Memory price will continue dropping in the next few years according to Gartner. Such trend renders it affordable for in-memory key-value stores (IMKVs) to maintain redundant memory-resident copies of each key-value pair to provision enhanced reliability and high availability services. Though contemporary IMKVs have reached unprecedented performance, delivering single-digit microsecond-scale latency with up to tens of millions queries per second throughput, existing replication protocols are unable to keep pace with such an advancement of IMKVs, either incurring unbearable latency overhead or demanding intensive resource usage. Consequently, the adoption of those replication techniques always results in substantial performance degradation.In this paper, we propose MacR, a RDMA-based high-performance and lightweight replication protocol for IMKVs. The design of MacR centers around sharing the remote backup memory to enable RDMA-based replication protocol, and synthesizes a collection of optimizations, including memory allocator cooperative replication and adaptive bulk data synchronization to control the number of network operations and to enhance the recovery performance. Performance evaluations with a variety of YCSB workloads demonstrate that MacR can efficiently outperform alternative replication methods in terms of the throughput while preserving sufficiently low latency overhead. It can also efficiently speed up the recovery process.

While key-value (KV) cache, such as memcached, dedicates a large volume of expensive memory to holding performance-critical data, it is important to improve memory efficiency, or to reduce cache miss ratio without adding more memory. As we find that optimizing replacement algorithms is of limited effect for this purpose, a promising approach is to use a compact data organization and data compression to increase effective cache size. However, this approach has the risk of degrading the cache's performance due to additional computation cost. A common perception is that a high-performance KV cache is not compatible with use of data compacting techniques.

This paper examines synchronization of computer clocks connected via a data network and proposes a skewless algorithm to synchronize them. Unlike existing solutions, which either estimate and compensate the frequency difference (skew) among clocks or introduce offset corrections that can generate jitter and possibly even backward jumps, our solution achieves synchronization without these problems. We first analyze the convergence property of the algorithm and provide explicit necessary and sufficient conditions on the parameters to guarantee synchronization. We then study the effect of noisy measurements (jitter) and frequency drift (wander) on the offsets and synchronization frequency, and further optimize the parameter values to minimize their variance. Our study reveals a few insights, for example, we show that our algorithm can converge even in the presence of timing loops and noise, provided that there is a well-defined leader. This marks a clear contrast with current standards such as NTP and PTP, where timing loops are specifically avoided. Furthermore, timing loops can even be beneficial in our scheme as it is demonstrated that highly connected subnetworks can collectively outperform individual clients when the time source has large jitter. The results are supported by experiments running on a cluster of IBM BladeCenter servers with Linux.

This paper examines synchronization of computer clocks connected via a data network and proposes a skewless algorithm to synchronize them. Unlike existing solutions, which either estimate and compensate the frequency difference (skew) among clocks or introduce offset corrections that can generate jitter and possibly even backward jumps, our algorithm achieves synchronization without these problems. We first analyze the convergence property of the algorithm and provide necessary and sufficient conditions on the parameters to guarantee synchronization. We then implement our solution on a cluster of IBM BladeCenter servers running Linux and study its performance. In particular, both analytically and experimentally, we show that our algorithm can converge in the presence of timing loops. This marks a clear contrast with current standards such as NTP and PTP, where timing loops are specifically avoided. Furthermore, timing loops can even be beneficial in our scheme. For example, it is demonstrated that highly connected subnetworks can collectively outperform individual clients when the time source has large jitter. It is also experimentally demonstrated that our algorithm outperforms other well-established software-based solutions such as the NTPv4 and IBM Coordinated Cluster Time (IBM CCT).

SUMMARY Even though Java is the de facto programming language for enterprise applications, there exist only a limited number of Java‐based benchmarks to understand the performance on emerging multicore systems. To bridge this gap, this paper presents a report generation benchmark that is developed on top of Open Source Apache Geronimo's DayTrader benchmark. Report generation and rendering is at the heart of many enterprise business analytics and business intelligence software products, and it is used by many enterprise applications. We evaluate the performance scalability of this benchmark on a state‐of‐the‐art Power7 multicore system with 8 Power7 cores and 32 hardware threads. The benchmark throughput scales linearly up to eight hardware threads, but beyond that point, the throughput falls sharply. Significant locking in the Java class libraries for non‐shared objects results in this performance drop. Splitting the locks on these shared classes results in near linear scaling from eight to 32 threads and improved the throughput by 80%. We also show that the Linux operating system load balancing could result in a degraded application performance in hardware multithreaded systems and simultaneous‐multithreads‐aware task scheduling results in uniform core‐resource utilization as well as improved application performance. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, an almost peer-to-peer (AP2P) clock synchronization protocol is proposed. AP2P is almost peer-to-peer in the sense that it provides the desirable features of a purely hierarchical (client/server) clock synchronization protocol while avoiding the undesirable consequences of a purely peer-to-peer one. In AP2P, a unique node is elected as a leader in a distributed manner. Each non-leader node adjusts its clock rate based on message exchanges with its neighbors, taking into consideration that neighbors that are closer to the leader have more effect on the adjustment than the neighbors that are further away from the leader. We compare the performance of AP2P with that of the server time protocol (STP), which is a purely hierarchical clock synchronization protocol. Simulation results, which have been conducted on several network topologies, have shown that AP2P can provide a clock synchronization accuracy that is indistinguishable from that of STP. Furthermore, AP2P is more fault-tolerant because it can recover from certain types of failures that STP cannot recover from.