation details for two solutions designed to ensure fault tolerance, using virtualisation and container-based replication of services. We also present an approach to enhance security using combined modern security models in large-scale distributed systems. The results and implementation details can serve as a methodology to assist distributed infrastructures in adopting such a middleware layer designed to enforce dependability in large-scale distributed systems.

Dynamic meta-scheduling architecture based on monitoring in distributed systems

The Scheduling process in Large Scale Distributed System (LSDS) became more important due of increases of users and applications. This paper presents a dynamic meta-scheduling architecture model for LSDS based on monitoring. Dynamic scheduling process tries to perform task allocation on the fly as the application executes. The monitoring is important in this process because can offer a full view of nodes in distributed systems. The proposed architecture is an agent framework and contains a Grid Monitoring Service, an Execution Services and a Discovery Services. The performance of used monitoring system - MonALISA is very important for dynamic scheduling because ensure the real-time process. The experimental results validate our architecture and scheduling model.

Large-Scale Distributed Computing and Applications: Models and Trends

Many applications follow the distributed computing paradigm, in which parts of the application are executed on different network-interconnected computers. The extension of these applications in terms of number of users or size has led to an unprecedented increase in the scale of the infrastructure that supports them. Large-Scale Distributed Computing and Applications: Models and Trends offers a coherent and realistic image of todays research results in large scale distributed systems, explains state-of-the-art technological solutions for the main issues regarding large scale distributed systems, and presents the benefits of using large scale distributed systems and the development process of scientific and commercial distributed applications.

Dynamic Meta-Scheduling Architecture Based on Monitoring in Distributed Systems

The Scheduling process in Large Scale Distributed System (LSDS) became more important due of increases of users and applications. This paper presents a dynamic meta-scheduling architecture model for LSDS based on monitoring. Dynamic scheduling process tries to perform task allocation on the fly as the application executes. The monitoring is important in this process because can offer a full view of nodes in distributed systems. The proposed architecture is an agent framework and contains a Grid Monitoring Service, an Execution Services and a Discovery Services. The performance of used monitoring system - MonALISA is very important for dynamic scheduling because ensure the real-time process. The experimental results validate our architecture and scheduling model.

Decentralized Grid Scheduling Using Genetic Algorithms

The chapter describes a solution to the key problem of ensuring high performance behavior of the Grid, namely the scheduling of tasks. It presents a distributed, fault-tolerant, scalable and efficient solution for optimizing task assignment. The scheduler uses a combination of genetic algorithms and lookup services for obtaining a scalable and highly reliable optimization tool. The experiments have been carried out on the MonALISA monitoring environment and its extensions. The results demonstrate very good behavior in comparison with other scheduling approaches.

An Agent Based Architecture for DAG Scheduling

This paper presents an efficient agent based DAG scheduling system. The proposed system has a decentralized architecture. It is able to manage tasks with dependencies, and bases its decisions on Grid resources’ status captured dynamically and used at schedule time. Fault tolerance mechanisms can also be easily implemented, providing a great degree of certainty that the schedule results will be correct and delivered before the imposed deadline. The system has been integrated and tested with MonAlisa farms and the ApMon, which is a MonAlisa extension. The results obtained so far in the performed experiments show that the system introduces a reasonable overhead while producing higher quality mappings than the centralized ones.