Improving the end-to-end latency of datacenter applications using coordination across application components

To handle millions of user requests every second and process hundreds of terabytes of data each day, many organizations have turned to large datacenter-scale computing systems. The applications running in these datacenters consist of a multitude of dependent logical components or stages which perform specific functionality. These stages are connected to form a directed acyclic graph (DAG), with edges representing input-output dependencies. Each stage can run over tens to thousands of machines, and involves multiple cluster sub-systems such as storage, network and compute. The scale and complexity of these applications can lead to significant delays in their end-to-end latency. However, the organizations running these applications have strict requirements on this latency as it directly affects their revenue and operational costs. Addressing this problem, the goal of this dissertation is to develop scheduling and resource allocation techniques to optimize for the end-to-end latency of datacenter applications. The key idea behind these techniques is to utilize coordination between different application components, allowing us to efficiently allocate cluster resources. In particular, we develop planning algorithms that coordinate the storage and compute sub-systems in datacenters to determine how many resources should be allocated to each stage in an application along with where in the cluster should they be allocated, to meet application requirements (e.g., completion time goals, minimize average completion time etc.). To further speed up applications at runtime, we develop a few latency reduction techniques: reissuing laggards elsewhere in the cluster, returning partial results and speeding up laggards by giving them extra resources. We perform a global optimization to coordinate across all the stages in an application DAG and determine which of these techniques works best for each stage, while ensuring that the cost incurred by these techniques is within a given end-to-end budget. We use application characteristics to predict and determine how resources should be allocated to different application components to meet the end-to-end latency requirements. We evaluate our techniques on two different kinds of datacenter applications: (a) web services, and (b) data analytics. With large-scale simulations and an implementation in Apache Yarn (Hadoop 2.0), we use workloads derived from production traces to show that our techniques can achieve more than 50% reduction in the 99th percentile latency of web services and up to 56% reduction in the median latency of data analytics jobs

PERICLES Deliverable 4.3:Content Semantics and Use Context Analysis Techniques

The current deliverable summarises the work conducted within task T4.3 of WP4, focusing on the extraction and the subsequent analysis of semantic information from digital content, which is imperative for its preservability. More specifically, the deliverable defines content semantic information from a visual and textual perspective, explains how this information can be exploited in long-term digital preservation and proposes novel approaches for extracting this information in a scalable manner. Additionally, the deliverable discusses novel techniques for retrieving and analysing the context of use of digital objects. Although this topic has not been extensively studied by existing literature, we believe use context is vital in augmenting the semantic information and maintaining the usability and preservability of the digital objects, as well as their ability to be accurately interpreted as initially intended.PERICLE

Hybrid, Job-Aware, and Preemptive Datacenter Scheduling

Scheduling in datacenters is an important, yet challenging problem. Datacenters are composed of a large number, typically tens of thousands, of commodity computers running a variety of data-parallel jobs. The role of the scheduler is to assign cluster resources to jobs, which is not trivial due to the large scale of the cluster, as well as the high scheduling load (tens of thousands of scheduling decisions per second). Additionally to scalability, modern datacenters face increasingly heterogeneous workloads composed of long batch jobs, e.g., data analytics, and latency-sensitive short jobs, e.g., operations of user-facing services. In such workloads, and especially if the cluster is highly utilized, it is challenging to avoid short running jobs getting stuck behind long running jobs, i.e. head-of-line blocking. Schedulers have evolved from being centralized (one single scheduler for the entire cluster) to distributed (many schedulers that take scheduling decisions in parallel). Although distributed schedulers can handle the large-scale nature of datacenters, they trade scheduling latency for accuracy. The complexity of scheduling in datacenters is exacerbated by the data-parallel nature of the jobs. That is, a job is composed of multiple tasks and the job completes only when all of its tasks complete. A scheduler that takes into account this fact, i.e. job-aware, could use this information to provide better scheduling decisions. Furthermore, to improve the quality of their scheduling decisions, most of datacenter schedulers use job runtime estimates. Obtaining accurate runtime estimates is, however, far from trivial, and erroneous estimates may lead to sub-optimal scheduling decisions. Considering these challenges, in this dissertation we argue the following: (i) that a hybrid centralized/distributed design can get the best of both worlds by scheduling long jobs in a centralized way and short jobs in a distributed way; (ii) such a hybrid scheduler can avoid head-of-line blocking and provide job-awareness by dynamically partitioning the cluster for short and long jobs and by executing a job to completion once it started; (iii) a scheduler can dispense with runtime estimates by sharing the resources of a node with preemption, and load balancing jobs among the nodes

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications