High Priority Requests Research Articles

Multi-section fission ionization chamber for measurement of [formula omitted] reaction in fission tagging method

The 239Pu(n,γ) reaction cross section is very important for operation of both thermal and fast reactors, when loaded with MOX fuels. According to the NEA/OECD High Priority Request List the precision of cross section data for this reaction should be improved. The cross section of (n,f) reaction is much higher compared to (n,γ) for this isotope. In such conditions the fission tagging technique could be applied to identify the fission background. In the past, this technique was successfully used for capture measurements at the n_TOF facility at CERN. The multi-section fission ionization chamber was constructed and used in the combination with Total Absorption Calorimeter (TAC) for detecting gamma rays for the precise measurement of 239Pu(n,γ) reaction cross section at the n_TOF facility.

Description and outlook of the 50,53Cr(n,γ) cross section measurement at n_TOF and HiSPANoS

Chromium is a very relevant element regarding criticality safety in nuclear reactors because of its presence in stainless steel, an important structural material. Currently, there are serious discrepancies between the different evaluations regarding the neutron capture cross sections of 50Cr and 53Cr, most probably related to the difficulty of reducing and then estimating the very large neutron scattering effects on the shape of the resonances. In this context, there is a recent entry in the Nuclear Energy Agency (NEA) High Priority Request List (HPRL) to measure these reactions between 1 and 100 keV with an accuracy of 8-10%. In response to this request, we have performed a time-of-flight experiment at CERN n_TOF (Switzerland) and a complementary activation experimenton 50Crat30and90keVatCNAHiSPANoS(Spain).Theexperiments are presented herein, together with a discussion on the quality of the preliminary data and the results to be expected.

Target Accuracy Requirements Exercise within WPEC/SG46 and Feedback on Nuclear Data Needs

The Target Accuracy Requirements (TAR) exercise was launched in the framework of the WPEC/S46 (2018-2022) on “Efficient and Effective Use of Integral Experiments for Nuclear Data Validation” [1]. This Exercise aims to quantify priority nuclear data needs or uncertainties reduction to meet design integral parameters target accuracies. To perform this work, the status and methodology of WPEC/SG26 (2005-2008) [2] was updated and reviewed. The overall objective of WPEC/SG46 Exercise on TAR to assess the effect of nuclear data uncertainty reduction on output relevant for reactor design and operation was successfully achieved. This work will be contributing in the short term to provide new entries in the NEA High Priority Request List (HPRL) [3].

Study of 239Pu(n,f) prompt fission neutron spectrum

The experimental data concerning 239Pu prompt fission neutron spectrum above 10 MeV are sparse, and no spectrum averaged cross section has been measured in this neutron field yet. The first measurement was performed at the IBR-2 reactor, which uses plutonium dioxide fuel. The present work aimed at the measurement of spectral averaged cross sections for high threshold reactions such as 46Ti(n,p)46Sc, 54Fe(n,p)54Mn, 58Ni(n,X)57Co, 89Y(n,2n)88Y, and 209Bi(n,3n)207Bi to explore the high energy region of the 239Pu prompt fission neutron spectrum. Measurement of spectrum averaged cross section for the 89Y(n,2n)88Y reaction is included in NEA high priority request list. The 58Ni(n,p)58Co reaction was employed as a monitor reaction. The experimental spectral averaged cross sections were derived from reaction rates measured by means of high purity Germanium spectrometry. The experimental values were compared with spectrum averaged cross sections compiled from either ENDF/B-VIII.0, GMA or JEFF-3.3 239Pu prompt fission neutron spectrum and IRDFF-II cross sections. The best agreement, i.e. within uncertainties, was achieved using GMA 239Pu prompt fission neutron spectrum for all reactions under study. Calculation results using JEFF-3.3 and ENDF/B-VIII.0 evaluations of the 239Pu prompt fission neutron spectrum agree well with the experimental data except for the high energy region reflected in the disagreement with the 209Bi(n,3n)207Bi reaction.

Fog Computing, Cloud Computing and IoT Environment: Advanced Broker Management System

Cloud computing is a massive amount of dynamic ad distributed resources that are delivered on request to clients over the Internet. Typical centralized cloud computing models may have difficulty dealing with challenges caused by IoT applications, such as network failure, latency, and capacity constraints. One of the introduced methods to solve these challenges is fog computing which makes the cloud closer to IoT devices. A system for dynamic congestion management brokerage is presented in this paper. With this proposed system, the IoT quality of service (QoS) requirements as defined by the service-level agreement (SLA) can be met as the massive amount of cloud requests come from the fog broker layer. In addition, a forwarding policy is introduced which helps the cloud service broker to select and forward the high-priority requests to the appropriate cloud resources from fog brokers and cloud users. This proposed idea is influenced by the weighted fair queuing (WFQ) Cisco queuing mechanism to simplify the management and control of the congestion that may possibly take place at the cloud service broker side. The system proposed in this paper is evaluated using iFogSim and CloudSim tools, and the results demonstrate that it improves IoT (QoS) compliance, while also avoiding cloud SLA violations.

Simulator for Performance Evaluation of ASON/GMPLS Network

The hierarchical control plane network architecture of Automatically Switched Optical Network with utilization of Generalized Multi-Protocol Label Switching protocols is compliant to next generation networks requirements and can supply connections with required quality of service, even with incomplete domain information. Considering connection control, connection management and network management, the controllers of this architecture could perform the same operations on the transport plane resources and could support control functions for Software Defined Networking controllers, which are considered as the future networking solutions. Therefore, it is very important to determine factors that have an influence on service control and resource management. One of the tools for achieving this goal is computer simulation. The paper proposes a simulator for the hierarchical control plane networks in an open source OMNeT++ environment, in order to evaluate network architecture performance for different network structures and traffic parameters. To demonstrate simulator capabilities, examples of typical simulations performance results for Polish and European network structures are presented and discussed. Performance metrics which have been used to evaluate the performance of this architecture are: mean values of Call Set-up Time, Connection Set-up Time, Call Release Time, Connection Release Time, loss probability for inter-domain connection requests and loss probability for low and high priority requests. General remarks concerned with presented simulator are also provided.

A cloud priority-based dynamic online double auction mechanism (PB-DODAM)

Double auctions are considered to be effective price-scheduling mechanisms to resolve cloud resource allocation and service pricing problems. Most of the classical double auction models use price-based mechanisms in which determination of the winner is based on the prices offered by the agents in the market. In cloud ecosystems, the services offered by cloud service providers are inherently time-constrained and if they are not sold, the allocated resources for the unsold services are wasted. Furthermore, cloud service users have time constraints to complete their tasks, otherwise, they would not need to request these services. These features, perishability and time-criticality, have not received much attention in most classical double auction models. In this paper, we propose a cloud priority-based dynamic online double auction mechanism (PB-DODAM), which is aligned with the dynamic nature of cloud supply and demand and the agents’ time constraints. In PB-DODAM, a heuristic algorithm which prioritizes the agents’ asks and bids based on their overall condition and time constraints for resource allocation and price-scheduling mechanisms is proposed. The proposed mechanism drastically increases resource allocation and traders’ profits in both low-risk and high-risk market conditions by raising the matching rate. Moreover, the proposed mechanism calculates the precise defer time to wait for any urgent or high-priority request without sacrificing the achieved performance in resource allocation and traders’ profits. Based on experimental results in different scenarios, the proposed mechanism outperforms the classical price-based online double auctions in terms of resource allocation efficiency and traders’ profits while fulfilling the double auction’s truthfulness pillar.

High Priority Requests in Grid Environment

Grid computing is an enhanced technology consisting of a pool of connected machines that belong to multiple organizations in different sites to form a distributed system. This system can be used to deal with complex scientific or business problems. It is developed to help share distributed resources that may be diverted in nature and solve many computing problems. The typical decentralized grid computing model faces many challenges, such as; different systems and software architectures, quickly handling the enormous amount of grid requesters, and finding the appropriate resources for the grid users. Some of the grid requests might need to get significant attention and fast response to the other requests. Usually, the Grid Broker (GB) works as a third party or mediator between grid service providers and grid service requesters. This paper introduced a new automated system that can help exploit the grid power, improve its functionality, and enhance its performance. This research presents a new architecture for the Grid Broker that can assist with high priority requests and be processed first. This system is also used to monitor and provision the grid providers' work during the job running. It uses a multi-agent system to facilitates its work and accomplish its tasks. The proposed approach is evaluated using the Jade simulator. The results show that using the proposed approach can enhance the way of dealing with high priority requests coming to the grid.

HPRL – International cooperation to identify and monitor priority nuclear data needs for nuclear applications

The OECD-NEA High Priority Request List (HPRL) is a point of reference to guide and stimulate the improvement of nuclear data for nuclear energy and other applications, and a tool to bridge the gap between data users and producers. The HPRL is application-driven and the requests are submitted by nuclear data users or representatives of the user’s communities. A panel of international experts reviews and monitors the requests in the framework of an Expert Group mandated by the NEA Nuclear Science Committee Working Party on International Nuclear Data Evaluation Cooperation (WPEC). After approval, individual requests are classified to three categories: high priority requests, general requests, and special purpose requests (e.g., dosimetry, standards). The HPRL is hosted by the NEA in the form of a relational database publicly available on the web. This paper provides an overview of HPRL entries, status and outlook. Examples of requests successfully completed are given and new requests are described with emphasis on updated nuclear data needs in the fields of nuclear energy, neutron standards and dosimetry.

Labeled Network Stack: A High-Concurrency and Low-Tail Latency Cloud Server Framework for Massive IoT Devices

Internet of Things (IoT) applications have massive client connections to cloud servers, and the number of networked IoT devices is remarkably increasing. IoT services require both low-tail latency and high concurrency in datacenters. This study aims to determine whether an order of magnitude improvement is possible in tail latency and concurrency in mainstream systems by proposing a hardware–software codesigned labeled network stack (LNS) for future datacenters. The key innovation is a cross-layered payload labeling mechanism that distinguishes different requests by payload across the full network stack, including application, TCP/IP, and Ethernet layers. This type of design enables prioritized data packet processing and forwarding along the full datapath, such that latency-insensitive requests cannot significantly interfere with high-priority requests. We build a prototype datacenter server to evaluate the LNS design against a commercial X86 server and the mTCP research, using a cloud-supported IoT application scenario. Experimental results show that the LNS design can provide an order of magnitude improvement in tail latency and concurrency. A single datacenter server node can support over 2 million concurrent long-living connections for IoT devices as a 99-percentile tail latency of 50 ms is maintained. In addition, the hardware–software codesign approach remarkably reduces the labeling and prioritization overhead and constrains the interference of high-priority requests to low-priority requests.

Analyzing the Models of Systems with Heterogeneous Servers

The mathematical model of a queueing system with heterogeneous servers, without queues, and with two types of requests is investigated. High-priority requests are processed in fast servers while low-priority calls are processed in slow servers. If all servers in some group are busy, then reassigning of requests to another group is allowed. Reassigning is based on random schemes and reassignment probability depends on the number of busy servers in appropriate group. Exact and approximate methods are developed for the analysis of characteristics of the system. Explicit approximate formulas to calculate the approximate values of characteristics are proposed.

Measurement of the 242Pu(n, γ) cross section from thermal to 500 keV at the Budapest research reactor and CERN n_TOF-EAR1 facilities

The design and operation of innovative nuclear systems requires a better knowledge of the capture and fission cross sections of the Pu isotopes. For the case of capture on 242Pu, a reduction of the uncertainty in the fast region down to 8-12% is required. Moreover, aiming at improving the evaluation of the fast energy range in terms of average parameters, the OECD NEA High Priority Request List (HPRL) requests high-resolution capture measurements with improved accuracy below 2 keV. The current uncertainties also affect the thermal point, where previous experiments deviate from each other by 20%. A fruitful collaboration betwen JGU Mainz and HZ Dresden-Rossendorf within the EC CHANDA project resulted in a 242Pu sample consisting of a stack of seven fission-like targets making a total of 95(4) mg of 242Pu electrodeposited on thin (11.5 μm) aluminum backings. This contribution presents the results of a set of measurements of the 242Pu(n, γ) cross section from thermal to 500 keV combining different neutron beams and techniques. The thermal point was determined at the Budapest Research Reactor by means of Neutron Activation Analysis and Prompt Gamma Analysis, and the resolved (1 eV - 4 keV) and unresolved (1 - 500 keV) resonance regions were measured using a set of four Total Energy detectors at the CERN n_TOF-EAR1.

Overview of the OECD-NEA Working Party on International Nuclear Data Evaluation Cooperation (WPEC)

The OECD Nuclear Energy Agency (NEA) Working Party on International Nuclear Data Evaluation Cooperation (WPEC) was established in 1989 to facilitate collaboration in nuclear data activities. Over its thirty year history, different Subgroups have been created to address topics in nearly every aspect of nuclear data, including: experimental measurements, evaluation, validation, model development, quality assurance of databases and the development of software tools.WPEC has recently completed activities on fission yield evaluation, the general nuclear database structure (GNDS) to replace the ENDF-6 format, methods to provide feedback to evaluation, studies of specific capture cross sections, new methods in thermal scattering kernel evaluation and the Collaborative International Evaluated Library Organisation (CIELO) Pilot Project. Ongoing activities in GNDS application programming interface (API) development, methods for covariance evaluation and quality assurance in nuclear data validation using the International Criticality Safety Benchmark Evaluation Project (ICSBEP) database are complemented by the work of two Expert Groups that oversee the High-Priority Request List (HPRL) for Nuclear Data and the continuous development of the GNDS. New activities on the use of integral experiments for nuclear data validation and adjustment, as well as the use of the Shielding Integral Benchmark Archive and Database (SINBAD) for validation have begun and will be coordinated alongside future Subgroups.After three decades we will review the status of WPEC, how it integrates other collections and activities organised by the NEA and how it dovetails with the initiatives of the IAEA and other bodies to effectively coordinate international activities in nuclear data.

Load Balancing of Fog Computing Centers: Minimizing Response Time of High Priority Requests

Fog computing is one of the enabling computing technology which primarily aims to fulfill the requirements of the Internet of Things (IoT). IoT is fast-growing networking and computing sector. The scalability of users, devices, and application is crucial for the success of IoT systems. The load balancing is an approach to distribute the load among computing nodes so that the computing nodes are not overloaded. In this paper, we propose the priority-based request servicing at fog computing centers. We particularly address the situation when the fog node in fog computing center (FCC) receives more workload than their capacity to handle it. The increased workload is shifted to nearby fog nodes rather than to the remote cloud. The proposed approach is able to minimize the offloading the high priority request to other nodes by 11% which proves the novelty of our proposed.

Analysis and Optimization of Systems with Heterogeneous Servers and Jump Priorities

Markov models of systems with heterogeneous servers, various types of requests, and jump priorities are proposed. It is assumed that there are high and low priority requests; the high priority requests are assigned to the server with the high service rate, and the low priority requests are assigned to the server with the low service rate. Models of two types are studied: with separate queues and the shared queue for requests of different types. Jump priorities determine the rules according to which the type of low priority requests changes depending on the state of the queue. Methods for calculating the distribution of the probability of the system states are developed, formulas for the calculation of the characteristics of the system are derived, and the problem of the optimization of these characteristics is solved. The results of numerical experiments are presented.

QoE Oriented Chunk Scheduling in P2P-VoD Streaming System

Peer-to-peer-based video on demand (P2P-VoD) streaming systems have been widely used in delivering multimedia content. However, There are several problems existing in the P2P-VoD streaming systems, such as unstable video playing, low-utilization rate of video, and high server load, severely influencing viewers’ quality-of-experience (QoE). How to reasonably and efficiently schedule video chunks is important to handle the above-mentioned issues. In this paper, we propose a novel QoE-oriented chunk scheduling in a P2P-VoD streaming system, containing a request queue-based load balancing (RQLB) strategy and an elimination index-based cache replacement (EICR) strategy. In the proposed RQLB strategy, we first define the priority of the chunk requests, making the high-priority requests handled in time. Subsequently, we construct the utilization function of peer, so that the relatively low-priority requests will be transferred to the candidate neighbors with low utilization and high reliability. In the designed EICR strategy, we first calculate the elimination index for determining the candidate video. Considering the fact that chunks in a video may have different popularities, we then select the chunk with the lowest cache value for replacement. Moreover, we implement a real P2P-VoD system based on the proposed chunk scheduling scheme, giving the detailed design process of the network elements. Experimental results show that our proposed strategies can effectively solve chunk scheduling problem in the P2P-VoD streaming system, guaranteeing viewers’ QoE.

Advanced Pre-fetching Based Dynamic Data Replication under Small World Network Model

The database replication keeps up contents of database such as tables, in various geographical locations that fabricate an arrangement of distributed database. The necessities of database replication are expanding with the time as the use of internet is also increasing. With the purpose of meeting these prerequisites, priorities among requests can be included. In this work two types of priorities i.e., low and high priority is considered in this research article. It is noticeable that the high priority requests are more vital in comparison to low priority requests and therefore low priority requests can be delayed or dropped. The rate of request loss can be lessened utilizing the conditions of load balancing where a portion of the contending requests are transferred to other nodes. In this paper, internet is modelled as small world network, and performance assessment of A-PDDRA (Advanced Pre-bringing Based Dynamic Data Replication Algorithm) is carried out on small world network while considering both priorities and load balancing using computer simulation.

Service Center Staffing with Cross‐Trained Agents and Heterogeneous Customers

We model a real‐world service center with cross‐trained agents serving customer requests that are heterogeneous with respect to complexity and priority levels: High priority requests preempt low priority requests and low‐skilled agents can only serve less complex requests, while high skilled agents can serve all requests. Our main aim is to dynamically assign requests to agents considering the priority and complexity levels of requests. We model this system as a Markov chain that is infinite in multiple dimensions and thus is not amenable to exact analysis. We therefore apply approximation and bounding techniques to develop a tractable, novel algorithm using the Matrix Analytic Method. Our algorithm closely approximates the operations of the real‐world service system under a simple but effective threshold‐based request‐assignment policy. Extensive computational results demonstrate the usefulness of our algorithm to minimize costs given an existing staffing configuration, as well as in helping to make long‐term staffing decisions. In addition, our algorithm also has at least two orders of magnitude shorter computation times than each replication of simulation. Hence, it is both fast and accurate.

Performance Simulation of Finite-Source Cognitive Radio Networks with Servers Subjects to Breakdowns and Repairs

The present paper deals with the performance evaluation of a cognitive radio network with the help of a queueing model. The queueing system contains two interconnected, not independent sub-systems. The first part is for the requests of the Primary Units (PU). The number of sources is finite, and each source generates high priority requests after a exponentially distributed time. The requests are sent to a single server unit or Primary Channel Service (PCS) with a preemptive priority queue. The service times are assumed to be exponentially distributed. The second sub-system is for the requests of the Secondary Units (SU), which is finite sources system too; the inter-request times and service times of the single server unit or Secondary system Channel Service (SCS) are assumed to be exponentially distributed, respectively. A generated high priority packet goes to the primary service unit. If the unit is idle, the service of the packet begins immediately. If the server is busy with a high priority request, the packet joins the preemptive priority queue. When the unit is engaged with a request from SUs, the service is interrupted and the interrupted low priority task is sent back to the SCS. Depending on the state of the secondary channel, the interrupted job is directed to either the server or the orbit. In case the requests from SUs find the SCS idle, the service starts, and if the SCS is busy, the packet looks for the PCS. In the case of an idle PCS, the service of the low-priority packet begins at the high-priority channel (PCS). If the PCS is busy, the packet goes to the orbit. From the orbit it retries to be served after an exponentially distributed time. The novelty of our investigation is that each server is subject to random breakdowns, in which case the interrupted request is sent to the queue or orbit, respectively. The operating and repair times of the servers are assumed to be generally distributed. Finally, all the random times included in the model construction are assumed to be independent of each other. The main aim of the paper is to analyze the effect of the nonreliability of the servers on the mean and variance of the response time for the SUs by using simulation.

An efficient cloud scheduler design supporting preemptible instances

Maximizing resource utilization by performing an efficient resource provisioning is a key factor for any cloud provider: commercial actors can maximize their revenues, whereas scientific and non-commercial providers can maximize their infrastructure utilization. Traditionally, batch systems have allowed data centers to fill their resources as much as possible by using backfilling and similar techniques. However, in an IaaS cloud, where virtual machines are supposed to live indefinitely, or at least as long as the user is able to pay for them, these policies are not easily implementable. In this work we present a new scheduling algorithm for IaaS providers that is able to support preemptible instances, that can be stopped by higher priority requests without introducing large modifications in the current cloud schedulers. This scheduler enables the implementation of new cloud usage and payment models that allow more efficient usage of the resources and potential new revenue sources for commercial providers. We also study the correctness and the performance overhead of the proposed scheduler against existing solutions.