35,837 research outputs found
The \uabQuality of Information\ubb Challenges in IoT Forensics: An Introduction
IoT technologies pose serious challenges to digital forensics. The acquisition of digital evidence is hindered by the number and extreme variety of IoT items, often lacking of physical interfaces, connected in unprotected networks, feeding data to uncontrolled cloud services. In this paper we introduce the main issues of \uabinformation quality\ubb in this field. After a short introduction, we provide an overview on digital forensics approach to preserve the \uabchain of custody\ubb, then we detect relevant IoT features in order to analyse main concerns in digital forensics. At the end, we propose a formula for benchmarking forensics trustworthiness (Information Quality Assessment)
Cloud Services Brokerage: A Survey and Research Roadmap
A Cloud Services Brokerage (CSB) acts as an intermediary between cloud
service providers (e.g., Amazon and Google) and cloud service end users,
providing a number of value adding services. CSBs as a research topic are in
there infancy. The goal of this paper is to provide a concise survey of
existing CSB technologies in a variety of areas and highlight a roadmap, which
details five future opportunities for research.Comment: Paper published in the 8th IEEE International Conference on Cloud
Computing (CLOUD 2015
Cloud Benchmarking for Performance
How can applications be deployed on the cloud to achieve maximum performance?
This question has become significant and challenging with the availability of a
wide variety of Virtual Machines (VMs) with different performance capabilities
in the cloud. The above question is addressed by proposing a six step
benchmarking methodology in which a user provides a set of four weights that
indicate how important each of the following groups: memory, processor,
computation and storage are to the application that needs to be executed on the
cloud. The weights along with cloud benchmarking data are used to generate a
ranking of VMs that can maximise performance of the application. The rankings
are validated through an empirical analysis using two case study applications;
the first is a financial risk application and the second is a molecular
dynamics simulation, which are both representative of workloads that can
benefit from execution on the cloud. Both case studies validate the feasibility
of the methodology and highlight that maximum performance can be achieved on
the cloud by selecting the top ranked VMs produced by the methodology.Comment: 6 pages, 6th IEEE International Conference on Cloud Computing
Technology and Science (IEEE CloudCom) 2014, Singapor
Cloud WorkBench - Infrastructure-as-Code Based Cloud Benchmarking
To optimally deploy their applications, users of Infrastructure-as-a-Service
clouds are required to evaluate the costs and performance of different
combinations of cloud configurations to find out which combination provides the
best service level for their specific application. Unfortunately, benchmarking
cloud services is cumbersome and error-prone. In this paper, we propose an
architecture and concrete implementation of a cloud benchmarking Web service,
which fosters the definition of reusable and representative benchmarks. In
distinction to existing work, our system is based on the notion of
Infrastructure-as-Code, which is a state of the art concept to define IT
infrastructure in a reproducible, well-defined, and testable way. We
demonstrate our system based on an illustrative case study, in which we measure
and compare the disk IO speeds of different instance and storage types in
Amazon EC2
Every Cloud Has a Push Data Lining: Incorporating Cloud Services in a Context-Aware Application
We investigated context-awareness by utilising multiple sources of context in a mobile device setting. In our experiment we developed a system consisting of a mobile client, running on the Android platform, integrated with a cloud-based service. These components were integrated using pushmessaging technology.One of the key featureswas the automatic adaptation of smartphones in accordance with implicit user needs. The novelty of our approach consists in the use of multiple sources of context input to the system, which included the use of calendar data and web based user configuration tool, as well as that of an external, cloud-based, configuration file storing user interface preferences which, pushed at log-on time irrespective of access device, frees the user from having to manually configure its interface.The systemwas evaluated via two rounds of user evaluations (n = 50 users), the feedback of which was generally positive and demonstrated the viability of using cloud-based services to provide an enhanced context-aware user experience
ALOJA: A benchmarking and predictive platform for big data performance analysis
The main goals of the ALOJA research project from BSC-MSR, are to explore and automate the characterization of cost-effectivenessof Big Data deployments. The development of the project over its first year, has resulted in a open source benchmarking platform, an online public repository of results with over 42,000 Hadoop job runs, and web-based analytic tools to gather insights about system's cost-performance1.
This article describes the evolution of the project's focus and research
lines from over a year of continuously benchmarking Hadoop under dif-
ferent configuration and deployments options, presents results, and dis
cusses the motivation both technical and market-based of such changes.
During this time, ALOJA's target has evolved from a previous low-level
profiling of Hadoop runtime, passing through extensive benchmarking
and evaluation of a large body of results via aggregation, to currently
leveraging Predictive Analytics (PA) techniques. Modeling benchmark
executions allow us to estimate the results of new or untested configu-
rations or hardware set-ups automatically, by learning techniques from
past observations saving in benchmarking time and costs.This work is partially supported the BSC-Microsoft Research Centre, the Span-
ish Ministry of Education (TIN2012-34557), the MINECO Severo Ochoa Research program (SEV-2011-0067) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft
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