Poster Abstract: Hierarchical Serverless Computing for the Mobile Edge

Lara, Eyal de; Gomes, Carolina Simoes; Langridge, Steve; Mortazavi, Seyed Hossein; Roodi, Meysam

doi:10.1109/sec.2016.37

Cited by 17 publications

(10 citation statements)

References 2 publications

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“…However, they did not consider the security mechanism in the study. Yan et al [11], De-Lara et al [12], Lakhan et al [13] and Li et al [14] and Li et al [15,16] suggested serverless and container-based application partitioning, resource allocation and scheduling methodology-based linear and dynamic optimization in fog-cloud. The objective is to minimize the execution, energy, response time, and delay and offloading cost of applications.…”

Section: Related Workmentioning

confidence: 99%

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Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Lakhan

Mohammed

Rashid

et al. 2021

Sensors

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The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications.

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Section: Related Workmentioning

confidence: 99%

“…Due to the external services, the security of the offloaded data of different users has posed a challenge. Therefore, secure and cost-efficient task scheduling in an edge computing, serverless, decentralized system is becoming a challenge [12].…”

Section: Introductionmentioning

confidence: 99%

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Lakhan

Mohammed

Rashid

et al. 2021

Sensors

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“…Due to its use of short, ephemeral functions, and the possibility of invoking a large number of these in parallel, serverless computing is being used for both event-driven workloads, where a VM would be idle most of the time, and for massively parallel, short-term compute, like in data analytics. Example use cases include Internet of Things and edge computing [33], parallel data processing [41], video processing [35], and Web backend APIs [6]. A common property of these workloads is that they are bursty -they make a large number of invocations of a few functions to either tackle bursts in event-driven workloads or exploit data parallelism for analytics.…”

Section: Concurrent Invocations Are Commonmentioning

confidence: 99%

Photons

Dukic

Bruno

Singla

et al. 2020

Proceedings of the 11th ACM Symposium on Cloud Computing

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Serverless computing allows users to create short, stateless functions and invoke hundreds of them concurrently to tackle massively parallel workloads. We observe that even though most of the footprint of a serverless function is fixed across its invocations-language runtime, libraries, and other application state-today's serverless platforms do not exploit this redundancy. Such an inefficiency has cascading negative impacts: longer startup times, lower throughput, higher latency, and higher cost. To mitigate these problems, we have built Photons, a framework leveraging workload parallelism to co-locate multiple instances of the same function within the same runtime. Concurrent invocations can then share the runtime and application state transparently, without compromising execution safety. Photons reduce function's memory consumption by 25% to 98% per invocation, with no performance degradation compared to today's serverless platforms. We also show that our approach can reduce the overall memory utilization by 30%, and the total number of cold starts by 52%. CCS Concepts • Computer systems organization → Cloud computing; • Software and its engineering → Virtual machines.

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“…These works have identified the challenge of auto-scaling, which is not predictable to the user. When a function is scaled down, the cold start problem can cause latency issues in a constrained environment [10], [47]- [49].…”

Section: Related Workmentioning

confidence: 99%

An Evaluation of Open Source Serverless Computing Frameworks Support at the Edge

Palade

Kazmi

Clarke

2019

2019 IEEE World Congress on Services (SERVICES)

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The proliferation of Internet of Things (IoT) and the success of resource-rich cloud services have pushed the data processing horizon towards the edge of the network. This has the potential to address bandwidth costs, and latency, availability and data privacy concerns. Serverless computing, a cloud computing model for stateless and event-driven applications, promises to further improve Quality of Service (QoS) by eliminating the burden of always-on infrastructure through ephemeral containers. Open source serverless frameworks have been introduced to avoid the vendor lock-in and computation restrictions of public cloud platforms and to bring the power of serverless computing to onpremises deployments. In an IoT environment, these frameworks can leverage the computational capabilities of devices in the local network to further improve QoS of applications delivered to the user. However, these frameworks have not been evaluated in a resource-constrained, edge computing environment. In this work we evaluate four open source serverless frameworks, namely, Kubeless, Apache OpenWhisk, OpenFaaS, Knative. Each framework is installed on a bare-metal, single master, Kubernetes cluster. We use the JMeter framework to evaluate the response time, throughput and success rate of functions deployed using these frameworks under different workloads. The evaluation results are presented and open research opportunities are discussed.

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Poster Abstract: Hierarchical Serverless Computing for the Mobile Edge

Cited by 17 publications

References 2 publications

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Photons

An Evaluation of Open Source Serverless Computing Frameworks Support at the Edge

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