Partial Virtual Channel Sharing: A Generic Methodology to Enhance Resource Management and Fault Tolerance in Networks-on-Chip

We present a novel Partial Virtual channel Sharing (PVS) NoC architecture which reduces the impact of faults on performance and also tolerates faults within the routing logic. Without PVS, failure of a component impairs the fault-free connected components, which leads to considerable performance degradation. Improving resource utilization is key in enhancing or sustaining performance with minimal overhead when faults or overload occurs. In the proposed architecture, autonomic virtual-channel buffer sharing is implemented with a novel algorithm that determines the sharing of buffers among a set of ports. The runtime allocation of the buffers depends on incoming load and fault occurrence. In addition, we propose an efficient technique for maintaining the accessibility of a processing element (PE) to the network even if its router is faulty. Our techniques can be used in any NoC topology and for both, 2D and 3D NoCs. The synthesis results for an integrated video conference application demonstrate 22 % reduction in average packet latency compared to state-of-the-art virtual channel (VC) based NoC architecture. Extensive quantitative simulation has been carried out with synthetic benchmarks. Simulation results reveal that the PVS architecture improves the performance significantly in presence of faults, compared to other VC-based NoC architectures.     

A genetic algorithm for the single machine preemptive scheduling problem with linear earliness and quadratic tardiness penalties

In this paper, we consider the single machine preemptive scheduling problem with linear earliness and quadratic tardiness penalties, with no machine idle time. The problem is strongly NP-hard. We proposed a new mathematical model, with non-linear terms and integer variables. We develop a genetic algorithm for solving the problem in medium and large size. The proposed procedure is compared with optimal solutions for the smaller instance sizes. The genetic procedure is also quite close to the optimum and provided an optimal solution for most of the test problems. Numerical examples show that the proposed algorithm is efficient and effective. Scheduling with early and tardy penalties has received extensive attention from the scheduling community because of its practical significance. Single machine scheduling environments actually occur in several practical applications. Also, the performance of many production systems is often determined by the schedules for a single bottleneck machine. Furthermore, the study of single machine problems frequently provides outcomes that prove functional for more complex scheduling areas.     

Improving the NAA laboratory pneumatic transfer system for using Tehran research reactor

Neutron activation analysis (NAA) is a precise multi-elemental method which performs well for qualitative and quantitative analyses. This method has been applied to the laboratory of Radiation Applications Research School (RARS) at University of Tehran. The new pneumatic transfer system (Rabbit) has been designed and constructed for transferring samples, in particular short half-life samples, to Tehran Research Reactor (TRR) core for NAA. With this system samples were transferred and returned to/from the reactor as fast as possible in both automatic and manual modes. This system has two distinct paths for sending and receiving with transfer time of 30 s to the reactor core and 36 s from the irradiation position to the counting station in NAA laboratory which is about 700 m from the laboratory. Experiment and calculation have been carried out for calibration of the neutron flux and spectrum.     

A walk around the decarboxylative C-S cross-coupling reactions

This review highlights a number of recent discoveries and advances in the construction of carbon-sulfur bonds through decarboxylative cross-coupling reactions between carboxylic acids and sulfur-containing coupling partners. Particular emphasis is placed on the mechanistic aspects of the reactions.     

Burst‐aware virtual machine migration for improving performance in the cloud

Burst is a common pattern in the user's requirements, which suddenly increases the workload of virtual machines (VMs) and reduces the performance and energy efficiency of cloud computing systems (CCS). Virtualization technology with the ability to migrate VMs attempts to solve this problem. By migration, VMs can be dynamically consolidated to the users' requests. Burst temporarily increases the workload. Ignoring this issue will lead to incorrect decisions regarding the migration of VMs. It increases the number of migrations and Service Level Agreement Violations (SLAVs) due to overload. This may cause waste of resources, increase in energy consumption, and misplaced VMs. Therefore, a burst‐aware method for these issues is proposed in this paper. The method consists of two algorithms: one for determining the migration time and the other for the placement of VMs. We use the PlanetLab real dataset and CloudSim simulator to evaluate the performance of the proposed method. The results confirm the advantages of the method regarding performance compared to benchmark methods.     

Quality of service‐aware approaches in fog computing

In recent years, fog computing, a novel paradigm, has emerged for location and latency‐sensitive applications. It is a powerful complement for cloud computing that enables provisioning services and resources outside the cloud near the end devices. In a fog system, the existence of several nonhomogenous devices, which are potentially mobile, led to quality of service (QoS) worries. QoS‐aware approaches are presented in various parts of the fog system, and several different QoS factors are taken into account. In spite of the importance of QoS in fog computing, no comprehensive study on QoS‐aware approaches exists in fog computing. Hence, this paper reviews the current research used to guarantee QoS in fog computing. This paper investigates the QoS‐ensuring techniques that fall into three categories: service/resource management, communication management, and application management (published between 2013 and October 2018). Regarding the selected approaches, this paper represents merits, demerits, tools, evaluation types, and QoS factors. Finally, on the basis of the reviewed studies, we suggest some open issues and challenges which are worth further studying and researching in QoS‐aware approaches in fog computing.     

MMLT: A mutual multilevel trust framework based on trusted third parties in multicloud environments

In this article, a mutual multilevel trust framework is proposed, which involves managing trust from the perspective of cloud users (CUs) and cloud service providers (CSPs) in a multicloud environment based on a set of trusted third parties (TTPs). These independent agents are trusted by CUs and CSPs and distributed on different clouds. The TTPs evaluate the CUs' trustworthiness based on the accuracy of feedback ratings and assess the CSPs' trustworthiness based on the quality of service monitoring information. They are connected themselves through the trusted release network, which enables a TTP to obtain trust information about CSPs and CUs from other clouds. With the objective of developing an effective trust management framework, a new approach has been provided to improve trust-based interactions, that is, able to rank the trusted cloud services (CSs) based on CU's priorities via fuzzy logic. Fuzzy logic is applied to manage the different priorities of CUs, all the CUs do not have the same priorities to use trusted CSs. Customizing service ranking allows CUs to apply trusted CSs based on their priorities. Experiments on real datasets well matched the analytical results, indicating that our proposed approach is effective and outperforms the existing approaches.     

RMRL: improved regret minimisation techniques using learning automata

Game theory as one of the most progressive areas in AI in last few years originates from the same root as AI. The unawareness of the other players and their decisions in such incomplete-information problems, make it necessary to use some learning techniques to enhance the decision-making process. Reinforcement learning techniques are studied in this research; regret minimisation (RM) and utility maximisation (UM) techniques as reinforcement learning approaches are widely applied to such scenarios to achieve optimum solutions. In spite of UM, RM techniques enable agents to overcome the shortage of information and enhance the performance of their choices based on regrets, instead of utilities. The idea of merging these two techniques are motivated by iteratively applying UM functions to RM techniques. The main contributions are as follows; first, proposing some novel updating methods based on UM of reinforcement learning approaches for RM; the proposed methods refine RM to accelerate the regret reduction, second, devising different procedures, all relying on RM techniques, in a multi-state predator-prey problem. Third, how the approach, called RMRL, enhances different RM techniques in this problem is studied. Estimated results support the validity of RMRL approach comparing with some UM and RM techniques.     

Kinematics,Singularity Study and Optimization of an Innovative Spherical Parallel Manipulator with Large Workspace

A novel design for three degree of freedom (DoF) mechanical arm, i.e. a 3-PUS/S Spherical Parallel Manipulator (SPM) with three rotational motions is proposed in this article. In addition, its kinematic equations, singularity and design optimization are studied according to its application. The proposed parallel robot that has three legs with three prismatic joints can rotate about Z-axis unlimitedly. Therefore, the manipulator has large workspace and good flexibility, hence being attractive to study. To complete the kinematic analysis of the manipulator, three stages are considered as follows. At the first, the kinematics of the SPM is explained to obtain the positions, velocities, and accelerations. Furthermore, the Jacobian and Hessian matrices of the 3-PUS/S Parallel Manipulator are derived. The results are verified by the use of CAD and Adams software. Next, the Jacobian matrix obtained from the kinematic equations is utilized to study the different types of singularities. Finally, the optimum dimensions of the manipulator based on kinematic and singularity features are studied by Genetic Algorithm (GA), and the Global Condition Index (GCI) is maximized. The results help the designers to achieve an ideal geometry for the parallel manipulator with good workspace and minimum singularity.