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Ningfang Mi

Assistant Professor
Department of Electrical and Computer Engineering
Northeastern University
Office: 302 Dana Research Center
Phone: (617)373-3028
Fax: (617)373-8970
Email:

I joined Department of Electrical and Computer Engineering at Northeastern University as an Assistant Professor in Fall 2009. I am looking for self-motivated PhD or Master's students to work in the area of capacity planning, resource management, energy/power management, performance evaluation, system modeling, simulation, virtulization, and cloud computing. Contact me if you are interested.


Research Interests
Storage systems, multi-tier systems, virtulized systems, performance evaluation, capacity planning, resource management, energy/power management, cloud computing, simulation, system modeling, and web characterization.

Education

Grants and Awards
  • 2011 The IBM Faculty Award ($20,000)
  • 2010 The AWS (Amazon Web Services) in Education Research Grant ($7500)
  • 2010 The Best Student Paper Award at the 22nd International Teletraffic Congres (ITC-22) for the paper titled "Fastrack for Taming Burstiness and Saving Power in Multi-Tiered Systems"
  • 2009 The Computer Management Group (CMG) Graduate Fellowship
  • 2008 The Best Paper Award at the ACM/IFIP/USENIX 9th International Middleware Conference for the paper titled "Burstiness in Multi-Tier Applications: Symptoms, Causes, and New Models"
Curriculum Vitae

[ps] [pdf] (updated by May, 2011)


Publications

Invited Publications

  1. Giuliano Casale, Ningfang Mi, and Evgenia Smirni, "Versatile Models of Systems Using MAP Queueing Networks", in the IEEE International Parallel and Distributed Processing Symposium (IPDPS), Next Generation Software (NGS) Workshop, 2008.
  2. Evgenia Smirni, Qi Zhang, Ningfang Mi, Alma Riska, and Giuliano Casale, " New Results on the Performance Effects of Autocorrelated Flows in Systems ", in the IEEE International Parallel and Distributed Processing Symposium (IPDPS), Next Generation Software (NGS) Workshop, Long Beach, CA, pp. 1-6, 2007.

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Research Projects
  • ARA: Adaptive Resource Allocation for Cloud Computing Environment

    Cloud computing platforms provide mechanisms that allow multiple instances of an application to run simultaneously across the cloud which could be aggressively aggregated together as the pattern of burst. We argue that the presence of burstiness can cause load unbalancing in clouds and consequently degrade the overall system performance. Motivated by this problem, we are developing novel methodologies for resource allocation in cloud systems, which attempt to counteract the deleterious effect of burstiness, improving overall system performance and availability and maintaining the cloud usersa€? Service Level Agreement (SLA)We expect that our new resource allocation methodologies are suitable for cloud systems (e.g., Amazon EC2) which attempt to reduce the effect of burstiness on system performance and optimize resource utilizations.

    This work is currently supported by AWS (Amazon Web Services) in Education Research Grant and has been presented in ICC'11. Project Link.

  • Impact of Autocorrelation in Multi-tiered Systems

    Multi-tiered systems, a prevalent architecture of today's web sites, is an example of closed systems because the hardware imposes a limit on the number of simultaneous connections. In collaboration with researchers at Seagate Research, we have built an e-commerce server according to the TPC-W benchmark to identify the presence of autocorrelation in different tiers of the system. We observed that autocorrelated flows severely degrade overall system performance even under medium loads. It implies that if autocorrelation is ignored, the throughput and utilization of specific devices - metrics often used in capacity planning and admission control - may give a distorted view of system performance.

    This work has been presented in Performance'07.

  • General Autocorrelation-driven Scheduling Policies

    Temporal dependence in workloads creates conditions in which a server, in order to remain available, should quickly process bursts of requests with large service requirements. We have studied on how to counteract the resulting peak congestion and maintain high availability by delaying/dropping selected requests that contribute to temporal locality. SWAP was proposed to approximate the shortest job first (SJF) scheduling without requiring any knowledge of job service times. We also designed ALoC, an autocorrelation-driven load control policy, that drops a percentage of the requests in order to meet pre-defined quality-of-service levels. To the best of our knowledge, this is the first direct application of autocorrelation of service times to autonomic load control.

    This work has been presented in DSN'08.

  • Background Job Scheduling Methodologies in Storage Systems

    Background activities are scheduled with low priority and served during system idle times. We presented that idle waiting, i.e., delay scheduling of a background job, is insufficient as a ``standalone'' technique to manage the trade-off between the performance of foreground and background tasks. We complemented ``idle-waiting'' with the ``estimation'' of background work to be served in every idle interval and then proposed a methodology that determines the schedulability of background work in storage systems, i.e., when and for how long idle times can be used for serving background tasks. An extensive set of trace-driven simulation experiments and measurements in a prototype on the Linux 2.6.22 kernel, show that the new approach meets the performance targets by finding a solution that is among the best. Finally, the effectiveness of two known as background activities, namely scrubbing and intra-disk data redundancy, is evaluated to detect and/or recover from latent sector errors.

    This work has been presented in TOS, DSN'08.

  • New Capacity Planning Models for Autocorrelated Workloads

    Building effective models of complex enterprise systems are central to capacity planning and resource provisioning. We found that if autocorrelated flows exist in the system, then classic queueing theory models such as MVA give wrong predictions. Indeed, We have devised a new class of queueing network models that overcome the weakness of classic models by capturing the performance effects of autocorrelation in the service process. However, there is a lack of understanding and of practical results on how to perform model parameterization, especially when this model parameterization must be derived from limited coarse measurements as is often encountered in practice. In collaboration with researchers at HP Labs, We have devised a new methodology to integrate workload autocorrelation in performance models, which well captures autocorrelation and variability of the true service process, despite inevitable inaccuracies that result from inexact and limited measurements.

    This work has been presented in HotMetrics'08, SIGMETRICS'08, Middleware'08.

  • Automated Detection of Application Performance Anomaly and Change

    Application servers are a core component of a multi-tier architecture that has become the industry standard for building scalable client-server applications. A client communicates with a service deployed as a multi-tier application via request-reply transactions. A typical server reply consists of the web page dynamically generated by the application server. Then, the application server may issue multiple database calls while preparing the reply. Understanding the cascading effects of the various tasks that are sprung by a single request-reply transaction is a challenging task. we have addressed the problem of efficiently diagnosing essential performance changes in application behavior in order to provide timely feedback to application designers and service providers. A new approach based on an application signature has been proposed to enable a quick performance comparison of the new application signature against the old one, while the application continues its execution in the production environment. Application signatures provide a simple and powerful solution that are further used for efficient capacity planning, anomaly detection, and provisioning of multi-tier applications in rapidly evolving IT environments.

    This work has been presented in NOMS'08, DSN'08.

 

Paper Reading Lists

Present Graduate Students:

  • Jianzhe Tai: Ph.D. student
  • Yi Yao: Ph.D. student
  • Jiahui Chen: M.S. student
  • Zhen Li: M.S. student

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Teaching

Fall 2009

  • EECE3326 Optimization Methods

Covers the design and implementation of algorithms to solve engineering problems using a high-level programming language. Reviews elementary data structures, such as arrays, stacks, queues, and lists, and introduces more advanced structures, such as trees and graphs and the use of recursion. Covers both the algorithms to manipulate these data structures as well as their use in problem solving. Emphasizes the importance of software engineering principles. Introduces algorithm complexity analysis and its application to developing efficient algorithms. Prereq. CS 1500.

Syllabus

Course website on blackboard.

Spring 2010

  • EECE7398 Simulation and Performance Evaluation

Covers topics on computer simulation and performance evaluation in computer systems. The course mainly covers both classic and timely techniques in the area of performance evaluation, including capacity planning to predict system performance, scheduling, and resource allocation in systems. The course also introduces some basic computational and mathematical techniques for modeling, simulating and analyzing the performance by using simulation, including models, random-number generation, statistics, and discrete event-driven simulation.

Syllabus

Fall 2010

  • EECE3326 Optimization Methods

Spring 2011

Fall 2011

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Service

Editorial board member of "Simulation Modelling Practice and Theory"

Demo/poster chair for ACM International Conference on Performance Engineering (ICPE 2012)

Publicity Co-Chair for ACM/IFIP/USENIX International Middleware Conference (2010)

TPC Member

  • QEST: International Conference on the Quantitative Evaluation of Systems (2010, 2011)
  • SIGMETRICS: International conference on Measurement and modeling of computer systems (2010) - Shadow Program Committee Member

Journal Reviewer

  • IEEEs Transactions on Computers
  • Journal of Parallel and Distributed Computing
  • International Journal of Performance Evaluation
  • IEEEs Transactions on Parallel and Distributed Systems
  • International Journal of Modeling, Simulation, and Scientific Computing
  • IEEEs Transactions on Dependable and Secure Computing
  • Journal of Simulation Modelling Practice and Theory

 

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Links

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