5,397 research outputs found
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Flip: Data-Centric Edge CGRA Accelerator
Coarse-Grained Reconfigurable Arrays (CGRA) are promising edge accelerators
due to the outstanding balance in flexibility, performance, and energy
efficiency. Classic CGRAs statically map compute operations onto the processing
elements (PE) and route the data dependencies among the operations through the
Network-on-Chip. However, CGRAs are designed for fine-grained static
instruction-level parallelism and struggle to accelerate applications with
dynamic and irregular data-level parallelism, such as graph processing. To
address this limitation, we present Flip, a novel accelerator that enhances
traditional CGRA architectures to boost the performance of graph applications.
Flip retains the classic CGRA execution model while introducing a special
data-centric mode for efficient graph processing. Specifically, it exploits the
natural data parallelism of graph algorithms by mapping graph vertices onto
processing elements (PEs) rather than the operations, and supporting dynamic
routing of temporary data according to the runtime evolution of the graph
frontier. Experimental results demonstrate that Flip achieves up to 36
speedup with merely 19% more area compared to classic CGRAs. Compared to
state-of-the-art large-scale graph processors, Flip has similar energy
efficiency and 2.2 better area efficiency at a much-reduced power/area
budget
Information protection in content-centric networks
Information-centric networks have distinct advantages with regard to securing sensitive content as a result of their new approaches to managing data in potential future internet architectures. These kinds of systems, because of their data-centric perspective, provide the opportunity to embed policy-centric content management components that can address looming problems in information distribution that both companies and federal agencies are beginning to face with respect to sensitive content. This information-centricity facilitates the application of security techniques that are very difficult and in some cases impossible to apply in traditional packetized networks. This work addresses the current state of the art in both these kinds of cross-domain systems and information-centric networking in general. It then covers other related work, outlining why information-centric networks are more powerful than traditional packetized networks with regard to usage management. Then, it introduces a taxonomy of types of policy-centric usage managed information network systems and an associated methodology for evaluating the individual taxonomic elements. It finally delves into experimental evaluation of the various defined architectural options and presents results of comparing experimental evaluation with anticipated outcomes
Gunrock: A High-Performance Graph Processing Library on the GPU
For large-scale graph analytics on the GPU, the irregularity of data access
and control flow, and the complexity of programming GPUs have been two
significant challenges for developing a programmable high-performance graph
library. "Gunrock", our graph-processing system designed specifically for the
GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on
operations on a vertex or edge frontier. Gunrock achieves a balance between
performance and expressiveness by coupling high performance GPU computing
primitives and optimization strategies with a high-level programming model that
allows programmers to quickly develop new graph primitives with small code size
and minimal GPU programming knowledge. We evaluate Gunrock on five key graph
primitives and show that Gunrock has on average at least an order of magnitude
speedup over Boost and PowerGraph, comparable performance to the fastest GPU
hardwired primitives, and better performance than any other GPU high-level
graph library.Comment: 14 pages, accepted by PPoPP'16 (removed the text repetition in the
previous version v5
Microservice-based Reference Architecture for Semantics-aware Measurement Systems
Cloud technologies have become more important than ever with the rising need for scalable
and distributed software systems. A pattern that is used in many such systems is a
microservice-based architecture (MSA). MSAs have become a blueprint for many large
companies and big software systems. In many scientific fields like energy and environmental
informatics, efficient and scalable software systems with a primary focus on measurement
data are a core requirement. Nowadays, there are many ways to solve research questions
using data-driven approaches. Most of them have a need for large amounts of measurement
data and according metadata. However, many measurement systems still follow deprecated
guidelines such as monolithic architectures, classic relational database principles and are
missing semantic awareness and interpretation of data. These problems and the resulting
requirements are tackled by the introduction of a reference architecture with a focus on
measurement systems that utilizes the principles of microservices.
The thesis first presents the systematic design of the reference architecture by using the
principles of Domain-driven Design (DDD). This process ensures that the reference architecture
is defined in a modular and sustainable way in contrast to complex monolithic
software systems. An extensive scientific analysis leads to the core parts of the concept
consisting of the data management and semantics for measurement systems. Different data
services define a concept for managing measurement data, according meta data and master
data describing the business objects of the application implemented by using the reference
architecture. Further concepts allow the reference architecture to define a way for the system
to understand and interpret the data using semantic information. Lastly, the introduction of
a frontend framework for dashboard applications represents an example for visualizing the
data managed by the microservices
CamFlow: Managed Data-sharing for Cloud Services
A model of cloud services is emerging whereby a few trusted providers manage
the underlying hardware and communications whereas many companies build on this
infrastructure to offer higher level, cloud-hosted PaaS services and/or SaaS
applications. From the start, strong isolation between cloud tenants was seen
to be of paramount importance, provided first by virtual machines (VM) and
later by containers, which share the operating system (OS) kernel. Increasingly
it is the case that applications also require facilities to effect isolation
and protection of data managed by those applications. They also require
flexible data sharing with other applications, often across the traditional
cloud-isolation boundaries; for example, when government provides many related
services for its citizens on a common platform. Similar considerations apply to
the end-users of applications. But in particular, the incorporation of cloud
services within `Internet of Things' architectures is driving the requirements
for both protection and cross-application data sharing.
These concerns relate to the management of data. Traditional access control
is application and principal/role specific, applied at policy enforcement
points, after which there is no subsequent control over where data flows; a
crucial issue once data has left its owner's control by cloud-hosted
applications and within cloud-services. Information Flow Control (IFC), in
addition, offers system-wide, end-to-end, flow control based on the properties
of the data. We discuss the potential of cloud-deployed IFC for enforcing
owners' dataflow policy with regard to protection and sharing, as well as
safeguarding against malicious or buggy software. In addition, the audit log
associated with IFC provides transparency, giving configurable system-wide
visibility over data flows. [...]Comment: 14 pages, 8 figure
A Systematic Review of Data Quality in CPS and IoT for Industry 4.0
The Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the backbones of Industry 4.0, where data quality is crucial for decision support. Data quality in these systems can deteriorate due to sensor failures or uncertain operating environments. Our objective is to summarize and assess the research efforts that address data quality in data-centric CPS/IoT industrial applications. We systematically review the state-of-the-art data quality techniques for CPS and IoT in Industry 4.0 through a systematic literature review (SLR) study. We pose three research questions, define selection and exclusion criteria for primary studies, and extract and synthesize data from these studies to answer our research questions. Our most significant results are (i) the list of data quality issues, their sources, and application domains, (ii) the best practices and metrics for managing data quality, (iii) the software engineering solutions employed to manage data quality, and (iv) the state of the data quality techniques (data repair, cleaning, and monitoring) in the application domains. The results of our SLR can help researchers obtain an overview of existing data quality issues, techniques, metrics, and best practices. We suggest research directions that require attention from the research community for follow-up work.acceptedVersio
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