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Network emulation allows unmodified code execution on lightweight containers to enable accurate and scalable networked application testing. However, such testbeds cannot guarantee fidelity under high workloads, especially when many processes concurrently request resources (e.g., CPU, disk I/O, GPU, and network bandwidth) that are more than the underlying physical machine can offer. A virtual time system enables the emulated hosts to maintain their own notion of virtual time. A container can stop advancing its time when not running (e.g., in an idle or suspended state). The existing virtual time systems focus on precise time management for CPU-intensive applications but are not designed to handle other operations, such as disk I/O, network I/O, and GPU computation. In this paper, we develop a lightweight virtual time system that integrates precise I/O time for container-based network emulation. We model and analyze the temporal error during I/O operations and develop a barrier-based time compensation mechanism in the Linux kernel. We also design and implement Dynamic Load Monitor (DLM) to mitigate the temporal error during I/O resource contention. VT-IO enables accurate virtual time advancement with precise I/O time measurement and compensation. The experimental results demonstrate a significant improvement in temporal error with the introduction of DLM. The temporal error is reduced from 7.889 seconds to 0.074 seconds when utilizing the DLM in the virtual time system. Remarkably, this improvement is achieved with an overall overhead of only 1.36% of the total execution time. 

In many real-world applications such as social network analysis and online advertising/marketing, one of the most important and popular problems is called influence maximization (IM), which finds a set of k seed users that maximize the expected number of influenced user nodes. In practice, however, maximizing the number of influenced nodes may be far from satisfactory for real applications such as opinion promotion and collective buying. In this paper, we explore the importance of stability and triangles in social networks, and formulate a novel problem in the influence spread scenario, named triangular stability maximization , over social networks, and generalize it to a general triangle influence maximization problem, which is proved to be NP-hard. We develop an efficient reverse influence sampling (RIS) based framework for the triangle IM with theoretical guarantees. To enable unbiased estimators, it demands probabilistic sampling of triangles, that is, sampling triangles according to their probabilities. We propose an edge-based triple sampling approach, which is exactly equivalent to probabilistic sampling and avoids costly triangle enumeration and materialization. We also design several pruning and reduction techniques, as well as a cost-model-guided heuristic algorithm. Extensive experiments and a case study over real-world graphs confirm the effectiveness of our proposed algorithms and the superiority of triangular stability maximization and triangle influence maximization. 

P4’s data-plane programmability allows for highly customizable and programmable packet processing, enabling rapid innovation in network applications, such as virtualization, security, load balancing, and traffic engineering. Researchers extensively use Mininet, a popular network emulator, integrated with BMv2, for fast and flexible prototyping of these P4-based applications, but due to its lower performance in terms of throughput and latency compared to a production-grade software switch like Open vSwitch, it is crucial to have an accurate and scalable emulation testbed. In this paper, we develop a lightweight virtual time system and integrate it into Mininet with BMv2 to enhance fidelity and scalability. By scaling the time of interactions between containers and the underlying physical machine by a time dilation factor (TDF), we can trade time with system resources, making the emulated P4 network appear to be faster from the viewpoint of the switch/host processes in the container. Our experimental results show that the testbed can accurately emulate much larger networks with high loads, scaled by a factor of TDF with extremely low system overhead. 

We present a unique virtual testbed that combines a data-plane programmable network emulator and a power distribution system simulator to evaluate smart grid security and resilience applications. The testbed employs a virtual time system for effective simulation synchronization and fidelity enhancement. We showcase the advantages of the simulation testbed through an anomaly detection case study. 

Abstract The susceptibility of a granular soil to suffusion is strongly dependent on its grain size distribution (GSD) and the mechanical and hydraulic conditions it is subjected to. This study investigates the onset of suffusion considering the effect of confining pressure and stress anisotropy using a fully resolved computational fluid dynamics and discrete element method (CFD–DEM). Three benchmarks, including the sedimentations of single and two adjacent spheres and the classic one‐dimensional (1D) consolidation are performed to demonstrate the capability of this method for high‐fidelity particle‐fluid simulations. A modified hydraulic criterion for the onset of suffusion considering stress anisotropy is presented. The microstructural changes of soil specimens before and during global suffusion are inspected, with emphasis on the evolutions of particle kinetic energy and displacements, force chain networks, and stress anisotropy. We found that the critical hydraulic gradient is negatively correlated with the confining pressure and the degree of stress anisotropy. Fine particles in the soil matrix are locally detached at small hydraulic gradients before the apparent global suffusion, as manifested by the variation of particle kinetic energy and coordination numbers. The roles of different contact types on force transmission and stress anisotropy in eroded specimens are also examined.