2,495 research outputs found
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing
Cloud computing is a reliable solution to provide distributed computation
power. However, real-time response is still challenging regarding the enormous
amount of data generated by the IoT devices in 5G and 6G networks. Thus,
multi-access edge computing (MEC), which consists of distributing the edge
servers in the proximity of end-users to have low latency besides the higher
processing power, is increasingly becoming a vital factor for the success of
modern applications. This paper addresses the problem of minimizing both, the
network delay, which is the main objective of MEC, and the number of edge
servers to provide a MEC design with minimum cost. This MEC design consists of
edge servers placement and base stations allocation, which makes it a joint
combinatorial optimization problem (COP). Recently, reinforcement learning (RL)
has shown promising results for COPs. However, modeling real-world problems
using RL when the state and action spaces are large still needs investigation.
We propose a novel RL framework with an efficient representation and modeling
of the state space, action space and the penalty function in the design of the
underlying Markov Decision Process (MDP) for solving our problem
Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation
With the wide deployment of public cloud computing infrastructures, using
clouds to host data query services has become an appealing solution for the
advantages on scalability and cost-saving. However, some data might be
sensitive that the data owner does not want to move to the cloud unless the
data confidentiality and query privacy are guaranteed. On the other hand, a
secured query service should still provide efficient query processing and
significantly reduce the in-house workload to fully realize the benefits of
cloud computing. We propose the RASP data perturbation method to provide secure
and efficient range query and kNN query services for protected data in the
cloud. The RASP data perturbation method combines order preserving encryption,
dimensionality expansion, random noise injection, and random projection, to
provide strong resilience to attacks on the perturbed data and queries. It also
preserves multidimensional ranges, which allows existing indexing techniques to
be applied to speedup range query processing. The kNN-R algorithm is designed
to work with the RASP range query algorithm to process the kNN queries. We have
carefully analyzed the attacks on data and queries under a precisely defined
threat model and realistic security assumptions. Extensive experiments have
been conducted to show the advantages of this approach on efficiency and
security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201
Adaptive content mapping for internet navigation
The Internet as the biggest human library ever assembled keeps on growing. Although all kinds of information carriers (e.g. audio/video/hybrid file formats) are available, text based documents dominate. It is estimated that about 80% of all information worldwide stored electronically exists in (or can be converted into) text form. More and more, all kinds of documents are generated by means of a text processing system and are therefore available electronically. Nowadays, many printed journals are also published online and may even discontinue to appear in print form tomorrow. This development has many convincing advantages: the documents are both available faster (cf. prepress services) and cheaper, they can be searched more easily, the physical storage only needs a fraction of the space previously necessary and the medium will not age. For most people, fast and easy access is the most interesting feature of the new age; computer-aided search for specific documents or Web pages becomes the basic tool for information-oriented work. But this tool has problems. The current keyword based search machines available on the Internet are not really appropriate for such a task; either there are (way) too many documents matching the specified keywords are presented or none at all. The problem lies in the fact that it is often very difficult to choose appropriate terms describing the desired topic in the first place. This contribution discusses the current state-of-the-art techniques in content-based searching (along with common visualization/browsing approaches) and proposes a particular adaptive solution for intuitive Internet document navigation, which not only enables the user to provide full texts instead of manually selected keywords (if available), but also allows him/her to explore the whole database
A survey on energy efficiency in information systems
Concerns about energy and sustainability are growing everyday involving a wide range
of fields. Even Information Systems (ISs) are being influenced by the issue of reducing
pollution and energy consumption and new fields are rising dealing with this topic. One
of these fields is Green Information Technology (IT), which deals with energy efficiency
with a focus on IT. Researchers have faced this problem according to several points of
view. The purpose of this paper is to understand the trends and the future development
of Green IT by analyzing the state-of-the-art and classifying existing approaches to
understand which are the components that have an impact on energy efficiency in ISs
and how this impact can be reduced. At first, we explore some guidelines that can help
to understand the efficiency level of an organization and of an IS. Then, we discuss
measurement and estimation of energy efficiency and identify which are the components
that mainly contribute to energy waste and how it is possible to improve energy efficiency,
both at the hardware and at the software level
Self-adaptation via concurrent multi-action evaluation for unknown context
Context-aware computing has been attracting growing attention in recent years. Generally, there are several ways for a context-aware system to select a course of action for a particular change of context. One way is for the system developers to encompass all possible context changes in the domain knowledge. Other methods include system inferences and adaptive learning whereby the system executes one action and evaluates the outcome and self-adapts/self-learns based on that. However, in situations where a system encounters unknown contexts, the iterative approach would become unfeasible when the size of the action space increases. Providing efficient solutions to this problem has been the main goal of this research project.
Based on the developed abstract model, the designed methodology replaces the single action implementation and evaluation by multiple actions implemented and evaluated concurrently. This parallel evaluation of actions speeds up significantly the evolution time taken to select the best action suited to unknown context compared to the iterative approach.
The designed and implemented framework efficiently carries out concurrent multi-action evaluation when an unknown context is encountered and finds the best course of action. Two concrete implementations of the framework were carried out demonstrating the usability and adaptability of the framework across multiple domains.
The first implementation was in the domain of database performance tuning. The concrete implementation of the framework demonstrated the ability of concurrent multi-action evaluation technique to performance tune a database when performance is regressed for an unknown reason.
The second implementation demonstrated the ability of the framework to correctly determine the threshold price to be used in a name-your-own-price channel when an unknown context is encountered.
In conclusion the research introduced a new paradigm of a self-adaptation technique for context-aware application. Among the existing body of work, the concurrent multi-action evaluation is classified under the abstract concept of experiment-based self-adaptation techniques
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