Scaling Social Media Applications Into Geo-Distributed Clouds

Wu, Yu; Wu, Chuan; Li, Bo; Zhang, Linquan; Li, Zongpeng; Lau, Francis C. M.

doi:10.1109/tnet.2014.2308254

Cited by 94 publications

(29 citation statements)

References 25 publications

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“…It leases the computation, bandwidth and storage resources with much lower long-term costs than those with over-provisioned self-owned servers, and reacts better and faster to user demand with the dramatically increasing scale. There have been pioneer studies on migrating video services to the cloud to accommodate worldwide-distributed and time-varying video demands [14], [2]. Aggarwal et al [17] showed that the cost of IPTV services can be noticeably reduced through a cloud infrastructure, and Wu et al [14] utilized a geo-distributed cloud to support large scale social media streaming applications.…”

Section: Background and Related Workmentioning

confidence: 99%

“…There have been pioneer studies on migrating video services to the cloud to accommodate worldwide-distributed and time-varying video demands [14], [2]. Aggarwal et al [17] showed that the cost of IPTV services can be noticeably reduced through a cloud infrastructure, and Wu et al [14] utilized a geo-distributed cloud to support large scale social media streaming applications. Wang et al [11] presented CALMS (Cloud-Assisted Live Media Streaming) to lease and adjust cloud server resources in a fine granularity, meeting with the temporal and spatial dynamics of demands from online users.…”

Section: Background and Related Workmentioning

confidence: 99%

“…None of existing algorithms can simultaneously satisfy these design requirements of the resource allocation in the global scale. For example, [8], [12], [18] considered the viewer dynamic in the live streaming systems, without analysis of the demand distribution in the different areas; though [11], [15], [9] deployed the distributed cloud servers or CDNs to handle the dynamic demand in the global scale, crowdsourced live streaming further elevate the challenges with highly dynamic and geo-distributed sourcers; [10], [14] explored the cloud service migration for video-on-demand, while live streaming raises more stringent delay constraint during the server resource allocation; [13] investigated the joint online transcoding and geo-distributed delivery for dynamic adaptive streaming neglecting the price policies of different datacenters. Generally, the resource allocation in crowdsourced live streaming should not only consider both the dynamic viewers and sourcers, but also balance the trade-off between the streaming performance and the system cost.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Given the large system scale and the high bandwidth, storage, and computation demands involved, cloud services with elastic resource provisioning is expected. We again consider a generic geo-distributed cloud infrastructure, which consists of multiple cloud sites distributed in different geographical locations (e.g., US East (N. Virginia) and EU (Ireland) in Amazon EC2 Cloud) [14]. Each cloud site resides in a data center, and contains a collection of interconnected and virtualized servers.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Elastic resource provisioning and computation offloading are where cloud computing platforms excel [14]. We have seen many new generation of cloud-based multimedia services that emerged in recent years, e.g., Netflix, which are rapidly changing the operation and business models in the market.…”

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confidence: 99%

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Cloud-Assisted Live Streaming for Crowdsourced Multimedia Content

Chen

Zhang

Wang

et al. 2015

IEEE Trans. Multimedia

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Empowered by today's rich tools for media generation and distribution, and the convenient Internet access, streaming crowdsourced multimedia content (crowdsourced streaming, in brief) generalizes the single-source streaming paradigm by including massive contributors for a video/data channel. It calls a joint optimization along the path from crowdsourcers, through streaming servers, to the end-users to minimize the overall latency. The dynamics of the video sources, together with the globalized request demands and the high computation demand from each sourcer, make crowdsourced live streaming challenging even with powerful support from modern cloud computing. In this paper, we present a generic framework that facilitates a cost-effective cloud service for crowdsourced live streaming. Through adaptively leasing, the cloud servers can be provisioned in a fine granularity to accommodate geo-distributed video crowdsourcers. We present an optimal solution to deal with service migration among cloud instances of diverse lease prices. It also addresses the location impact to the streaming quality. To understand the performance of the proposed strategies in the real world, we have built a prototype system running over the planetlab and the Amazon/Microsoft Cloud. Our extensive experiments demonstrate that the effectiveness of our solution in terms of deployment cost and streaming quality.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Cloud-Assisted Live Streaming for Crowdsourced Multimedia Content

Chen

Zhang

Wang

et al. 2015

IEEE Trans. Multimedia

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An eigenvalue‐based edge infrastructure for cloud‐based CDN

Sahoo

Turuk

2019

Int J Communication

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In today's dynamic video landscape, an end user needs services to be delivered to any devices anytime with less delay over the Internet. Now users' expectation has changed; they want faster time-to-market, cost reduction, and the ability to adjust according to the evolving requirements, which are a limit for the traditional server-based approach. The explosive growth of the internet multimedia application needs a new approach to content delivery to overcome the limitations of server-based techniques. Cloud-based content delivery networks (CCDNs) have recently started to emerge where contents are cached from the cloud storage and delivered through the distribution service to meet quality of services (QoS) of requested services. In this paper, we dealt with CCDN deployment problem and proposed a new eigenvalue-based edge infrastructure for a network service provider to serve the users with a variation on proximity interest concerning operational cost and user QoS satisfaction. The edge infrastructure designing is a two-step process: (a) ideal location search for placing edge server and (b) edge server placement and capacity provisioning. The performance of the proposed approach is appraised via modeling and simulation.Performance evaluation outcomes are exhibited to manifest the effectiveness and competitiveness of our approach when compared with existing algorithms.

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Optimal Resource Allocation for Brokers in Media Cloud

2018

Computational Data and Social Networks

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Scaling Social Media Applications Into Geo-Distributed Clouds

Cited by 94 publications

References 25 publications

Cloud-Assisted Live Streaming for Crowdsourced Multimedia Content

Cloud-Assisted Live Streaming for Crowdsourced Multimedia Content

An eigenvalue‐based edge infrastructure for cloud‐based CDN

Optimal Resource Allocation for Brokers in Media Cloud

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