263 research outputs found
Microservices-based IoT Applications Scheduling in Edge and Fog Computing: A Taxonomy and Future Directions
Edge and Fog computing paradigms utilise distributed, heterogeneous and
resource-constrained devices at the edge of the network for efficient
deployment of latency-critical and bandwidth-hungry IoT application services.
Moreover, MicroService Architecture (MSA) is increasingly adopted to keep up
with the rapid development and deployment needs of the fast-evolving IoT
applications. Due to the fine-grained modularity of the microservices along
with their independently deployable and scalable nature, MSA exhibits great
potential in harnessing both Fog and Cloud resources to meet diverse QoS
requirements of the IoT application services, thus giving rise to novel
paradigms like Osmotic computing. However, efficient and scalable scheduling
algorithms are required to utilise the said characteristics of the MSA while
overcoming novel challenges introduced by the architecture. To this end, we
present a comprehensive taxonomy of recent literature on microservices-based
IoT applications scheduling in Edge and Fog computing environments.
Furthermore, we organise multiple taxonomies to capture the main aspects of the
scheduling problem, analyse and classify related works, identify research gaps
within each category, and discuss future research directions.Comment: 35 pages, 10 figures, submitted to ACM Computing Survey
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
Adapting Microservices in the Cloud with FaaS
This project involves benchmarking, microservices and Function-as-a-service (FaaS) across the dimensions of performance and cost. In order to do a comparison this paper proposes a benchmark framework
From Kubernetes to Knactor: A State-Centric Rethink of Service Integration
Microservices are increasingly used in modern applications, leading to a
growing need for effective service integration solutions. However, we argue
that traditional API-centric integration mechanisms (e.g., RPC, REST, and
Pub/Sub) hamper the modularity of microservices. These mechanisms introduce
rigid code-level coupling, scatter integration logic, and hinder visibility
into cross-service state exchanges. Ultimately, these limitations complicate
the maintenance and evolution of microservice-based applications. In response,
we propose a rethinking of service integration and present Knactor, a new
state-centric integration framework to restore the modularity that
microservices were intended to offer. Knactor decouples service integration
from service development, allowing integration to be implemented as explicit
state exchanges among multiple services. Our initial case study suggests that
Knactor simplifies service integration and creates new opportunities for
optimizations
Scalability Benchmarking of Cloud-Native Applications Applied to Event-Driven Microservices
Cloud-native applications constitute a recent trend for designing large-scale software systems. This thesis introduces the Theodolite benchmarking method, allowing researchers and practitioners to conduct empirical scalability evaluations of cloud-native applications, their frameworks, configurations, and deployments. The benchmarking method is applied to event-driven microservices, a specific type of cloud-native applications that employ distributed stream processing frameworks to scale with massive data volumes. Extensive experimental evaluations benchmark and compare the scalability of various stream processing frameworks under different configurations and deployments, including different public and private cloud environments. These experiments show that the presented benchmarking method provides statistically sound results in an adequate amount of time. In addition, three case studies demonstrate that the Theodolite benchmarking method can be applied to a wide range of applications beyond stream processing
Edge computing platforms for Internet of Things
Internet of Things (IoT) has the potential to transform many domains of human activity, enabled by the collection of data from the physical world at a massive scale. As the projected growth of IoT data exceeds that of available network capacity, transferring it to centralized cloud data centers is infeasible. Edge computing aims to solve this problem by processing data at the edge of the network, enabling applications with specialized requirements that cloud computing cannot meet.
The current market of platforms that support building IoT applications is very fragmented, with offerings available from hundreds of companies with no common architecture. This threatens the realization of IoT's potential: with more interoperability, a new class of applications that combine the collected data and use it in new ways could emerge.
In this thesis, promising IoT platforms for edge computing are surveyed. First, an understanding of current challenges in the field is gained through studying the available literature on the topic. Second, IoT edge platforms having the most potential to meet these challenges are chosen and reviewed for their capabilities. Finally, the platforms are compared against each other, with a focus on their potential to meet the challenges learned in the first part.
The work shows that AWS IoT for the edge and Microsoft Azure IoT Edge have mature feature sets. However, these platforms are tied to their respective cloud platforms, limiting interoperability and the possibility of switching providers. On the other hand, open source EdgeX Foundry and KubeEdge have the potential for more standardization and interoperability in IoT but are limited in functionality for building practical IoT applications
CloudOps: Towards the Operationalization of the Cloud Continuum: Concepts, Challenges and a Reference Framework
The current trend of developing highly distributed, context aware, heterogeneous computing intense and data-sensitive applications is changing the boundaries of cloud computing. Encouraged by the growing IoT paradigm and with flexible edge devices available, an ecosystem of a combination of resources, ranging from high density compute and storage to very lightweight embedded computers running on batteries or solar power, is available for DevOps teams from what is known as the Cloud Continuum. In this dynamic context, manageability is key, as well as controlled operations and resources monitoring for handling anomalies. Unfortunately, the operation and management of such heterogeneous computing environments (including edge, cloud and network services) is complex and operators face challenges such as the continuous optimization and autonomous (re-)deployment of context-aware stateless and stateful applications where, however, they must ensure service continuity while anticipating potential failures in the underlying infrastructure. In this paper, we propose a novel CloudOps workflow (extending the traditional DevOps pipeline), proposing techniques and methods for applications’ operators to fully embrace the possibilities of the Cloud Continuum. Our approach will support DevOps teams in the operationalization of the Cloud Continuum. Secondly, we provide an extensive explanation of the scope, possibilities and future of the CloudOps.This research was funded by the European project PIACERE (Horizon 2020 Research and Innovation Programme, under grant agreement No. 101000162)
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