AppSachet: Distributed App Delivery from the Edge Cloud

Bhardwaj, Ketan; Agrawal, Pragya; Gavrilovska, Ada; Schwan, Karsten

doi:10.1007/978-3-319-29003-4_6

Cited by 18 publications

(10 citation statements)

References 8 publications

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“…In this visionary model, cognitive systems will rely on machine learning algorithms and the data that is generated to continually acquire knowledge, model problems and determine solutions. Examples include the use of IBM Watson for speech and facial recognition and sentiment analysis 44 . APIs and SaaS supporting Watson are currently available.…”

Section: Self-learning Systemsmentioning

confidence: 99%

Next generation cloud computing: New trends and research directions

Varghese

Buyya

2018

Future Generation Computer Systems

621

270

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The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.

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Section: Self-learning Systemsmentioning

confidence: 99%

Next generation cloud computing: New trends and research directions

Varghese

Buyya

2018

Future Generation Computer Systems

621

270

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“…Hence, in this paper, we consider an alternate execution model of offloading workloads from the cloud server to an edge node. Current research in offloading workloads from a cloud server to an edge node focuses on caching [32], contextaware web browsing [33] and video pre-processing [34]. There is minimal research addressing resource management.…”

Section: Related Workmentioning

confidence: 99%

ENORM: A Framework For Edge NOde Resource Management

Wang

Varghese

Matthaiou

et al. 2019

IEEE Trans. Serv. Comput.

140

136

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Current computing techniques using the cloud as a centralised server will become untenable as billions of devices get connected to the Internet. This raises the need for fog computing, which leverages computing at the edge of the network on nodes, such as routers, base stations and switches, along with the cloud. However, to realise fog computing the challenge of managing edge nodes will need to be addressed. This paper is motivated to address the resource management challenge. We develop the first framework to manage edge nodes, namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for provisioning and auto-scaling edge node resources are proposed. The feasibility of the framework is demonstrated on a Pok éMon Go-like online game use-case. The benefits of using ENORM are observed by reduced application latency between 20%-80% and reduced data transfer and communication frequency between the edge node and the cloud by up to 95%. These results highlight the potential of fog computing for improving the quality of service and experience.

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“…Such projects focus mainly on offering distributed orchestration frameworks through which Edge and Fog resources can be assigned to running services. Such solutions can be also extended to exploit Edge-based distributed caching policies [ 31 ], which may increase the chance to react to runtime fluctuations in the workload before a performance issue arises. However, achieving the goal to have all Edge and Fog nodes as general-purpose computation and storage resources is still difficult.…”

Section: Background and Related Workmentioning

confidence: 99%

A Capillary Computing Architecture for Dynamic Internet of Things: Orchestration of Microservices from Edge Devices to Fog and Cloud Providers

Taherizadeh

Stankovski

Grobelnik

2018

Sensors

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The adoption of advanced Internet of Things (IoT) technologies has impressively improved in recent years by placing such services at the extreme Edge of the network. There are, however, specific Quality of Service (QoS) trade-offs that must be considered, particularly in situations when workloads vary over time or when IoT devices are dynamically changing their geographic position. This article proposes an innovative capillary computing architecture, which benefits from mainstream Fog and Cloud computing approaches and relies on a set of new services, including an Edge/Fog/Cloud Monitoring System and a Capillary Container Orchestrator. All necessary Microservices are implemented as Docker containers, and their orchestration is performed from the Edge computing nodes up to Fog and Cloud servers in the geographic vicinity of moving IoT devices. A car equipped with a Motorhome Artificial Intelligence Communication Hardware (MACH) system as an Edge node connected to several Fog and Cloud computing servers was used for testing. Compared to using a fixed centralized Cloud provider, the service response time provided by our proposed capillary computing architecture was almost four times faster according to the 99th percentile value along with a significantly smaller standard deviation, which represents a high QoS.

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AppSachet: Distributed App Delivery from the Edge Cloud

Cited by 18 publications

References 8 publications

Next generation cloud computing: New trends and research directions

Next generation cloud computing: New trends and research directions

ENORM: A Framework For Edge NOde Resource Management

A Capillary Computing Architecture for Dynamic Internet of Things: Orchestration of Microservices from Edge Devices to Fog and Cloud Providers

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