6,895 research outputs found
Control and Communication Protocols that Enable Smart Building Microgrids
Recent communication, computation, and technology advances coupled with
climate change concerns have transformed the near future prospects of
electricity transmission, and, more notably, distribution systems and
microgrids. Distributed resources (wind and solar generation, combined heat and
power) and flexible loads (storage, computing, EV, HVAC) make it imperative to
increase investment and improve operational efficiency. Commercial and
residential buildings, being the largest energy consumption group among
flexible loads in microgrids, have the largest potential and flexibility to
provide demand side management. Recent advances in networked systems and the
anticipated breakthroughs of the Internet of Things will enable significant
advances in demand response capabilities of intelligent load network of
power-consuming devices such as HVAC components, water heaters, and buildings.
In this paper, a new operating framework, called packetized direct load control
(PDLC), is proposed based on the notion of quantization of energy demand. This
control protocol is built on top of two communication protocols that carry
either complete or binary information regarding the operation status of the
appliances. We discuss the optimal demand side operation for both protocols and
analytically derive the performance differences between the protocols. We
propose an optimal reservation strategy for traditional and renewable energy
for the PDLC in both day-ahead and real time markets. In the end we discuss the
fundamental trade-off between achieving controllability and endowing
flexibility
Leveraging Semantic Web Technologies for Managing Resources in a Multi-Domain Infrastructure-as-a-Service Environment
This paper reports on experience with using semantically-enabled network
resource models to construct an operational multi-domain networked
infrastructure-as-a-service (NIaaS) testbed called ExoGENI, recently funded
through NSF's GENI project. A defining property of NIaaS is the deep
integration of network provisioning functions alongside the more common storage
and computation provisioning functions. Resource provider topologies and user
requests can be described using network resource models with common base
classes for fundamental cyber-resources (links, nodes, interfaces) specialized
via virtualization and adaptations between networking layers to specific
technologies.
This problem space gives rise to a number of application areas where semantic
web technologies become highly useful - common information models and resource
class hierarchies simplify resource descriptions from multiple providers,
pathfinding and topology embedding algorithms rely on query abstractions as
building blocks.
The paper describes how the semantic resource description models enable
ExoGENI to autonomously instantiate on-demand virtual topologies of virtual
machines provisioned from cloud providers and are linked by on-demand virtual
connections acquired from multiple autonomous network providers to serve a
variety of applications ranging from distributed system experiments to
high-performance computing
Genetic Algorithm-based Mapper to Support Multiple Concurrent Users on Wireless Testbeds
Communication and networking research introduces new protocols and standards
with an increasing number of researchers relying on real experiments rather
than simulations to evaluate the performance of their new protocols. A number
of testbeds are currently available for this purpose and a growing number of
users are requesting access to those testbeds. This motivates the need for
better utilization of the testbeds by allowing concurrent experimentations. In
this work, we introduce a novel mapping algorithm that aims to maximize
wireless testbed utilization using frequency slicing of the spectrum resources.
The mapper employs genetic algorithm to find the best combination of requests
that can be served concurrently, after getting all possible mappings of each
request via an induced sub-graph isomorphism stage. The proposed mapper is
tested on grid testbeds and randomly generated topologies. The solution of our
mapper is compared to the optimal one, obtained through a brute-force search,
and was able to serve the same number of requests in 82.96% of testing
scenarios. Furthermore, we show the effect of the careful design of testbed
topology on enhancing the testbed utilization by applying our mapper on a
carefully positioned 8-nodes testbed. In addition, our proposed approach for
testbed slicing and requests mapping has shown an improved performance in terms
of total served requests, about five folds, compared to the simple allocation
policy with no slicing.Comment: IEEE Wireless Communications and Networking Conference (WCNC) 201
Autonomous resource-aware scheduling of large-scale media workflows
The media processing and distribution industry generally requires considerable resources to be able to execute the various tasks and workflows that constitute their business processes. The latter processes are often tied to critical constraints such as strict deadlines. A key issue herein is how to efficiently use the available computational, storage and network resources to be able to cope with the high work load. Optimizing resource usage is not only vital to scalability, but also to the level of QoS (e.g. responsiveness or prioritization) that can be provided. We designed an autonomous platform for scheduling and workflow-to-resource assignment, taking into account the different requirements and constraints. This paper presents the workflow scheduling algorithms, which consider the state and characteristics of the resources (computational, network and storage). The performance of these algorithms is presented in detail in the context of a European media processing and distribution use-case
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