22,577 research outputs found
On data skewness, stragglers, and MapReduce progress indicators
We tackle the problem of predicting the performance of MapReduce
applications, designing accurate progress indicators that keep programmers
informed on the percentage of completed computation time during the execution
of a job. Through extensive experiments, we show that state-of-the-art progress
indicators (including the one provided by Hadoop) can be seriously harmed by
data skewness, load unbalancing, and straggling tasks. This is mainly due to
their implicit assumption that the running time depends linearly on the input
size. We thus design a novel profile-guided progress indicator, called
NearestFit, that operates without the linear hypothesis assumption and exploits
a careful combination of nearest neighbor regression and statistical curve
fitting techniques. Our theoretical progress model requires fine-grained
profile data, that can be very difficult to manage in practice. To overcome
this issue, we resort to computing accurate approximations for some of the
quantities used in our model through space- and time-efficient data streaming
algorithms. We implemented NearestFit on top of Hadoop 2.6.0. An extensive
empirical assessment over the Amazon EC2 platform on a variety of real-world
benchmarks shows that NearestFit is practical w.r.t. space and time overheads
and that its accuracy is generally very good, even in scenarios where
competitors incur non-negligible errors and wide prediction fluctuations.
Overall, NearestFit significantly improves the current state-of-art on progress
analysis for MapReduce
Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach
Many algorithms in workflow scheduling and resource provisioning rely on the
performance estimation of tasks to produce a scheduling plan. A profiler that
is capable of modeling the execution of tasks and predicting their runtime
accurately, therefore, becomes an essential part of any Workflow Management
System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS)
platforms that use clouds for deploying scientific workflows, task runtime
prediction becomes more challenging because it requires the processing of a
significant amount of data in a near real-time scenario while dealing with the
performance variability of cloud resources. Hence, relying on methods such as
profiling tasks' execution data using basic statistical description (e.g.,
mean, standard deviation) or batch offline regression techniques to estimate
the runtime may not be suitable for such environments. In this paper, we
propose an online incremental learning approach to predict the runtime of tasks
in scientific workflows in clouds. To improve the performance of the
predictions, we harness fine-grained resources monitoring data in the form of
time-series records of CPU utilization, memory usage, and I/O activities that
are reflecting the unique characteristics of a task's execution. We compare our
solution to a state-of-the-art approach that exploits the resources monitoring
data based on regression machine learning technique. From our experiments, the
proposed strategy improves the performance, in terms of the error, up to
29.89%, compared to the state-of-the-art solutions.Comment: Accepted for presentation at main conference track of 11th IEEE/ACM
International Conference on Utility and Cloud Computin
Synapse: Synthetic Application Profiler and Emulator
We introduce Synapse motivated by the needs to estimate and emulate workload
execution characteristics on high-performance and distributed heterogeneous
resources. Synapse has a platform independent application profiler, and the
ability to emulate profiled workloads on a variety of heterogeneous resources.
Synapse is used as a proxy application (or "representative application") for
real workloads, with the added advantage that it can be tuned at arbitrary
levels of granularity in ways that are simply not possible using real
applications. Experiments show that automated profiling using Synapse
represents application characteristics with high fidelity. Emulation using
Synapse can reproduce the application behavior in the original runtime
environment, as well as reproducing properties when used in a different
run-time environments
Resource and Application Models for Advanced Grid Schedulers
As Grid computing is becoming an inevitable future, managing, scheduling and monitoring dynamic, heterogeneous resources will present new challenges. Solutions will have to be agile and adaptive, support self-organization and autonomous management, while maintaining optimal resource utilisation. Presented in this paper are basic principles and architectural concepts for efficient resource allocation in heterogeneous Grid environment
- …