21,645 research outputs found
Amorphous Placement and Retrieval of Sensory Data in Sparse Mobile Ad-Hoc Networks
Abstract—Personal communication devices are increasingly being equipped with sensors that are able to passively collect information from their surroundings – information that could be stored in fairly small local caches. We envision a system in which users of such devices use their collective sensing, storage, and communication resources to query the state of (possibly remote) neighborhoods. The goal of such a system is to achieve the highest query success ratio using the least communication overhead (power). We show that the use of Data Centric Storage (DCS), or directed placement, is a viable approach for achieving this goal, but only when the underlying network is well connected. Alternatively, we propose, amorphous placement, in which sensory samples are cached locally and informed exchanges of cached samples is used to diffuse the sensory data throughout the whole network. In handling queries, the local cache is searched first for potential answers. If unsuccessful, the query is forwarded to one or more direct neighbors for answers. This technique leverages node mobility and caching capabilities to avoid the multi-hop communication overhead of directed placement. Using a simplified mobility model, we provide analytical lower and upper bounds on the ability of amorphous placement to achieve uniform field coverage in one and two dimensions. We show that combining informed shuffling of cached samples upon an encounter between two nodes, with the querying of direct neighbors could lead to significant performance improvements. For instance, under realistic mobility models, our simulation experiments show that amorphous placement achieves 10% to 40% better query answering ratio at a 25% to 35% savings in consumed power over directed placement.National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 0202067
Dynamic Resource Management in Clouds: A Probabilistic Approach
Dynamic resource management has become an active area of research in the
Cloud Computing paradigm. Cost of resources varies significantly depending on
configuration for using them. Hence efficient management of resources is of
prime interest to both Cloud Providers and Cloud Users. In this work we suggest
a probabilistic resource provisioning approach that can be exploited as the
input of a dynamic resource management scheme. Using a Video on Demand use case
to justify our claims, we propose an analytical model inspired from standard
models developed for epidemiology spreading, to represent sudden and intense
workload variations. We show that the resulting model verifies a Large
Deviation Principle that statistically characterizes extreme rare events, such
as the ones produced by "buzz/flash crowd effects" that may cause workload
overflow in the VoD context. This analysis provides valuable insight on
expectable abnormal behaviors of systems. We exploit the information obtained
using the Large Deviation Principle for the proposed Video on Demand use-case
for defining policies (Service Level Agreements). We believe these policies for
elastic resource provisioning and usage may be of some interest to all
stakeholders in the emerging context of cloud networkingComment: IEICE Transactions on Communications (2012). arXiv admin note:
substantial text overlap with arXiv:1209.515
Dynamics of new strain emergence on a temporal network
Multi-strain competition on networks is observed in many contexts, including
infectious disease ecology, information dissemination or behavioral adaptation
to epidemics. Despite a substantial body of research has been developed
considering static, time-aggregated networks, it remains a challenge to
understand the transmission of concurrent strains when links of the network are
created and destroyed over time. Here we analyze how network dynamics shapes
the outcome of the competition between an initially endemic strain and an
emerging one, when both strains follow a susceptible-infected-susceptible
dynamics, and spread at time scales comparable with the network evolution one.
Using time-resolved data of close-proximity interactions between patients
admitted to a hospital and medical health care workers, we analyze the impact
of temporal patterns and initial conditions on the dominance diagram and
coexistence time. We find that strong variations in activity volume cause the
probability that the emerging strain replaces the endemic one to be highly
sensitive to the time of emergence. The temporal structure of the network
shapes the dominance diagram, with significant variations in the replacement
probability (for a given set of epidemiological parameters) observed from the
empirical network and a randomized version of it. Our work contributes towards
the description of the complex interplay between competing pathogens on
temporal networks.Comment: 9 pages, 4 figure
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