13,046 research outputs found
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
Paradigms for computational nucleic acid design
The design of DNA and RNA sequences is critical for many endeavors, from DNA nanotechnology, to PCR‐based applications, to DNA hybridization arrays. Results in the literature rely on a wide variety of design criteria adapted to the particular requirements of each application. Using an extensively studied thermodynamic model, we perform a detailed study of several criteria for designing sequences intended to adopt a target secondary structure. We conclude that superior design methods should explicitly implement both a positive design paradigm (optimize affinity for the target structure) and a negative design paradigm (optimize specificity for the target structure). The commonly used approaches of sequence symmetry minimization and minimum free‐energy satisfaction primarily implement negative design and can be strengthened by introducing a positive design component. Surprisingly, our findings hold for a wide range of secondary structures and are robust to modest perturbation of the thermodynamic parameters used for evaluating sequence quality, suggesting the feasibility and ongoing utility of a unified approach to nucleic acid design as parameter sets are refined further. Finally, we observe that designing for thermodynamic stability does not determine folding kinetics, emphasizing the opportunity for extending design criteria to target kinetic features of the energy landscape
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