Mechanisms for outsourcing computation via a decentralized market

Eisele, Scott; Eghtesad, Taha; Troutman, Nicholas; Lászka, Áron; Dubey, Abhishek

doi:10.1145/3401025.3401737

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Existing solutions for harnessing this power, such as volunteer computing (e.g., BOINC), are centralized platforms in which an entity can control participation and pricing. MODiCuM [32], a distributed-ledger based platform for decentralized computation, offers a system for an open market of computational resources. It offers a smart contractbased protocol, incentivizing creators, and resource providers to join an open-market, allowing users to harness the computational power at the edge.…”

Section: Related Work a Internet Of Things And Task Assignmentmentioning

confidence: 99%

Route Planning Through Distributed Computing by Road Side Units

et al. 2020

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Cities are embracing data-intensive applications to maximize their constrained transportation networks. Platforms such as Google offer route planning services to mitigate the effect of traffic congestion. These use remote servers that require an Internet connection, which exposes data to increased risk of network failures and latency issues. Edge computing, an alternative to centralized architectures, offers computational power at the edge that could be used for similar services. Road side units (RSU), Internet of Things (IoT) devices within a city, offer an opportunity to offload computation to the edge. To provide an environment for processing on RSUs, we introduce RSU-Edge, a distributed edge computing system for RSUs. We design and develop a decentralized route planning service over RSU-Edge. In the service, the city is divided into grids and assigned an RSU. Users send trip queries to the service and obtain routes. For maximum accuracy, tasks must be allocated to optimal RSUs. However, this overloads RSUs, increasing delay. To reduce delays, tasks may be reallocated from overloaded RSUs to its neighbors. The distance between the optimal and actual allocation causes accuracy loss due to stale data. The problem is identifying the most efficient allocation of tasks such that response constraints are met while maintaining acceptable accuracy. We created the system and present an analysis of a case study in Nashville, Tennessee that shows the effect of our algorithm on route accuracy and query response, given varying neighbor levels. We find that our system can respond to 1000 queries up to 57.17% faster, with only a model accuracy loss of 5.57% to 7.25% compared to using only optimal grid allocation. INDEX TERMS Distributed computing, Middleware, Transportation, Vehicle routing, Road side units I. INTRODUCTION Urban cities are growing at a rapid pace. This growth, bolstered by businesses, commerce, and opportunities, is bringing more and more people into these urban landscapes, putting a strain on the cities' limited infrastructure. For example, roads are faced with an influx of motorists resulting in heavy traffic, which makes living in cities unbearable. To meet the growing demands, cities and private companies are turning to data-intensive applications to make the most out of the limited resources. Companies such as Google, Apple, and Waze 1 created traffic-aware navigation apps that leverage crowd-sourced information and cloud environments. These applications allow users access to up-to-date routing information for their daily commutes. 1 https://www.waze.com/ Cloud environments running on centralized architectures can process data effectively for existing services, however, future use cases such as autonomous vehicles have their own challenges [1], such as requiring ultra-reliable low-latency communications [2]. These requirements expose the limitations of the cloud and call for a new architecture that better fits the requirements for real-time analysis and processing of city-level data. One way to resolve ...

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Section: Related Work a Internet Of Things And Task Assignmentmentioning

confidence: 99%

Route Planning Through Distributed Computing by Road Side Units

et al. 2020

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Decentralized Computation Market for Stream Processing Applications

Eisele

Wilbur

Eghtesad

et al. 2022

2022 IEEE International Conference on Cloud Engineering (IC2E)

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Mechanisms for outsourcing computation via a decentralized market

Eisele

Eghtesad

Troutman

et al. 2020

Proceedings of the 14th ACM International Conference on Distributed and Event-Based Systems

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As the number of personal computing and IoT devices grows rapidly, so does the amount of computational power that is available at the edge. Many of these devices are often idle and constitute an untapped resource which could be used for outsourcing computation. Existing solutions for harnessing this power, such as volunteer computing (e.g., BOINC), are centralized platforms in which a single organization or company can control participation and pricing. By contrast, an open market of computational resources, where resource owners and resource users trade directly with each other, could lead to greater participation and more competitive pricing.To provide an open market, we introduce MODiCuM, a decentralized system for outsourcing computation. MODiCuM deters participants from misbehaving-which is a key problem in decentralized systems-by resolving disputes via dedicated mediators and by imposing enforceable fines. However, unlike other decentralized outsourcing solutions, MODiCuM minimizes computational overhead since it does not require global trust in mediation results. We provide analytical results proving that MODiCuM can deter misbehavior, and we evaluate the overhead of MODiCuM using experimental results based on an implementation of our platform. CCS CONCEPTS• Information systems → Computing platforms; • Computing methodologies → Distributed algorithms; • Computer systems organization → Cloud computing.

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Mechanisms for outsourcing computation via a decentralized market

Cited by 4 publications

References 18 publications

Route Planning Through Distributed Computing by Road Side Units

Route Planning Through Distributed Computing by Road Side Units

Decentralized Computation Market for Stream Processing Applications

Mechanisms for outsourcing computation via a decentralized market

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