4,480 research outputs found
Towards Measuring and Understanding Performance in Infrastructure- and Function-as-a-Service Clouds
Context. Cloud computing has become the de facto standard for deploying modern software systems, which makes its performance crucial to the efficient functioning of many applications. However, the unabated growth of established cloud services, such as Infrastructure-as-a-Service (IaaS), and the emergence of new services, such as Function-as-a-Service (FaaS), has led to an unprecedented diversity of cloud services with different performance characteristics.Objective. The goal of this licentiate thesis is to measure and understand performance in IaaS and FaaS clouds. My PhD thesis will extend and leverage this understanding to propose solutions for building performance-optimized FaaS cloud applications.Method.\ua0To achieve this goal, quantitative and qualitative research methods are used, including experimental research, artifact analysis, and literature review.Findings.\ua0The thesis proposes a cloud benchmarking methodology to estimate application performance in IaaS clouds, characterizes typical FaaS applications, identifies gaps in literature on FaaS performance evaluations, and examines the reproducibility of reported FaaS performance experiments. The evaluation of the benchmarking methodology yielded promising results for benchmark-based application performance estimation under selected conditions. Characterizing 89 FaaS applications revealed that they are most commonly used for short-running tasks with low data volume and bursty workloads. The review of 112 FaaS performance studies from academic and industrial sources found a strong focus on a single cloud platform using artificial micro-benchmarks and discovered that the majority of studies do not follow reproducibility principles on cloud experimentation.Future Work. Future work will propose a suite of application performance benchmarks for FaaS, which is instrumental for evaluating candidate solutions towards building performance-optimized FaaS applications
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White paper – On the use of LiDAR data at AmeriFlux sites
Our aim is to inform the AmeriFlux community on existing and upcoming LiDAR technologies (atmospheric Doppler
or Raman LiDAR often deployed at flux sites are not considered here), how it is currently used at flux sites, and how
we believe it could, in the future, further contribute to the AmeriFlux vision. Heterogeneity in vegetation and ground
properties at various spatial scales is omnipresent at flux sites, and 3D mapping of canopy, understory, and ground
surface can help move the science forward
Performance Evaluation of Serverless Applications and Infrastructures
Context. Cloud computing has become the de facto standard for deploying modern web-based software systems, which makes its performance crucial to the efficient functioning of many applications. However, the unabated growth of established cloud services, such as Infrastructure-as-a-Service (IaaS), and the emergence of new serverless services, such as Function-as-a-Service (FaaS), has led to an unprecedented diversity of cloud services with different performance characteristics. Measuring these characteristics is difficult in dynamic cloud environments due to performance variability in large-scale distributed systems with limited observability.Objective. This thesis aims to enable reproducible performance evaluation of serverless applications and their underlying cloud infrastructure.Method. A combination of literature review and empirical research established a consolidated view on serverless applications and their performance. New solutions were developed through engineering research and used to conduct performance benchmarking field experiments in cloud environments.Findings. The review of 112 FaaS performance studies from academic and industrial sources found a strong focus on a single cloud platform using artificial micro-benchmarks and discovered that most studies do not follow reproducibility principles on cloud experimentation. Characterizing 89 serverless applications revealed that they are most commonly used for short-running tasks with low data volume and bursty workloads. A novel trace-based serverless application benchmark shows that external service calls often dominate the median end-to-end latency and cause long tail latency. The latency breakdown analysis further identifies performance challenges of serverless applications, such as long delays through asynchronous function triggers, substantial runtime initialization for coldstarts, increased performance variability under bursty workloads, and heavily provider-dependent performance characteristics. The evaluation of different cloud benchmarking methodologies has shown that only selected micro-benchmarks are suitable for estimating application performance, performance variability depends on the resource type, and batch testing on the same instance with repetitions should be used for reliable performance testing.Conclusions. The insights of this thesis can guide practitioners in building performance-optimized serverless applications and researchers in reproducibly evaluating cloud performance using suitable execution methodologies and different benchmark types
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
On Observability and Monitoring of Distributed Systems: An Industry Interview Study
Business success of companies heavily depends on the availability and
performance of their client applications. Due to modern development paradigms
such as DevOps and microservice architectural styles, applications are
decoupled into services with complex interactions and dependencies. Although
these paradigms enable individual development cycles with reduced delivery
times, they cause several challenges to manage the services in distributed
systems. One major challenge is to observe and monitor such distributed
systems. This paper provides a qualitative study to understand the challenges
and good practices in the field of observability and monitoring of distributed
systems. In 28 semi-structured interviews with software professionals we
discovered increasing complexity and dynamics in that field. Especially
observability becomes an essential prerequisite to ensure stable services and
further development of client applications. However, the participants mentioned
a discrepancy in the awareness regarding the importance of the topic, both from
the management as well as from the developer perspective. Besides technical
challenges, we identified a strong need for an organizational concept including
strategy, roles and responsibilities. Our results support practitioners in
developing and implementing systematic observability and monitoring for
distributed systems
Jet Momentum Resolution for the CMS Experiment and Distributed Data Caching Strategies
Accurately measured jets are mandatory for precision measurements of the Standard Model of particle physics as well as for searches for new physics.
The increased instantaneous luminosity and center-of-mass energy at LHC Run 2 pose challenges for pileup mitigation and the measurement of jet characteristics.
This thesis concentrates on using Z + jets events to calibrate the energy scale of jets recorded by the CMS detector in 2018.
Furthermore, it proposes a new procedure for determining the jet momentum resolution using Z + jets events.
This procedure is expected to allow cross-checking complementary measurement approaches and increasing the accuracy of the jet momentum resolution at the CMS experiment.
Data-intensive end-user analyses in High Energy Physics such as the presented calibration of jets put enormous challenges on the computing infrastructure since requiring high data throughput.
Besides the particle physics analysis, this thesis also focuses on accelerating data processing within a distributed computing infrastructure via a coordinated distributed caching approach.
Coordinated placement of critical data within distributed caches and matching workflows to the most suitable host in terms of cached data allows for optimizing processing efficiency.
Improving the processing of data-intensive workflows aims at shortening turnaround cycles and thus deriving physics results, e.g. the jet calibration results, faster
Model-driven Scheduling for Distributed Stream Processing Systems
Distributed Stream Processing frameworks are being commonly used with the
evolution of Internet of Things(IoT). These frameworks are designed to adapt to
the dynamic input message rate by scaling in/out.Apache Storm, originally
developed by Twitter is a widely used stream processing engine while others
includes Flink, Spark streaming. For running the streaming applications
successfully there is need to know the optimal resource requirement, as
over-estimation of resources adds extra cost.So we need some strategy to come
up with the optimal resource requirement for a given streaming application. In
this article, we propose a model-driven approach for scheduling streaming
applications that effectively utilizes a priori knowledge of the applications
to provide predictable scheduling behavior. Specifically, we use application
performance models to offer reliable estimates of the resource allocation
required. Further, this intuition also drives resource mapping, and helps
narrow the estimated and actual dataflow performance and resource utilization.
Together, this model-driven scheduling approach gives a predictable application
performance and resource utilization behavior for executing a given DSPS
application at a target input stream rate on distributed resources.Comment: 54 page
A Survey of Green Networking Research
Reduction of unnecessary energy consumption is becoming a major concern in
wired networking, because of the potential economical benefits and of its
expected environmental impact. These issues, usually referred to as "green
networking", relate to embedding energy-awareness in the design, in the devices
and in the protocols of networks. In this work, we first formulate a more
precise definition of the "green" attribute. We furthermore identify a few
paradigms that are the key enablers of energy-aware networking research. We
then overview the current state of the art and provide a taxonomy of the
relevant work, with a special focus on wired networking. At a high level, we
identify four branches of green networking research that stem from different
observations on the root causes of energy waste, namely (i) Adaptive Link Rate,
(ii) Interface proxying, (iii) Energy-aware infrastructures and (iv)
Energy-aware applications. In this work, we do not only explore specific
proposals pertaining to each of the above branches, but also offer a
perspective for research.Comment: Index Terms: Green Networking; Wired Networks; Adaptive Link Rate;
Interface Proxying; Energy-aware Infrastructures; Energy-aware Applications.
18 pages, 6 figures, 2 table
Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition
The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future
RoboPianist: A Benchmark for High-Dimensional Robot Control
We introduce a new benchmarking suite for high-dimensional control, targeted
at testing high spatial and temporal precision, coordination, and planning, all
with an underactuated system frequently making-and-breaking contacts. The
proposed challenge is mastering the piano through bi-manual dexterity, using a
pair of simulated anthropomorphic robot hands. We call it RoboPianist, and the
initial version covers a broad set of 150 variable-difficulty songs. We
investigate both model-free and model-based methods on the benchmark,
characterizing their performance envelopes. We observe that while certain
existing methods, when well-tuned, can achieve impressive levels of performance
in certain aspects, there is significant room for improvement. RoboPianist
provides a rich quantitative benchmarking environment, with human-interpretable
results, high ease of expansion by simply augmenting the repertoire with new
songs, and opportunities for further research, including in multi-task
learning, zero-shot generalization, multimodal (sound, vision, touch) learning,
and imitation. Supplementary information, including videos of our control
policies, can be found at https://kzakka.com/robopianist
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