56 research outputs found
RFaaS: RDMA-Enabled FaaS Platform for Serverless High-Performance Computing
The rigid MPI programming model and batch scheduling dominate
high-performance computing. While clouds brought new levels of elasticity into
the world of computing, supercomputers still suffer from low resource
utilization rates. To enhance supercomputing clusters with the benefits of
serverless computing, a modern cloud programming paradigm for pay-as-you-go
execution of stateless functions, we present rFaaS, the first RDMA-aware
Function-as-a-Service (FaaS) platform. With hot invocations and decentralized
function placement, we overcome the major performance limitations of FaaS
systems and provide low-latency remote invocations in multi-tenant
environments. We evaluate the new serverless system through a series of
microbenchmarks and show that remote functions execute with negligible
performance overheads. We demonstrate how serverless computing can bring
elastic resource management into MPI-based high-performance applications.
Overall, our results show that MPI applications can benefit from modern cloud
programming paradigms to guarantee high performance at lower resource costs
Rise of the Planet of Serverless Computing: A Systematic Review
Serverless computing is an emerging cloud computing paradigm, being adopted to develop a wide range of software applications.
It allows developers to focus on the application logic in the granularity of function, thereby freeing developers from tedious and
error-prone infrastructure management. Meanwhile, its unique characteristic poses new challenges to the development and deployment
of serverless-based applications. To tackle these challenges, enormous research efforts have been devoted. This paper provides a
comprehensive literature review to characterize the current research state of serverless computing. Specifically, this paper covers 164
papers on 17 research directions of serverless computing, including performance optimization, programming framework, application
migration, multi-cloud development, testing and debugging, etc. It also derives research trends, focus, and commonly-used platforms
for serverless computing, as well as promising research opportunities
FogROS2: An Adaptive Platform for Cloud and Fog Robotics Using ROS 2
Mobility, power, and price points often dictate that robots do not have
sufficient computing power on board to run contemporary robot algorithms at
desired rates. Cloud computing providers such as AWS, GCP, and Azure offer
immense computing power on demand, but tapping into that power from a robot is
non-trivial. We present FogROS2, an open-source platform to facilitate cloud
and fog robotics that is compatible with the emerging Robot Operating System 2
(ROS 2) standard. FogROS2 is completely redesigned and distinct from its
predecessor FogROS1 in 9 ways, and has lower latency, overhead, and startup
times; improved usability, and additional automation, such as region and
computer type selection. Additionally, FogROS2 was added to the official
distribution of ROS 2, gaining performance, timing, and additional improvements
associated with ROS 2. In examples, FogROS2 reduces SLAM latency by 50 %,
reduces grasp planning time from 14 s to 1.2 s, and speeds up motion planning
28x. When compared to FogROS1, FogROS2 reduces network utilization by up to
3.8x, improves startup time by 63 %, and network round-trip latency by 97 % for
images using video compression. The source code, examples, and documentation
for FogROS2 are available at https://github.com/BerkeleyAutomation/FogROS2, and
is available through the official ROS 2 repository at
https://index.ros.org/p/fogros2
QoS-Aware Resource Management for Multi-phase Serverless Workflows with Aquatope
Multi-stage serverless applications, i.e., workflows with many computation
and I/O stages, are becoming increasingly representative of FaaS platforms.
Despite their advantages in terms of fine-grained scalability and modular
development, these applications are subject to suboptimal performance, resource
inefficiency, and high costs to a larger degree than previous simple serverless
functions.
We present Aquatope, a QoS-and-uncertainty-aware resource scheduler for
end-to-end serverless workflows that takes into account the inherent
uncertainty present in FaaS platforms, and improves performance predictability
and resource efficiency. Aquatope uses a set of scalable and validated Bayesian
models to create pre-warmed containers ahead of function invocations, and to
allocate appropriate resources at function granularity to meet a complex
workflow's end-to-end QoS, while minimizing resource cost. Across a diverse set
of analytics and interactive multi-stage serverless workloads, Aquatope
significantly outperforms prior systems, reducing QoS violations by 5x, and
cost by 34% on average and up to 52% compared to other QoS-meeting methods
Data-centric serverless cloud architecture
Serverless has become a new dominant cloud architecture thanks to its high scalability
and flexible, pay-as-you-go billing model. In serverless, developers compose their
cloud services as a set of functions while providers take responsibility for scaling each
function’s resources according to traffic changes. Hence, the provider needs to timely
spawn, or tear down, function instances (i.e., HTTP servers with user-provider handles),
which cannot hold state across function invocations.
Performance of a modern serverless cloud is bound by data movement. Serverless
architecture separates compute resources and data management to allow function instances
to run on any node in a cloud datacenter. This flexibility comes at the cost of
the necessity to move function initialization state across the entire datacenter when
spawning new instances on demand. Furthermore, to facilitate scaling, cloud providers
restrict the serverless programming model to stateless functions (which cannot hold or
share state across different functions), which lack efficient support for cross-function
communication.
This thesis consists of four following research contributions that pave the way for
a data-centric serverless cloud architecture. First, we introduce STeLLAR, an opensource
serverless benchmarking framework, which enables an accurate performance
characterization of serverless deployments. Using STeLLAR, we study three leading
serverless clouds and identify that all of them follow the same conceptual architecture
that comprises three essential subsystems, namely the worker fleet, the scheduler, and
the storage. Our analysis quantifies the aspect of the data movement problem that is
related to moving state from the storage to workers when spawning function instances
(“cold-start” delays). Also, we study two state-of-the-art production methods of crossfunction
communication that involve either the storage or the scheduler subsystems, if
the data is transmitted as part of invocation HTTP requests (i.e., inline).
Second, we introduce vHive, an open-source ecosystem for serverless benchmarking
and experimentation, with the goal of enabling researchers to study and innovate across
the entire serverless stack. In contrast to the incomplete academic prototypes and
proprietary infrastructure of the leading commercial clouds, vHive is representative of
the leading clouds and comprises only fully open-source production-grade components,
such as Kubernetes orchestrator and AWS Firecracker hypervisor technologies. To
demonstrate vHive’s utility, we analyze the cold-start delays, revealing that the high
cold-start latency of function instances is attributable to frequent page faults as the
function’s state is brought from disk into guest memory one page at a time. Our analysis
further reveals that serverless functions operate over stable working sets - even across
function invocations.
Third, to reduce the cold-start delays of serverless functions, we introduce a novel
snapshotting mechanism that records and prefetches their memory working sets. This
mechanism, called REAP, is implemented in userspace and consists of two phases.
During the first invocation of a function, all accessed memory pages are recorded and
their contents are stored compactly as a part of the function snapshot. Starting from the
second cold invocation, the contents of the recorded pages are retrieved from storage
and installed in the guest memory before the new function instance starts to process the
invocation, allowing to avoid the majority of page faults, hence significantly accelerating
the function’s cold starts.
Finally, to accelerate the cross-function data communication, we propose Expedited
Data Transfers (XDT), an API-preserving high-performance data communication
method for serverless. In production clouds, function transmit intermediate data to other
functions either inline or through a third-party storage service. The former approach is
restricted to small transfer sizes, the latter supports arbitrary transfers but suffers from
performance and cost overheads. XDT enables direct function-to-function transfers
in a way that is fully compatible with the existing autoscaling infrastructure. With
XDT, a trusted component of the sender function buffers the payload in its memory
and sends a secure reference to the receiver, which is picked by the load balancer and
autoscaler based on the current load. Using the reference, the receiver instance pulls the
transmitted data directly from sender’s memory, obviating the need for intermediary
storage
Technologies and Applications for Big Data Value
This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems
Technologies and Applications for Big Data Value
This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems
Enhancing Usability, Security, and Performance in Mobile Computing
We have witnessed the prevalence of smart devices in every aspect of human life. However, the ever-growing smart devices present significant challenges in terms of usability, security, and performance. First, we need to design new interfaces to improve the device usability which has been neglected during the rapid shift from hand-held mobile devices to wearables. Second, we need to protect smart devices with abundant private data against unauthorized users. Last, new applications with compute-intensive tasks demand the integration of emerging mobile backend infrastructure. This dissertation focuses on addressing these challenges. First, we present GlassGesture, a system that improves the usability of Google Glass through a head gesture user interface with gesture recognition and authentication. We accelerate the recognition by employing a novel similarity search scheme, and improve the authentication performance by applying new features of head movements in an ensemble learning method. as a result, GlassGesture achieves 96% gesture recognition accuracy. Furthermore, GlassGesture accepts authorized users in nearly 92% of trials, and rejects attackers in nearly 99% of trials. Next, we investigate the authentication between a smartphone and a paired smartwatch. We design and implement WearLock, a system that utilizes one\u27s smartwatch to unlock one\u27s smartphone via acoustic tones. We build an acoustic modem with sub-channel selection and adaptive modulation, which generates modulated acoustic signals to maximize the unlocking success rate against ambient noise. We leverage the motion similarities of the devices to eliminate unnecessary unlocking. We also offload heavy computation tasks from the smartwatch to the smartphone to shorten response time and save energy. The acoustic modem achieves a low bit error rate (BER) of 8%. Compared to traditional manual personal identification numbers (PINs) entry, WearLock not only automates the unlocking but also speeds it up by at least 18%. Last, we consider low-latency video analytics on mobile devices, leveraging emerging mobile backend infrastructure. We design and implement LAVEA, a system which offloads computation from mobile clients to edge nodes, to accomplish tasks with intensive computation at places closer to users in a timely manner. We formulate an optimization problem for offloading task selection and prioritize offloading requests received at the edge node to minimize the response time. We design and compare various task placement schemes for inter-edge collaboration to further improve the overall response time. Our results show that the client-edge configuration has a speedup ranging from 1.3x to 4x against running solely by the client and 1.2x to 1.7x against the client-cloud configuration
Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial
Este documento contiene el proyecto docente e investigador del candidato Germán Moltó Martínez presentado como requisito para el concurso de acceso a plazas de Cuerpos Docentes Universitarios. Concretamente, el documento se centra en el concurso para la plaza 6708 de Catedrático de Universidad en el área de Ciencia de la Computación en el Departamento de Sistemas Informáticos y Computación de la Universitat Politécnica de València. La plaza está adscrita a la Escola Técnica Superior d'Enginyeria Informàtica y tiene como perfil las asignaturas "Infraestructuras de Cloud Público" y "Estructuras de Datos y Algoritmos".También se incluye el Historial Académico, Docente e Investigador, así como la presentación usada durante la defensa.Germán Moltó Martínez (2022). Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial. http://hdl.handle.net/10251/18903
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