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

This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of DRL from fundamental concepts to advanced models. Then, we review DRL approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks, such as 5G and beyond. Furthermore, we present applications of DRL for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying DRL.

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly expanding body of work on diffusion models, categorizing the research into three key areas: efficient sampling, improved likelihood estimation, and handling data with special structures. We also discuss the potential for combining diffusion models with other generative models for enhanced results. We further review the wide-ranging applications of diffusion models in fields spanning from computer vision, natural language generation, temporal data modeling, to interdisciplinary applications in other scientific disciplines. This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration. Github: https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy.

pdf/diffusion-models-a-comprehensive-survey-of-methods-and-3ox7uzgu.pdf

Diffusion Models: A Comprehensive Survey of Methods and Applications

Deep learning workloads are common in today's production clusters due to the proliferation of deep learning driven AI services (e.g., speech recognition, machine translation). A deep learning training job is resource-intensive and time-consuming. Efficient resource scheduling is the key to the maximal performance of a deep learning cluster. Existing cluster schedulers are largely not tailored to deep learning jobs, and typically specifying a fixed amount of resources for each job, prohibiting high resource efficiency and job performance. This paper proposes Optimus, a customized job scheduler for deep learning clusters, which minimizes job training time based on online resource-performance models. Optimus uses online fitting to predict model convergence during training, and sets up performance models to accurately estimate training speed as a function of allocated resources in each job. Based on the models, a simple yet effective method is designed and used for dynamically allocating resources and placing deep learning tasks to minimize job completion time. We implement Optimus on top of Kubernetes, a cluster manager for container orchestration, and experiment on a deep learning cluster with 7 CPU servers and 6 GPU servers, running 9 training jobs using the MXNet framework. Results show that Optimus outperforms representative cluster schedulers by about 139% and 63% in terms of job completion time and makespan, respectively.

Optimus: an efficient dynamic resource scheduler for deep learning clusters

This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.

Due to their complexity, modern systems expose many configuration parameters which users must tune to maximize performance. Auto-tuning has emerged as an alternative in which a black-box optimizer iteratively evaluates configurations to find efficient ones. Unfortunately, for many systems, such as distributed systems, evaluating performance takes too long and the space of configurations is too large for the optimizer to converge within a reasonable time. We present BOAT, a framework which allows developers to build efficient bespoke auto-tuners for their system, in situations where generic auto-tuners fail. At BOAT's core is structured Bayesian optimization (SBO), a novel extension of the Bayesian optimization algorithm. SBO leverages contextual information provided by system developers, in the form of a probabilistic model of the system's behavior, to make informed decisions about which configurations to evaluate. In a case study, we tune the scheduling of a neural network computation on a heterogeneous cluster. Our auto-tuner converges within ten iterations. The optimized configurations outperform those found by generic auto-tuners in thirty iterations by up to 2X.

BOAT: Building Auto-Tuners with Structured Bayesian Optimization

Reinforcement learning approaches have long appealed to the data management community due to their ability to learn to control dynamic behavior from raw system performance. Recent successes in combining deep neural networks with reinforcement learning have sparked significant new interest in this domain. However, practical solutions remain elusive due to large training data requirements, algorithmic instability, and lack of standard tools. In this work, we introduce LIFT, an end-to-end software stack for applying deep reinforcement learning to data management tasks. While prior work has frequently explored applications in simulations, LIFT centers on utilizing human expertise to learn from demonstrations, thus lowering online training times. We further introduce TensorForce, a TensorFlow library for applied deep reinforcement learning exposing a unified declarative interface to common RL algorithms, thus providing a backend to LIFT. We demonstrate the utility of LIFT in two case studies in database compound indexing and resource management in stream processing. Results show LIFT controllers initialized from demonstrations can outperform human baselines and heuristics across latency metrics and space usage by up to 70%.

LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations

Today, web performance is primarily governed by round-trip latencies between end devices and cloud services. To improve performance, services need to minimize the delay of accessing data. In this paper, we propose a novel approach to low latency that relies on existing content delivery and web caching infrastructure. The main idea is to enable application-independent caching of query results and records with tunable consistency guarantees, in particular bounded staleness. Quaestor (Query Store) employs two key concepts to incorporate both expiration-based and invalidation-based web caches: (1) an Expiring Bloom Filter data structure to indicate potentially stale data, and (2) statistically derived cache expiration times to maximize cache hit rates. Through a distributed query invalidation pipeline, changes to cached query results are detected in real-time. The proposed caching algorithms offer a new means for data-centric cloud services to trade latency against staleness bounds, e.g. in a database-as-a-service. Quaestor is the core technology of the backend-as-a-service platform Baqend, a cloud service for low-latency websites. We provide empirical evidence for Quaestor's scalability and performance through both simulation and experiments. The results indicate that for read-heavy workloads, up to tenfold speed-ups can be achieved through Quaestor's caching.

Quaestor: query web caching for database-as-a-service providers

In this paper, we present an innovative solution for providing automated polyglot persistence based on service level agreements defined over functional and non-functional requirements of database systems. Complex applications require polyglot persistence to deal with a wide range of database related needs. Until now, the overhead and the required know-how to manage multiple database systems prevents many applications from employing efficient polyglot persistence solutions. Instead, developers are often forced to implement one-size-fits-all solutions that do not scale well and cannot easily be upgraded. Therefore, we introduce the concept for a Polyglot Persistence Mediator (PPM), which allows for runtime decisions on routing data to different backends according to schema-based annotations. This enables applications to either employ polyglot persistence right from the beginning or employ new systems at any point with minimal overhead. We have implemented and evaluated the concept of automated polyglot persistence for a REST-based Database-as-a-Service setting. Evaluations were performed on various EC2 setups, showing a scalable writeperformance increase of 50-100% for a typical polyglot persistence scenario as well as drastically reduced latencies for reads and queries.

/pdf/towards-automated-polyglot-persistence-48qpe240b7.pdf

Towards Automated Polyglot Persistence.

In the last years, many database-as-a-service (DBaaS) systems have started to offer their functionalities through REST APIs. Examples are record stores like DynamoDB and Azure Tables, object stores such as Amazon S3 as well as many NoSQL database systems, for instance Riak, CouchDB and ElasticSearch. Yet today, there has been no systematic effort on deriving a unified REST interface which takes into account the different data models, schemas, consistency concepts, transactions, access-control mechanisms and query languages to expose cloud data stores through a common interface without restricting their functionality. This work motivates the design of such a REST API as well as the challenges of providing it in an extensible, scalable and highly-available fashion. To this end, we propose the REST middleware ORESTES that consists of an independently scalable tier of HTTP servers that map the unified REST API to aggregate-oriented (NoSQL) data stores. It extracts a wide range of DBaaS concerns (e.g. schema management and access control) and provides them in a modular, database-independent fashion at the middleware-level. To tackle the latency problem of cloud-based web applications we introduce the Bloom filter-bounded staleness cache consistency algorithm. It leverages the global web caching infrastructure for geo-replication to allow consistent low latency reads. We furthermore show the first steps towards a Polyglot Persistence Mediator that exploits the decoupling of the REST API from the data store to route data and operations based on SLAs.

/pdf/towards-a-scalable-and-unified-rest-api-for-cloud-data-3eqzqtqd23.pdf

Towards a Scalable and Unified REST API for Cloud Data Stores.

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