Scalable Cloud Service For Multimedia Analysis Based on Deep Learning

Xiang, Yangyang; Li, Lusong; Lyu, Shuen; Munshi, Harsh; Zhu, Honghong

doi:10.1109/icmew.2018.8551579

Cited by 4 publications

(10 citation statements)

References 5 publications

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“…The most dominant among the advantages is scalability since AIaaS providers can elastically provision and release hardware resources available to the platform and thus scale horizontally in accordance with the user-defined configurations and Lit, Int Provider lock in (-) Int (+): factor drives adoption; (-): factor inhibits adoption Lit = literature review findings; Int = interview findings requirements if the consumption of computing resources for the defined AI model has increased [14]. Scalability of the cloud, combined with the number of available hardware resources, results in a large amount of processing power provisioned by the cloud and enables the AIaaS to respond to extensive requests with scalable and responsive utilization of CPUs and GPUs [33]. This is particularly beneficial, because when using AI, organizations' hardware requirements typically change frequently and quickly.…”

Section: These] Places […] Don't Have the Internal Skills To Develop mentioning

confidence: 99%

Drivers and Inhibitors for Organizations’ Intention to Adopt Artificial Intelligence as a Service

Pandl¹,

Teigeler²,

Lins³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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The adoption of artificial intelligence promises tremendous economic benefits for organizations. Yet, many organizations struggle to unlock the full potential of this technology. To ease the adoption of artificial intelligence for organizations, several cloud providers have begun offering artificial intelligence as a service (AIaaS). Extant research on AIaaS exhibits a strong focus on technical aspects and has opposing views on what drives or inhibits the adoption of AIaaS within organizations. In this research, we synthesize extant research on AIaaS adoption factors and conduct semi-structured interviews with practitioners. Our research yields 12 factors that drive and another 12 factors that inhibit the adoption of AIaaS in practice. We thereby close a gap in scholarly knowledge on adopting this emerging service technology, especially on inhibiting factors, and help guide future research on related behavioral and technical aspects.

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Section: These] Places […] Don't Have the Internal Skills To Develop mentioning

confidence: 99%

Drivers and Inhibitors for Organizations’ Intention to Adopt Artificial Intelligence as a Service

Pandl¹,

Teigeler²,

Lins³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…The most dominant advantage is scalability because AIaaS providers can elastically provision and release hardware resources available to the platform and thus scale horizontally in accordance with the user-defined configurations and requirements if the consumption of computing resources for the defined AI model has increased (Boag et al 2018;Elshawi et al 2018;Pandl et al 2021). The scalability of the cloud, combined with the number of available hardware resources, results in a large amount of processing power provisioned by the cloud and enables the AIaaS to respond to extensive requests with scalable and responsive utilization of CPUs and GPUs (Bao et al 2018). Since AI algorithms are based on the knowledge inferred from a substantial quantity of data, the processing is performed by allocating significant computational resources that require the cloud's capability (Rouhani et al 2018).…”

Section: Inheriting Cloud Characteristicsmentioning

confidence: 99%

Artificial Intelligence as a Service

et al. 2021

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is undoubtedly one of the most actively debated technologies, providing auspicious opportunities to contribute to individuals' well-being, the success and innovativeness of organizations, and societies' prosperity and advancement (Thiebes et al. 2020). The McKinsey Global Institute predicts that the utilization of AI could yield an additional worldwide economic output of USD 13 trillion by 2030 (Bughin et al. 2018).

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“…In another paper deep learning mechanism was used for multimedia analysis and in turn scaling the cloud service [16]. The paper mainly discussed the technical details of image analysis.…”

Section: Related Workmentioning

confidence: 99%

“…This analysis would scale horizontally based on user's request. The system able to provide high accuracy, handled request along with scaling GPU and CPU [16]. The authors proposed a platform and framework.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Workload Analysis of Cloud Resources using Time Series and Machine Learning Prediction

Bhagavathiperumal

Goyal

2019

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)

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Most of the businesses now-a-days have started using cloud platforms to host their software applications. A Cloud platform is shared resource that provides various services like software as a service (SAAS), infrastructure as a service (IAAS) or anything as a service (XAAS) that is required to develop and deploy any business application. These cloud services are provided as virtual machines (VM) that can handle the end user's requirements. The cloud providers must ensure efficient resource handling mechanisms for different time intervals to avoid wastage of resources.Auto-scaling mechanisms would take care of using these resources appropriately along with providing an excellent quality of service. Auto-scaling supports the cloud service providers achieve the goal of supplying the required resources automatically. It use methods that will calculate the number of requests and decides the resources to release based on workload. The workload consists of some quantity of application program running on the machine and usually some number of users connected to and communicating with the computer's applications. The researchers have used various approaches to perform autoscaling which is a process to predict the workload that is required to handle the end users request and provide required resources as Virtual Machines (VM) disruptively. Along with providing uninterrupted service, the businesses also only pay for the service they use, thus increasing the popularity of Cloud computing. Based on the workload identified the resources are provisioned.The resource provisioning techniques is a model used for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, applications, and services) required resources are released. In this regard, the aim of this paper is to develop a framework to predict the workload using deep learning which would be able to handle provisioning of cloud resources dynamically. This framework would handle the user request efficiently and allocate the required virtual machines. As a result, an efficient dynamic method of provisioning of cloud services would be implemented supporting both the cloud providers and users.

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Scalable Cloud Service For Multimedia Analysis Based on Deep Learning

Cited by 4 publications

References 5 publications

Drivers and Inhibitors for Organizations’ Intention to Adopt Artificial Intelligence as a Service

Drivers and Inhibitors for Organizations’ Intention to Adopt Artificial Intelligence as a Service

Artificial Intelligence as a Service

Workload Analysis of Cloud Resources using Time Series and Machine Learning Prediction

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