Competition for the last-level cache (LLC) is a long-standing issue in multi-tenant cloud environments, often leading to severe performance interference among co-located virtual machines. LLC management in the cloud faces unique challenges, including unpredictable tenant workloads, misaligned performance metrics, and the need to ensure fairness under service level agreements (SLAs). Existing LLC allocation methods fall short in addressing these challenges. We present Cacheman, a comprehensive LLC management system designed from real-world cloud deployment experience. Cacheman introduces a novel gradient-based sharing mechanism for LLC ways, enabling smooth LLC allocation adjustments that simultaneously improve fairness and utilization efficiency. Its real-time allocation algorithm promptly detects and mitigates unfair LLC allocation, adapting to dynamic workloads with second-scale responsiveness. Additionally, Cacheman supports performance consistency for tenants running distributed applications by enforcing negotiated upper bounds on cache usage. Extensive experiments demonstrate that Cacheman effectively achieves its multi-dimensional goals, and long-term production deployment further shows that it significantly reduces SLA violations caused by LLC contention.

Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall short of reaching high data scalability. In this paper, we present a general-purpose, distributed, information-centric random walk-based graph embedding framework, DistGER, which can scale to embed billion-edge graphs. DistGER incrementally computes information-centric random walks. It further leverages a multi-proximity-aware, streaming, parallel graph partitioning strategy, simultaneously achieving high local partition quality and excellent workload balancing across machines. DistGER also improves the distributed Skip-Gram learning model to generate node embeddings by optimizing the access locality, CPU throughput, and synchronization efficiency. Experiments on real-world graphs demonstrate that compared to state-of-the-art distributed graph embedding frameworks, including KnightKing, DistDGL, and Pytorch-BigGraph, DistGER exhibits 2.33×--129× acceleration, 45% reduction in cross-machines communication, and >10% effectiveness improvement in downstream tasks.

Disaggregated memory architecture has risen in popularity for large datacenters with the advantage of improved resource utilization, failure isolation, and elasticity. Replicated state machines (RSMs) have been extensively used for reliability and consistency. In traditional RSM protocols, each replica stores replicated data and has the computing power to participate in some part of the protocols. However, traditional RSM protocols fail to work in the disaggregated memory architecture due to asymmetric resources on CPU nodes and memory nodes. This article proposes ECHO, a fast one-sided RDMA-based RSM protocol with lightweight log replication and remote applying, efficient linearizability guarantee, and fast coordinator failure recovery. ECHO enables all operations in the protocol to be efficiently executed using only one-sided RDMA, without the participation of any computing resource in the memory pool. To provide lightweight log replication and remote applying, ECHO couples the replicated log and the state machine to avoid dual-copy and performs remote applying by updating pointers. To enable efficient remote log state management, ECHO leverages a hitchhiked log state updating scheme to eliminate extra network round trips. To provide efficient linearizability guarantee, ECHO performs immediate remote applying after log replication and leverages the local locks at the coordinator to ensure linear consistency. Moreover, ECHO adopts a commit-aware log cache to make data visible immediately after being committed. To achieve fast failure recovery, ECHO leverages a commit point identification scheme to reduce the overhead of log consistency recovery. Experimental results demonstrate that ECHO outperforms the state-of-the-art RSM protocol (namely Sift) in multiple scenarios. For example, ECHO achieves 27%–52% higher throughput on typical write-intensive workloads. Moreover, ECHO reduces the consistency recovery time by three orders of magnitude for coordinator failure.

Data security is a key issue that non-volatile memory (NVM) system designers must consider. However, this is challenging because implementing security mechanisms such as bonsai merkle tree (BMT) in NVM needs to ensure crash recovery due to the non-volatile property of NVM. Existing schemes fail to efficiently guarantee the atomic BMT root update and instant system recovery required for BMT crash recovery, resulting in large write traffic and performance overhead. In this paper, we propose DR-TREE, a dynamic and recoverable BMT scheme for secure NVM, which reduces the update overhead of BMT root and achieves fast crash recovery with low write traffic. DR-TREE dynamically builds BMT and adjusts the updated BMT levels according to memory write requests, thus reducing unnecessary update overhead of BMT root. Next, based on the locality of memory write requests, DR-TREE merges repeated BMT updates, further decreasing the update overhead of BMT root. Moreover, DR-TREE achieves fast crash recovery with extremely low write traffic by delaying the partial recovery process. Experiments show that compared to the state-of-the-art design, DR-TREE improves the performance by 44.6%, decreases write traffic by 78.2% and achieves the system recovery in 5ms.