Real-time identification and modelling in pervasive mining

This paper introduces an architecture for identifying and modelling in real-time at a copper mine using new technologies as M2M and cloud computing with a server in the cloud and an Android client inside the mine. The proposed design brings up pervasive mining, a system with wider coverage, higher communication efficiency, better fault-tolerance, and anytime anywhere availability. This solution was designed for a plant inside the mine which cannot tolerate interruption and for which their identification in situ, in real time, is an essential part of the system to control aspects such as instability by adjusting their corresponding parameters without stopping the process.

Determining service trustworthiness in inter loud computing environments

Deployment of applications and scientific workflows that require resources from multiple distributed platforms are fuelling the federation of autonomous clouds to create cyber infrastructure environments. As the scope of federated cloud computing enlarges to ubiquitous and pervasive computing, there will be a need to assess and maintain the trustworthiness of the cloud computing entities. In this paper, we present a fully distributed framework that enable interested parties determine the trustworthiness of federated cloud computing entities.

IaaS clouds vs. clusters for HPC: a performance study

The increasing amount of data collected in the fields of physics and bio-informatics allows researchers to build realistic, and therefore accurate, models/simulations and gain a deeper understanding of complex systems. This analysis is often at the cost of greatly increased processing requirements. Cloud computing, which provides on demand resources, can offset increased analysis requirements. While beneficial to researchers, adaption of clouds has been slow due to network and performance uncertainties. We compare the performance of cloud computers to clusters to make clear the advantages and limitations of clouds. Focus has been put on understanding how virtualization and the underlying network effects performance of High Performance Computing (HPC) applications. Collected results indicate that performance comparable to high performance clusters is achievable on cloud computers depending on the type of application run.

A VMD plugin for NAMD simulations on Amazon EC2

VMD and NAMD are two major molecular dynamics simulation software packages, which can work together for mining structural information of bio-molecules. Carrying out such molecular dynamics simulations can help researchers to understand the roles and functions of various bio-molecules in life science research. Recently, clouds have provided HPC clusters on demand that allow users to benefit from their flexibility, elasticity, and lower costs. Although cloud computing promises to provide seamless access to HPC clusters through the abstraction of services, which hide the details of the underlying software and hardware infrastructure, users without in depth computing knowledge are still forced to cope with many low level system and programming details. Therefore, we have designed and developed a software plugin of VMD, which can provide an integrated framework for NAMD to be executed on Amazon EC2. The proposed Amazon EC2 Plugin for VMD frees users from performing many tedious computing tasks such as launching, connecting and terminating Amazon EC2 compute instances; configuring a HPC cluster; and installing middleware and software applications before the system is readily available for any scientific investigation. This allows VMD/NAMD users to spend less time getting applications to work on HPC clusters but more time for bio-research.

An effective network traffic classification method with unknown flow detection

Traffic classification technique is an essential tool for network and system security in the complex environments such as cloud computing based environment. The state-of-the-art traffic classification methods aim to take the advantages of flow statistical features and machine learning techniques, however the classification performance is severely affected by limited supervised information and unknown applications. To achieve effective network traffic classification, we propose a new method to tackle the problem of unknown applications in the crucial situation of a small supervised training set. The proposed method possesses the superior capability of detecting unknown flows generated by unknown applications and utilizing the correlation information among real-world network traffic to boost the classification performance. A theoretical analysis is provided to confirm performance benefit of the proposed method. Moreover, the comprehensive performance evaluation conducted on two real-world network traffic datasets shows that the proposed scheme outperforms the existing methods in the critical network environment.

Guest editors' introduction : Special issue on trust, security, and privacy in parallel and distributed systems

In modern computing paradigms, most computing systems, e.g., cluster computing, grid computing, cloud computing, the Internet, telecommunication networks, Cyber- Physical Systems (CPS), and Machine-to-Machine communication networks (M2M), are parallel and distributed systems. While providing improved expandability, manageability, efficiency, and reliability, parallel and distributed systems increase their security weaknesses to an unprecedented scale. As the system devices are widely connected, their vulnerabilities are shared by the entire system. Because tasks are allocated to, and information is exchanged among the system devices that may belong to different users, trust, security, and privacy issues have yet to be resolved. This special issue of the IEEE Transactions on Parallel and Distributed Systems (TPDS) highlights recent advances in trust, security, and privacy for emerging parallel and distributed systems. This special issue was initiated by Dr. Xu Li, Dr. Patrick McDaniel, Dr. Radha Poovendran, and Dr. Guojun Wang. Due to a large number of submissions, Dr. Zhenfu Cao, Dr. Keqiu Li, and Dr. Yang Xiang were later invited to the editorial team. Dr. Xu Li was responsible for coordinating the paper review process. In response to the call for papers, we received 150 effective submissions, out of which 24 are included in this special issue after rigorous review and careful revision, presenting an acceptance ratio of 16 percent. The accepted papers are divided into three groups, covering issues related to trust, security, and privacy, respectively.

Can we beat DDoS attacks in clouds?

Cloud is becoming a dominant computing platform. Naturally, a question that arises is whether we can beat notorious DDoS attacks in a cloud environment. Researchers have demonstrated that the essential issue of DDoS attack and defense is resource competition between defenders and attackers. A cloud usually possesses profound resources and has full control and dynamic allocation capability of its resources. Therefore, cloud offers us the potential to overcome DDoS attacks. However, individual cloud hosted servers are still vulnerable to DDoS attacks if they still run in the traditional way. In this paper, we propose a dynamic resource allocation strategy to counter DDoS attacks against individual cloud customers. When a DDoS attack occurs, we employ the idle resources of the cloud to clone sufficient intrusion prevention servers for the victim in order to quickly filter out attack packets and guarantee the quality of the service for benign users simultaneously. We establish a mathematical model to approximate the needs of our resource investment based on queueing theory. Through careful system analysis and real-world data set experiments, we conclude that we can defeat DDoS attacks in a cloud environment. © 2013 IEEE.

Security from the transparent computing aspect

Transparent computing is an emerging computing paradigm where the users can enjoy any kind of service over networks on-demand with any devices, without caring about the underlying deployment details. In transparent computing, all software resources (even the OS) are stored on remote servers, from which the clients can request the resources for local execution in a block-streaming way. This paradigm has many benefits including cross-platform experience, user orientation, and platform independence. However, due to its fundamental features, e.g., separation of computation and storage in clients and servers respectively, and block-streaming-based scheduling and execution, transparent computing faces many new security challenges that may become its biggest obstacle. In this paper, we propose a Transparent Computing Security Architecture (TCSA), which builds user-controlled security for transparent computing by allowing the users to configure the desired security environments on demand. We envision, TCSA, which allows the users to take the initiative to protect their own data, is a promising solution for data security in transparent computing. © 2014 IEEE.

Software engineering for multi-tenancy computing challenges and implications

Multi-tenancy is a cloud computing phenomenon. Multiple instances of an application occupy and share resources from a large pool, allowing different users to have their own version of the same application running and coexisting on the same hardware but in isolated virtual spaces. In this position paper we survey the current landscape of multi-tenancy, laying out the challenges and complexity of software engineering where multi-tenancy issues are involved. Multitenancy allows cloud service providers to better utilise computing resources, supporting the development of more exible services to customers based on economy of scale, reducing overheads and infrastructural costs. Nevertheless, there are major challenges in migration from single tenant applications to multi-tenancy. These have not been fully explored in research or practice to date. In particular, the reengineering effort of multi-tenancy in Software-as-a-Service cloud applications requires many complex and important aspects that should be taken into consideration, such as security, scalability, scheduling, data isolation, etc. Our study emphasizes scheduling policies and cloud provisioning and deployment with regards to multi-tenancy issues. We employ CloudSim and MapReduce in our experiments to simulate and analyse multi-tenancy models, scenarios, performance, scalability, scheduling and reliability on cloud platforms.

ANGEL: agent-based scheduling for real-time tasks in virtualized clouds

The success of cloud computing makes an increasing number of real-time applications such as signal processing and weather forecasting run in the cloud. Meanwhile, scheduling for real-time tasks is playing an essential role for a cloud provider to maintain its quality of service and enhance the system's performance. In this paper, we devise a novel agent-based scheduling mechanism in cloud computing environment to allocate real-time tasks and dynamically provision resources. In contrast to traditional contract net protocols, we employ a bidirectional announcement-bidding mechanism and the collaborative process consists of three phases, i.e., basic matching phase, forward announcement-bidding phase and backward announcement-bidding phase. Moreover, the elasticity is sufficiently considered while scheduling by dynamically adding virtual machines to improve schedulability. Furthermore, we design calculation rules of the bidding values in both forward and backward announcement-bidding phases and two heuristics for selecting contractors. On the basis of the bidirectional announcement-bidding mechanism, we propose an agent-based dynamic scheduling algorithm named ANGEL for real-time, independent and aperiodic tasks in clouds. Extensive experiments are conducted on CloudSim platform by injecting random synthetic workloads and the workloads from the last version of the Google cloud tracelogs to evaluate the performance of our ANGEL. The experimental results indicate that ANGEL can efficiently solve the real-time task scheduling problem in virtualized clouds.

Cost-effective authentic and anonymous data sharing with forward security

Data sharing has never been easier with the advances of cloud computing, and an accurate analysis on the shared data provides an array of benefits to both the society and individuals. Data sharing with a large number of participants must take into account several issues, including efficiency, data integrity and privacy of data owner. Ring signature is a promising candidate to construct an anonymous and authentic data sharing system. It allows a data owner to anonymously authenticate his data which can be put into the cloud for storage or analysis purpose. Yet the costly certificate verification in the traditional public key infrastructure (PKI) setting becomes a bottleneck for this solution to be scalable. Identity-based (ID-based) ring signature, which eliminates the process of certificate verification, can be used instead. In this paper, we further enhance the security of ID-based ring signature by providing forward security: If a secret key of any user has been compromised, all previous generated signatures that include this user still remain valid. This property is especially important to any large scale data sharing system, as it is impossible to ask all data owners to re-authenticate their data even if a secret key of one single user has been compromised. We provide a concrete and efficient instantiation of our scheme, prove its security and provide an implementation to show its practicality.

Inference of personal sensors in the internet of things

Smartphone technology has become more popular and innovative over the last few years, and technology companies are now introducing wearable devices into the market. By emerging and converging with technologies such as Cloud, Internet of Things (IoT) and Virtualization, requirements to personal sensor devices are immense and essential to support existing networks, e.g. mobile health (mHealth) as well as IoT users. Traditional physiological and biological medical sensors in mHealth provide health data either periodically or on-demand. Both of these situations can cause rapid battery consumption, consume significant bandwidth, and raise privacy issues, because these sensors do not consider or understand sensor status when converged together. The aim of this research is to provide a novel approach and solution to managing and controlling personal sensors that can be used in various areas such as the health, military, aged care, IoT and sport. This paper presents an inference system to transfer health data collected by personal sensors efficiently and effectively to other networks in a secure and effective manner without burdening workload on sensor devices.

Arquitetura para publicação e universalização do acesso a transdutores inteligentes

Pós-graduação em Engenharia Elétrica - FEIS

Help-me-Here: sistema para atendimento a chamada de emergência rápida e inteligente

This final paper is a reflection on mobile service of urgency by raising the main difficulties encountered by organizations, and seeking innovative solutions through new technological features like smartphones, tablets application and cloud computing to develop a scalable system and globalized, based on geolocation that is able to overcome this difficulty being accessible to the whole population