This paper presents a transparent Container Network Memory storage device, coined as CNetMem, aiming to address the open problem of unpredictable performance degradation of containers when the working set of an application no longer fits in container memory. First, CNetMem will enable application tenants running in a container to park their working set memory/file to a faster network memory storage by organizing a group of remote memory nodes as remote memory donors. This allows CNetMem to take advantage of remote idle memory on a cluster before resorting to a slow local I/O subsystem like local disk without any modification of host OS or application. Second, CNetMem provides a hybrid batching technique to remove or alleviate performance bottlenecks in the I/O performance critical path for remote memory read/write with replication or disk backup for fault tolerance. Third, CNetMem introduces a rank-based node selection algorithm to find the optimal node for placing remote memory blocks across cluster. This helps CNetMem to reduce the performance impact due to remote memory eviction. Extensive experiments are conducted on three big data applications and four machine learning workloads. The results show that CNetMem achieves up to 172× throughput improvements compared to vanilla Linux and up to 5.9× completion time improvements over existing approaches in big data and ML workload.

An efficient approach to managing big data workloads is to enable applications to work directly with huge pages. This can effectively avoid or reduce the memory fragmentation problem due to high frequent memory allocation and deallocation and significantly minimize the performance degradation of big data applications. This paper presents XPage, a huge page memory management framework, with three novel features. First, XPage by design can provide automated huge page managements with transparency to both OS and applications. Second, Xpage represents a memory management redesign that brings performance and memory saving to memory intensive applications by supporting dynamic huge page memory management without resorting to splitting huge pages for memory fragmentation. Third but not the least, XPage can efficiently minimize the internal fragmentation without impacting performance of applications. We conduct extensive experiments to evaluate the effectiveness of XPage in minimizing internal memory fragmentation in the presence of dynamic memory intensive big data workloads, by comparing XPage with vanilla Linux using 4KB base page and Linux with 2MB huge page.

The volume of trajectory data has become tremendously large in recent years. How to efficiently maintain and compute such trajectory data becomes a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road networks. By treating the mined patterns as dictionary items, we have the chance to encode a long trajectory by shorter paths, thus leading to smaller space cost. Meanwhile, we design an error-bounded temporal compression on top of the identified spatial patterns for much low space cost. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of both space saving and runtime of trajectory compression.