867 research outputs found
Traffic and task allocation in networks and the cloud
Communication services such as telephony, broadband and TV are increasingly migrating into Internet Protocol(IP) based networks because of the consolidation of telephone and data networks. Meanwhile, the increasingly wide application of Cloud Computing enables the accommodation of tens of thousands of applications from the general public or enterprise users which make use of Cloud services on-demand through IP networks such as the Internet. Real-Time services over IP (RTIP) have also been increasingly significant due to the convergence of network services, and the real-time needs of the Internet of Things (IoT) will strengthen this trend. Such Real-Time applications have strict Quality of Service (QoS) constraints, posing a major challenge for IP networks. The Cognitive Packet Network (CPN) has been designed as a QoS-driven protocol that addresses user-oriented QoS demands by adaptively routing packets based on online sensing and measurement. Thus in this thesis we first describe our design for a novel ``Real-Time (RT) traffic over CPN'' protocol which uses QoS goals that match the needs of voice packet delivery in the presence of other background traffic under varied traffic conditions; we present its experimental evaluation via measurements of key QoS metrics such as packet delay, delay variation (jitter) and packet loss ratio. Pursuing our investigation of packet routing in the Internet, we then propose a novel Big Data and Machine Learning approach for real-time Internet scale Route Optimisation based on Quality-of-Service using an overlay network, and evaluate is performance. Based on the collection of data sampled each minutes over a large number of source-destinations pairs, we observe that intercontinental Internet Protocol (IP) paths are far from optimal with respect to metrics such as end-to-end round-trip delay. On the other hand, our machine learning based overlay network routing scheme exploits large scale data collected from communicating node pairs to select overlay paths, while it uses IP between neighbouring overlay nodes. We report measurements over a week long experiment with several million data points shows substantially better end-to-end QoS than is observed with pure IP routing. Pursuing the machine learning approach, we then address the challenging problem of dispatching incoming tasks to servers in Cloud systems so as to offer the best QoS and reliable job execution; an experimental system (the Task Allocation Platform) that we have developed is presented and used to compare several task allocation schemes, including a model driven algorithm, a reinforcement learning based scheme, and a ``sensible’’ allocation algorithm that assigns tasks to sub-systems that are observed to provide lower response time. These schemes are compared via measurements both among themselves and against a standard round-robin scheduler, with two architectures (with homogenous and heterogenous hosts having different processing capacities) and the conditions under which the different schemes offer better QoS are discussed. Since Cloud systems include both locally based servers at user premises and remote servers and multiple Clouds that can be reached over the Internet, we also describe a smart distributed system that combines local and remote Cloud facilities, allocating tasks dynamically to the service that offers the best overall QoS, and it includes a routing overlay which minimizes network delay for data transfer between Clouds. Internet-scale experiments that we report exhibit the effectiveness of our approach in adaptively distributing workload across multiple Clouds.Open Acces
HICSS - Modeling Privacy Preservation in Smart Connected Toys by Petri-Nets
Children data privacy must be considered as integral and factored into the system design of Smart Connected Toy (SCT). The challenge is that SCTs are capable to gather significant amount volunteered and non-volunteered data, which lacks privacy considerations. It is imperative to adopt a modeling technique that autonomously preserves privacy and secure children’s data in SCT transactions. This paper surveys the current data flow modeling techniques, which most of them do not have elements to address the privacy of Personal Identifiable Information (PII). This paper shows a Petri-Net simulation which provides privacy assurance in order to minimize the risk of privacy violation of a child’s PII and related data
Modeling Privacy Preservation in Smart Connected Toys by Petri-Nets
Children data privacy must be considered as integral and factored into the system design of Smart Connected Toy (SCT). The challenge is that SCTs are capable to gather significant amount volunteered and non-volunteered data, which lacks privacy considerations. It is imperative to adopt a modeling technique that autonomously preserves privacy and secure children’s data in SCT transactions. This paper surveys the current data flow modeling techniques, which most of them do not have elements to address the privacy of Personal Identifiable Information (PII). This paper shows a Petri-Net simulation which provides privacy assurance in order to minimize the risk of privacy violation of a child’s PII and related data
Evaluating Resilience of Cyber-Physical-Social Systems
Nowadays, protecting the network is not the only security concern. Still, in cyber security,
websites and servers are becoming more popular as targets due to the ease with which
they can be accessed when compared to communication networks. Another threat in
cyber physical social systems with human interactions is that they can be attacked and
manipulated not only by technical hacking through networks, but also by manipulating
people and stealing users’ credentials. Therefore, systems should be evaluated beyond cy-
ber security, which means measuring their resilience as a piece of evidence that a system
works properly under cyber-attacks or incidents. In that way, cyber resilience is increas-
ingly discussed and described as the capacity of a system to maintain state awareness for
detecting cyber-attacks. All the tasks for making a system resilient should proactively
maintain a safe level of operational normalcy through rapid system reconfiguration to
detect attacks that would impact system performance. In this work, we broadly studied
a new paradigm of cyber physical social systems and defined a uniform definition of it.
To overcome the complexity of evaluating cyber resilience, especially in these inhomo-
geneous systems, we proposed a framework including applying Attack Tree refinements
and Hierarchical Timed Coloured Petri Nets to model intruder and defender behaviors
and evaluate the impact of each action on the behavior and performance of the system.Hoje em dia, proteger a rede não é a única preocupação de segurança. Ainda assim, na
segurança cibernética, sites e servidores estão se tornando mais populares como alvos
devido à facilidade com que podem ser acessados quando comparados às redes de comu-
nicação. Outra ameaça em sistemas sociais ciberfisicos com interações humanas é que eles
podem ser atacados e manipulados não apenas por hackers técnicos através de redes, mas
também pela manipulação de pessoas e roubo de credenciais de utilizadores. Portanto, os
sistemas devem ser avaliados para além da segurança cibernética, o que significa medir
sua resiliência como uma evidência de que um sistema funciona adequadamente sob
ataques ou incidentes cibernéticos. Dessa forma, a resiliência cibernética é cada vez mais
discutida e descrita como a capacidade de um sistema manter a consciência do estado para
detectar ataques cibernéticos. Todas as tarefas para tornar um sistema resiliente devem
manter proativamente um nível seguro de normalidade operacional por meio da reconfi-
guração rápida do sistema para detectar ataques que afetariam o desempenho do sistema.
Neste trabalho, um novo paradigma de sistemas sociais ciberfisicos é amplamente estu-
dado e uma definição uniforme é proposta. Para superar a complexidade de avaliar a
resiliência cibernética, especialmente nesses sistemas não homogéneos, é proposta uma
estrutura que inclui a aplicação de refinamentos de Árvores de Ataque e Redes de Petri
Coloridas Temporizadas Hierárquicas para modelar comportamentos de invasores e de-
fensores e avaliar o impacto de cada ação no comportamento e desempenho do sistema
Novel applications and contexts for the cognitive packet network
Autonomic communication, which is the development of self-configuring, self-adapting, self-optimising and self-healing communication systems, has gained much attention in the network research community. This can be explained by the increasing demand for more sophisticated networking technologies with physical realities that possess computation capabilities and can operate successfully with minimum human intervention. Such systems are driving innovative applications and services that improve the quality of life of citizens both socially and economically. Furthermore, autonomic communication, because of its decentralised approach to communication, is also being explored by the research community as an alternative to centralised control infrastructures for efficient management of large networks. This thesis studies one of the successful contributions in the autonomic communication research, the Cognitive Packet Network (CPN). CPN is a highly scalable adaptive routing protocol that
allows for decentralised control in communication. Consequently, CPN has achieved significant successes, and because of the direction of research, we expect it to continue to find relevance. To investigate this hypothesis, we research new applications and contexts for CPN. This thesis first studies Information-Centric Networking (ICN), a future Internet architecture
proposal. ICN adopts a data-centric approach such that contents are directly addressable at the network level and in-network caching is easily supported. An optimal caching strategy for an information-centric network is first analysed, and approximate solutions are developed and evaluated. Furthermore, a CPN inspired forwarding strategy for directing requests in such a way that exploits the in-network caching capability of ICN is proposed. The proposed strategy is evaluated via discrete event simulations and shown to be more effective in its search for local cache hits compared to the conventional methods. Finally, CPN is proposed to implement the routing system of an Emergency Cyber-Physical System for guiding evacuees in confined spaces in emergency situations. By exploiting CPN’s QoS capabilities, different paths are assigned to evacuees based on their ongoing health conditions using well-defined path metrics. The proposed system is evaluated via discrete-event simulations and shown to improve survival chances compared to a static system that treats evacuees in the same way.Open Acces
On the analysis of big data indexing execution strategies
Efficient response to search queries is very crucial for data analysts to obtain timely results from big data spanned over heterogeneous machines. Currently, a number of big-data processing frameworks are available in which search operations are performed in distributed and parallel manner. However, implementation of indexing mechanism results in noticeable reduction of overall query processing time. There is an urge to assess the feasibility and impact of indexing towards query execution performance. This paper investigates the performance of state-of-the-art clustered indexing approaches over Hadoop framework which is de facto standard for big data processing. Moreover, this study leverages a comparative analysis of non-clustered indexing overhead in terms of time and space taken by indexing process for varying volume data sets with increasing Index Hit Ratio. Furthermore, the experiments evaluate performance of search operations in terms of data access and retrieval time for queries that use indexes. We then validated the obtained results using Petri net mathematical modeling. We used multiple data sets in our experiments to manifest the impact of growing volume of data on indexing and data search and retrieval performance. The results and highlighted challenges favorably lead researchers towards improved implication of indexing mechanism in perspective of data retrieval from big data. Additionally, this study advocates selection of a non-clustered indexing solution so that optimized search performance over big data is obtained
ACHIEVING AUTONOMIC SERVICE ORIENTED ARCHITECTURE USING CASE BASED REASONING
Service-Oriented Architecture (SOA) enables composition of large and complex
computational units out of the available atomic services. However, implementation of
SOA, for its dynamic nature, could bring about challenges in terms of service
discovery, service interaction, service composition, robustness, etc. In the near future,
SOA will often need to dynamically re-configuring and re-organizing its topologies of
interactions between the web services because of some unpredictable events, such as
crashes or network problems, which will cause service unavailability. Complexity and
dynamism of the current and future global network system require service architecture
that is capable of autonomously changing its structure and functionality to meet
dynamic changes in the requirements and environment with little human intervention.
This then needs to motivate the research described throughout this thesis.
In this thesis, the idea of introducing autonomy and adapting case-based reasoning
into SOA in order to extend the intelligence and capability of SOA is contributed and
elaborated. It is conducted by proposing architecture of an autonomic SOA
framework based on case-based reasoning and the architectural considerations of
autonomic computing paradigm. It is then followed by developing and analyzing
formal models of the proposed architecture using Petri Net. The framework is also
tested and analyzed through case studies, simulation, and prototype development. The
case studies show feasibility to employing case-based reasoning and autonomic
computing into SOA domain and the simulation results show believability that it
would increase the intelligence, capability, usability and robustness of SOA. It was
shown that SOA can be improved to cope with dynamic environment and services
unavailability by incorporating case-based reasoning and autonomic computing
paradigm to monitor and analyze events and service requests, then to plan and execute
the appropriate actions using the knowledge stored in knowledge database
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