773 research outputs found
Mage: Online Interference-Aware Scheduling in Multi-Scale Heterogeneous Systems
Heterogeneity has grown in popularity both at the core and server level as a
way to improve both performance and energy efficiency. However, despite these
benefits, scheduling applications in heterogeneous machines remains
challenging. Additionally, when these heterogeneous resources accommodate
multiple applications to increase utilization, resources are prone to
contention, destructive interference, and unpredictable performance. Existing
solutions examine heterogeneity either across or within a server, leading to
missed performance and efficiency opportunities. We present Mage, a practical
interference-aware runtime that optimizes performance and efficiency in systems
with intra- and inter-server heterogeneity. Mage leverages fast and online data
mining to quickly explore the space of application placements, and determine
the one that minimizes destructive interference between co-resident
applications. Mage continuously monitors the performance of active
applications, and, upon detecting QoS violations, it determines whether
alternative placements would prove more beneficial, taking into account any
overheads from migration. Across 350 application mixes on a heterogeneous CMP,
Mage improves performance by 38% and up to 2x compared to a greedy scheduler.
Across 160 mixes on a heterogeneous cluster, Mage improves performance by 30%
on average and up to 52% over the greedy scheduler, and by 11% over the
combination of Paragon [15] for inter- and intra-server heterogeneity
Hipster: hybrid task manager for latency-critical cloud workloads
In 2013, U. S. data centers accounted for 2.2% of the country's total electricity consumption, a figure that is projected to increase rapidly over the next decade. Many important workloads are interactive, and they demand strict levels of quality-of-service (QoS) to meet user expectations, making it challenging to reduce power consumption due to increasing performance demands. This paper introduces Hipster, a technique that combines heuristics and reinforcement learning to manage latency-critical workloads. Hipster's goal is to improve resource efficiency in data centers while respecting the QoS of the latency-critical workloads. Hipster achieves its goal by exploring heterogeneous multi-cores and dynamic voltage and frequency scaling (DVFS). To improve data center utilization and make best usage of the available resources, Hipster can dynamically assign remaining cores to batch workloads without violating the QoS constraints for the latency-critical workloads. We perform experiments using a 64-bit ARM big.LITTLE platform, and show that, compared to prior work, Hipster improves the QoS guarantee for Web-Search from 80% to 96%, and for Memcached from 92% to 99%, while reducing the energy consumption by up to 18%.Peer ReviewedPostprint (author's final draft
D-SPACE4Cloud: A Design Tool for Big Data Applications
The last years have seen a steep rise in data generation worldwide, with the
development and widespread adoption of several software projects targeting the
Big Data paradigm. Many companies currently engage in Big Data analytics as
part of their core business activities, nonetheless there are no tools and
techniques to support the design of the underlying hardware configuration
backing such systems. In particular, the focus in this report is set on Cloud
deployed clusters, which represent a cost-effective alternative to on premises
installations. We propose a novel tool implementing a battery of optimization
and prediction techniques integrated so as to efficiently assess several
alternative resource configurations, in order to determine the minimum cost
cluster deployment satisfying QoS constraints. Further, the experimental
campaign conducted on real systems shows the validity and relevance of the
proposed method
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