ABSTRACT Virtual Network Embedding (VNE) plays a crucial role in optimizing physical network (PN) resource utilization in network virtualization and delivering service benefits such as isolation, cost efficiency, flexibility, security, and Quality of Service (QoS) to end users. Despite its importance, VNE faces significant challenges, such as assigning resources to Virtual Network Requests (VNRs) to drive lower energy consumption, which can unfavorably affect network performance. VNE constitutes two corresponding subproblems: virtual machine embedding and virtual link embedding, and both problems are treated as hard. In this context, minimizing energy consumption remains vital for SPs by effectively utilizing PN resources, as it not only increases the revenue‐to‐cost ratio but also enhances the acceptance of VNRs. This work introduces a novel heuristic framework called the Multi‐Attributed Traffic Intensity Based Energy Aware Embedding for Online Virtual Network Requests (IViN) framework, designed to enhance the acceptance ratio while minimizing energy consumption. IViN considers a multi‐attribute approach from system and network features in its heuristic ranking mechanism to rank virtual machines and servers during virtual machine embedding, followed by a virtual link assignment using the shortest path approach. These attributes play a crucial role in effectively capturing the dependencies between network elements. This helps IViN achieve energy‐sensitive resource allocation and improves VNR acceptance and revenue‐to‐cost ratio. We validate the proposed approach by comparing it with existing methods through simulation experiments. The results show that IViN outperforms the baseline techniques by achieving improvements of 41%, 60%, and 34% in acceptance ratio, revenue‐to‐cost ratio, and energy consumption, respectively.

Soft robotic grippers offer advantages over rigid end effectors but are typically coupled to a rigid robot for locomotion. In contrast, this paper details a soft robot for both locomotion and grasping. The system is a type of boundaryconstrained granular swarm robot, which is composed of a closed-loop series of active (capable of locomotion) sub-robots. Prior work has shown how this type of robot is capable of locomotion and grasping. For this paper, we propose a new grasping strategy and demonstrate real-time grasp quality evaluation using pressure sensors and the Ferrari-Canny grasp metric. The grasping strategy leverages gradient-based control via distance functions and dynamic system planning to achieve desired robot geometries for effective grasping. Previous research primarily used pull tests to evaluate grasping efficacy, which lacked realtime feedback on grasp quality. Simulated and experimental results confirm the effectiveness of this method.

This paper describes Simultaneous Localization and Mapping (SLAM) techniques for mobile soft robots using on-board local sensors. The paper focuses on planar boundary-constrained swarms, which are comprised of identical modular sub-units, each flexibly connected to its neighbor. The sub-units themselves are not necessarily soft, but as the robot's size increases with respect to the size of the sub-units, the robot as a whole approaches a continuous system that exhibits the characteristics and behavior of a soft robot. Previous versions of this system have demonstrated grasping, shape formation, and tunneling; however, all prior embodiments have relied on external sensing for pose estimation. This paper is the first to demonstrate a fully self-sufficient boundary constrained swarm soft robot that does not rely on external pose estimation. The robot successfully navigates a maze-like environment while localizing and mapping the environment.

Soft robots possess unique capabilities for adapting to the environment and interacting with it safely. However, their deformable nature also poses challenges for controlling their movement. In particular, the large deformations of a soft robot make it difficult to localize its individual body parts, which in turn impedes effective control. This paper introduces a novel localization framework designed for soft robots that are constrained by boundaries and benefit from unique hardware architecture. To this end, we propose a method that exploits the flexible boundaries of the robot to create an onboard sensor capable of measuring the relative distances between its sub-robots. This measurement data is incorporated into a linear Kalman filter for accurate localization. We evaluate the framework's performance in benchmark and dynamic cases and demonstrate its effectiveness in improving localization accuracy compared to an IMU-based approach. The results also show that the proposed method achieves sufficient localization accuracy for contact-based mapping, enabling the robot to sense the location of obstacles in the environment. Finally, we validate the proposed framework using a physical prototype of a boundary-constrained soft robot and demonstrate its ability to accurately estimate the robot's shape. This framework has the potential to enable soft robots to autonomously navigate and map unknown environments, which could be beneficial for a variety of exploration tasks.

Bayesian networks (BNs) are a widely used graphical model in machine learning. As learning the structure of BNs is NP-hard, high-performance computing methods are necessary for constructing large-scale networks. In this article, we present a parallel framework to scale BN structure learning algorithms to tens of thousands of variables. Our framework is applicable to learning algorithms that rely on the discovery of Markov blankets (MBs) as an intermediate step. We demonstrate the applicability of our framework by parallelizing three different algorithms:Grow-Shrink(GS),Incremental Association MB(IAMB), andInterleaved IAMB(Inter-IAMB). Our implementations are available as part of an open-source software calledramBLe, and are able to construct BNs from real data sets with tens of thousands of variables and thousands of observations in less than a minute on 1024 cores, with a speedup of up to 845X and 82.5% efficiency. Furthermore, we demonstrate using simulated data sets that our proposed parallel framework can scale to BNs of even higher dimensionality. Our implementations were selected for the reproducibility challenge component of the 2021 student cluster competition (SCC’21), which tasked undergraduate teams from around the world with reproducing the results that we obtained using the implementations. We discuss details of the challenge and the results of the experiments conducted by the top teams in the competition. The results of these experiments indicate that our key results are reproducible, despite the use of completely different data sets and experiment infrastructure, and validate the scalability of our implementations.

As robots become more advanced and capable, developing trust is an important factor of human-robot interaction and cooperation. However, as multiple environmental and social factors can influence trust, it is important to develop more elaborate scenarios and methods to measure human-robot trust. A widely used measurement of trust in social science is the investment game. In this study, we propose a scaled-up, immersive, science fiction Human-Robot Interaction (HRI) scenario for intrinsic motivation on human-robot collaboration, built upon the investment game and aimed at adapting the investment game for human-robot trust. For this purpose, we utilise two Neuro-Inspired Companion (NICO) - robots and a projected scenery. We investigate the applicability of our space mission experiment design to measure trust and the impact of non-verbal communication. We observe a correlation of 0.43 (p=0.02)between self-assessed trust and trust measured from the game and a positive impact of non-verbal communication on trust (p=0.0008) and robot perception for anthropomorphism (p=0.007) and animacy (p=0.00002). We conclude that our scenario is an appropriate method to measure trust in human-robot interaction and also to study how non-verbal communication influences a human's trust in robots.

Bayesian networks (BNs) are a widely used graphical model in machine learning. As learning the structure of BNs is NP-hard, high-performance computing methods are necessary for constructing large-scale networks. In this paper, we present a parallel framework to scale BN structure learning algorithms to tens of thousands of variables. Our framework is applicable to learning algorithms that rely on the discovery of Markov blankets (MBs) as an intermediate step. We demonstrate the applicability of our framework by parallelizing three different algorithms: Grow-Shrink (GS), Incremental Association MB (IAMB), and Interleaved IAMB (Inter-IAMB). Our implementations are able to construct BNs from real data sets with tens of thousands of variables and thousands of observations in less than a minute on 1024 cores, with a speedup of up to 845X and 82.5% efficiency. Furthermore, we demonstrate using simulated data sets that our proposed parallel framework can scale to BNs of even higher dimensionality.

In this paper, we study the acceleration of applications that identify all the occurrences of thousands of string-patterns in an input data-stream using the Automata Processor (AP). For this evaluation, we use two applications from two fields, namely, cybersecurity and bioinformatics. The first application, called Fast-SNAP, scans network data for 4312 signatures of intrusion derived from the popular open-source Snort database. Using the resources of a single AP-board, Fast-SNAP can scan for all these signatures at 1 Gbps. The second application, called PROTOMATA, looks for all the occurrences of 1,309 motifs from the PROSITE database in protein sequences. PROTOMATA is up to 68 times faster than the state-of-the-art CPU implementation. As a comparison, we emulate the execution of the same NFAs by programming FPGAs using state-of-the-art techniques. We find that the performance derived by using the resources of a single AP-board, which houses 32 AP-chips, is comparable to that of the resources of five to six large FPGAs. The design techniques used in this paper are generic and may be applicable to the development of similar applications on the AP.

The Micron Automata Processor (AP) is a novel co-processor accelerator that supports the parallel execution of multiple Nondeterministic Finite Automata (NFA) programmed directly into hardware over a single data-stream. In this paper, we present a number of programming techniques to develop automata that execute efficiently on this processor. First, we present general techniques to transform NFAs defined in their classical representation to the representation used by the AP, and optimize the same. Then, we present automata development techniques using simple but powerful generic building blocks. All the above techniques are generic in nature and can be useful to application developers working on this new upcoming co-processor architecture.

In this paper, we study the acceleration of applications that require searching for all occurrences of thousands of string-patterns in an input data-stream, using the Automata Processor (AP). For this purpose, we use two applications from two fields, namely, network security and bioinformatics. The first application, called Fast-SNAP (for Fast-SNort using AP), scans network data for 4312 signatures of intrusion derived from the popular open-source Snort database. Using the resources of a single AP board, Fast-SNAP can scan for all these signatures at 10.3 Gbps. The second application, called PROTOMATA (for PROTein autOMATA), looks for all occurrences of 1308 protein motifs from the PROSITE database in protein sequences. PROTOMATA is up to half a million times faster than its single-CPU-based counterpart. The techniques developed to program these applications may be useful in the design and development of similar applications using this new hardware accelerator.