A QoS-Based Joint Scheduling and Caching Algorithm for Multimedia Objects

Liu, Jiangchuan; Li, Bo

doi:10.1023/b:wwwj.0000028181.13079.80

Cited by 9 publications

(4 citation statements)

References 29 publications

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“…A substantial body of literature has been devoted to content replication and scheduling in a CDN or cache network [12] [13]. Our work differs from those work in that we focus on a geo-distributed cloud platform, with significantly different charging models and elastic "pay-per-use" usage patterns, which calls for a more flexible online algorithm.…”

Section: Related Workmentioning

confidence: 96%

Scaling social media applications into geo-distributed clouds

et al. 2012

2012 Proceedings IEEE INFOCOM

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Federation of geo-distributed cloud services is a trend in cloud computing which, by spanning multiple data centers at different geographical locations, can provide a cloud platform with much larger capacities. Such a geo-distributed cloud is ideal for supporting large-scale social media streaming applications (e.g., YouTube-like sites) with dynamic contents and demands, owing to its abundant on-demand storage/bandwidth capacities and geographical proximity to different groups of users. Although promising, its realization presents challenges on how to efficiently store and migrate contents among different cloud sites (i.e. data centers), and to distribute user requests to the appropriate sites for timely responses at modest costs. These challenges escalate when we consider the persistently increasing contents and volatile user behaviors in a social media application. By exploiting social influences among users, this paper proposes efficient proactive algorithms for dynamic, optimal scaling of a social media application in a geo-distributed cloud. Our key contribution is an online content migration and request distribution algorithm with the following features: (1) future demand prediction by novelly characterizing social influences among the users in a simple but effective epidemic model; (2) oneshot optimal content migration and request distribution based on efficient optimization algorithms to address the predicted demand, and (3) a ∆(t)-step look-ahead mechanism to adjust the one-shot optimization results towards the offline optimum. We verify the effectiveness of our algorithm using solid theoretical analysis, as well as large-scale experiments under dynamic realistic settings on a home-built cloud platform.

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Section: Related Workmentioning

confidence: 96%

Scaling social media applications into geo-distributed clouds

et al. 2012

2012 Proceedings IEEE INFOCOM

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“…[28,29] propose using video proxies to increase quality of the streaming service. Also, [30,31] improve the performance through caching and prefetching strategies.…”

Section: Media Streaming Servermentioning

confidence: 99%

Numeric Analysis for Relationship-Aware Scalable Streaming Scheme

Lee¹,

Jung²,

Ahn³

et al. 2014

Journal of Applied Mathematics

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Frequent packet loss of media data is a critical problem that degrades the quality of streaming services over mobile networks. Packet loss invalidates frames containing lost packets and other related frames at the same time. Indirect loss caused by losing packets decreases the quality of streaming. A scalable streaming service can decrease the amount of dropped multimedia resulting from a single packet loss. Content providers typically divide one large media stream into several layers through a scalable streaming service and then provide each scalable layer to the user depending on the mobile network. Also, a scalable streaming service makes it possible to decode partial multimedia data depending on the relationship between frames and layers. Therefore, a scalable streaming service provides a way to decrease the wasted multimedia data when one packet is lost. However, the hierarchical structure between frames and layers of scalable streams determines the service quality of the scalable streaming service. Even if whole packets of layers are transmitted successfully, they cannot be decoded as a result of the absence of reference frames and layers. Therefore, the complicated relationship between frames and layers in a scalable stream increases the volume of abandoned layers. For providing a high-quality scalable streaming service, we choose a proper relationship between scalable layers as well as the amount of transmitted multimedia data depending on the network situation. We prove that a simple scalable scheme outperforms a complicated scheme in an error-prone network. We suggest an adaptive set-top box (AdaptiveSTB) to lower the dependency between scalable layers in a scalable stream. Also, we provide a numerical model to obtain the indirect loss of multimedia data and apply it to various multimedia streams. Our AdaptiveSTB enhances the quality of a scalable streaming service by removing indirect loss.

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“…Video proxies: Others have proposed video proxies (e.g., MiddleMan [13,42]) and some have focused on caching or prefectching strategies coupled with server-side scheduling that help offer better video experience and QoS [41,34]. However, iProxy is the first to argue for an information-centric approach that achieves better caching of important information by aggregating multiple related URLs, and co-designs an effective video adaptation scheme into the cache.…”

Section: Related Workmentioning

confidence: 99%

An information-aware QoE-centric mobile video cache

Shen¹

2013

Proceedings of the 19th Annual International Conference on Mobile Computing &Amp; Networking - MobiCom '13

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Recent years have seen a tremendous growth in the volume of video traffic in mobile settings. In this paper, we present the design of a mobile video-centric proxy cache, named iProxy, that offers improved performance in terms of both hit rates and streaming quality. Our thesis in designing iProxy is that we need to elevate the traditional view of caching from "data" to "information" in order to optimally meet the stringent requirements of video streaming in mobile settings. iProxy relies on recent advances on informationbound references (IBRs) to collapse multiple related cache entries into a single one, improving hitrate while lowering storage costs. iProxy incorporates a novel dynamic linear rate adaptation scheme to ensure high stream quality in face of channel diversity and device heterogeneity. Our evaluation of iProxy using realistic traffic traces shows that it can improve hitrate, but we need to use novel information-aware replacement policies for optimal performance. We show that our linear encoder can adapt well to changes in bandwidth, and yield better bit rates, lower buffering and lower start up delays than state-of-the-art schemes.

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A QoS-Based Joint Scheduling and Caching Algorithm for Multimedia Objects

Cited by 9 publications

References 29 publications

Scaling social media applications into geo-distributed clouds

Scaling social media applications into geo-distributed clouds

Numeric Analysis for Relationship-Aware Scalable Streaming Scheme

An information-aware QoE-centric mobile video cache

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