3,492 research outputs found
Soft Cache Hits and the Impact of Alternative Content Recommendations on Mobile Edge Caching
Caching popular content at the edge of future mobile networks has been widely
considered in order to alleviate the impact of the data tsunami on both the
access and backhaul networks. A number of interesting techniques have been
proposed, including femto-caching and "delayed" or opportunistic cache access.
Nevertheless, the majority of these approaches suffer from the rather limited
storage capacity of the edge caches, compared to the tremendous and rapidly
increasing size of the Internet content catalog. We propose to depart from the
assumption of hard cache misses, common in most existing works, and consider
"soft" cache misses, where if the original content is not available, an
alternative content that is locally cached can be recommended. Given that
Internet content consumption is increasingly entertainment-oriented, we believe
that a related content could often lead to complete or at least partial user
satisfaction, without the need to retrieve the original content over expensive
links. In this paper, we formulate the problem of optimal edge caching with
soft cache hits, in the context of delayed access, and analyze the expected
gains. We then show using synthetic and real datasets of related video contents
that promising caching gains could be achieved in practice
Estimating Self-Sustainability in Peer-to-Peer Swarming Systems
Peer-to-peer swarming is one of the \emph{de facto} solutions for distributed
content dissemination in today's Internet. By leveraging resources provided by
clients, swarming systems reduce the load on and costs to publishers. However,
there is a limit to how much cost savings can be gained from swarming; for
example, for unpopular content peers will always depend on the publisher in
order to complete their downloads. In this paper, we investigate this
dependence. For this purpose, we propose a new metric, namely \emph{swarm
self-sustainability}. A swarm is referred to as self-sustaining if all its
blocks are collectively held by peers; the self-sustainability of a swarm is
the fraction of time in which the swarm is self-sustaining. We pose the
following question: how does the self-sustainability of a swarm vary as a
function of content popularity, the service capacity of the users, and the size
of the file? We present a model to answer the posed question. We then propose
efficient solution methods to compute self-sustainability. The accuracy of our
estimates is validated against simulation. Finally, we also provide closed-form
expressions for the fraction of time that a given number of blocks is
collectively held by peers.Comment: 27 pages, 5 figure
Control-theoretical load-balancing for cloud applications with brownout
Cloud applications are often subject to unexpected events like flash crowds and hardware failures. Without a predictable behaviour, users may abandon an unresponsive application. This problem has been partially solved on two separate fronts: first, by adding a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience, and, second, by introducing replicas -- copies of the applications having the same functionalities -- for redundancy and adding a load-balancer to direct incoming traffic. However, existing load-balancing strategies interfere with brownout self-adaptivity. Load-balancers are often based on response times, that are already controlled by the self-adaptive features of the application, hence they are not a good indicator of how well a replica is performing. In this paper, we present novel load-balancing strategies, specifically designed to support brownout applications. They base their decision not on response time, but on user experience degradation. We implemented our strategies in a self-adaptive application simulator, together with some state-of-the-art solutions. Results obtained in multiple scenarios show that the proposed strategies bring significant improvements when compared to the state-of-the-art ones
Modeling and Control of Server-based Systems
When deploying networked computing-based applications, proper resource management of the server-side resources is essential for maintaining quality of service and cost efficiency. The work presented in this thesis is based on six papers, all investigating problems that relate to resource management of server-based systems. Using a queueing system approach we model the performance of a database system being subjected to write-heavy traffic. We then evaluate the model using simulations and validate that it accurately mimics the behavior of a real test bed. In collaboration with Ericsson we model and design a per-request admission control scheme for a Mobile Service Support System (MSS). The model is then validated and the control scheme is evaluated in a test bed. Also, we investigate the feasibility to estimate the state of a server in an MSS using an event-based Extended Kalman Filter. In the brownout paradigm of server resource management, the amount of work required to serve a client is adjusted to compensate for temporary resource shortages. In this thesis we investigate how to perform load balancing over self-adaptive server instances. The load balancing schemes are evaluated in both simulations and test bed experiments. Further, we investigate how to employ delay-compensated feedback control to automatically adjust the amount of resources to deploy to a cloud application in the presence of a large, stochastic delay. The delay-compensated control scheme is evaluated in simulations and the conclusion is that it can be made fast and responsive compared to an industry-standard solution
Random Access Protocols for Massive MIMO
5G wireless networks are expected to support new services with stringent
requirements on data rates, latency and reliability. One novel feature is the
ability to serve a dense crowd of devices, calling for radically new ways of
accessing the network. This is the case in machine-type communications, but
also in urban environments and hotspots. In those use cases, the high number of
devices and the relatively short channel coherence interval do not allow
per-device allocation of orthogonal pilot sequences. This article motivates the
need for random access by the devices to pilot sequences used for channel
estimation, and shows that Massive MIMO is a main enabler to achieve fast
access with high data rates, and delay-tolerant access with different data rate
levels. Three pilot access protocols along with data transmission protocols are
described, fulfilling different requirements of 5G services
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