9,411 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
Mirroring Mobile Phone in the Clouds
This paper presents a framework of Mirroring Mobile Phone in the Clouds (MMPC) to speed up data/computing intensive applications on a mobile phone by taking full advantage of the super computing power of the clouds. An application on the mobile phone is dynamically partitioned in such a way that the heavy-weighted part is always running on a mirrored server in the clouds while the light-weighted part remains on the mobile phone. A performance improvement (an energy consumption reduction of 70% and a speed-up of 15x) is achieved at the cost of the communication overhead between the mobile phone and the clouds (to transfer the application codes and intermediate results) of a desired application. Our original contributions include a dynamic profiler and a dynamic partitioning algorithm compared with traditional approaches of either statically partitioning a mobile application or modifying a mobile application to support the required partitioning
Reproducible Experiment Platform
Data analysis in fundamental sciences nowadays is an essential process that
pushes frontiers of our knowledge and leads to new discoveries. At the same
time we can see that complexity of those analyses increases fast due to
a)~enormous volumes of datasets being analyzed, b)~variety of techniques and
algorithms one have to check inside a single analysis, c)~distributed nature of
research teams that requires special communication media for knowledge and
information exchange between individual researchers. There is a lot of
resemblance between techniques and problems arising in the areas of industrial
information retrieval and particle physics. To address those problems we
propose Reproducible Experiment Platform (REP), a software infrastructure to
support collaborative ecosystem for computational science. It is a Python based
solution for research teams that allows running computational experiments on
shared datasets, obtaining repeatable results, and consistent comparisons of
the obtained results. We present some key features of REP based on case studies
which include trigger optimization and physics analysis studies at the LHCb
experiment.Comment: 21st International Conference on Computing in High Energy Physics
(CHEP2015), 6 page
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