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Big Data Analytics-Enhanced Cloud Computing: Challenges, Architectural Elements, and Future Directions  [PDF]
Rajkumar Buyya,Kotagiri Ramamohanarao,Chris Leckie,Rodrigo N. Calheiros,Amir Vahid Dastjerdi,Steve Versteeg
Computer Science , 2015,
Abstract: The emergence of cloud computing has made dynamic provisioning of elastic capacity to applications on-demand. Cloud data centers contain thousands of physical servers hosting orders of magnitude more virtual machines that can be allocated on demand to users in a pay-as-you-go model. However, not all systems are able to scale up by just adding more virtual machines. Therefore, it is essential, even for scalable systems, to project workloads in advance rather than using a purely reactive approach. Given the scale of modern cloud infrastructures generating real time monitoring information, along with all the information generated by operating systems and applications, this data poses the issues of volume, velocity, and variety that are addressed by Big Data approaches. In this paper, we investigate how utilization of Big Data analytics helps in enhancing the operation of cloud computing environments. We discuss diverse applications of Big Data analytics in clouds, open issues for enhancing cloud operations via Big Data analytics, and architecture for anomaly detection and prevention in clouds along with future research directions.
Detection and Mitigation of MITM Attacks in Storage Cloud Infrastructures  [cached]
Jaqueline Carmilema,Miguel Medrano,Denys Alberto Flores
Redifis , 2012,
Abstract: Cloud computing is changing IT service provision around the world, not only within large, but also small enterprises. These are benefited by tremendous advantages in cost reductionand customer satisfaction; however, behind this, there is a constant concern about risk management and vulnerability analysis when IT services are placed in the Cloud with thirdpartiesmanaging the security of the core-business infrastructure. Moreover, when moving to the Cloud, providers struggle to ensure high integrity, availability and confidentiality of informationbecause Cloud Services are still exposed to traditional threats due to the inherited protocols working underneath, which have not evolved as quick as these services. In this article, we briefly explainsome important concepts of Cloud Computing in order to understand the impact of ARP Spoofing, via a Man-In-The-Middle Attack (MITM) within a small experimental Storage Cloud environment so that a possible way to mitigate this particular problem to guarantee secure connections can be provided.
Big Data in Critical Infrastructures Security Monitoring: Challenges and Opportunities  [PDF]
L. Aniello,A. Bondavalli,A. Ceccarelli,C. Ciccotelli,M. Cinque,F. Frattini,A. Guzzo,A. Pecchia,A. Pugliese,L. Querzoni,S. Russo
Computer Science , 2014,
Abstract: Critical Infrastructures (CIs), such as smart power grids, transport systems, and financial infrastructures, are more and more vulnerable to cyber threats, due to the adoption of commodity computing facilities. Despite the use of several monitoring tools, recent attacks have proven that current defensive mechanisms for CIs are not effective enough against most advanced threats. In this paper we explore the idea of a framework leveraging multiple data sources to improve protection capabilities of CIs. Challenges and opportunities are discussed along three main research directions: i) use of distinct and heterogeneous data sources, ii) monitoring with adaptive granularity, and iii) attack modeling and runtime combination of multiple data analysis techniques.
CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services  [PDF]
Rodrigo N. Calheiros,Rajiv Ranjan,Cesar A. F. De Rose,Rajkumar Buyya
Computer Science , 2009,
Abstract: Cloud computing focuses on delivery of reliable, secure, fault-tolerant, sustainable, and scalable infrastructures for hosting Internet-based application services. These applications have different composition, configuration, and deployment requirements. Quantifying the performance of scheduling and allocation policy on a Cloud infrastructure (hardware, software, services) for different application and service models under varying load, energy performance (power consumption, heat dissipation), and system size is an extremely challenging problem to tackle. To simplify this process, in this paper we propose CloudSim: a new generalized and extensible simulation framework that enables seamless modelling, simulation, and experimentation of emerging Cloud computing infrastructures and management services. The simulation framework has the following novel features: (i) support for modelling and instantiation of large scale Cloud computing infrastructure, including data centers on a single physical computing node and java virtual machine; (ii) a self-contained platform for modelling data centers, service brokers, scheduling, and allocations policies; (iii) availability of virtualization engine, which aids in creation and management of multiple, independent, and co-hosted virtualized services on a data center node; and (iv) flexibility to switch between space-shared and time-shared allocation of processing cores to virtualized services.
Phenomenology Tools on Cloud Infrastructures using OpenStack  [PDF]
I. Campos,E. Fernandez del Castillo,S. Heinemeyer,A. Lopez-Garcia,F. v. d. Pahlen
Computer Science , 2012, DOI: 10.1140/epjc/s10052-013-2375-0
Abstract: We present a new environment for computations in particle physics phenomenology employing recent developments in cloud computing. On this environment users can create and manage "virtual" machines on which the phenomenology codes/tools can be deployed easily in an automated way. We analyze the performance of this environment based on "virtual" machines versus the utilization of "real" physical hardware. In this way we provide a qualitative result for the influence of the host operating system on the performance of a representative set of applications for phenomenology calculations.
A Cloud Service Architecture for Analyzing Big Monitoring Data  [PDF]
Samneet Singh,Yan Liu
- , 2016, DOI: 10.1109/TST.2016.7399283
Abstract: Cloud monitoring is of a source of big data that are constantly produced from traces of infrastructures, platforms, and applications. Analysis of monitoring data delivers insights of the system’s workload and usage pattern and ensures workloads are operating at optimum levels. The analysis process involves data query and extraction, data analysis, and result visualization. Since the volume of monitoring data is big, these operations require a scalable and reliable architecture to extract, aggregate, and analyze data in an arbitrary range of granularity. Ultimately, the results of analysis become the knowledge of the system and should be shared and communicated. This paper presents our cloud service architecture that explores a search cluster for data indexing and query. We develop REST APIs that the data can be accessed by different analysis modules. This architecture enables extensions to integrate with software frameworks of both batch processing (such as Hadoop) and stream processing (such as Spark) of big data. The analysis results are structured in Semantic Media Wiki pages in the context of the monitoring data source and the analysis process. This cloud architecture is empirically assessed to evaluate its responsiveness when processing a large set of data records under node failures.
Methodology Developments in Sensor Placement for Health Monitoring of Civil Infrastructures
Ting-Hua Yi,Hong-Nan Li
International Journal of Distributed Sensor Networks , 2012, DOI: 10.1155/2012/612726
Abstract: Optimal sensor placement (OSP) technique plays a key role in the structural health monitoring (SHM) of large-scale civil infrastructures. This paper outlines an overview of current research and development in the field of OSP problems in a perspective of both researchers and engineers. The paper begins with a definition of the model of sensor placement and provides the basic issues covering relevant methodologies. The primary evaluation criteria and main sensor placement methods are then discussed in details. Following that, the linkage between several influential sensor placement methods is described. Finally, existing problems and promising research efforts in the OSP problem of civil SHM are discussed.
Monitoring Large-Scale Cloud Systems with Layered Gossip Protocols  [PDF]
Jonathan Stuart Ward,Adam Barker
Computer Science , 2013,
Abstract: Monitoring is an essential aspect of maintaining and developing computer systems that increases in difficulty proportional to the size of the system. The need for robust monitoring tools has become more evident with the advent of cloud computing. Infrastructure as a Service (IaaS) clouds allow end users to deploy vast numbers of virtual machines as part of dynamic and transient architectures. Current monitoring solutions, including many of those in the open-source domain rely on outdated concepts including manual deployment and configuration, centralised data collection and adapt poorly to membership churn. In this paper we propose the development of a cloud monitoring suite to provide scalable and robust lookup, data collection and analysis services for large-scale cloud systems. In lieu of centrally managed monitoring we propose a multi-tier architecture using a layered gossip protocol to aggregate monitoring information and facilitate lookup, information collection and the identification of redundant capacity. This allows for a resource aware data collection and storage architecture that operates over the system being monitored. This in turn enables monitoring to be done in-situ without the need for significant additional infrastructure to facilitate monitoring services. We evaluate this approach against alternative monitoring paradigms and demonstrate how our solution is well adapted to usage in a cloud-computing context.
An Overview of the Commercial Cloud Monitoring Tools: Research Dimensions, Design Issues, and State-of-the-Art  [PDF]
Khalid Alhamazani,Rajiv Ranjan,Karan Mitra,Fethi Rabhi,Samee Ullah Khan,Adnene Guabtni,Vasudha Bhatnagar
Computer Science , 2013,
Abstract: Cloud monitoring activity involves dynamically tracking the Quality of Service (QoS) parameters related to virtualized resources (e.g., VM, storage, network, appliances, etc.), the physical resources they share, the applications running on them and data hosted on them. Applications and resources configuration in cloud computing environment is quite challenging considering a large number of heterogeneous cloud resources. Further, considering the fact that at each point of time, there will be a different and specific cloud service which may be massively required. Hence, cloud monitoring tools can assist a cloud providers or application developers in: (i) keeping their resources and applications operating at peak efficiency; (ii) detecting variations in resource and application performance; (iii) accounting the Service Level Agreement (SLA) violations of certain QoS parameters; and (iv) tracking the leave and join operations of cloud resources due to failures and other dynamic configuration changes. In this paper, we identify and discuss the major research dimensions and design issues related to engineering cloud monitoring tools. We further discuss how aforementioned research dimensions and design issues are handled by current academic research as well as by commercial monitoring tools.
Ahmed S. Al-Masah,Ali M. Al-Sharafi
International Journal of Advances in Engineering and Technology , 2013,
Abstract: Although, building a monitoring system for IT infrastructure is an important issue, it is not an easy process. Monitoring software might be complicated, inflexible and expensive. Among many IT service cloud computing has its remarkable benefits for network monitoring service. Cloud Computing is a style of computing where IT-related capabilities are provided “as a service” Advantages of cloud computing such as monitoring capabilities, pricing, and ease of use have been created a remarkable milestone for network monitoring framework. Providing a monitoring service on the cloud can be a valuable addition for today’s development growth of cloud computing.
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