Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, ...theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature Provides a theoretical understanding as well as practical implementation hints Provides a step-by-step introduction to each algorithm.
All grown-up; 18 years of LHC@home Cameron, David; Field, Laurence; Van der Veken, Frederik ...
EPJ Web of Conferences,
2024, Letnik:
295
Journal Article, Conference Proceeding
Recenzirano
Odprti dostop
LHC@home was launched as a BOINC project in 2004 as an outreach project for CERN’s 50 years anniversary. Initially focused on the accelerator physics simulation code SixTrack, the project was ...expanded in 2011 to run other physics simulation codes on Linux thanks to virtualisation. Later on the experiment and theory applications running on the LHC@home platform have evolved to use containers and take advantage of the CVMFS file system as well as content delivery networks. Furthermore, a substantial part of the contributed computing capacity nowadays is provided as opportunistic back-fill from data centers with spare capacity, in addition to enthusiastic volunteers. The paper will address the challenges with this distributed computing model, new applications to exploit GPUs and the future outlook for volunteer computing.
DIRAC is a widely used framework for distributed computing. It provides a layer between users and computing resources by offering a common interface to a number of heterogeneous resource providers. ...In these proceedings we describe a new implementation of the DIRAC to Cloud interface.
Improved Pilot Logging in DIRAC Fayer, Simon; Martyniak, Janusz; Stagni, Federico
EPJ Web of Conferences,
01/2024, Letnik:
295
Conference Proceeding, Journal Article
Recenzirano
Odprti dostop
DIRAC is a widely used framework for distributed computing. It works by building a layer between the users and the resources offering a common interface to a number of heterogeneous providers. DIRAC, ...like many other workload management systems, uses pilot jobs to check and configure the worker-node environment before fetching a user payload. The log messages generated by these pilot jobs are crucial for diagnosing problems in the infrastructure. In these proceedings we present a configurable, resource independent remote pilot logging system.
The computing challenges at the HL–LHC require fundamental changes to the distributed computing models that have served experiments well throughout LHC. ATLAS planning for HL–LHC computing started ...back in 2020 with a Conceptual Design Report outlining various challenges to explore. This was followed in 2022 by a roadmap defining concrete milestones and associated effort required. Today, ATLAS is proceeding further with a set of "demonstrators" with focused R&D in specific topics described in the roadmap. The demonstrators cover areas such as optimised tape writing and access, data recreation on–demand and the use of commercial clouds.
This article gives a survey of state-of-the-art methods for processing remotely sensed big data and thoroughly investigates existing parallel implementations on diverse popular high-performance ...computing platforms. The pros/cons of these approaches are discussed in terms of capability, scalability, reliability, and ease of use. Among existing distributed computing platforms, cloud computing is currently the most promising solution to efficient and scalable processing of remotely sensed big data due to its advanced capabilities for high-performance and service-oriented computing. We further provide an in-depth analysis of state-of-the-art cloud implementations that seek for exploiting the parallelism of distributed processing of remotely sensed big data. In particular, we study a series of scheduling algorithms (GSs) aimed at distributing the computation load across multiple cloud computing resources in an optimized manner. We conduct a thorough review of different GSs and reveal the significance of employing scheduling strategies to fully exploit parallelism during the remotely sensed big data processing flow. We present a case study on large-scale remote sensing datasets to evaluate the parallel and distributed approaches and algorithms. Evaluation results demonstrate the advanced capabilities of cloud computing in processing remotely sensed big data and the improvements in computational efficiency obtained by employing scheduling strategies.
Monitoring services play a crucial role in the day-to-day operation of distributed computing systems. The ATLAS Experiment at LHC uses the Production and Distributed Analysis workload management ...system (PanDA WMS), which allows a million computational jobs to run daily at over 170 computing centers of the WLCG and opportunistic resources, utilizing 600k cores simultaneously on average. The BigPanDA monitor is an essential part of the monitoring infrastructure for the ATLAS Experiment that provides a wide range of views, from top-level summaries to a single computational job and its logs. Over the past few years of the PanDA WMS advancement in the ATLAS Experiment, several new components were developed, such as Harvester, iDDS, Data Carousel, and Global Shares. Due to its modular architecture, the BigPanDA monitor naturally grew into a platform where the relevant data from all PanDA WMS components and accompanying services are accumulated and displayed in the form of interactive charts and tables. Moreover the system has been adopted by other experiments beyond HEP. In this paper we describe the evolution of the BigPanDA monitor system, the development of new modules, and the integration process into other experiments.
Message from the TPC Co-Chairs: ICDCSW 2022 Yang, Yuanyuan; Ye, Fan
2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW),
2022-July
Conference Proceeding
Welcome to ICDCS 2022 in Bologna, Italy! It is an exciting moment to return to in-person conference after two years in a row of virtual presentations amid the COVID-19 pandemic. ICDCS is a premier ...venue for research results in distributed computing systems. It is our great pleasure to introduce the ICDCS 2022 program, which consists of excellent keynote talks, high-quality paper presentations, social activities including a reception and a banquet, and many opportunities for researchers around the world to gather and exchange the latest work and ideas in distributed computing.
