Automating Fault Tolerance in High-Performance Computational Biological Jobs Using Multi-Agent Approaches

Background: Large-scale biological jobs on high-performance computing systems require manual intervention if one or more computing cores on which they execute fail. This places not only a cost on the maintenance of the job, but also a cost on the time taken for reinstating the job and the risk of losing data and execution accomplished by the job before it failed. Approaches which can proactively detect computing core failures and take action to relocate the computing core's job onto reliable cores can make a significant step towards automating fault tolerance. Method: This paper describes an experimental investigation into the use of multi-agent approaches for fault tolerance. Two approaches are studied, the first at the job level and the second at the core level. The approaches are investigated for single core failure scenarios that can occur in the execution of parallel reduction algorithms on computer clusters. A third approach is proposed that incorporates multi-agent technology both at the job and core level. Experiments are pursued in the context of genome searching, a popular computational biology application. Result: The key conclusion is that the approaches proposed are feasible for automating fault tolerance in high-performance computing systems with minimal human intervention. In a typical experiment in which the fault tolerance is studied, centralised and decentralised checkpointing approaches on an average add 90% to the actual time for executing the job. On the other hand, in the same experiment the multi-agent approaches add only 10% to the overall execution time.Comment: Computers in Biology and Medicin

A hierarchical run-time adaptive resource allocation framework for large-scale MPSoC systems

In the embedded computer system domain, MPSoC systems have become increasingly popular due to the ever-increasing performance demands of modern embedded applications. The number of processing elements in these MPSoCs also steadily increases. Whereas current MPSoCs still contain a limited number of processing elements, future MPSoCs will feature tens up to hundreds of (heterogeneous) processing elements that are all integrated on a single chip. On these future large-scale MPSoC systems, the mapping of applications onto the hardware resources plays an important role to fully explore the parallelism of applications. In this article, a hierarchical run-time adaptive resource allocation framework which uses an intelligent task remapping approach is proposed to improve the system performance for large-scale MPSoCs

Evaluating Byzantine-Based Blockchain Consensus Algorithms for Sarawak’s Digitalized Pepper Value Chain

A chosen network structure of Practical Byzantine Fault Tolerance (PBFT), a Byzantine-based consensus algorithm, is proposed to minimize some of the identified pain points faced by the pepper stakeholders. Byzantine-based consensus algorithms are used to achieve the same agreement on a single data value, including transactions and block state, and to maintain system continuity even when several nodes have failed to respond or transmit inconsistent messages in the blockchain network