skip to main content

Search for: All records

Creators/Authors contains: "Gavrilovska, Ada"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Emerging hybrid memory systems that comprise technologies such as Intel's Optane DC Persistent Memory, exhibit disparities in the access speeds and capacity ratios of their heterogeneous memory components. This breaks many assumptions and heuristics designed for traditional DRAM-only platforms. High application performance is feasible via dynamic data movement across memory units, which maximizes the capacity use of DRAM while ensuring efficient use of the aggregate system resources. Newly proposed solutions use performance models and machine intelligence to optimize which and how much data to move dynamically. However, the decision of when to move this data is based on empirical selection of time intervals, or left to the applications. Our experimental evaluation shows that failure to properly conFigure the data movement frequency can lead to 10%-100% performance degradation for a given data movement policy; yet, there is no established methodology on how to properly conFigure this value for a given workload, platform and policy. We propose Cori, a system-level tuning solution that identifies and extracts the necessary application-level data reuse information, and guides the selection of data movement frequency to deliver gains in application performance and system resource efficiency. Experimental evaluation shows that Cori configures data movement frequencies that provide applicationmore »performance within 3% of the optimal one, and that it can achieve this up to 5 x more quickly than random or brute-force approaches. System-level validation of Cori on a platform with DRAM and Intel's Optane DC PMEM confirms its practicality and tuning efficiency.« less
  2. Application performance improvements in emerging systems with hybrid memory components, such as DRAM and Intel’s Optane DC persistent memory, are possible via periodic data movements, that maximize the DRAM use and system resource efficiency. Similarly, predominantly used NUMA DRAM-only systems benefit from data balancing solutions, such as AutoNUMA, which periodically remap an application and its data on the same NUMA node. Although there has been a significant body of research focused on the clever selection of the data to be moved periodically, there is little insight as to how to select the frequency of the data movements, i.e., the duration of the monitoring period. Our experimental analysis shows that fine-tuning the period frequency can boost application performance on average by 70% for systems with locally attached memory units and 5x when accessing remote memory via interconnection networks. Thus, there is potential for significant performance improvements just by cleverly selecting the frequency of the data movements apart from choosing the data itself. While existing solutions empirically set the duration of the period, our work provides insights into the application-level properties that influence the choice of the period. More specifically, we show that there is a correlation between the application-level data reusemore »distance and migration frequency. Future work aims to solidify this correlation and build a profiling solution that provides users with the data movement frequency which dynamic data management solutions can then use to enhance performance.« less
  3. For hosting data-serving and caching workloads based on key-value stores in clouds, the cost of memory represents a significant portion of the hosting expenses. The emergence of cheaper, but slower, types of memories, such as NVDIMMs, opens opportunities to reduce the hosting costs for such workloads. The question explored in this paper is how to determine adequate allocations of different memory types in future systems with heterogeneous memory components, so as to retain desired performance SLOs and maximize the cost efficiency of the memory resource. We develop Mnemo, a memory sizing and data tiering consultant, that permits quick exploration of the cost-benefit tradeoffs associated with different configurations of the hybrid memory components used by key-value store workloads. Using experimental evaluation with different workload patterns, Mnemo is able to afford applications such as Redis, Memcached and DynamoDB, with substantial reduction in their hosting costs, at negligible impact on application performance, thus improving the overall system memory cost efficiency.
  4. Server systems with large amounts of physical memory can benefit from using some of the available memory capacity for in-memory snapshots of the ongoing computations. In-memory snapshots are useful for services such as scaling of new workload instances, debugging, during scheduling, etc., which do not require snapshot persistence across node crashes/reboots. Since increasingly more frequently servers run containerized workloads, using technologies such as Docker, the snapshot, and the subsequent snapshot restore mechanisms, would be applied at granularity of containers. However, CRIU, the current approach to snapshot/restore containers, suffers from expensive filesystem write/read operations on image files containing memory pages, which dominate the runtime costs and impact the potential benefits of manipulating in-memory process state. In this paper, we demonstrate that these overheads can be eliminated by using MVAS -- kernel support for multiple independent virtual address spaces (VAS), designed specifically for machines with large memory capacities. The resulting VAS-CRIU stores application memory as a separate snapshot address space in DRAM and avoids costly file system operations. This accelerates the snapshot/restore of address spaces by two orders of magnitude, resulting in an overall reduction in snapshot time by up to 10× and restore time by up to 9×. We demonstrate themore »utility of VAS-CRIU for container management services such as fine-grained snapshot generation and container instance scaling.« less
  5. The increasing demand of big data analytics for more main memory capacity in datacenters and exascale computing environments is driving the integration of heterogeneous memory technologies. The new technologies exhibit vastly greater differences in access latencies, bandwidth and capacity compared to the traditional NUMA systems. Leveraging this heterogeneity while also delivering application performance enhancements requires intelligent data placement. We present Kleio, a page scheduler with machine intelligence for applications that execute across hybrid memory components. Kleio is a hybrid page scheduler that combines existing, lightweight, history-based data tiering methods for hybrid memory, with novel intelligent placement decisions based on deep neural networks. We contribute new understanding toward the scope of benefits that can be achieved by using intelligent page scheduling in comparison to existing history-based approaches, and towards the choice of the deep learning algorithms and their parameters that are effective for this problem space. Kleio incorporates a new method for prioritizing pages that leads to highest performance boost, while limiting the resulting system resource overheads. Our performance evaluation indicates that Kleio reduces on average 80% of the performance gap between the existing solutions and an oracle with knowledge of future access pattern. Kleio provides hybrid memory systems with fastmore »and effective neural network training and prediction accuracy levels, which bring significant application performance improvements with limited resource overheads, so as to lay the grounds for its practical integration in future systems.« less