Scalable Edge Computing for Low Latency Data Dissemination in Topic-Based Publish/Subscribe

Khare, Shweta; Sun, Hongyang; Zhang, Kaiwen; Gascon‐Samson, Julien; Gokhale, Aniruddha; Koutsoukos, Xenofon; Abdel-Aziz, Hamzah

doi:10.1109/sec.2018.00023

Cited by 12 publications

(9 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Khare et al. present techniques to realize a scalable broker architecture that balances data publication and processing load for publish‐process‐subscribe systems operating at the edges and ensures Quality‐of‐Service (QoS) on a per‐topic basis 8 …”

Section: Related Workmentioning

confidence: 99%

“…However, due to the limited resources on edge servers, duplicating messages on numerous edge servers could cause exhaustion of storage capacity. An efficient way of using computational resources consumed by Pub/Sub brokers, which perform analysis of the streaming messages in the publish‐process‐subscribe paradigm, has been presented 8 . In contrast, we mainly focus on utilizing storage capacity on edge servers efficiently while enabling one topic to be managed on multiple edge servers so that the published messages are processed at nearby edge servers and the generated notifications are immediately delivered to neighboring subscribers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Topic allocation method on edge servers for latency‐sensitive notification service

2021

View full text Add to dashboard Cite

The importance of real-time notification has been growing for social services and Intelligent Transporting System (ITS). As an advanced version of Pub/Sub systems, publish-process-subscribe systems, where published messages are spooled and processed on edge servers, have been proposed to achieve datadriven intelligent notifications. In this paper, we present a system that allows a topic to be managed on multiple edge servers so that messages are processed near the publishers, even when publishers spread over a wide area. Duplicating messages on geographically distributed servers could enable immediate notification to neighboring subscribers. However, the duplicated message spool may cause exhaustion of resources. We prepare a formal model of our publishprocess-subscribe system and formulate the topic allocation as an optimization problem under the resource constraints of edge servers. As the optimization problem is NP-hard, we propose heuristics leveraging the locality and the pub/sub relationships observed between clients to use the edge server resources efficiently. Our performance evaluation shows that our method reduces the delay to deliver notifications and the effectiveness of the strategy exploiting the relationships between clients.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Topic allocation method on edge servers for latency‐sensitive notification service

2021

View full text Add to dashboard Cite

show abstract

“…Besides validating the efficacy of FECBench on applications drawn from the benchmarking suites, we have applied FECBench to interference-aware load balancing of topics for a publish-process-subscribe system [46]. The Publish/Subscribe (pub/sub) communication pattern allows asynchronous and anonymous exchange of information (topic of interest) between publishers (data producers) and subscribers (data receivers).…”

Section: E Fecbench In Action: a Concrete Use Casementioning

confidence: 99%

FECBench: A Holistic Interference-aware Approach for Application Performance Modeling

Barve

Shekhar

Chhokra

et al. 2019

2019 IEEE International Conference on Cloud Engineering (IC2E)

Self Cite

View full text Add to dashboard Cite

Services hosted in multi-tenant cloud platforms often encounter performance interference due to contention for non-partitionable resources, which in turn causes unpredictable behavior and degradation in application performance. To grapple with these problems and to define effective resource management solutions for their services, providers often must expend significant efforts and incur prohibitive costs in developing performance models of their services under a variety of interference scenarios on different hardware. This is a hard problem due to the wide range of possible co-located services and their workloads, and the growing heterogeneity in the runtime platforms including the use of fog and edge-based resources, not to mention the accidental complexity in performing application profiling under a variety of scenarios. To address these challenges, we present FECBench (Fog/Edge/Cloud Benchmarking), an open source framework comprising a set of 106 applications covering a wide range of application classes to guide providers in building performance interference prediction models for their services without incurring undue costs and efforts. Through the design of FECBench, we make the following contributions. First, we develop a technique to build resource stressors that can stress multiple system resources all at once in a controlled manner, which helps to gain insights into the impact of interference on an application's performance. Second, to overcome the need for exhaustive application profiling, FECBench intelligently uses the design of experiments (DoE) approach to enable users to build surrogate performance models of their services. Third, FECBench maintains an extensible knowledge base of application combinations that create resource stresses across the multidimensional resource design space. Empirical results using realworld scenarios to validate the efficacy of FECBench show that the predicted application performance has a median error of only 7.6% across all test cases, with 5.4% in the best case and 13.5% in the worst case.

show abstract

“…• processing power was measured in threads, assuming equal power in all threads [7] • one thread uses a specific amount of time for a service type • function execution takes a finite amount of time per content type, assuming execution sessions established • for simulation efficiency, the smallest step of processing time is 0.1s (realistically it could be smaller) • EDPs, EDR cluster, Data Providers and Cloud Providers are securely bootstrapped, for data and function verification • all EDPs and EDRs use their private keys and pre-fetched certificates/trust anchors to produce secure data • freshness period accounts for only one processing cycle • under service strain, the retrieving EDR will compress and send processing messages to the cloud before processing • compression is executed on individual messages, and not on the aggregated messages, as a file, for ease of simulation These assumptions will be revisited in further work, to tackle more complex network and management problems. The EDP generation rates used in this paper were based on CISCO predictions [3,5] for 2022-24, several case studies ( [2,11,16]) and reasonable approximations.…”

Section: Setup and Assumptionsmentioning

confidence: 99%

Edge Data Repositories - The design of a store-process-send system at the Edge

Nicolaescu

Ascigil

Psaras

2019

Proceedings of the 1st ACM CoNEXT Workshop on Emerging in-Network Computing Paradigms

View full text Add to dashboard Cite

The Edge of the Internet is currently accommodating large numbers of devices and these numbers will dramatically increase with the advancement of technology. Edge devices and their associated service bandwidth requirements are predicted to become a major problem in the near future. As a result, the popularity of data management, analysis and processing at the edges is also increasing. This paper proposes Edge Data Repositories and their performance analysis. In this context, provide a service quality and resource allocation feedback algorithm for the processing and storage capabilities of Edge Data Repositories. A suitable simulation environment was created for this system, with the help of the ONE Simulator. The simulations were further used to evaluate the Edge Data Repository cluster within different scenarios, providing a range of service models. From there, with the help and adaptation of a few basic networks management concepts, the feedback algorithm was developed. As an initial step, we assess and provide measurable performance feedback for the most essential parts of our envisioned system: network metrics and service and resource status, through this algorithm.

show abstract

Scalable Edge Computing for Low Latency Data Dissemination in Topic-Based Publish/Subscribe

Cited by 12 publications

References 36 publications

Topic allocation method on edge servers for latency‐sensitive notification service

Topic allocation method on edge servers for latency‐sensitive notification service

FECBench: A Holistic Interference-aware Approach for Application Performance Modeling

Edge Data Repositories - The design of a store-process-send system at the Edge

Contact Info

Product

Resources

About