Maternal moments

Sanfilippo, Katie Rose M.

doi:10.1186/isrctn11696836

Cited by 22 publications

(32 citation statements)

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“…There are key-value stores such as Redis [66], RAMCloud [2], MemepiC [60], [138], Masstree [249], MICA [64], Mercury [250], Citrusleaf/Aerospike [34], Kyoto/Tokyo Cabinet [251], Pilaf [252], document stores such as MongoDB [65], Couchbase [253], graph databases such as Trinity [46], Bitsy [254], RDF databases such as OWLIM [255], WhiteDB [50], etc. There are some systems that are partially in-memory, such as MongoDB [65], MonetDB [256], MDB [257], as they use memory-mapped files to store data such that the data can be accessed as if it was in the memory.…”

Section: In-memory Nosql Databasesmentioning

confidence: 99%

“…In this section, we will introduce some representative inmemory NoSQL databases, including MemepiC [60], [138], MongoDB [65], RAMCloud [2], [75], [126], [258], [259], Redis [66] and some graph databases.…”

Section: In-memory Nosql Databasesmentioning

confidence: 99%

“…Indexes designed for in-memory databases are quite different from traditional indexes designed for disk-based databases such as the B þ -tree, because traditional indexes mainly care about the I/O efficiency instead of memory and cache utilization. Hash-based indexes are commonly used in key-value stores, e.g., Memcached [61], Redis [66], RAMCloud [75], and can be further optimized for better cache utilization by reducing pointer chasing [63]. However hash-based indexes do not support range queries, which are crucial for data analytics and thus, tree-based indexes have also been proposed, such as T-Tree [80], Cache Sensitive Search Trees (CSS-Trees) [81], Cache Sensitive B þ -Trees (CSB þ -Trees) [82], D-Tree [83], BD-Tree [84], Fast Architecture Sensitive Tree (FAST) [85], Bw-tree [86] and Adaptive Radix Tree (ART) [87], some of which also consider pointer reduction.…”

Section: Introductionmentioning

confidence: 99%

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In-Memory Big Data Management and Processing: A Survey

Zhang

Chen

Ooi

et al. 2015

IEEE Trans. Knowl. Data Eng.

362

156

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Growing main memory capacity has fueled the development of in-memory big data management and processing. By eliminating disk I/O bottleneck, it is now possible to support interactive data analytics. However, in-memory systems are much more sensitive to other sources of overhead that do not matter in traditional I/O-bounded disk-based systems. Some issues such as fault-tolerance and consistency are also more challenging to handle in in-memory environment. We are witnessing a revolution in the design of database systems that exploits main memory as its data storage layer. Many of these researches have focused along several dimensions: modern CPU and memory hierarchy utilization, time/space efficiency, parallelism, and concurrency control. In this survey, we aim to provide a thorough review of a wide range of in-memory data management and processing proposals and systems, including both data storage systems and data processing frameworks. We also give a comprehensive presentation of important technology in memory management, and some key factors that need to be considered in order to achieve efficient in-memory data management and processing.

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Section: In-memory Nosql Databasesmentioning

confidence: 99%

Section: In-memory Nosql Databasesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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In-Memory Big Data Management and Processing: A Survey

Zhang

Chen

Ooi

et al. 2015

IEEE Trans. Knowl. Data Eng.

362

156

View full text Add to dashboard Cite

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“…The trace is indefinitely replayed at the speed of 3.3 Mb/s. The attacker(s) generates a (D)DoS flooding attack towards the server listening on ports 69 (TFTP) and/or 80 (HTTP), by using hping3 [26] (1 pkt/1200 µs). During each experiment, we captured the traffic exchanged between the switch and the controller (control plane traffic) using tcpdump, and we observed also the reaction time of the detection process.…”

Section: B Scenario and Parametersmentioning

confidence: 99%

Statesec: Stateful monitoring for DDoS protection in software defined networks

Boite

Nardin

Rebecchi

et al. 2017

2017 IEEE Conference on Network Softwarization (NetSoft)

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Abstract-Software-Defined Networking (SDN) allows for fast reactions to security threats by dynamically enforcing simple forwarding rules as counter-measures. However, in classic SDN all the intelligence resides at the controller, with the switches only capable of performing stateless forwarding as ruled by the controller. It follows that the controller, in addition to network management and control duties, must collect and process any piece of information required to take advanced (stateful) forwarding decisions. This threatens both to overload the controller and to congest the control channel. On the other hand, stateful SDN represents a new concept, developed both to improve reactivity and to offload the controller and the control channel by delegating local treatments to the switches. In this paper, we adopt this stateful paradigm to protect end-hosts from Distributed Denial of Service (DDoS). We propose StateSec, a novel approach based on in-switch processing capabilities to detect and mitigate DDoS attacks. StateSec monitors packets matching configurable traffic features (e.g., IP src/dst, port src/dst) without resorting to the controller. By feeding an entropy-based algorithm with such monitoring features, StateSec detects and mitigates several threats such as (D)DoS and port scans with high accuracy. We implemented StateSec and compared it with a state-of-the-art approach to monitor traffic in SDN. We show that StateSec is more efficient: it achieves very accurate detection levels, limiting at the same time the control plane overhead.

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“…Today's multi-and many-core platforms support higher levels of workload parallelism (in mobile and server workloads) and multi-tenancy (in server workloads), placing greater demands on the memory system. Furthermore, the new generation of big data analytics applications in the enterprise and cloud are inherently memory intensive whereby workloads demand access to high-performance, yet low-cost, memory devices in preference to the much slower storage system [spa,sap,Fit04,Sal].…”

Section: Managing Tiered Memory Systems With Multi-clockmentioning

confidence: 99%

Optimizing Main Memory Usage in Modern Computing Systems to Improve Overall System Performance

Campello¹

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Operating Systems use fast, CPU-addressable main memory to maintain an application's temporary data as anonymous data and to cache copies of persistent data stored in slower block-based storage devices. However, the use of this faster memory comes at a high cost. Therefore, several techniques have been implemented to use main memory more efficiently in the literature. In this thesis we introduce three distinct approaches to improve overall system performance by optimizing main memory usage.First, DRAM and host-side caching of file system data are used for speeding up virtual machine performance in today's virtualized data centers. The clustering of VM images that share identical pages, coupled with data deduplication, has the potential to optimize main memory usage, since it provides more opportunity for sharing resources across processes and across different VMs. In our first approach, we study the use of content and semantic similarity metrics and a new algorithm to cluster VM images and place them in hosts where through deduplication we improve main memory usage.Second, while careful VM placement can improve memory usage by eliminating duplicate data, caches in current systems employ complex machinery to manage the cached data. Writing data to a page not present in the file system page cache causes the operating system to synchronously fetch the page into memory, blocking the writing process. In this thesis, we address this limitation with a new approach to managing page writes involving v buffering the written data elsewhere in memory and unblocking the writing process immediately. This buffering allows the system to service file writes faster and with less memory resources.In our last approach, we investigate the use of emerging byte-addressable persistent memory technology to extend main memory as a less costly alternative to exclusively using expensive DRAM. We motivate and build a tiered memory system wherein persistent memory and DRAM co-exist and provide improved application performance at lower cost and power consumption with the goal of placing the right data in the right memory tier at the right time. The proposed approach seamlessly performs page migration across memory tiers as access patterns change and/or to handle tier memory pressure.

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Maternal moments

Cited by 22 publications

References 0 publications

In-Memory Big Data Management and Processing: A Survey

In-Memory Big Data Management and Processing: A Survey

Statesec: Stateful monitoring for DDoS protection in software defined networks

Optimizing Main Memory Usage in Modern Computing Systems to Improve Overall System Performance

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