Multisource Stream Aggregation in the Cloud

Zhanikeev, Marat

doi:10.1002/9781118909690.ch11

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…At yet a deeper level, the storage virtualization technology is often discussed as part of the SSD vs HDD argument (Zhanikeev, 2013), where a hybrid storage facility is built from SSD and/or HDD units in such a way that performance is maximized. Note that the academic viewpoint is that the best optimization is one that is based on realtime measurements of read/write operations.…”

Section: Related Researchmentioning

confidence: 99%

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Fog Caching and a Trace-Based Analysis of its Offload Effect

Zhanikeev

2017

International Journal of Information Technologies and Systems Approach

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Many years of research on Content Delivery Networks (CDNs) offers a number of effective methods for caching of content replicas or forwarding requests. However, recently CDNs have aggressively started migrating to clouds. Clouds present a new kind of distribution environment as each location can support multiple caching options varying in the level of persistence of stored content. A subclass of clouds located at network edge is referred to as fog clouds. Fog clouds help by allowing CDNs to offload popular content to network edge, closer to end users. However, due to the fact that fog clouds are extremely heterogeneous and vary wildly in network and caching performance, traditional caching technology is no longer applicable. This paper proposes a multi-level caching technology specific to fog clouds. To deal with the heterogeneity problem and, at the same time, avoid centralized control, this paper proposes a function that allows CDN services to discover local caching facilities dynamically, at runtime. Using a combination of synthetic models and real measurement dataset, this paper analyzes efficiency of offload both at the local level of individual fog locations and at the global level of the entire CDN infrastructure. Local analysis shows that the new method can reduce inter-cloud traffic by between 16 and 18 times while retaining less than 30% of total content in a local cache. Global analysis further shows that, based on existing measurement datasets, centralized optimization is preferred to distributed coordination among services.

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Section: Related Researchmentioning

confidence: 99%

“…MiniCache helps individual VMs to perform local caching but the cache is not persistent and is destroyed with the VM itself. The other side of the MiniCache technology is virtualization of physical devices (Kuenzer, 2013), which makes it similar to the RAM+SSD+HDD optimization engines discussed in literature (Zhanikeev, 2013).…”

Section: Fog Caching Basicsmentioning

confidence: 99%

Fog Caching and a Trace-Based Analysis of its Offload Effect

Zhanikeev

2017

International Journal of Information Technologies and Systems Approach

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“…Among the three physical interfaces in the current prototype, the storage interface-implemented as a S3-compatible Web API-is useful for providing a cloud-based video streaming service with a high level of regional content distribution. 3 Each VM (a video source, in this case) uses its persistent memory as highfrequency cache, but also discoversthrough the CVP's API-and employs a large local storage space as a lowerfrequency extension to its own cache. In traditional federations, such a service would use third-party, nonlocal storage for low-frequency content and would have to sync multimedia files over the network, potentially causing traffic congestion in the process.…”

Section: Cvp Adoptionmentioning

confidence: 99%