77 research outputs found
From Dataflow Specification to Multiprocessor Partitioned Time-triggered Real-time Implementation *
International audienceOur objective is to facilitate the development of complex time-triggered systems by automating the allocation and scheduling steps. We show that full automation is possible while taking into account the elements of complexity needed by a complex embedded control system. More precisely, we consider deterministic functional specifications provided (as often in an industrial setting) by means of synchronous data-flow models with multiple modes and multiple relative periods. We first extend this functional model with an original real-time characterization that takes advantage of our time-triggered framework to provide a simpler representation of complex end-to-end flow requirements. We also extend our specifications with additional non-functional properties specifying partitioning, allocation , and preemptability constraints. Then, weprovide novel algorithms for the off-line scheduling of these extended specifications onto partitioned time-triggered architectures Ă la ARINC 653. The main originality of our work is that it takes into account at the same time multiple complexity elements: various types of non-functional properties (real-time, partitioning, allocation, preemptability) and functional specifications with conditional execution and multiple modes. Allocation of time slots/windows to partitions can be fullyor partially provided, or synthesized by our tool. Our algorithms allow the automatic allocation and scheduling onto multi-processor (distributed) sys-tems with a global time base, taking into account communication costs. We demonstrate our technique on a model of space flight software systemwith strong real-time determinism requirements
A Survey of Microarchitectural Timing Attacks and Countermeasures on Contemporary Hardware
Microarchitectural timing channels expose hidden hardware states though timing. We survey recent attacks that exploit microarchitectural features in shared hardware, especially as they are relevant for cloud computing. We classify types of attacks according to a taxonomy of the shared resources leveraged for such attacks. Moreover, we take a detailed look at attacks used against shared caches. We survey existing countermeasures. We finally discuss trends in attacks, challenges to combating them, and future directions, especially with respect to hardware support
Model-Based Design, Analysis, and Implementations for Power and Energy-Efficient Computing Systems
Modern computing systems are becoming increasingly complex. On one end of
the spectrum, personal computers now commonly support multiple processing
cores, and, on the other end, Internet services routinely employ thousands of
servers in distributed locations to provide the desired service to its users. In
such complex systems, concerns about energy usage and power consumption
are increasingly important. Moreover, growing awareness of environmental
issues has added to the overall complexity by introducing new variables to the
problem. In this regard, the ability to abstractly focus on the relevant details
allows model-based design to help significantly in the analysis and solution of
such problems.
In this dissertation, we explore and analyze model-based design for energy
and power considerations in computing systems. Although the presented techniques
are more generally applicable, we focus their application on large-scale
Internet services operating in U.S. electricity markets. Internet services are becoming
increasingly popular in the ICT ecosystem of today. The physical infrastructure
to support such services is commonly based on a group of cooperative
data centers (DCs) operating in tandem. These DCs are geographically
distributed to provide security and timing guarantees for their customers. To
provide services to millions of customers, DCs employ hundreds of thousands
of servers. These servers consume a large amount of energy that is traditionally
produced by burning coal and employing other environmentally hazardous
methods, such as nuclear and gas power generation plants. This large energy
consumption results in significant and fast-growing financial and environmental
costs. Consequently, for protection of local and global environments, governing
bodies around the globe have begun to introduce legislation to encourage
energy consumers, especially corporate entities, to increase the share of
renewable energy (green energy) in their total energy consumption. However,
in U.S. electricity markets, green energy is usually more expensive than energy
generated from traditional sources like coal or petroleum.
We model the overall problem in three sub-areas and explore different approaches
aimed at reducing the environmental foot print and operating costs
of multi-site Internet services, while honoring the Quality of Service (QoS) constraints
as contracted in service level agreements (SLAs).
Firstly, we model the load distribution among member DCs of a multi-site Internet
service. The use of green energy is optimized considering different factors
such as (a) geographically and temporally variable electricity prices, (b)
the multitude of available energy sources to choose from at each DC, (c) the necessity
to support more than one SLA, and, (d) the requirements to offer more
than one service at each DC. Various approaches are presented for solving this
problem and extensive simulations using Google’s setup in North America are
used to evaluate the presented approaches.
Secondly, we explore the area of shaving the peaks in the energy demand of
large electricity consumers, such as DCs by using a battery-based energy storage
system. Electrical demand of DCs is typically peaky based on the usage
cycle of their customers. Resultant peaks in the electrical demand require development
and maintenance of a costlier energy delivery mechanism, and are
often met using expensive gas or diesel generators which often have a higher
environmental impact. To shave the peak power demand, a battery can be used
which is charged during low load and is discharged during the peak loads.
Since the batteries are costly, we present a scheme to estimate the size of battery
required for any variable electrical load. The electrical load is modeled using
the concept of arrival curves from Network Calculus. Our analysis mechanism
can help determine the appropriate battery size for a given load arrival curve
to reduce the peak.
Thirdly, we present techniques to employ intra-DC scheduling to regulate the
peak power usage of each DC. The model we develop is equally applicable to
an individual server with multi-/many-core chips as well as a complete DC
with an intermix of homogeneous and heterogeneous servers. We evaluate
these approaches on single-core and multi-core chip processors and present the
results.
Overall, our work demonstrates the value of model-based design for intelligent
load distribution across DCs, storage integration, and per DC optimizations
for efficient energy management to reduce operating costs and environmental
footprint for multi-site Internet services
Dynamic Voltage Scaling for Energy- Constrained Real-Time Systems
The problem of reducing energy consumption is dominating the design of several real-time systems.
The Dynamic Voltage Scaling (DVS) technique, provided by most microprocessors, allow to balance
computational speed versus energy consumption.
We present some novel energy-aware scheduling algorithms that allow to expoit this technique while
meeting real-time constraints. In particular, we present the GRUB-PA algorithm which, unlike most
existing algorithms, allows to reduce energy consumption on real-time systems consisting of any kind of task.
We also present a working implementation of the algorithm on Linux
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On thermal sensor calibration and software techniques for many-core thermal management
The high power density of a many-core processor results in increased temperature which negatively impacts system reliability and performance. Dynamic thermal management applies thermal-aware techniques at run time to avoid overheating using temperature information collected from on-chip thermal sensors. Temperature sensing and thermal control schemes are two critical technologies for successfully maintaining thermal safety. In this dissertation, on-line thermal sensor calibration schemes are developed to provide accurate temperature information.
Software-based dynamic thermal management techniques are proposed using calibrated thermal sensors. Due to process variation and silicon aging, on-chip thermal sensors require periodic calibration before use in DTM. However, the calibration cost for thermal sensors can be prohibitively high as the number of on-chip sensors increases. Linear models which are suitable for on-line calculation are employed to estimate temperatures at multiple sensor locations using performance counters. The estimated temperature and the actual sensor thermal profile show a very high similarity with correlation coefficient ~0.9 for SPLASH2 and SPEC2000 benchmarks.
A calibration approach is proposed to combine potentially inaccurate temperature values obtained from two sources: thermal sensor readings and temperature estimations. A data fusion strategy based on Bayesian inference, which combines information from these two sources, is demonstrated. The result shows the strategy can effectively recalibrate sensor readings in response to inaccuracies caused by process variation and environmental noise. The average absolute error of the corrected sensor temperature readings is
A dynamic task allocation strategy is proposed to address localized overheating in many-core systems. Our approach employs reinforcement learning, a dynamic machine learning algorithm that performs task allocation based on current temperatures and a prediction regarding which assignment will minimize the peak temperature. Our results show that the proposed technique is fast (scheduling performed in \u3c1 \u3ems) and can efficiently reduce peak temperature by up to 8 degree C in a 49-core processor (6% on average) versus a leading competing task allocation approach for a series of SPLASH-2 benchmarks. Reinforcement learning has also been applied to 3D integrated circuits to allocate tasks with thermal awareness
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