Distributed content storage for just-in-time streaming

Dandapat, Sourav Kumar; Jain, Sanyam; Choudhury, Romit Roy; Ganguly, Niloy

doi:10.1145/2377677.2377689

Cited by 12 publications

(18 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is often used in the context of prefetching related content that is expected to be used in the near future according to users' access patterns or the content's structural relationship. Authors in [14], [15], [19] propose to prefetch parts of large content objects to different AP locations by utilizing a client's mobility pattern. While Sprinkler [14] assumes that mobile client's route is known as prerequisite, other work [15], [19] requires mobility prediction.…”

Section: B Caching and Prefetchingmentioning

confidence: 99%

“…Authors in [14], [15], [19] propose to prefetch parts of large content objects to different AP locations by utilizing a client's mobility pattern. While Sprinkler [14] assumes that mobile client's route is known as prerequisite, other work [15], [19] requires mobility prediction. These studies propose a personalized mobility models where each mobile client predicts its future connection based on its own history.…”

Section: B Caching and Prefetchingmentioning

confidence: 99%

“…We envision that these APs, when equipped with storage, can be utilized as a distributed content caching system (similar to the concept of PacketCloud [12] and EdgeClouds [13]), such that a mobile client downloads chunks of content from APs it connects to over time, thus avoiding reaching out to the original content server. The download performance (i.e., latency, throughput) can be greatly improved when the content chunks are available locally at the AP [12], [13], [14], [15]. However, achieving the above goal is not trivial: we need to determine what chunks should be placed at which AP(s) prior to the client's arrival at that AP.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

EdgeBuffer: Caching and prefetching content at the edge in the MobilityFirst future Internet architecture

Zhang

et al. 2015

2015 IEEE 16th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

View full text Add to dashboard Cite

The prevalence of mobile devices especially smartphones has attracted research on mobile content delivery techniques. In this paper, we propose to take advantage of the storage available at wireless access points to bring content closer to mobile devices, hence improving the downloading performance. Specifically, we propose to have a separate popularity based cache and a prefetch buffer at the network edge to capture both longterm and short-term content access patterns. Further, we point out that it is insufficient to rely on a device's past history to predict when and where to prefetch, especially in urban settings; instead, we propose to derive a prediction model based on the aggregated network-level statistics.We discuss the proposed mobile content caching/prefetching method in the context of the MobilityFirst future Internet architecture. In MobilityFirst, when mobile clients move between network attachment points (e.g., Wi-Fi access points), their network association records are logged by the network, which then naturally facilitates the network-level mobility prediction. Through detailed simulations with real taxi mobility traces, we show that such a strategy is more effective than earlier schemes in satisfying content requests at the edge (higher cache hit ratios), leading to shorter content download latencies. Specifically, the fraction of requests satisfied at the edge increases by a factor of 2.9 compared to a caching only approach, and by 45% compared to individual user-based prediction and prefetching.

show abstract

Section: B Caching and Prefetchingmentioning

confidence: 99%

Section: B Caching and Prefetchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

EdgeBuffer: Caching and prefetching content at the edge in the MobilityFirst future Internet architecture

Zhang

et al. 2015

2015 IEEE 16th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

View full text Add to dashboard Cite

show abstract

“…Related work can be split in caching at the edge and offloading on mobile devices. In the context of caching at the edge, traditional solutions concern adding storage capacity to small cells [15,27] and/or to WiFi Access Points [14,31]. However, a large number of small cells is required for an extensive coverage, which comes at a high cost [5].…”

Section: Related Workmentioning

confidence: 99%

Streaming content from a vehicular cloud

Vigneri

Spyropoulos

Barakat

2016

Proceedings of the Eleventh ACM Workshop on Challenged Networks

View full text Add to dashboard Cite

Network densification via small cells is considered as a key step to cope with the data tsunami. Caching data at small cells or even user devices is also considered as a promising way to alleviate the backhaul congestion this densification might cause. However, the former suffers from high deployment and maintenance costs, and the latter from limited resources and privacy issues with user devices. We argue that an architecture with (public or private) vehicles acting as mobile caches and communication relays might be a promising middle ground. In this paper, we assume such a vehicular cloud is in place to provide video streaming to users, and that the operator can decide which content to store in the vehicle caches. Users can then greedily fill their playout buffer with video pieces of the streamed content from encountered vehicles, and turn to the infrastructure immediately when the playout buffer is empty, to ensure uninterrupted streaming. Our main contribution is to model the playout buffer in the user device with a queuing approach, and to provide a mathematical formulation for the idle periods of this buffer, which relate to the bytes downloaded from the cellular infrastructure. We also solve the resulting content allocation problem, and perform trace-based simulations to finally show that up to 50% of the original traffic could be offloaded from the main infrastructure.

show abstract

“…In the proposed scheme, the authors use APES as a cache server through vehicular destination prediction. In [9], Dandapat et al proposed a method for video streaming through optimal placement of APES. The authors proposed efficient storage method to realize just-in-time video streaming.…”

Section: B Ap Equipped With External Storage (Apes)mentioning

confidence: 99%

An energy efficient upload transmission method in storage-embedded wireless mesh networks

Arai

Suto

Nishiyama

2014

2014 IEEE International Conference on Communications (ICC)

View full text Add to dashboard Cite

The recent increase of wireless devices (i.e., STAtions (STAs)) brings forward an increase of energy consumption. We address the energy consumption issues for STAs in Wireless Mesh Networks (WMNs). One of the available techniques for power-saving is the sleep technique. However, since the common transmission mode from the STA to the server is performed based on end-to-end transmission, this results in increase of energy consumption of the STAs since they cannot enter sleep mode until end-to-end communications are completed. To cope with the issue, we focus on an Access Point equipped with External Storage (APES), which provide end-to-end communication instead of STAs. Using this, STAs can shorten the transmission time and decrease the energy consumption since they communicate with neighboring APES. In this paper, we propose a novel method to select the adequate APES as a proxy server based on the number of STAs and the amount of traffic from STAs. The proposed method effectively transmits data and reduces the energy consumption of the STAs. Moreover, we validate the efficiency of the proposed APES selection method through numerical analysis.

show abstract

Distributed content storage for just-in-time streaming

Cited by 12 publications

References 1 publication

EdgeBuffer: Caching and prefetching content at the edge in the MobilityFirst future Internet architecture

EdgeBuffer: Caching and prefetching content at the edge in the MobilityFirst future Internet architecture

Streaming content from a vehicular cloud

An energy efficient upload transmission method in storage-embedded wireless mesh networks

Contact Info

Product

Resources

About