Distributed Computing Applications Research Articles

Thermal Aware System-Wide Reliability Optimization for Automotive Distributed Computing Applications

As the automotive industry is shifting the paradigm towards autonomous driving, safety guarantee has become a paramount consideration. Temperature plays a key role in the system-wide reliability of the electronic control systems (ECS) used in the automotive. A vehicle is usually subjected to harsh temperature conditions from its operating environment. The increasing power density of IC chips in the ECS further exacerbates the operating temperature and thermal gradient condition on the chip, thereby significantly impacting the vehicle’s reliability. In this paper, we study how to map a periodic distributed automotive application on a heterogeneous multiple-core processing architecture with temperature and system-level reliability issues in check. We first present a mathematical programming model to bound the peak operating temperature for the ECS. Then we propose a more sophisticated approach based on the genetic algorithm to effectively bound the peak temperature and optimize the system-wide reliability of the ECS by maximizing its mean-time-to-failure (MTTF). To this end, we present an algorithm to guarantee the peak temperature for periodic applications with variable execution times to ensure our approach’s effectiveness. We also present several computationally efficient techniques for system-wide MTTF computation, which show several-order-of-magnitude speed-up over the state-of-the-art method when tested using synthetic cases and practical benchmarks.

Trigger-Based Incremental Data Processing with Unified Sync and Async Model

In recent years, more and more applications in the cloud have needs to process large-scale on-line datasets, which evolve over time as new entries are added and existing entries are modified. Several programming frameworks, such as Percolator and Oolong, are proposed for such incremental data processing and can achieve efficient processing with an event-driven abstraction. However, these frameworks are inherently asynchronous, leaving the heavy burden of managing synchronization to applications' developers, which further significantly restricts their usabilities. In this study, we propose a trigger-based incremental computing framework in the cloud, called Domino, with both synchronous and asynchronous mechanisms to coordinate parallel triggers. With this new framework, both synchronous and asynchronous applications can be seamlessly developed. Use cases and extensive evaluation results confirm that it can deliver sufficient performance, and also is easy to use for incremental applications in large-scale distributed computing.

Computational Strategies for Scalable Genomics Analysis.

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications.

AGIS: Integration of new technologies used in ATLAS Distributed Computing

The variety of the ATLAS Distributed Computing infrastructure requires a central information system to define the topology of computing resources and to store different parameters and configuration data which are needed by various ATLAS software components. The ATLAS Grid Information System (AGIS) is the system designed to integrate configuration and status information about resources, services and topology of the computing infrastructure used by ATLAS Distributed Computing applications and services. Being an intermediate middleware system between clients and external information sources (like central BDII, GOCDB, MyOSG), AGIS defines the relations between experiment specific used resources and physical distributed computing capabilities. Being in production during LHC Runl AGIS became the central information system for Distributed Computing in ATLAS and it is continuously evolving to fulfil new user requests, enable enhanced operations and follow the extension of the ATLAS Computing model. The ATLAS Computing model and data structures used by Distributed Computing applications and services are continuously evolving and trend to fit newer requirements from ADC community. In this note, we describe the evolution and the recent developments of AGIS functionalities, related to integration of new technologies recently become widely used in ATLAS Computing, like flexible computing utilization of opportunistic Cloud and HPC resources, ObjectStore services integration for Distributed Data Management (Rucio) and ATLAS workload management (PanDA) systems, unified storage protocols declaration required for PandDA Pilot site movers and others. The improvements of information model and general updates are also shown, in particular we explain how other collaborations outside ATLAS could benefit the system as a computing resources information catalogue. AGIS is evolving towards a common information system, not coupled to a specific experiment.

AGIS: Evolution of Distributed Computing information system for ATLAS

ATLAS, a particle physics experiment at the Large Hadron Collider at CERN, produces petabytes of data annually through simulation production and tens of petabytes of data per year from the detector itself. The ATLAS computing model embraces the Grid paradigm and a high degree of decentralization of computing resources in order to meet the ATLAS requirements of petabytes scale data operations. It has been evolved after the first period of LHC data taking (Run-1) in order to cope with new challenges of the upcoming Run- 2. In this paper we describe the evolution and recent developments of the ATLAS Grid Information System (AGIS), developed in order to integrate configuration and status information about resources, services and topology of the computing infrastructure used by the ATLAS Distributed Computing applications and services.

AGIS: The ATLAS Grid Information System

ATLAS, a particle physics experiment at the Large Hadron Collider at CERN, produced petabytes of data annually through simulation production and tens of petabytes of data per year from the detector itself. The ATLAS computing model embraces the Grid paradigm and a high degree of decentralization and computing resources able to meet ATLAS requirements of petabytes scale data operations. In this paper we describe the ATLAS Grid Information System (AGIS), designed to integrate configuration and status information about resources, services and topology of the computing infrastructure used by the ATLAS Distributed Computing applications and services.

AGIS: The ATLAS Grid Information System

ATLAS is a particle physics experiment at the Large Hadron Collider at CERN. The experiment produces petabytes of data annually through simulation production and tens petabytes of data per year from the detector itself. The ATLAS Computing model embraces the Grid paradigm and a high degree of decentralization and computing resources able to meet ATLAS requirements of petabytes scale data operations. In this paper we present ATLAS Grid Information System (AGIS) designed to integrate configuration and status information about resources, services and topology of whole ATLAS Grid needed by ATLAS Distributed Computing applications and services.

P2P Networks with IP Based Communication

P2P communities can be seen as truly Distributed Computing applications in which group members communicate with one another to exchange information. The authors consider security issues in Peer to Peer Networks. For secure exchange of data between the group members the authors present a cryptography protocol and an Identity mechanism which can able to check even the Trust of the Peers based on the available reputation information. The authors are encapsulating the reputations of both the provider and the requester. So the requester cannot (gainfully) maliciously abort the transaction in the middle. In other words, the requester cannot take the service from the provider and then logoff without giving a recommendation to the provider.

Multiscale modeling and distributed computing to predict cosmesis outcome after a lumpectomy

Surgery for early stage breast carcinoma is either total mastectomy (complete breast removal) or surgical lumpectomy (only tumor removal). The lumpectomy or partial mastectomy is intended to preserve a breast that satisfies the woman’s cosmetic, emotional and physical needs. But in a fairly large number of cases the cosmetic outcome is not satisfactory. Today, predicting that surgery outcome is essentially based on heuristic. Modeling such a complex process must encompass multiple scales, in space from cells to tissue, as well as in time, from minutes for the tissue mechanics to months for healing.The goal of this paper is to present a first step in multiscale modeling of the long time scale prediction of breast shape after tumor resection. This task requires coupling very different mechanical and biological models with very different computing needs. We provide a simple illustration of the application of heterogeneous distributed computing and modular software design to speed up the model development. Our computational framework serves currently to test hypothesis on breast tissue healing in a pilot study with women who have been elected to undergo BCT and are being treated at the Methodist Hospital in Houston, TX.

ATLAS Grid Information System

ATLAS is a particle physics experiment at the Large Hadron Collider at CERN. The detector produces huge volumes of data, of the order of petabytes per year. To satisfy ATLAS requirements for petabyte scale production and distributed analysis processing, a grid technology is used to distribute, store and analyse these immense amounts of data. In this paper we present ATLAS Grid Information System (AGIS) designed to integrate configuration and status information about resources, services and topology of whole ATLAS Grid needed by ATLAS Distributed Computing applications.

Implementation of an Intelligent Urban Traffic Management System Based on a City Grid Infrastructure

Grid computing is becoming the preferred basis for large-scale distributed computing and Internet applications. ShanghaiGrid launched in Shanghai, China is a grid infrastructure that aggregates several heterogeneous supercomputers, data centers and other applications scattered in different organizations in Shanghai. One of the major focuses in the ShanghaiGrid project is research and development of an intelligent urban traffic management system based on the ShanghaiGrid system software. The system software provides a platform in a service-oriented way for resource encapsulation and management, service scheduling and accounting, data aggregation and adaptive transmission, as well as service composition and reliability support. This paper presents the main components of the system software, and investigates key techniques for the implementation of ShanghaiGrid and the intelligent urban traffic management system, concentrating on grid transaction and workflow management. Finally, we propose a new wireless sensor network model and routing protocol characteristic to mobile vehicles for dynamical traffic data collection.

Distributed Computing Software Building-Blocks for Ubiquitous Computing Societies

The steady approach of advanced nations toward realization of ubiquitous computing societies has given birth to rapidly growing demands for new-generation distributed computing (DC) applications. Consequently, economic and reliable construction of new-generation DC applications is currently a major issue faced by the software technology research community. What is needed is a new-generation DC software engineering technology which is at least multiple times more effective in constructing new-generation DC applications than the currently practiced technologies are. In particular, this author believes that a new-generation building-block (BB), which is much more advanced than the current-generation DC object that is a small extension of the object model embedded in languages C++, Java, and C#, is needed. Such a BB should enable systematic and economic construction of DC applications that are capable of taking critical actions with 100-microsecond-level or even 10-microsecond-level timing accuracy, fault tolerance, and security enforcement while being easily expandable and taking advantage of all sorts of network connectivity. Some directions considered worth pursuing for finding such BBs are discussed.

Orchestration of Network-Wide Active Measurements for Supporting Distributed Computing Applications

Recent computing applications such as videoconferencing and grid computing run their tasks on distributed computing resources connected through networks. For such applications, knowledge of the network status such as delay, jitter, and available bandwidth can help them select proper network resources to meet the Quality-of-Service (QoS) requirements. Also, the applications can dynamically change the resource selection if the current selection is found to experience poor performance. For such purposes, Internet Service Providers (ISPs) have started to instrument their networks with Network Measurement Infrastructures (NMIs) that run active measurement tasks periodically and/or on demand. However, one problem that most network engineers have overlooked is the measurement conflict problem, which happens when multiple active measurement tasks inject probing packets into the same network segment at the same time, resulting in misleading reports of network performance due to their combined effects. This paper proposes enhanced Earliest Deadline First (EDF) algorithms that allow Concurrent Executions to orchestrate offline/online measurement jobs in a conflict-free manner. The simulation study shows that our measurement scheduling mechanism can improve the schedulable utilization of offline measurement tasks up to 300 percent and the response time of on-demand jobs up to 50 percent. Further, we implement and deploy our scheduling mechanism in a real working NMI for monitoring the Internet2 Abilene network. As a case study, we show the utility of our algorithms in the widely used Network Weather Service (NWS).

Simulation of resource synchronization in a dynamic real‐time distributed computing environment

AbstractToday, more and more distributed computer applications are being modeled and constructed using real‐time principles and concepts. In 1989, the Object Management Group (OMG) formed a Real‐Time Special Interest Group (RT SIG) with the goal of extending the Common Object Request Broker Architecture (CORBA) standard to include real‐time specifications. This group's most recent efforts have focused on the requirements of dynamic distributed real‐time systems.One open problem in this area is resource access synchronization for tasks employing dynamic priority scheduling. This paper presents two resource synchronization protocols that the authors have developed which meet the requirements of dynamic distributed real‐time systems as specified by Dynamic Scheduling Real‐Time CORBA (DSRT CORBA). The proposed protocols can be applied to both Earliest Deadline First (EDF) and Least Laxity First (LLF) dynamic scheduling algorithms, allow distributed nested critical sections, and avoid unnecessary runtime overhead. In order to evaluate the performance of the proposed protocols, we analyzed each protocol's schedulability. Since the schedulability of the system is affected by numerous system configuration parameters, we have designed simulation experiments to isolate and illustrate the impact of each individual system parameter. Simulation experiments show the proposed protocols have better performance than one would realize by applying a schema that utilizes dynamic priority ceiling update. Copyright © 2004 John Wiley & Sons, Ltd.

Cryptographically resilient functions

This correspondence studies resilient functions which have applications in fault-tolerant distributed computing, quantum cryptographic key distribution, and random sequence generation for stream ciphers. We present a number of new methods for synthesizing resilient functions. An interesting aspect of these methods is that they are applicable both to linear and nonlinear resilient functions. Our second major contribution is to show that every linear resilient function can be transformed into a large number of nonlinear resilient functions with the same parameters. As a result, we obtain resilient functions that are highly nonlinear and have a high algebraic degree.

A distributed decision-making system

A message based model of a Distributed Decision Making, DDM, System is presented. This model is developed from a survey of the needs of group decision-making processes within organisations and distributed computer applications; a review of this survey is given. DDM extends the DSS concept to one that enables two or more decision making parties to co-operate in using DSS tools. It assumes a framework of organisational decision making which concentrates on the organisation as a political system where communication is important and conflict between groups just as likely as consensus. A brief description of a prototype implementation of the model is given. The use of the prototype has upheld the principles from which DDM was designed.

Distributed computer systems: Four case studies

Distributed computer applications built from off-the-shelf hard-ware and software are increasingly common. This paper examines four such distributed systems with contrasting degrees of decentralized hardware, control, and redundancy. The first is a one-site system, the second is a node-replicated system at a remote site for disaster backup, the third is a multi-site system with central control, and the fourth is a multi-site system with node autonomy. The application, design rationale, and experience of each of these systems are briefly sketched.