Memory-based key-value cache systems, such as Memcached and Redis, have become indispensable components of data center infrastructures and have been used to cache performance-critical data to avoid expensive back-end database accesses. As the memory is usually not large enough to hold all the items, cache replacement must be performed to evict some cached items to make room for the newly coming items when there is no free space. Many real-world workloads target small items and have frequent bursts of scans (a scan is a sequence of one-time access requests). The commonly used LRU policy does not work well under such workloads since LRU needs a large amount of metadata and tends to discard hot items with scans. Small decreases in hit ratio can result in large end-to-end losses in these systems. This paper presents MemSC, which is a scan-resistant and compact cache replacement framework for Memcached. MemSC assigns a multi-granularity reference flag for each item, which requires only a few bits (two bits are enough for general use) per item to support scan-resistant cache replacement policies. To evaluate MemSC, we implement three representative cache replacement policies (MemSC-HM, MemSC-LH, and MemSC-LF) on MemSC and test them using various workloads. The experimental results show that MemSC outperforms prior techniques. Compared with the optimized LRU policy in Memcached, MemSC-LH reduces the cache miss ratio and the memory usage of the resulting system by up to 23% and 14% respectively.

MemSC: A Scan-Resistant and Compact Cache Replacement Framework for Memory-Based Key-Value Cache Systems

Sample based distributed scheduling methods have been shown to be promising lower overhead alternatives to their centralized counterparts. These methods can make fast decisions based on information gathered from just a small number of worker nodes instead of the whole cluster. Most recent works in the field tend to adopt a combination of probe actions and worker-end queues in their design. However, as individual worker nodes are becoming increasingly powerful thanks to the rapid hardware evolution, we argue that one-node sampling is now a viable choice. Specifically, we show that it is now possible to achieve even lower scheduling latency by latency by abolishing probes and worker-end queues altogether. With this insight, we introduce Tiresias, a low overhead distributed scheduler based on one-node sampling and a novel task hopping mechanism. Comparing to Sparrow's approach, experiment on Google trace shows Tiresias could reduce 20% and 60% of Sparrow's 50th percentile and 90th percentile job runtime, respectively. In addition, our experiment also shows Tiresias is especially effective in reducing the delay of small jobs in non-highly loaded clusters.

Tiresias: Low-Overhead Sample Based Scheduling with Task Hopping

The modern operating system (OS) is inclined to be less dependent on devices and more involved with data. Data manipulation now tends to be the core functionality of future OSes. This is also one principle of the sea-cloud computing environment. By analyzing the challenges and problems of traditional OSes, this paper proposes the design and implementation paradigm of a next-generation OS, DataOS. DataOS introduces data abstraction and an in-kernel data stack that can directly support data collection, transfer, processing, storing, and service-providing. DataOS presents a novel solution not only for performance improvement of current big data systems but also for operating system evolvement. As a proof of concept, we implement the first version of the DataOS prototype that includes a data stack and ports the Key/Value storage system. The results of experiments confirm that DataOS outperforms the classical Memcached in terms of both real-time and concurrency and validate the feasibility and efficiency of the philosophy and design of DataOS.

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DataOS: principle, design and prototype implementation

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DataOS: principle, design and prototype implementation