The .NET Runtime Environment

Multiagent control system (MACS) has become a promising solution for solving complex control problems. Using the advantages of MACS-based design approaches, a novel solution for advanced control of mechatronic systems has been developed in this paper. The study has aimed at integrating learning control into MACS. Specifically, learning feedforward control (LFFC) is implemented as a pattern for incorporation in MACS. The major novelty of this work is that the feedback control part is realized in a real-time periodic MACS, while the LFFC algorithm is done on-line, asynchronously, and in a separate non-real-time aperiodic MACS. As a result, a MACS-based LFFC design method has been developed. A second-order B-spline neural network (BSN) is used as a function approximator for LFFC whose input-output mapping can be adapted during control and is intended to become equal to the inverse model of the plant. To provide real-time features for the MACS-based LFFC system, the open robot control software (OROCOS) has been employed as development and runtime environment. A case study using a simulated linear motor in the presence of nonlinear cogging and friction force as well as mass variations is used to illustrate the proposed method. A MACS-based LFFC system has been designed and implemented for the simulated plant. The system consists of a setpoint generator, a feedback controller, and a time-index LFFC that can learn on-line. Simulation results have demonstrated the applicability of the design method.

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The runtime environment for Scheme, a Scheme implementation on the 88000

ACM SIGARCH Computer Architecture News ◽

10.1145/68182.68199 ◽

1989 ◽

Vol 17 (2) ◽

pp. 172-182

Author(s):

Steven R. Vegdahl ◽

Uwe F. Pleban

Keyword(s):

Runtime Environment

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Unified Development for Mixed Multi-GPU and Multi-coprocessor Environments Using a Lightweight Runtime Environment

2014 IEEE 28th International Parallel and Distributed Processing Symposium ◽

10.1109/ipdps.2014.58 ◽

2014 ◽

Cited By ~ 17

Author(s):

Azzam Haidar ◽

Chongxiao Cao ◽

Asim Yarkhan ◽

Piotr Luszczek ◽

Stanimire Tomov ◽

...

Keyword(s):

Runtime Environment

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eTPM: A Trusted Cloud Platform Enclave TPM Scheme Based on Intel SGX Technology

Sensors ◽

10.3390/s18113807 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3807 ◽

Cited By ~ 3

Author(s):

Haonan Sun ◽

Rongyu He ◽

Yong Zhang ◽

Ruiyun Wang ◽

Wai Hung Ip ◽

...

Keyword(s):

Virtual Machines ◽

Trusted Computing ◽

Information Leakage ◽

Research Topic ◽

Cloud Environment ◽

Cloud Platform ◽

Computing Technology ◽

Security Issues ◽

Runtime Environment ◽

Cloud Users

Today cloud computing is widely used in various industries. While benefiting from the services provided by the cloud, users are also faced with some security issues, such as information leakage and data tampering. Utilizing trusted computing technology to enhance the security mechanism, defined as trusted cloud, has become a hot research topic in cloud security. Currently, virtual TPM (vTPM) is commonly used in a trusted cloud to protect the integrity of the cloud environment. However, the existing vTPM scheme lacks protections of vTPM itself at a runtime environment. This paper proposed a novel scheme, which designed a new trusted cloud platform security component, ‘enclave TPM (eTPM)’ to protect cloud and employed Intel SGX to enhance the security of eTPM. The eTPM is a software component that emulates TPM functions which build trust and security in cloud and runs in ‘enclave’, an isolation memory zone introduced by SGX. eTPM can ensure its security at runtime, and protect the integrity of Virtual Machines (VM) according to user-specific policies. Finally, a prototype for the eTPM scheme was implemented, and experiment manifested its effectiveness, security, and availability.

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A Runtime Environment for Distributed Mashups in Multi-device Scenarios

Lecture Notes in Computer Science - Network and Parallel Computing ◽

10.1007/978-3-642-35606-3_63 ◽

2012 ◽

pp. 532-541

Author(s):

Oliver Mroß ◽

Klaus Meißner

Keyword(s):

Runtime Environment

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Selective Service Provenance in the VRESCo Runtime

Web Service Composition and New Frameworks in Designing Semantics ◽

10.4018/978-1-4666-1942-5.ch017 ◽

2012 ◽

pp. 372-394

Author(s):

Anton Michlmayr ◽

Florian Rosenberg ◽

Philipp Leitner ◽

Schahram Dustdar

Keyword(s):

Performance Evaluation ◽

Information Systems ◽

Web Service ◽

Scientific Workflows ◽

Data Provenance ◽

Provenance Information ◽

Selective Service ◽

Runtime Environment ◽

Service Oriented ◽

History Of

In general, provenance describes the origin and well-documented history of a given object. This notion has been applied in information systems, mainly to provide data provenance of scientific workflows. Similar to this, provenance in Service-oriented Computing has also focused on data provenance. However, the authors argue that in service-centric systems the origin and history of services is equally important. This paper presents an approach that addresses service provenance. The authors show how service provenance information can be collected and retrieved, and how security mechanisms guarantee integrity and access to this information, while also providing user-specific views on provenance. Finally, the paper gives a performance evaluation of the authors’ approach, which has been integrated into the VRESCo Web service runtime environment.

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Standalone containers with ATLAS offline software

EPJ Web of Conferences ◽

10.1051/epjconf/202024507010 ◽

2020 ◽

Vol 245 ◽

pp. 07010

Author(s):

Marcelo Vogel ◽

Mikhail Borodin ◽

Alessandra Forti ◽

Lukas Heinrich

Keyword(s):

File Systems ◽

Network Connectivity ◽

Data Reconstruction ◽

Atlas Experiment ◽

Network Connection ◽

Software Packages ◽

Processing Data ◽

Runtime Environment ◽

Detector Simulation ◽

Runtime Environments

This paper describes the deployment of the offline software of the ATLAS experiment at LHC in containers for use in production workflows such as simulation and reconstruction. To achieve this goal we are using Docker and Singularity, which are both lightweight virtualization technologies that can encapsulate software packages inside complete file systems. The deployment of offline releases via containers removes the interdependence between the runtime environment needed for job execution and the configuration of the computing nodes at the sites. Docker or Singularity would provide a uniform runtime environment for the grid, HPCs and for a variety of opportunistic resources. Additionally, releases may be supplemented with a detector’s conditions data, thus removing the need for network connectivity at computing nodes, which is normally quite restricted for HPCs. In preparation to achieve this goal, we have built Docker and Singularity images containing single full releases of ATLAS software for running detector simulation and reconstruction jobs in runtime environments without a network connection. Unlike similar efforts to produce containers by packing all possible dependencies of every possible workflow into heavy images (≈ 200GB), our approach is to include only what is needed for specific workflows and to manage dependencies efficiently via software package managers. This approach leads to more stable packaged releases where the dependencies are clear and the resulting images have more portable sizes ( 16GB). In an effort to cover a wider variety of workflows, we are deploying images that can be used in raw data reconstruction. This is particularly challenging due to the high database resource consumption during the access to the experiment’s conditions payload when processing data. We describe here a prototype pipeline in which images are provisioned only with the conditions payload necessary to satisfy the jobs’ requirements. This database-on-demand approach would keep images slim, portable and capable of supporting various workflows in a standalone fashion in environments with no network connectivity.

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