7,343 research outputs found
Managing Uncertainty: A Case for Probabilistic Grid Scheduling
The Grid technology is evolving into a global, service-orientated
architecture, a universal platform for delivering future high demand
computational services. Strong adoption of the Grid and the utility computing
concept is leading to an increasing number of Grid installations running a wide
range of applications of different size and complexity. In this paper we
address the problem of elivering deadline/economy based scheduling in a
heterogeneous application environment using statistical properties of job
historical executions and its associated meta-data. This approach is motivated
by a study of six-month computational load generated by Grid applications in a
multi-purpose Grid cluster serving a community of twenty e-Science projects.
The observed job statistics, resource utilisation and user behaviour is
discussed in the context of management approaches and models most suitable for
supporting a probabilistic and autonomous scheduling architecture
SLO-aware Colocation of Data Center Tasks Based on Instantaneous Processor Requirements
In a cloud data center, a single physical machine simultaneously executes
dozens of highly heterogeneous tasks. Such colocation results in more efficient
utilization of machines, but, when tasks' requirements exceed available
resources, some of the tasks might be throttled down or preempted. We analyze
version 2.1 of the Google cluster trace that shows short-term (1 second) task
CPU usage. Contrary to the assumptions taken by many theoretical studies, we
demonstrate that the empirical distributions do not follow any single
distribution. However, high percentiles of the total processor usage (summed
over at least 10 tasks) can be reasonably estimated by the Gaussian
distribution. We use this result for a probabilistic fit test, called the
Gaussian Percentile Approximation (GPA), for standard bin-packing algorithms.
To check whether a new task will fit into a machine, GPA checks whether the
resulting distribution's percentile corresponding to the requested service
level objective, SLO is still below the machine's capacity. In our simulation
experiments, GPA resulted in colocations exceeding the machines' capacity with
a frequency similar to the requested SLO.Comment: Author's version of a paper published in ACM SoCC'1
Energy-Aware Cloud Management through Progressive SLA Specification
Novel energy-aware cloud management methods dynamically reallocate
computation across geographically distributed data centers to leverage regional
electricity price and temperature differences. As a result, a managed VM may
suffer occasional downtimes. Current cloud providers only offer high
availability VMs, without enough flexibility to apply such energy-aware
management. In this paper we show how to analyse past traces of dynamic cloud
management actions based on electricity prices and temperatures to estimate VM
availability and price values. We propose a novel SLA specification approach
for offering VMs with different availability and price values guaranteed over
multiple SLAs to enable flexible energy-aware cloud management. We determine
the optimal number of such SLAs as well as their availability and price
guaranteed values. We evaluate our approach in a user SLA selection simulation
using Wikipedia and Grid'5000 workloads. The results show higher customer
conversion and 39% average energy savings per VM.Comment: 14 pages, conferenc
A Novel Beamformed Control Channel Design for LTE with Full Dimension-MIMO
The Full Dimension-MIMO (FD-MIMO) technology is capable of achieving huge
improvements in network throughput with simultaneous connectivity of a large
number of mobile wireless devices, unmanned aerial vehicles, and the Internet
of Things (IoT). In FD-MIMO, with a large number of antennae at the base
station and the ability to perform beamforming, the capacity of the physical
downlink shared channel (PDSCH) has increased a lot. However, the current
specifications of the 3rd Generation Partnership Project (3GPP) does not allow
the base station to perform beamforming techniques for the physical downlink
control channel (PDCCH), and hence, PDCCH has neither the capacity nor the
coverage of PDSCH. Therefore, PDCCH capacity will still limit the performance
of a network as it dictates the number of users that can be scheduled at a
given time instant. In Release 11, 3GPP introduced enhanced PDCCH (EPDCCH) to
increase the PDCCH capacity at the cost of sacrificing the PDSCH resources. The
problem of enhancing the PDCCH capacity within the available control channel
resources has not been addressed yet in the literature. Hence, in this paper,
we propose a novel beamformed PDCCH (BF-PDCCH) design which is aligned to the
3GPP specifications and requires simple software changes at the base station.
We rely on the sounding reference signals transmitted in the uplink to decide
the best beam for a user and ingeniously schedule the users in PDCCH. We
perform system level simulations to evaluate the performance of the proposed
design and show that the proposed BF-PDCCH achieves larger network throughput
when compared with the current state of art algorithms, PDCCH and EPDCCH
schemes
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