Papers by Muhammad Jibril
Improving narrative text writing skill by using series of pictures at the eight grade of SMPN 1 DAU
Proceedings of the 17th International Workshop on Data Management on New Hardware (DaMoN 2021)
Persistent memory (PMem)-also known as non-volatile memory (NVM)-offers new opportunities not onl... more Persistent memory (PMem)-also known as non-volatile memory (NVM)-offers new opportunities not only for the design of data structures and system architectures but also for failure recovery in databases. However, instant recovery can mean not only to bring the system up as fast as possible but also to continue long-running queries which have been interrupted by a system failure. In this work, we discuss how PMem can be utilized to implement query recovery for analytical graph queries. Furthermore, we investigate the trade-off between the overhead of managing the query state in PMem at query runtime as well as the recovery and restart costs.
Proceedings of the 2022 International Conference on Management of Data
Distributed in-memory processing frameworks accelerate iterative workloads by caching suitable da... more Distributed in-memory processing frameworks accelerate iterative workloads by caching suitable datasets in memory rather than recomputing them in each iteration. Selecting appropriate datasets to cache as well as allocating a suitable cluster configuration for caching these datasets play a crucial role in achieving optimal performance. In practice, both are tedious, time-consuming tasks and are often neglected by end users, who are typically not aware of workload semantics, sizes of intermediate data, and cluster specification. To address these problems, we present Juggler, an end-to-end framework, which autonomously selects appropriate datasets for caching and recommends a correspondingly suitable cluster configuration to end users, with the aim of achieving optimal execution time and cost. We evaluate Juggler on various iterative, real-world, machine learning applications. Compared with our baseline, Juggler reduces execution time to 25.1 % and cost to 58.1 %, on average, as a result of selecting suitable datasets for caching. It recommends optimal cluster configuration in 50 % of cases and near-to-optimal configuration in the remaining cases. Moreover, Juggler achieves an average performance prediction accuracy of 90 %.
Graph databases are used for different applications like analyzing large networks, representing a... more Graph databases are used for different applications like analyzing large networks, representing and querying knowledge graphs, and managing master data and complex data structures. Besides graph analytics, the transactional processing of concurrent updates and queries represents a challenging data management task. In this paper, we investigate the usage of persistent memory as a very promising technology for graph processing. We present a novel architecture for transactional processing of queries and updates on a property graph model that exploits and addresses the specific characteristics of persistent memory by hybrid storage andmemorymanagement as well as a just-in-time query compilation approach. Our experimental evaluation on interactive short read and update queryworkloads show that PMem-based systems that are well-designed to exploit PMem characteristics outperform traditional disk-based systems significantly and have only a small overhead compared to DRAM-only systems. Moreo...
Distributed and Parallel Databases, 2021
After the introduction of Persistent Memory in the form of Intel’s Optane DC Persistent Memory on... more After the introduction of Persistent Memory in the form of Intel’s Optane DC Persistent Memory on the market in 2019, it has found its way into manifold applications and systems. As Google and other cloud infrastructure providers are starting to incorporate Persistent Memory into their portfolio, it is only logical that cloud applications have to exploit its inherent properties. Persistent Memory can serve as a DRAM substitute, but guarantees persistence at the cost of compromised read/write performance compared to standard DRAM. These properties particularly affect the performance of index structures, since they are subject to frequent updates and queries. However, adapting each and every index structure to exploit the properties of Persistent Memory is tedious. Hence, we require a general technique that hides this access gap, e.g., by using DRAM caching strategies. To exploit Persistent Memory properties for analytical index structures, we proposeselective caching. It is based on ...
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Papers by Muhammad Jibril