Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.

/pdf/tensorflow-a-system-for-large-scale-machine-learning-10wgdjhl6k.pdf

TensorFlow: a system for large-scale machine learning

Clearing the clouds away from the true potential and obstacles posed by this computing capability.

https://dl.acm.org/doi/pdf/10.1145/1721654.1721672

A view of cloud computing

Data Mining Practical Machine Learning Tools and Techniques

This paper presents the design and evaluation of Pastry, a scalable, distributed object location and routing substrate for wide-area peer-to-peer ap- plications. Pastry performs application-level routing and object location in a po- tentially very large overlay network of nodes connected via the Internet. It can be used to support a variety of peer-to-peer applications, including global data storage, data sharing, group communication and naming. Each node in the Pastry network has a unique identifier (nodeId). When presented with a message and a key, a Pastry node efficiently routes the message to the node with a nodeId that is numerically closest to the key, among all currently live Pastry nodes. Each Pastry node keeps track of its immediate neighbors in the nodeId space, and notifies applications of new node arrivals, node failures and recoveries. Pastry takes into account network locality; it seeks to minimize the distance messages travel, according to a to scalar proximity metric like the number of IP routing hops. Pastry is completely decentralized, scalable, and self-organizing; it automatically adapts to the arrival, departure and failure of nodes. Experimental results obtained with a prototype implementation on an emulated network of up to 100,000 nodes confirm Pastry's scalability and efficiency, its ability to self-organize and adapt to node failures, and its good network locality properties.

/pdf/pastry-scalable-decentralized-object-location-and-routing-1u2equbbbp.pdf

Pastry: Scalable, Decentralized Object Location, and Routing for Large-Scale Peer-to-Peer Systems

CHART seeks to improve the performance of operational DoD internets through the introduction of an intelligent network overlay. TCP performance ? particularly between CONUS and forward-deployed components located in combat theaters ? can be severely degraded due to high loss rates and long latencies. The lack of current information about network conditions in the core NIPR- net/SIPRnet further compounds the problem, because end hosts lack the data required to make intelligent routing decisions. Deploying CHART's enhanced control plane improves measurement and monitoring of unreliable communication links to provide current network state information to routers implemented in both software and hardware, enabling intelligent routing around faulty links. We describe the design of software and hardware routers sharing a common network `sensing' infrastructure, the implementation of end-to-end Quality of Service via flow state aware routers, and a new network-aware TCP/IP stack for Linux end systems. Performance test results demonstrate that bulk file transfer throughput can be increased by as much as an order of magnitude in networks with severely impaired communication links.

/pdf/increasing-tcp-throughput-with-an-enhanced-internet-control-jwo58dtcdi.pdf

Increasing TCP Throughput with an Enhanced Internet Control Plane

We present a novel domain analysis methodology that enables network researchers to quickly select the most accurate modeling and analysis method or methods for a given wired or wireless network path and network characteristic of interest e.g., delay, loss, or error process). Our approach includes two classical models, and two data preconditioning models developed in the Tapas project.

/pdf/choosing-an-accurate-network-path-model-55e8v3xqup.pdf

Choosing an accurate network path model

1 Executive Summary This report1 summarizes recommendations from a workshop on research challenges in distributed computer systems, sponsored by the National Science Foundation. A program committee solicited input from the research community by asking researchers to submit position papers that identified grand challenges in distributed systems, invited researchers based on their submissions, and selected a few position papers for presentation at the workshop. Most of the workshop was organized around break-out sessions, in which we refined the research challenges and identified the facilities needed to carry out future research as well as what the distributed systems community can contribute to the facility. Information about the workshop, including the full program, the selected submissions, and the slides of the presentations and the break-out sessions, is available at http://www.pdos.lcs.mit.edu/ kaashoek/nsf/. The workshop attendees identified a number of challenge applications whose implementation will require research advances in the design and engineering of distributed systems. Examples include: managing a large number of personal devices and data, improving the auto commute through data dissemination and using sensors and actuators in the car to avoid accidents, rapidly deploying fault-tolerant distributed systems to assist in disaster recovery, and understanding and affecting the planet in real-time. Each of these applications has security, storage, fault-tolerance, and usability requirements that can be addressed only if there are new research advances. To spur these advances, the community has a need for a facility to support experimentation. Facilities such as Planetlab [2] and Emulab [3] have demonstrated that the right facility can spark research progress. The new applications, however, require a scale of facility that is unavailable at present, and that includes product versions of recent research advances. Such a facility would allow researchers to leverage the recent results in tackling the next set of challenges. The report makes the following recommendations for the research community and NSF. For the research community:

/pdf/report-of-the-nsf-workshop-on-research-challenges-in-ci1t5bw8ef.pdf

Report of the NSF Workshop on Research Challenges in Distributed Computer Systems

Problem-based learning techniques involving small collaborative teams of students are widely regarded as being effective in teaching engineering design courses. However, the heavy requirements upon the teaching staff to support and coordinate such teams typically limit these courses to small numbers of students. In this paper, we describe the design of a computer-based tool for supporting small collaborative teams with the ultimate goal of scaling these engineering design courses to larger numbers of students. The system, deployed on laptops with wireless networking, utilizes student self-evaluations and instrumented project management tools to provide feedback of individual and team performance to the students and teaching staff. We present an example of the usage and data collected from this system during a pilot test in an undergraduate computer science design course. Based on that experience, we discuss future plans to refine the system for early detection of problem teams and to instrument asynchronous and instant messaging tools to support social network analysis of team communication.

/pdf/supporting-collaborative-teams-in-engineering-education-3htuv0el3u.pdf

Supporting Collaborative Teams in Engineering Education

Education in entrepreneurship continues to be a developing area of curricula for computer science and information systems students. Entrepreneurship is enabled frequently by cloud computing methods that furnish benefits to especially medium and small-sized firms. Expanding upon an earlier foundation paper, the authors of this paper present an enhanced model program for including cloud computing as a discipline for further learning technology entrepreneurship. In the program, students can learn skills for leveraging cloud computing practices in the context of an enterprise strategy. This paper will be beneficial to educators exploring new initiatives in industry that might improve innovation projects in a technology entrepreneurship program.

/pdf/cloud-computing-as-a-core-discipline-in-a-technology-5bu6zt50nh.pdf

Anthony D. Joseph

Papers

Increasing TCP Throughput with an Enhanced Internet Control Plane

Choosing an accurate network path model

Report of the NSF Workshop on Research Challenges in Distributed Computer Systems

Supporting Collaborative Teams in Engineering Education

Cloud Computing as a Core Discipline in a Technology Entrepreneurship Program