EMOMA: Exact Match in One Memory Access

Pontarelli, Salvatore; Reviriego, Pedro; Mitzenmacher, Michael

doi:10.1109/tkde.2018.2818716

Cited by 18 publications

(12 citation statements)

References 29 publications

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“…In [27], Pontarelli et al proposed a related model where a system has both faster on-chip memory and slower, larger o-chip memory, and can only perform a limited number of o-chip memory accesses per packet. In [18], Kim et al implemented a practical o-chip memory for PISA switches.…”

Section: Related Workmentioning

confidence: 99%

BeauCoup

Chen

Landau-Feibish

Braverman

et al. 2020

Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication on the Applications, Technologies,

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Network administrators constantly monitor network trac for congestion and attacks. They need to perform a large number of measurements on the trac simultaneously, to detect dierent types of anomalies such as heavy hitters or super-spreaders. Existing techniques often focus on a single statistic (e.g., trac volume) or trafc attribute (e.g., destination IP). However, performing numerous heterogeneous measurements within the constrained memory architecture of modern network devices poses signicant challenges, due to the limited number of memory accesses allowed per packet. We propose BeauCoup, a system based on the coupon collector problem, that supports multiple distinct counting queries simultaneously while making only a small constant number of memory accesses per packet. We implement BeauCoup on PISA commodity programmable switches, satisfying the strict memory size and access constraints while using a moderate portion of other data-plane hardware resources. Evaluations show BeauCoup achieves the same accuracy as other sketch-based or sampling-based solutions using 4x fewer memory access.

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Section: Related Workmentioning

confidence: 99%

BeauCoup

Chen

Landau-Feibish

Braverman

et al. 2020

Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication on the Applications, Technologies,

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“…EMOMA [44] is a lookup data structure with a full version of (2,4)-Cuckoo holding the key-value mappings. A counting block bloom filter (CBBF) is placed in cache to maintain the bucket choice of each key, such that each lookup costs exactly one off-chip memory load.…”

Section: Related Workmentioning

confidence: 99%

Ludo Hashing

Shi

Qian

2020

Proc. ACM Meas. Anal. Comput. Syst.

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Key-value lookup engines running in fast memory are crucial components of many networked and distributed systems such as packet forwarding, virtual network functions, content distribution networks, distributed storage, and cloud/edge computing. These lookup engines must be memory-efficient because fast memory is small and expensive. This work presents a new key-value lookup design, called Ludo Hashing, which costs the least space (3.76 + 1.05l bits per key-value item for l-bit values) among known compact lookup solutions including the recently proposed partial-key Cuckoo and Bloomier perfect hashing. In addition to its space efficiency, Ludo Hashing works well with most practical systems by supporting fast lookups, fast updates, and concurrent writing/reading. We implement Ludo Hashing and evaluate it with both micro-benchmark and two network systems deployed in CloudLab. The results show that in practice Ludo Hashing saves 40% to 80%+ memory cost compared to existing dynamic solutions. It costs only a few GB memory for 1 billion key-value items and achieves high lookup throughput: over 65 million queries per second on a single node with multiple threads.

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“…There are two common deploying approaches to address this issue [28,29,30,31]. The first one is sequence-based storage.…”

Section: A Dch-based Deploying Algorithm In the Heterogeneous Sensmentioning

confidence: 99%

Modeling and Deploying IoT-Aware Business Process Applications in Sensor Networks

Cheng

Zhao

Cheng

et al. 2018

Sensors

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The concept of the Internet of Things (IoT) is an important part of the next generation of information. Wireless sensor networks are composed of independent distributed smart sensor nodes and gateways. These discrete sensors constantly gather external physical information, such as temperature, sound, and vibration. Owing to the diversity of sensor devices and the complexity of the sensor sensing environment, the direct modeling of an IoT-aware business process application is particularly difficult. In addition, how to effectively deploy those designed applications to discrete servers in the heterogeneous sensor networks is also a pressing problem. In this paper, we propose a resource-oriented modeling approach and a dynamic consistent hashing (DCH)-based deploying algorithm to solve the above problems. Initially, we extended the graphic and machine-readable model of Business Process Model Notation (BPMN) 2.0 specification, making it able to support the direct modeling of an IoT-aware business process application. Furthermore, we proposed the DCH-based deploying algorithm to solve the problem of dynamic load balancing and access efficiency in the distributed execution environment. Finally, we designed an actual extended BPMN plugin in Eclipse. The approach presented in this paper has been validated to be effective.

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EMOMA: Exact Match in One Memory Access

Cited by 18 publications

References 29 publications

BeauCoup

BeauCoup

Ludo Hashing

Modeling and Deploying IoT-Aware Business Process Applications in Sensor Networks

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