Multi-tenant clouds enhance resource sharing among Virtual Machines (VMs) to boost overall utilization and reduce power consumption. However, this also introduces interference among workloads from different tenants and impedes VM performance isolation. In this article, we first demonstrate that the last-level cache (LLC) in CPUs, which is inherently shared by all VMs on the same physical machine, becomes a significant contending resource for LLC-critical workloads, leading to notable performance imbalances under the default hardware caching strategy. Although recent studies on LLC scheduling have progressed, they often require detailed profiling of user workloads or rely on hyperparameter tuning, limiting their applicability to private clusters or specific scenarios. We propose SpiderSense, a software-initiated LLC partitioner for managing Virtual Machine Monitors (VMM), to address these limitations. SpiderSense leverages modern yet off-the-shelf server CPU features to adaptively orchestrate LLC allocation among running black-boxed user VMs. SpiderSense dynamically samples VMs and calculates their fair share of LLC to allocate them while fully improving performance isolation among VMs. We experiment with SpiderSense using typical LLC-critical workloads, representative of the types of applications that stress LLC performance, such as Memcached and Llama. Our results show that SpiderSense improves performance by up to 40% in numerous colocation scenarios compared to current solutions.

An efficient formation-keeping strategy is essential for unmanned surface vehicles (USVs) to achieve complex cooperation missions in the Marine Internet of Things (MIoT) system. However, traditional methods make generating an efficient strategy to adapt to different formation patterns difficult in dynamic MIoT. To address this, we enhance the deep deterministic policy gradient (DDPG) algorithm and propose a novel formation control strategy generation approach. First, we design a generic reward mechanism based on the virtual leader–follower strategy to adapt to different formation patterns, simplify the design process, and optimize the formation control. Then, we adopt the intrinsic curiosity module (ICM) to alleviate the problem of sparse rewards and the prioritized experience replay (PER) mechanism to improve the utilization of experience and accelerate the learning rate. In addition, a Gaussian noise model is integrated into the DDPG approach to simulate various external disturbances, which can improve the robustness of the generated strategy. Finally, we built a virtual simulation environment based on Unity3D and conducted field tests to verify the feasibility and superiority of our approach.

Lensless cameras provide a framework to build thin imaging systems by replacing the lens in a conventional camera with an amplitude or phase mask near the sensor. Existing methods for lensless imaging can recover the depth and intensity of the scene, but they require solving computationally-expensive inverse problems. Furthermore, existing methods struggle to recover dense scenes with large depth variations. In this paper, we propose a lensless imaging system that captures a small number of measurements using different patterns on a programmable mask. In this context, we make three contributions. First, we present a fast recovery algorithm to recover textures on a fixed number of depth planes in the scene. Second, we consider the mask design problem, for programmable lensless cameras, and provide a design template for optimizing the mask patterns with the goal of improving depth estimation. Third, we use a refinement network as a post-processing step to identify and remove artifacts in the reconstruction. These modifications are evaluated extensively with experimental results on a lensless camera prototype to showcase the performance benefits of the optimized masks and recovery algorithms over the state of the art.

Mask-based lensless cameras offer an alternative option to conventional cameras. Compared to conventional cameras, lensless cameras can be extremely thin, flexible, and lightweight. Despite these advantages, the quality of images recovered from the lensless cameras is often poor because of the ill-conditioning of the underlying linear system. In this paper, we propose a new method to address the problem of illconditioning by combining coded illumination patterns with the mask-based lensless imaging. We assume that the object is illuminated with multiple binary patterns and the camera acquires a sequence of images for different illumination patterns. We propose a low-complexity, recursive algorithm that avoids storing all the images or creating a large system matrix. We present simulation results on standard test images under various extreme conditions and demonstrate that the quality of the image improves significantly with a small number of illumination patterns.