As the speed of integrated circuits increases, so does their power consumption.
Most of this power is turned into heat, which must be dissipated effectively in order
for the circuit to avoid thermal damage. Thermal control therefore has emerged as an
important issue in design and management of circuits and systems. Dynamic speed
scaling, where the input power is temporarily reduced by appropriately slowing down
the circuit, is one of the major techniques to manage power so as to maintain safe
temperature levels.
In this study, we focus on thermally-constrained hard real-time systems, where
timing guarantees must be met without exceeding safe temperature levels within the
microprocessor. Speed scaling mechanisms provided in many of today’s processors
provide opportunities to temporarily increase the processor speed beyond levels that
would be safe over extended time periods. This dissertation addresses the problem
of safely controlling the processor speed when scheduling mixed workloads with both
hard-real-time periodic tasks and non-real-time, but latency-sensitive, aperiodic jobs.
We first introduce the Transient Overclocking Server, which safely reduces the
response time of aperiodic jobs in the presence of hard real-time periodic tasks and
thermal constraints. We then propose a design-time (off-line) execution-budget allocation
scheme for the application of the Transient Overclocking Server. We show
that there is an optimal budget allocation which depends on the temporal character istics of the aperiodic workload. In order to provide a quantitative framework for the
allocation of budget during system design, we present a queuing model and validate
the model with results from a discrete-event simulator.
Next, we describe an on-line thermally-aware transient overclocking method to
reduce the response time of aperiodic jobs efficiently at run-time. We describe a modified
Slack-Stealing algorithm to consider the thermal constraints of systems together
with the deadline constraints of periodic tasks. With the thermal model and temperature
data provided by embedded thermal sensors, we compute slack for aperiodic
workload at run-time that satisfies both thermal and temporal constraints. We show
that the proposed Thermally-Aware Slack-Stealing algorithm minimizes the response
times of aperiodic jobs while guaranteeing both the thermal safety of the system and
the schedulability of the real-time tasks. The two proposed speed control algorithms
are examples of so-called proactive schemes, since they rely on a prediction of the
thermal trajectory to control the temperature before safe levels are exceeded.
In practice, the effectiveness of proactive speed control for the thermal management
of a system relies on the accuracy of the thermal model that underlies the
prediction of the effects of speed scaling and task execution on the temperature of
the processor. Due to variances in the manufacturing of the circuit and of the environment
it is to operate, an accurate thermal model can be gathered at deployment
time only. The absence of power data makes a straightforward derivation of a model
impossible.
We, therefore, study and describe a methodology to infer efficiently the thermal
model based on the monitoring of system temperatures and number of instructions
used for task executions.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-08-8578 |
| Date | 2010 August 1900 |
| Creators | Ahn, Youngwoo |
| Contributors | Annapareddy, Narasimha, Bettati, Riccardo |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | application/pdf |
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