4,728 research outputs found
AN EVALUATION OF CPU EFFICIENCY UNDER DYNAMIC QUANTUM ALLOCATION
A model for a time-sharing operating system is developed in order to assess the effects of dynamic quantum allocation and overhead variability on central processing unit (CPU) efficiency. CPU efficiency is determined by the proportion of time devoted to user-oriented (problem state) tasks within a busy period. Computational results indicate that a dynamic quantum allocation strategy produces significant differences in CPU efficiency compared to a constant quantum. The differences are affected significantly
by the variability among allocated quantum values and the demand on the system. Overhead variability also has a pronounced effect. A function that depicts overhead as decreasing with demand produces more stable values of CPU efficiency. The interaction between demand and the amount of overhead is observed to be significant
DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS
Grid, an infrastructure for resource sharing, currently has shown its importance in
many scientific applications requiring tremendously high computational power. Grid
computing enables sharing, selection and aggregation of resources for solving
complex and large-scale scientific problems. Grids computing, whose resources are
distributed, heterogeneous and dynamic in nature, introduces a number of fascinating
issues in resource management. Grid scheduling is the key issue in grid environment
in which its system must meet the functional requirements of heterogeneous domains,
which are sometimes conflicting in nature also, like user, application, and network.
Moreover, the system must satisfy non-functional requirements like reliability,
efficiency, performance, effective resource utilization, and scalability. Thus, overall
aim of this research is to introduce new grid scheduling algorithms for resource
allocation as well as for job scheduling for enabling a highly efficient and effective
utilization of the resources in executing various applications.
The four prime aspects of this work are: firstly, a model of the grid scheduling
problem for dynamic grid computing environment; secondly, development of a new
web based simulator (SyedWSim), enabling the grid users to conduct a statistical
analysis of grid workload traces and provides a realistic basis for experimentation in
resource allocation and job scheduling algorithms on a grid; thirdly, proposal of a new
grid resource allocation method of optimal computational cost using synthetic and
real workload traces with respect to other allocation methods; and finally, proposal of
some new job scheduling algorithms of optimal performance considering parameters
like waiting time, turnaround time, response time, bounded slowdown, completion
time and stretch time. The issue is not only to develop new algorithms, but also to
evaluate them on an experimental computational grid, using synthetic and real
workload traces, along with the other existing job scheduling algorithms.
Experimental evaluation confirmed that the proposed grid scheduling algorithms
possess a high degree of optimality in performance, efficiency and scalability
Quantum Monte Carlo for large chemical systems: Implementing efficient strategies for petascale platforms and beyond
Various strategies to implement efficiently QMC simulations for large
chemical systems are presented. These include: i.) the introduction of an
efficient algorithm to calculate the computationally expensive Slater matrices.
This novel scheme is based on the use of the highly localized character of
atomic Gaussian basis functions (not the molecular orbitals as usually done),
ii.) the possibility of keeping the memory footprint minimal, iii.) the
important enhancement of single-core performance when efficient optimization
tools are employed, and iv.) the definition of a universal, dynamic,
fault-tolerant, and load-balanced computational framework adapted to all kinds
of computational platforms (massively parallel machines, clusters, or
distributed grids). These strategies have been implemented in the QMC=Chem code
developed at Toulouse and illustrated with numerical applications on small
peptides of increasing sizes (158, 434, 1056 and 1731 electrons). Using 10k-80k
computing cores of the Curie machine (GENCI-TGCC-CEA, France) QMC=Chem has been
shown to be capable of running at the petascale level, thus demonstrating that
for this machine a large part of the peak performance can be achieved.
Implementation of large-scale QMC simulations for future exascale platforms
with a comparable level of efficiency is expected to be feasible
Karma: Resource Allocation for Dynamic Demands
The classical max-min fairness algorithm for resource allocation provides
many desirable properties, e.g., Pareto efficiency, strategy-proofness and
fairness. This paper builds upon the observation that max-min fairness
guarantees these properties under a strong assumption -- user demands being
static over time -- and that, for the realistic case of dynamic user demands,
max-min fairness loses one or more of these properties.
We present Karma, a generalization of max-min fairness for dynamic user
demands. The key insight in Karma is to introduce "memory" into max-min
fairness -- when allocating resources, Karma takes users' past allocations into
account: in each quantum, users donate their unused resources and are assigned
credits when other users borrow these resources; Karma carefully orchestrates
exchange of credits across users (based on their instantaneous demands, donated
resources and borrowed resources), and performs prioritized resource allocation
based on users' credits. We prove theoretically that Karma guarantees Pareto
efficiency, online strategy-proofness, and optimal fairness for dynamic user
demands (without future knowledge of user demands). Empirical evaluations over
production workloads show that these properties translate well into practice:
Karma is able to reduce disparity in performance across users to a bare minimum
while maintaining Pareto-optimal system-wide performance.Comment: Accepted for publication in USENIX OSDI 202
Computing server power modeling in a data center: survey,taxonomy and performance evaluation
Data centers are large scale, energy-hungry infrastructure serving the
increasing computational demands as the world is becoming more connected in
smart cities. The emergence of advanced technologies such as cloud-based
services, internet of things (IoT) and big data analytics has augmented the
growth of global data centers, leading to high energy consumption. This upsurge
in energy consumption of the data centers not only incurs the issue of surging
high cost (operational and maintenance) but also has an adverse effect on the
environment. Dynamic power management in a data center environment requires the
cognizance of the correlation between the system and hardware level performance
counters and the power consumption. Power consumption modeling exhibits this
correlation and is crucial in designing energy-efficient optimization
strategies based on resource utilization. Several works in power modeling are
proposed and used in the literature. However, these power models have been
evaluated using different benchmarking applications, power measurement
techniques and error calculation formula on different machines. In this work,
we present a taxonomy and evaluation of 24 software-based power models using a
unified environment, benchmarking applications, power measurement technique and
error formula, with the aim of achieving an objective comparison. We use
different servers architectures to assess the impact of heterogeneity on the
models' comparison. The performance analysis of these models is elaborated in
the paper
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