7,832 research outputs found
Addressing the Challenges in Federating Edge Resources
This book chapter considers how Edge deployments can be brought to bear in a
global context by federating them across multiple geographic regions to create
a global Edge-based fabric that decentralizes data center computation. This is
currently impractical, not only because of technical challenges, but is also
shrouded by social, legal and geopolitical issues. In this chapter, we discuss
two key challenges - networking and management in federating Edge deployments.
Additionally, we consider resource and modeling challenges that will need to be
addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and
Paradigms; Editors Buyya, Sriram
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
An Analysis of Distributed Systems Syllabi With a Focus on Performance-Related Topics
We analyze a dataset of 51 current (2019-2020) Distributed Systems syllabi
from top Computer Science programs, focusing on finding the prevalence and
context in which topics related to performance are being taught in these
courses. We also study the scale of the infrastructure mentioned in DS courses,
from small client-server systems to cloud-scale, peer-to-peer, global-scale
systems. We make eight main findings, covering goals such as performance, and
scalability and its variant elasticity; activities such as performance
benchmarking and monitoring; eight selected performance-enhancing techniques
(replication, caching, sharding, load balancing, scheduling, streaming,
migrating, and offloading); and control issues such as trade-offs that include
performance and performance variability.Comment: Accepted for publication at WEPPE 2021, to be held in conjunction
with ACM/SPEC ICPE 2021: https://doi.org/10.1145/3447545.3451197 This article
is a follow-up of our prior ACM SIGCSE publication, arXiv:2012.0055
Reference Exascale Architecture (Extended Version)
While political commitments for building exascale systems have been made, turning these systems into platforms for a wide range of exascale applications faces several technical, organisational and skills-related challenges. The key technical challenges are related to the availability of data. While the first exascale machines are likely to be built within a single site, the input data is in many cases impossible to store within a single site. Alongside handling of extreme-large amount of data, the exascale system has to process data from different sources, support accelerated computing, handle high volume of requests per day, minimize the size of data flows, and be extensible in terms of continuously increasing data as well as an increase in parallel requests being sent. These technical challenges are addressed by the general reference exascale architecture. It is divided into three main blocks: virtualization layer, distributed virtual file system, and manager of computing resources. Its main property is modularity which is achieved by containerization at two levels: 1) application containers - containerization of scientific workflows, 2) micro-infrastructure - containerization of extreme-large data service-oriented infrastructure. The paper also presents an instantiation of the reference architecture - the architecture of the PROCESS project (PROviding Computing solutions for ExaScale ChallengeS) and discusses its relation to the reference exascale architecture. The PROCESS architecture has been used as an exascale platform within various exascale pilot applications. This paper also presents performance modelling of exascale platform with its validation
Scientific High Performance Computing (HPC) Applications On The Azure Cloud Platform
Cloud computing is emerging as a promising platform for compute and data intensive scientific applications. Thanks to the on-demand elastic provisioning capabilities, cloud computing has instigated curiosity among researchers from a wide range of disciplines. However, even though many vendors have rolled out their commercial cloud infrastructures, the service offerings are usually only best-effort based without any performance guarantees. Utilization of these resources will be questionable if it can not meet the performance expectations of deployed applications. Additionally, the lack of the familiar development tools hamper the productivity of eScience developers to write robust scientific high performance computing (HPC) applications. There are no standard frameworks that are currently supported by any large set of vendors offering cloud computing services. Consequently, the application portability among different cloud platforms for scientific applications is hard. Among all clouds, the emerging Azure cloud from Microsoft in particular remains a challenge for HPC program development both due to lack of its support for traditional parallel programming support such as Message Passing Interface (MPI) and map-reduce and due to its evolving application programming interfaces (APIs). We have designed newer frameworks and runtime environments to help HPC application developers by providing them with easy to use tools similar to those known from traditional parallel and distributed computing environment set- ting, such as MPI, for scientific application development on the Azure cloud platform. It is challenging to create an efficient framework for any cloud platform, including the Windows Azure platform, as they are mostly offered to users as a black-box with a set of application programming interfaces (APIs) to access various service components. The primary contributions of this Ph.D. thesis are (i) creating a generic framework for bag-of-tasks HPC applications to serve as the basic building block for application development on the Azure cloud platform, (ii) creating a set of APIs for HPC application development over the Azure cloud platform, which is similar to message passing interface (MPI) from traditional parallel and distributed setting, and (iii) implementing Crayons using the proposed APIs as the first end-to-end parallel scientific application to parallelize the fundamental GIS operations
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