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.

Recently, learning-based image compression (LIC) methods have surpassed manually designed approaches in both compression quality and bitrate. However, increasing computational demands and insufficient optimizations in codec performance have hindered the advancement of LIC acceleration. Most researches focus on optimizing specific components, often neglecting the sources of underutilization during the execution of LIC models. Generally, efficient LIC acceleration encounters three primary challenges: 1) extra overheads introduced by individual optimizations; 2) load and computation imbalances in small kernels; and 3) mismatches between hardware configurations and the LIC models. To address these challenges, we propose a framework named extensive accelerator for LIC (X-LIC) for efficiently exploring the design space under constrained resources. First, we quantitatively characterize a representative LIC model, including its latency, computation size, and temporal utilization across various accelerators. We design a hardware-optimized quantization method to compensate for the lack of LIC-oriented research, particularly regarding data precision, distortion, and resource consumption. Additionally, we propose a parameterized LIC accelerator architecture that integrates seamlessly with existing loop optimization models and supports various LIC operators. Two optimization schemes are proposed for redundant computation in transposed convolution and load and computation imbalance in small kernels. Experimental results show that our framework demonstrates significant flexibility across a broad design space, achieving an average of 78%–95% of the theoretical peak performance and up to 688.2/759.1 GOP/s en/de-coder performance with INT8 precision. As a result, the en/de-coder performance can reach up to 33/36 FPS in 720P resolution. An FPGA demo of X-LIC is available athttps://github.com/sjtu-tcloud/X-LIC.

Resource underutilization has troubled data centers for several decades. On the CPU front, live migration plays a crucial role in reallocating CPU resources. Nevertheless, contemporary Virtual Machine (VM) live migration methods are burdened by substantial resource consumption. In terms of memory management, disaggregated memory offers an effective solution to enhance memory utilization, but leaves a gap in addressing CPU underutilization. Our findings highlight a considerable opportunity to optimize live migration in the context of disaggregated memory systems. We introduce Anemoi, a resource management system that seamlessly integrates VM live migration with memory disaggregation to address the aforementioned gap. In the context of disaggregated memory, remote memory becomes accessible from destination nodes, effectively eliminating the need for extensive network transmission of memory pages, and thereby significantly reducing migration time. In addition, we propose using memory replicas as an optimization to the live migration system. To mitigate the overhead of potential excessive memory consumption, we develop a dedicated compression algorithm. Our evaluations demonstrate that Anemoi leads to a notable 69% reduction in network bandwidth utilization and an impressive 83% reduction in migration time compared to traditional VM live migration. Additionally, our compression algorithm achieves an outstanding space-saving rate of 83.6%.

Resource underutilization has troubled data centers for several decades. Memory disaggregation provides an efficient way to improve memory utilization while leaving a missing puzzle piece on CPU underutilization. Live migration is an essential method for CPU resource reallocation. However, the state-of-the-art Virtual Machines (VM) live migration suffers from significant resource consumption. We discover the substantial potential for optimizing live migration in disaggregated memory systems. We propose Anemoi, a source management system incorporating VM live migration into memory disaggregation to fill in the missing piece. Disaggregated memory enables the read-only replica to be accessible from destination nodes, eliminating considerable network transmission for memory pages and saving migration time. Regarding the potential overwhelming memory consumption of duplicate read-only replicas, we design a dedicated compression algorithm. The evaluation shows that Anemoi reduces the network bandwidth use and the migration time by 69% and 83%, respectively, compared to VM live migration. The compression can achieve a space-saving of 83.6%.