2,601 research outputs found
Reclaiming the energy of a schedule: models and algorithms
We consider a task graph to be executed on a set of processors. We assume
that the mapping is given, say by an ordered list of tasks to execute on each
processor, and we aim at optimizing the energy consumption while enforcing a
prescribed bound on the execution time. While it is not possible to change the
allocation of a task, it is possible to change its speed. Rather than using a
local approach such as backfilling, we consider the problem as a whole and
study the impact of several speed variation models on its complexity. For
continuous speeds, we give a closed-form formula for trees and series-parallel
graphs, and we cast the problem into a geometric programming problem for
general directed acyclic graphs. We show that the classical dynamic voltage and
frequency scaling (DVFS) model with discrete modes leads to a NP-complete
problem, even if the modes are regularly distributed (an important particular
case in practice, which we analyze as the incremental model). On the contrary,
the VDD-hopping model leads to a polynomial solution. Finally, we provide an
approximation algorithm for the incremental model, which we extend for the
general DVFS model.Comment: A two-page extended abstract of this work appeared as a short
presentation in SPAA'2011, while the long version has been accepted for
publication in "Concurrency and Computation: Practice and Experience
Dynamic and Leakage Power-Composition Profile Driven Co-Synthesis for Energy and Cost Reduction
Recent research has shown that combining dynamic voltage scaling (DVS) and adaptive body bias (ABB) techniques achieve the highest reduction in embedded systems energy dissipation [1]. In this paper we show that it is possible to produce comparable energy saving to that obtained using combined DVS and ABB techniques but with reduced hardware cost achieved by employing processing elements (PEs) with separate DVS or ABB capability. A co-synthesis methodology which is aware of tasks’ power-composition profile (the ratio of the dynamic power to the leakage power) is presented. The methodology selects voltage scaling capabilities (DVS, ABB, or combined DVS and ABB) for the PEs, maps, schedules, and voltage scales applications given as task graphs with timing constraints, aiming to dynamic and leakage energy reduction at low hardware cost. We conduct detailed experiments, including a real-life example, to demonstrate the effectiveness of our methodology. We demonstrate that it is possible to produce designs that contain PEs with only DVS or ABB technique but have energy dissipation that are only 4.4% higher when compared with the same designs that employ PEs with combined DVS and ABB capabilities
Approximation Algorithms for Energy Minimization in Cloud Service Allocation under Reliability Constraints
We consider allocation problems that arise in the context of service
allocation in Clouds. More specifically, we assume on the one part that each
computing resource is associated to a capacity constraint, that can be chosen
using Dynamic Voltage and Frequency Scaling (DVFS) method, and to a probability
of failure. On the other hand, we assume that the service runs as a set of
independent instances of identical Virtual Machines. Moreover, there exists a
Service Level Agreement (SLA) between the Cloud provider and the client that
can be expressed as follows: the client comes with a minimal number of service
instances which must be alive at the end of the day, and the Cloud provider
offers a list of pairs (price,compensation), this compensation being paid by
the Cloud provider if it fails to keep alive the required number of services.
On the Cloud provider side, each pair corresponds actually to a guaranteed
success probability of fulfilling the constraint on the minimal number of
instances. In this context, given a minimal number of instances and a
probability of success, the question for the Cloud provider is to find the
number of necessary resources, their clock frequency and an allocation of the
instances (possibly using replication) onto machines. This solution should
satisfy all types of constraints during a given time period while minimizing
the energy consumption of used resources. We consider two energy consumption
models based on DVFS techniques, where the clock frequency of physical
resources can be changed. For each allocation problem and each energy model, we
prove deterministic approximation ratios on the consumed energy for algorithms
that provide guaranteed probability failures, as well as an efficient
heuristic, whose energy ratio is not guaranteed
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