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Distributed in-memory key-value stores (KVSs) have become a critical data-serving layer in cloud computing and big data infrastructure. Unfortunately, KVSs have demonstrated a gap between achieved and available performance, QoS, and energy efficiency on commodity platforms. Two research thrusts have focused on improving key-value performance: hardware-centric research has started to explore specialized platforms for KVSs, and software-centric research revisited the KVS application to address fundamental software bottlenecks. Unlike prior research focusing on hardware or software in isolation, the authors aimed to full-stack (software through hardware) architect high-performance and efficient KVS platforms. Their full-system characterization identifies the critical hardware/software ingredients for high-performance KVS systems and suggests optimizations to achieve record-setting performance and energy efficiency: 120~167 million requests per second (RPS) on a single commodity server. They propose a future many-core platform and via detailed simulations demonstrate the capability of achieving a billion RPS with a single server platform. [ABSTRACT FROM PUBLISHER]

Abstract: Consolidated environments are progressively accommodating diverse and unpredictable workloads in conjunction with virtual desktop infrastructure and cloud computing. Unpredictable workloads, however, aggravate the semantic gap between the virtual machine monitor and guest operating systems, leading to inefficient resource management. In particular, CPU management for virtual machines has a critical impact on I/O performance in cases where the virtual machine monitor is agnostic about the internal workloads of each virtual machine. This paper presents virtual machine scheduling techniques for transparently bridging the semantic gap that is a result of consolidated workloads. To enable us to achieve this goal, we ensure that the virtual machine monitor is aware of task-level I/O-boundedness inside a virtual machine using inference techniques, thereby improving I/O performance without compromising CPU fairness. In addition, we address performance anomalies arising from the indirect use of I/O devices via a driver virtual machine at the scheduling level. The proposed techniques are implemented on the Xen virtual machine monitor and evaluated with micro-benchmarks and real workloads on Linux and Windows guest operating systems. [Copyright &y& Elsevier]