167,144 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
Energy-Aware Cloud Management through Progressive SLA Specification
Novel energy-aware cloud management methods dynamically reallocate
computation across geographically distributed data centers to leverage regional
electricity price and temperature differences. As a result, a managed VM may
suffer occasional downtimes. Current cloud providers only offer high
availability VMs, without enough flexibility to apply such energy-aware
management. In this paper we show how to analyse past traces of dynamic cloud
management actions based on electricity prices and temperatures to estimate VM
availability and price values. We propose a novel SLA specification approach
for offering VMs with different availability and price values guaranteed over
multiple SLAs to enable flexible energy-aware cloud management. We determine
the optimal number of such SLAs as well as their availability and price
guaranteed values. We evaluate our approach in a user SLA selection simulation
using Wikipedia and Grid'5000 workloads. The results show higher customer
conversion and 39% average energy savings per VM.Comment: 14 pages, conferenc
Sensor Search Techniques for Sensing as a Service Architecture for The Internet of Things
The Internet of Things (IoT) is part of the Internet of the future and will
comprise billions of intelligent communicating "things" or Internet Connected
Objects (ICO) which will have sensing, actuating, and data processing
capabilities. Each ICO will have one or more embedded sensors that will capture
potentially enormous amounts of data. The sensors and related data streams can
be clustered physically or virtually, which raises the challenge of searching
and selecting the right sensors for a query in an efficient and effective way.
This paper proposes a context-aware sensor search, selection and ranking model,
called CASSARAM, to address the challenge of efficiently selecting a subset of
relevant sensors out of a large set of sensors with similar functionality and
capabilities. CASSARAM takes into account user preferences and considers a
broad range of sensor characteristics, such as reliability, accuracy, location,
battery life, and many more. The paper highlights the importance of sensor
search, selection and ranking for the IoT, identifies important characteristics
of both sensors and data capture processes, and discusses how semantic and
quantitative reasoning can be combined together. This work also addresses
challenges such as efficient distributed sensor search and
relational-expression based filtering. CASSARAM testing and performance
evaluation results are presented and discussed.Comment: IEEE sensors Journal, 2013. arXiv admin note: text overlap with
arXiv:1303.244
A self-adapting latency/power tradeoff model for replicated search engines
For many search settings, distributed/replicated search engines deploy a large number of machines to ensure efficient retrieval. This paper investigates how the power consumption of a replicated search engine can be automatically reduced when the system has low contention, without compromising its efficiency. We propose a novel self-adapting model to analyse the trade-off between latency and power consumption for distributed search engines. When query volumes are high and there is contention for the resources, the model automatically increases the necessary number of active machines in the system to maintain acceptable query response times. On the other hand, when the load of the system is low and the queries can be served easily, the model is able to reduce the number of active machines, leading to power savings. The model bases its decisions on examining the current and historical query loads of the search engine. Our proposal is formulated as a general dynamic decision problem, which can be quickly solved by dynamic programming in response to changing query loads. Thorough experiments are conducted to validate the usefulness of the proposed adaptive model using historical Web search traffic submitted to a commercial search engine. Our results show that our proposed self-adapting model can achieve an energy saving of 33% while only degrading mean query completion time by 10 ms compared to a baseline that provisions replicas based on a previous day's traffic
Reducing Electricity Demand Charge for Data Centers with Partial Execution
Data centers consume a large amount of energy and incur substantial
electricity cost. In this paper, we study the familiar problem of reducing data
center energy cost with two new perspectives. First, we find, through an
empirical study of contracts from electric utilities powering Google data
centers, that demand charge per kW for the maximum power used is a major
component of the total cost. Second, many services such as Web search tolerate
partial execution of the requests because the response quality is a concave
function of processing time. Data from Microsoft Bing search engine confirms
this observation.
We propose a simple idea of using partial execution to reduce the peak power
demand and energy cost of data centers. We systematically study the problem of
scheduling partial execution with stringent SLAs on response quality. For a
single data center, we derive an optimal algorithm to solve the workload
scheduling problem. In the case of multiple geo-distributed data centers, the
demand of each data center is controlled by the request routing algorithm,
which makes the problem much more involved. We decouple the two aspects, and
develop a distributed optimization algorithm to solve the large-scale request
routing problem. Trace-driven simulations show that partial execution reduces
cost by for one data center, and by for geo-distributed
data centers together with request routing.Comment: 12 page
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