1,762 research outputs found
Randomized online computation with high probability guarantees
We study the relationship between the competitive ratio and the tail
distribution of randomized online minimization problems. To this end, we define
a broad class of online problems that includes some of the well-studied
problems like paging, k-server and metrical task systems on finite metrics, and
show that for these problems it is possible to obtain, given an algorithm with
constant expected competitive ratio, another algorithm that achieves the same
solution quality up to an arbitrarily small constant error a with high
probability; the "high probability" statement is in terms of the optimal cost.
Furthermore, we show that our assumptions are tight in the sense that removing
any of them allows for a counterexample to the theorem. In addition, there are
examples of other problems not covered by our definition, where similar high
probability results can be obtained.Comment: 20 pages, 2 figure
Online Coded Caching
We consider a basic content distribution scenario consisting of a single
origin server connected through a shared bottleneck link to a number of users
each equipped with a cache of finite memory. The users issue a sequence of
content requests from a set of popular files, and the goal is to operate the
caches as well as the server such that these requests are satisfied with the
minimum number of bits sent over the shared link. Assuming a basic Markov model
for renewing the set of popular files, we characterize approximately the
optimal long-term average rate of the shared link. We further prove that the
optimal online scheme has approximately the same performance as the optimal
offline scheme, in which the cache contents can be updated based on the entire
set of popular files before each new request. To support these theoretical
results, we propose an online coded caching scheme termed coded least-recently
sent (LRS) and simulate it for a demand time series derived from the dataset
made available by Netflix for the Netflix Prize. For this time series, we show
that the proposed coded LRS algorithm significantly outperforms the popular
least-recently used (LRU) caching algorithm.Comment: 15 page
Online Service with Delay
In this paper, we introduce the online service with delay problem. In this
problem, there are points in a metric space that issue service requests
over time, and a server that serves these requests. The goal is to minimize the
sum of distance traveled by the server and the total delay in serving the
requests. This problem models the fundamental tradeoff between batching
requests to improve locality and reducing delay to improve response time, that
has many applications in operations management, operating systems, logistics,
supply chain management, and scheduling.
Our main result is to show a poly-logarithmic competitive ratio for the
online service with delay problem. This result is obtained by an algorithm that
we call the preemptive service algorithm. The salient feature of this algorithm
is a process called preemptive service, which uses a novel combination of
(recursive) time forwarding and spatial exploration on a metric space. We hope
this technique will be useful for related problems such as reordering buffer
management, online TSP, vehicle routing, etc. We also generalize our results to
servers.Comment: 30 pages, 11 figures, Appeared in 49th ACM Symposium on Theory of
Computing (STOC), 201
Context-aware Cluster Based Device-to-Device Communication to Serve Machine Type Communications
Billions of Machine Type Communication (MTC) devices are foreseen to be
deployed in next ten years and therefore potentially open a new market for next
generation wireless network. However, MTC applications have different
characteristics and requirements compared with the services provided by legacy
cellular networks. For instance, an MTC device sporadically requires to
transmit a small data packet containing information generated by sensors. At
the same time, due to the massive deployment of MTC devices, it is inefficient
to charge their batteries manually and thus a long battery life is required for
MTC devices. In this sense, legacy networks designed to serve human-driven
traffics in real time can not support MTC efficiently. In order to improve the
availability and battery life of MTC devices, context-aware device-to-device
(D2D) communication is exploited in this paper. By applying D2D communication,
some MTC users can serve as relays for other MTC users who experience bad
channel conditions. Moreover, signaling schemes are also designed to enable the
collection of context information and support the proposed D2D communication
scheme. Last but not least, a system level simulator is implemented to evaluate
the system performance of the proposed technologies and a large performance
gain is shown by the numerical results
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