Browsing by Subject "Performance prediction"
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Item Open Access Microeconomic Models for Managing Shared Datacenters(2017) Llull, QiuyunAs demands for users’ applications’ data increase, the world’s computing platforms are moving towards more capable machines – servers and warehouse-scale datacenters. Diverse users share datacenters for complex computation and compete for shared resources. In some systems, such as public clouds where users pay for reserved hardware, management policies pursue performance goals. In contrast, private systems consist of users who voluntarily combine their resources and subscribe to a common management policy. These users reserve the right to opt-out from shared systems if resources are managed poorly. The system management framework needs to ensure fairness among strategic users, encouraging users to participate while guaranteeing individual performance and preserving the system’s performance. Microeconomic models are well suited for studying individual behavior and the allocation of scarce resources. In this thesis, we present three pieces of work on task colocation, resource allocation, and task scheduling problems to demonstrate the effectiveness of a microeconomic approach.
Colocating applications on shared hardware (i.e., chip-multiprocessors) improves server utilization but introduces resource contention into the memory subsystem. In the first work, we design a colocation framework based on cooperative game theory to manage shared resource contention. Our framework uses a recommendation system to predict individual applications preferences for colocated tasks. It then uses these predictions to drive novel colocation mechanisms to guarantee user fairness and preserve system performance. Attractive system outcomes encourage strategic users to participate in the datacenter.
Processor allocations are inefficient when they are based on static reservations because reservations are often conservative; users rarely know their application’s needs across time, especially when applications have complex phase behavior. In the second work, we propose a fast, lightweight performance prediction framework to help users capture their phase behaviors in parallel applications. We design a dynamic and distributed core allocation framework so that users can trade resources for better efficiency based on predicted performance. Our management framework provides efficient allocations and game-theoretic fairness guarantees. In the last work, we characterize applications’ sensitivity to non-uniform memory access (NUMA) in big memory servers. We develop performance and energy models for communication costs in a blade server. We use this model to perform case studies on NUMA-aware scheduling policies and task queue management. Our parameterized models lay the foundation for the coordinated design of scheduling policies and hardware configurations. This method can be further used to design locality-aware schedulers with microeconomic models, e.g., dynamic pricing strategies for city parking.
Item Open Access Predicting Application Performance in the Cloud(2011) Zong, XuanranDespite the exceptional prominence of the cloud computing, the customers are
lack of direct sense to select the cloud that delivers the best performance,
due to the performance heterogeneity of each cloud provider. Existing solutions
either migrate the application to each cloud and evaluate the performance
individually, or benchmark each cloud along various dimensions and predict the
overall performance of the application. However, the former incurs significant
migration and configuration overhead, while the latter may suffer from coarse
prediction accuracy.
This thesis introduces two systems to address this issue. CloudProphet predicts the web
application performance by tracing and replaying the on-premise resource demand
on the cloud machines. DTRCP further predicts the performance for general
applications. In particular, it addresses the execution path divergence
manifested during replaying the on-premise resource demand. Our experiment
results show that both systems can accurately predict the application
performance.
Item Open Access Towards Systematic and Accurate Environment Selection for Emerging Cloud Applications(2012) Li, AngAs cloud computing is gaining popularity, many application owners are migrating their
applications into the cloud. However, because of the diversity of the cloud environments
and the complexity of the modern applications, it is very challenging to find out which
cloud environment is best fitted for one's application.
In this dissertation, we design and build systems to help application owners select the
most suitable cloud environments for their applications. The first part of this thesis focuses
on how to compare the general fitness of the cloud environments. We present CloudCmp,
a novel comparator of public cloud providers. CloudCmp measures the elastic computing,
persistent storage, and networking services offered by a cloud along metrics that directly
reflect their impact on the performance of customer applications. CloudCmp strives to
ensure fairness, representativeness, and compliance of these measurements while limiting
measurement cost. Applying CloudCmp to four cloud providers that together account
for most of the cloud customers today, we find that their offered services vary widely in
performance and costs, underscoring the need for thoughtful cloud environment selection.
From case studies on three representative cloud applications, we show that CloudCmp can
guide customers in selecting the best-performing provider for their applications.
The second part focuses on how to let customers compare cloud environments in the
context of their own applications. We describe CloudProphet, a novel system that can
accurately estimate an application's performance inside a candidate cloud environment
without the need of migration. CloudProphet generates highly portable shadow programs
to mimic the behavior of a real application, and deploys them inside the cloud to estimate
the application's performance. We use the trace-and-replay technique to automatically
generate high-fidelity shadows, and leverage the popular dispatcher-worker pattern
to accurately extract and enforce the inter-component dependencies. Our evaluation in
three popular cloud platforms shows that CloudProphet can help customers pick the bestperforming
cloud environment, and can also accurately estimate the performance of a
variety of applications.