1,685 research outputs found
Pricing and Resource Allocation in Caching Services With Multiple Levels of Quality of Service
Network caches are the storage centers in the supply chain for content delivery—the digital equivalent of warehouses. Operated by access networks and other operators, they provide benefits to content publishers in the forms of bandwidth cost reduction, response time improvement, and handling of flash crowds. Yet, caching has not been fully embraced by publishers, because its use can interfere with site personalization strategies and/or collection of visitor information for business intelligence purposes. While recent work has focused on technological solutions to these issues, this paper provides the first study of the managerial issues related to the design and provisioning of incentive-compatible caching services. Starting with a single class of caching service, we find conditions under which the profit-maximizing cache operator should offer the service for free. This occurs when the access networks’ bandwidth costs are high and a large fraction of content publishers value personalization and business intelligence. Some publishers will still opt out of the service, i.e., cache bust, as observed in practice. We next derive the conditions under which the profit-maximizing cache operator should provision two vertically differentiated service classes, namely, premium and best effort. Interestingly, caching service differentiation is different from traditional vertical differentiation models, in that the premium and best-effort market segments do not abut. Thus, optimal prices for the two service classes can be set independently and cannibalization does not occur. It is possible for the cache operator to continue to offer the best-effort service for free while charging for the premium service. Furthermore, consumers are better off because more content is cached and delivered faster to them. Finally, we find that declining bandwidth costs will put negative pressure on cache operator profits, unless consumer adoption of broadband connectivity and the availability of multimedia content provide the necessary increase in traffic volume for the caches
Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks
Cooperative video caching and transcoding in mobile edge computing (MEC)
networks is a new paradigm for future wireless networks, e.g., 5G and 5G
beyond, to reduce scarce and expensive backhaul resource usage by prefetching
video files within radio access networks (RANs). Integration of this technique
with other advent technologies, such as wireless network virtualization and
multicarrier non-orthogonal multiple access (MC-NOMA), provides more flexible
video delivery opportunities, which leads to enhancements both for the
network's revenue and for the end-users' service experience. In this regard, we
propose a two-phase RAF for a parallel cooperative joint multi-bitrate video
caching and transcoding in heterogeneous virtualized MEC networks. In the cache
placement phase, we propose novel proactive delivery-aware cache placement
strategies (DACPSs) by jointly allocating physical and radio resources based on
network stochastic information to exploit flexible delivery opportunities.
Then, for the delivery phase, we propose a delivery policy based on the user
requests and network channel conditions. The optimization problems
corresponding to both phases aim to maximize the total revenue of network
slices, i.e., virtual networks. Both problems are non-convex and suffer from
high-computational complexities. For each phase, we show how the problem can be
solved efficiently. We also propose a low-complexity RAF in which the
complexity of the delivery algorithm is significantly reduced. A Delivery-aware
cache refreshment strategy (DACRS) in the delivery phase is also proposed to
tackle the dynamically changes of network stochastic information. Extensive
numerical assessments demonstrate a performance improvement of up to 30% for
our proposed DACPSs and DACRS over traditional approaches.Comment: 53 pages, 24 figure
Wireless Communications in the Era of Big Data
The rapidly growing wave of wireless data service is pushing against the
boundary of our communication network's processing power. The pervasive and
exponentially increasing data traffic present imminent challenges to all the
aspects of the wireless system design, such as spectrum efficiency, computing
capabilities and fronthaul/backhaul link capacity. In this article, we discuss
the challenges and opportunities in the design of scalable wireless systems to
embrace such a "bigdata" era. On one hand, we review the state-of-the-art
networking architectures and signal processing techniques adaptable for
managing the bigdata traffic in wireless networks. On the other hand, instead
of viewing mobile bigdata as a unwanted burden, we introduce methods to
capitalize from the vast data traffic, for building a bigdata-aware wireless
network with better wireless service quality and new mobile applications. We
highlight several promising future research directions for wireless
communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications
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