記憶體快取長久以來一直用於填補處理器和儲存裝置之間的性能差距，以減少數據密集型計算的數據存取時間。
以往，關於記憶體快取的研究主要集中在最大化單一機器上的快取命中率。然而在本文中，針對平行數據分系應用，我們認為分散式快取系統應該以協作的方式運行。這些應用經常被許多新興科技，如：大數據和AI(人工智能)所使用，以在更短的時間內對更大量的數據進行數據挖掘及複雜的分析。
平行數據分析工作由多個平行的子任務所組成。因此，一個工作的完成時間會受其最慢的子任務所限制。這意味著在快取該工作的所有子任務的所有輸入之前，該工作將無法從快取中受益。
為了解決這個問題，我們提出了一個協作式快取記憶體設計系統。該系統根據數據存取的模式、規律，定期地重新安排節點之間的快取配置，同時考慮任務之間的相依性及網路的局部化特性。
我們以事件驅動模擬器來評估我們的方法，實驗包含一個合成的工作負載和一個真實世界工作負載。
結果顯示，與非協作式快取系統相比，我們的方法可以將平均工作完成時間減少多達41%；與其他協作式快取系統相比，可以減少平均工作完成時間多達35%。

Memory caching has long been used to fill up the performance gap between processor and disk for reducing the data access time of data-intensive computations. Previous studies on caching mostly focus on optimizing the hit rate of a single machine. But in this paper, we argue that the caching decision of a distributed memory system should be performed in a cooperative manner for the parallel data analytic applications, which are commonly used by emerging technologies, such as Big Data and AI (Artificial Intelligence), to perform data mining and sophisticated analytics on larger data volume in a shorter time.
A parallel data analytic job consists of multiple parallel tasks. Hence, the completion time of a job is bounded by its slowest task, meaning that the job cannot benefit from caching until all inputs of its tasks are cached. To address the problem, we proposed a cooperative caching design that periodically rearranges the cache placement among nodes according to the data access pattern while taking the task dependency and network locality into account. Our approach is evaluated by a trace-driven simulator using both synthetic workload and real-world traces. The results show that we can reduce the average completion times up to 33% compared to a non-collaborative caching polices and 25% compared to other start-of-the-art collaborative caching policies.

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