اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدDesigning a scalable framework for declarative automation on distributed systems
70
0
0.0
(
0
)
تأليف
J. Lowell Wofford
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
As distributed systems grow in scale and complexity, the need for flexible automation of systems management functions also grows. We outline a framework for building tools that provide distributed, scalable, declarative, modular, and continuous automation for distributed systems. We focus on four points of design: 1) a state-management approach that prescribes source-of-truth for configured and discovered system states; 2) a technique to solve the declarative unification problem for a class of automation problems, providing state convergence and modularity; 3) an eventual-consistency approach to state synchronization which provides automation at scale; 4) an event-driven architecture that provides always-on state enforcement. We describe the methodology, software architecture for the framework, and constraints for these techniques to apply to an automation problem. We overview a reference application built on this framework that provides state-aware system provisioning and node lifecycle management, highlighting key advantages. We conclude with a discussion of current and future applications.
قيم البحث
اقرأ أيضاً
Serverless computing has emerged as a promising alternative to infrastructure- (IaaS) and platform-as-a-service (PaaS)cloud platforms for applications with ample parallelism and intermittent activity. Serverless promises greater resource elasticity,
significant cost savings, and simplified application deployment. All major cloud providers, including Amazon, Google, and Microsoft, have introduced serverless to their public cloud offerings. For serverless to reach its potential, there is a pressing need for programming frameworks that abstract the deployment complexity away from the user. This includes simplifying the process of writing applications for serverless environments, automating task and data partitioning, and handling scheduling and fault tolerance. We present Ripple, a programming framework designed to specifically take applications written for single-machine execution and allow them to take advantage of the task parallelism of serverless. Ripple exposes a simple interface that users can leverage to express the high-level dataflow of a wide spectrum of applications, including machine learning (ML) analytics, genomics, and proteomics. Ripple also automates resource provisioning, meeting user-defined QoS targets, and handles fault tolerance by eagerly detecting straggler tasks. We port Ripple over AWS Lambda and show that, across a set of diverse applications, it provides an expressive and generalizable programming framework that simplifies running data-parallel applications on serverless, and can improve performance by up to 80x compared to IaaS/PaaS clouds for similar costs.
In large-scale distributed file systems, efficient meta- data operations are critical since most file operations have to interact with metadata servers first. In existing distributed hash table (DHT) based metadata management systems, the lookup serv
ice could be a performance bottleneck due to its significant CPU overhead. Our investigations showed that the lookup service could reduce system throughput by up to 70%, and increase system latency by a factor of up to 8 compared to ideal scenarios. In this paper, we present MetaFlow, a scalable metadata lookup service utilizing software-defined networking (SDN) techniques to distribute lookup workload over network components. MetaFlow tackles the lookup bottleneck problem by leveraging B-tree, which is constructed over the physical topology, to manage flow tables for SDN-enabled switches. Therefore, metadata requests can be forwarded to appropriate servers using only switches. Extensive performance evaluations in both simulations and testbed showed that MetaFlow increases system throughput by a factor of up to 3.2, and reduce system latency by a factor of up to 5 compared to DHT-based systems. We also deployed MetaFlow in a distributed file system, and demonstrated significant performance improvement.
In the current era of Big Data, data engineering has transformed into an essential field of study across many branches of science. Advancements in Artificial Intelligence (AI) have broadened the scope of data engineering and opened up new application
s in both enterprise and research communities. Aggregations (also termed reduce in functional programming) are an integral functionality in these applications. They are traditionally aimed at generating meaningful information on large data-sets, and today, they are being used for engineering more effective features for complex AI models. Aggregations are usually carried out on top of data abstractions such as tables/ arrays and are combined with other operations such as grouping of values. There are frameworks that excel in the said domains individually. But, we believe that there is an essential requirement for a data analytics tool that can universally integrate with existing frameworks, and thereby increase the productivity and efficiency of the entire data analytics pipeline. Cylon endeavors to fulfill this void. In this paper, we present Cylons fast and scalable aggregation operations implemented on top of a distributed in-memory table structure that universally integrates with existing frameworks.
This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning applications on production big data platforms. It allows deep learning applicatio
ns to run on the Apache Hadoop/Spark cluster so as to directly process the production data, and as a part of the end-to-end data analysis pipeline for deployment and management. Unlike existing deep learning frameworks, BigDL implements distributed, data parallel training directly on top of the functional compute model (with copy-on-write and coarse-grained operations) of Spark. We also share real-world experience and war stories of users that have adopted BigDL to address their challenges(i.e., how to easily build end-to-end data analysis and deep learning pipelines for their production data).
The industry and academia have proposed many distributed graph processing systems. However, the existing systems are not friendly enough for users like data analysts and algorithm engineers. On the one hand, the programing models and interfaces diffe
r a lot in the existing systems, leading to high learning costs and program migration costs. On the other hand, these graph processing systems are tightly bound to the underlying distributed computing platforms, requiring users to be familiar with distributed computing. To improve the usability of distributed graph processing, we propose a unified distributed graph programming framework UniGPS. Firstly, we propose a unified cross-platform graph programming model VCProg for UniGPS. VCProg hides details of distributed computing from users. It is compatible with the popular graph programming models Pregel, GAS, and Push-Pull. VCProg programs can be executed by compatible distributed graph processing systems without modification, reducing the learning overheads of users. Secondly, UniGPS supports Python as the programming language. We propose an interprocess-communication-based execution environment isolation mechanism to enable Java/C++-based systems to call user-defined methods written in Python. The experimental results show that UniGPS enables users to process big graphs beyond the memory capacity of a single machine without sacrificing usability. UniGPS shows near-linear data scalability and machine scalability.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
