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Li Zhang and co-authors will be honored at INFOCOM 2021 in May with the test of time award for their research paper that focused on improving the network scalability of cloud data centers.

AWS scientist Li Zhang awarded IEEE INFOCOM test of time paper award

Zhang and co-authors are honored for paper that focused on improving the network scalability of cloud data centers through an optimized traffic-aware algorithm for placing virtual machines on host machines. 

Amazon scientist Li Zhang and two former colleagues will be honored at INFOCOM 2021 on May 11, 2021, for a paper they wrote 11 years ago that has had a significant impact on the computer networking research community.

The paper, Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement, was first published in the 2010 INFOCOM proceedings, and recently awarded the 2021 IEEE INFOCOM test of time paper award.

Li Zhang awarded IEEE INFOCOM test of time paper award
Amazon scientist Li Zhang

Zhang wrote the award-winning paper with colleagues from IBM Research, where for more than 20 years he focused his research on optimizing the performance of individual computers, clusters of machines, and eventually cloud data centers. The paper, he says, exemplifies the motto he has followed throughout his long research career: Faster, Stronger, and More Efficient. The idea for the paper emerged from a visit Zhang made to a large IBM hosting center, where he witnessed firsthand the challenge of optimizing the utilization of virtual machines.

In their paper, the authors noted that virtual machine (VM) placement on host machines within data centers was consolidated for CPU, physical memory, and power consumption savings, yet failed to consider network resources. As a result, the authors said, this could lead to situations in which VM pairs with heavy traffic among them were placed on host machines with large network costs between them.

“To understand how often this happens in practice,” the authors wrote, “we conducted a measurement study in operational data centers and observed three apparent trends: there is a low correlation between the average pairwise traffic rate and the end-to-end cost; traffic distribution for individual VMs is highly uneven; VM pairs with relatively heavier traffic rate tend to constantly exhibit the higher rate and conversely VM pairs with low traffic rate tend to exhibit the low rate. These three observations suggest that there is a great potential in optimizing VM placement to save bandwidth and realizing such potential is feasible.”

Image from Li Zhang data center network paper
This illustration, from the test of time award-winning paper, shows network topologies and corresponding cost matrices for four data center network architectures. The paper was presented as part of the main technical program at IEEE INFOCOM 2010.

More than 10 years later, Zhang says he’s impressed with how rapidly enterprises have transitioned their workloads to the cloud, and while data center system utilization has improved significantly, “I still think there is room to improve.”
Zhang joined Amazon last year as a principal product manager technical for SageMaker JumpStart, Amazon SageMaker built-in algorithms that help data scientists and machine learning practitioners get started with training and deploying their models, and for the use of reinforcement learning (RL) with Amazon SageMaker.

Zhang says he’s enjoying his role at Amazon as it’s a natural extension of his previous research that evolved from datacenter networking, to big data analytics, machine learning, and to efficient, scale-out training of deep neural networks. Where previously his research focused more at the infrastructure level, Zhang says he’s now applying his mathematics and optimization expertise to “more at the algorithm or application level, which has more direct benefit to end users. I’m really enjoying that.”  

Zhang’s co-authors were Xiaoqiao Meng, now an engineering manager at Facebook, and Vasilis Pappas, now a software engineer at Google. Guoliang Xue, professor of computer science and engineering at Arizona State University, and chair of the IEEE INFOCOM steering committee, informed the authors of their award in a January 29 email.

“This is an extraordinary achievement,” said Xue, adding that the authors will be honored during the opening session of INFOCOM 2021, which will be held virtually this year.


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Amazon strives to be Earth's most customer-centric company. To achieve this, we hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment.Economists for Amazon will be expected to work directly with senior management on key business problems faced in retail and marketing. The role allows you to influence company-wide strategy by applying the frontier of economic thinking to forecasting, program evaluation, customer behavior and other areas. Economists at Amazon will be expected to develop new techniques to process large data sets, use econometric models and methods to address complex problems, design and test hypothesis on customer pain points and key growth ideas and contribute to design of automated systems around the company. Economists in our team will work closely with other research scientists, machine learning experts, and economists globally to design new frameworks for understanding business outcome and what impacts customer experiences. Our economists and scientists work closely with software engineers to put algorithms into practice.We are an interdisciplinary team on the cutting edge of economics, statistical analysis, and machine learning whose mission is to solve AI and ML problems that have high risk with high returns Our team seeks an outstanding econometrician with demonstrated experience tackling large-scale time-series forecasting challenges. We prize creative problem solvers with the ability to adapt and extend cutting-edge forecasting models to the unique and interesting problems we have in measuring customer experiences and Amazon business outcome. Our problems include forecasting key customer experiences metrics at different levels of granularities and explaining the drivers of the changes in these metrics. The ideal candidate combines acumen in statistics and applied time-series modeling to grapple with these and other challenges and guide decision-making at the highest levels of accuracy and model explain-ability.
US, CA, Cupertino
Amazon's Simple Storage Service (S3) offers industry-leading scalability, data availability, security, and performance. S3’s Automated Reasoning Group (S3-ARG) develops and applies automated reasoning techniques to deliver correct, secure, durable, and available distributed systems and storage services. S3 is complex: it consists of over 300 microservices each of which is a distributed system. With a very large number of servers and 10s of millions of requests per second, it is also highly concurrent. S3 has a sizable codebase, developed and maintained by a large team of engineers. Getting such a complex and fast-evolving system right requires developing cutting edge automated reasoning methods that are continuously integrated into the software development process. This is what our team does.We work on techniques ranging from deductive proofs to model checking, from static analysis to runtime verification of protocols. We work both at the design and the code level, and connecting the two is essential for us. We partner with development teams to make sure that our methods are deployed across S3 and that correctness is maintained as the software evolves. We have had significant success with adoption and we are key contributors to recent S3 launches such as strong consistency. We are developing innovative methods all the time. We publish our results at conferences and journals.We are a diverse team and are looking for teammates who are enthusiastic to work on these problems and further the state of the art with us. We are seeking candidates who are deep in one area of expertise but also broad enough to take on the most complex cloud computing challenges. If you are interested in exploring, please e-mail s3-arg-jobs@amazon.com.About UsInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, WA, Seattle
At Alexa Shopping, we strive to enable shopping in everyday life. We allow customers to instantly order whatever they need, by simply interacting with their Smart Devices such as Amazon Show, Spot, Echo, Dot or Tap. Our Services allow you to shop, no matter where you are or what you are doing, you can go from 'I want that' to 'that's on the way' in a matter of seconds. We are seeking the industry's best to help us create new ways to interact, search and shop. Join us, and you'll be taking part in changing the future of everyday lifeWe are seeking a Data Scientist to be part of the NLU science team for Alexa Shopping. This is a strategic role to shape and deliver our technical strategy in developing and deploying NLU, Machine Learning solutions to our hardest customer facing problems. Our goal is to delight customers by providing a conversational interaction. These initiatives are at the heart of the organization and recognized as the innovations that will allow us to build a differentiated product that exceeds customer expectations. We're a high energy, fast growth business excited to have the opportunity to shape Alexa Shopping NLU is defined for years to come. If this role seems like a good fit, please reach out, we'd love to talk to you.This role requires working closely with business, engineering and other scientists within Alexa Shopping and across Amazon to deliver ground breaking features. You will lead high visibility and high impact programs collaborating with various teams across Amazon. You will work with a team of Language Engineers and Scientists to launch new customer facing features and improve the current features.
US, NY, New York
MULTIPLE POSITIONS AVAILABLEEntity: Amazon Web Services, Inc.Title: Data Scientist ILocation: New York, NYPosition Responsibilities:Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, WA, Seattle
MULTIPLE POSITIONS AVAILABLEEntity: Amazon.Com Services LLCTitle: Applied ScientistWorksite: Seattle, WAPosition Responsibilities:Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, CO, Denver
Excited by using massive amounts of data to develop Machine Learning (ML) and Deep Learning (DL) models? Want to help public sector, medical center and non-profit agencies derive business value through the adoption of Artificial Intelligence (AI)? Eager to learn from many different enterprise’s use cases of AWS ML and DL? Thrilled to be key part of Amazon, who has been investing in Machine Learning for decades, pioneering and shaping the world’s AI technology?At Amazon Web Services (AWS), we are helping large enterprises build ML and DL models on the AWS Cloud. We are applying predictive technology to large volumes of data and against a wide spectrum of problems. Our Professional Services organization works together with our AWS customers to address their business needs using AI.AWS Professional Services is a unique consulting team. We pride ourselves on being customer obsessed and highly focused on the AI enablement of our customers. If you have experience with AI, including building ML or DL models, we’d like to have you join our team. You will get to work with an innovative company, with great teammates, and have a lot of fun helping our customers.If you do not live in a market where we have an open Data Scientist position, please feel free to apply. Our Data Scientists can live in any location where we have a Professional Service office.A successful candidate will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term solutions. It will be a person who likes to have fun, loves to learn, and wants to innovate in the world of AI. Major responsibilities include:· Understand the customer’s business need and guide them to a solution using our AWS AI Services, AWS AI Platforms, AWS AI Frameworks, and AWS AI EC2 Instances .· Assist customers by being able to deliver a ML / DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization.· Use Deep Learning frameworks like PyTorch, Tensorflow, and MxNet to help our customers build DL models.· Use SparkML and Amazon Machine Learning (AML) to help our customers build ML models.· Work with our Professional Services Big Data consultants to analyze, extract, normalize, and label relevant data.· Work with our Professional Services DevOps consultants to help our customers operationalize models after they are built.· Assist customers with identifying model drift and retraining models.· Research and implement novel ML and DL approaches, including using FPGA.· This position can have periods of up to 10% travel.
US, WA, Seattle
At Alexa Shopping, we strive to enable shopping in everyday life. We allow customers to instantly order whatever they need, by simply interacting with their Smart Devices such as Amazon Show, Spot, Echo, Dot or Tap. Our Services allow you to shop, no matter where you are or what you are doing, you can go from 'I want that' to 'that's on the way' in a matter of seconds. We are seeking the industry's best to help us create new ways to interact, search and shop. Join us, and you'll be taking part in changing the future of everyday lifeWe are seeking a Data Scientist to be part of the Correction and Automated Recovery (CARe) team for Alexa Shopping. Our team focuses on solving the hard problem of recovering from shopping errors in the Alexa pipeline and improve customer CX. The solutions will help address unique shopping challenges and maximize the self-learning benefits for shopping. This is a strategic role to shape and deliver our science strategy in developing and deploying Machine Learning solutions to our hardest customer facing problems. The initiatives are at the heart of the organization and recognized as the innovations that are ground breaking and will allow us to exceed customer expectations. If this role seems like a good fit, please reach out, we'd love to talk to you.This role requires working closely with business, engineering and other scientists within Alexa, Shopping and across Amazon to deliver ground breaking features. You will lead high visibility and high impact programs collaborating with various teams across Amazon. You will work with a team of SDEs and Scientists to launch new customer facing features and improve the current features.
US, MA, Cambridge
The Alexa Artificial Intelligence (AI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background, to help build industry-leading Speech and Language technology.About the hiring groupThe Alexa AI team has a mission to push the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), and Audio Signal Processing, in order to provide the best-possible experience for our customers.Job responsibilitiesAs an Applied Scientist with the Alexa AI team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.