144 research outputs found
A trust label system for communicating trust in cloud services.
Cloud computing is rapidly changing the digital service landscape. A proliferation of Cloud providers has emerged, increasing the difficulty of consumer decisions. Trust issues have been identified as a factor holding back Cloud adoption. The risks and challenges inherent in the adoption of Cloud services are well recognised in the computing literature. In conjunction with these risks, the relative novelty of the online environment as a context for the provision of business services can increase consumer perceptions of uncertainty. This uncertainty is worsened in a Cloud context due to the lack of transparency, from the consumer perspective, into the service types, operational conditions and the quality of service offered by the diverse providers. Previous approaches failed to provide an appropriate medium for communicating trust and trustworthiness in Clouds. A new strategy is required to improve consumer confidence and trust in Cloud providers. This paper presents the operationalisation of a trust label system designed to communicate trust and trustworthiness in Cloud services. We describe the technical details and implementation of the trust label components. Based on a use case scenario, an initial evaluation was carried out to test its operations and its usefulness for increasing consumer trust in Cloud services.N/
Real-Time Virtualization and Cloud Computing
In recent years, we have observed three major trends in the development of complex real-time embedded systems. First, to reduce cost and enhance flexibility, multiple systems are sharing common computing platforms via virtualization technology, instead of being deployed separately on physically isolated hosts. Second, multi-core processors are increasingly being used in real-time systems. Third, developers are exploring the possibilities of deploying real-time applications as virtual machines in a public cloud. The integration of real-time systems as virtual machines (VMs) atop common multi-core platforms in a public cloud raises significant new research challenges in meeting the real-time latency requirements of applications.
In order to address the challenges of running real-time VMs in the cloud, we first present RT-Xen, a novel real-time scheduling framework within the popular Xen hypervisor. We start with single-core scheduling in RT-Xen, and present the first work that empirically studies and compares different real-time scheduling schemes on a same platform. We then introduce RT-Xen 2.0, which focuses on multi-core scheduling and spanning multiple design spaces, including priority schemes, server schemes, and scheduling policies. Experimental results demonstrate that when combined with compositional scheduling theory, RT-Xen can deliver real-time performance to an application running in a VM, while the default credit scheduler cannot. After that, we present RT-OpenStack, a cloud management system designed to support co-hosting real-time and non-real-time VMs in a cloud. RT-OpenStack studies the problem of running real-time VMs together with non-real-time VMs in a public cloud. Leveraging the resource interface and real-time scheduling provided by RT-Xen, RT-OpenStack provides real-time performance guarantees to real-time VMs, while achieving high resource utilization by allowing non-real-time VMs to share the remaining CPU resources through a novel VM-to-host mapping scheme. Finally, we present RTCA, a real-time communication architecture for VMs sharing a same host, which maintains low latency for high priority inter-domain communication (IDC) traffic in the face of low priority IDC traffic
MACHS: Mitigating the Achilles Heel of the Cloud through High Availability and Performance-aware Solutions
Cloud computing is continuously growing as a business model for hosting information and communication technology applications. However, many concerns arise regarding the quality of service (QoS) offered by the cloud. One major challenge is the high availability (HA) of cloud-based applications. The key to achieving availability requirements is to develop an approach that is immune to cloud failures while minimizing the service level agreement (SLA) violations. To this end, this thesis addresses the HA of cloud-based applications from different perspectives. First, the thesis proposes a componentâs HA-ware scheduler (CHASE) to manage the deployments of carrier-grade cloud applications while maximizing their HA and satisfying the QoS requirements. Second, a Stochastic Petri Net (SPN) model is proposed to capture the stochastic characteristics of cloud services and quantify the expected availability offered by an application deployment. The SPN model is then associated with an extensible policy-driven cloud scoring system that integrates other cloud challenges (i.e. green and cost concerns) with HA objectives. The proposed HA-aware solutions are extended to include a live virtual machine migration model that provides a trade-off between the migration time and the downtime while maintaining HA objective. Furthermore, the thesis proposes a generic input template for cloud simulators, GITS, to facilitate the creation of cloud scenarios while ensuring reusability, simplicity, and portability. Finally, an availability-aware CloudSim extension, ACE, is proposed. ACE extends CloudSim simulator with failure injection, computational paths, repair, failover, load balancing, and other availability-based modules
Data-Driven Methods for Data Center Operations Support
During the last decade, cloud technologies have been evolving at
an impressive pace, such that we are now living in a cloud-native
era where developers can leverage on an unprecedented landscape
of (possibly managed) services for orchestration, compute, storage,
load-balancing, monitoring, etc. The possibility to have on-demand
access to a diverse set of configurable virtualized resources allows
for building more elastic, flexible and highly-resilient distributed
applications. Behind the scenes, cloud providers sustain the heavy
burden of maintaining the underlying infrastructures, consisting in
large-scale distributed systems, partitioned and replicated among
many geographically dislocated data centers to guarantee scalability,
robustness to failures, high availability and low latency. The larger the
scale, the more cloud providers have to deal with complex interactions
among the various components, such that monitoring, diagnosing and
troubleshooting issues become incredibly daunting tasks.
To keep up with these challenges, development and operations
practices have undergone significant transformations, especially in
terms of improving the automations that make releasing new software,
and responding to unforeseen issues, faster and sustainable at scale.
The resulting paradigm is nowadays referred to as DevOps. However,
while such automations can be very sophisticated, traditional DevOps
practices fundamentally rely on reactive mechanisms, that typically
require careful manual tuning and supervision from human experts.
To minimize the risk of outagesâand the related costsâit is crucial to
provide DevOps teams with suitable tools that can enable a proactive
approach to data center operations.
This work presents a comprehensive data-driven framework to address
the most relevant problems that can be experienced in large-scale
distributed cloud infrastructures. These environments are indeed characterized
by a very large availability of diverse data, collected at each
level of the stack, such as: time-series (e.g., physical host measurements,
virtual machine or container metrics, networking components
logs, application KPIs); graphs (e.g., network topologies, fault graphs
reporting dependencies among hardware and software components,
performance issues propagation networks); and text (e.g., source code,
system logs, version control system history, code review feedbacks).
Such data are also typically updated with relatively high frequency,
and subject to distribution drifts caused by continuous configuration
changes to the underlying infrastructure. In such a highly dynamic scenario,
traditional model-driven approaches alone may be inadequate
at capturing the complexity of the interactions among system components. DevOps teams would certainly benefit from having robust
data-driven methods to support their decisions based on historical
information. For instance, effective anomaly detection capabilities may
also help in conducting more precise and efficient root-cause analysis.
Also, leveraging on accurate forecasting and intelligent control
strategies would improve resource management.
Given their ability to deal with high-dimensional, complex data,
Deep Learning-based methods are the most straightforward option for
the realization of the aforementioned support tools. On the other hand,
because of their complexity, this kind of models often requires huge
processing power, and suitable hardware, to be operated effectively
at scale. These aspects must be carefully addressed when applying
such methods in the context of data center operations. Automated
operations approaches must be dependable and cost-efficient, not to
degrade the services they are built to improve.
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An Introduction to Software Ecosystems
This chapter defines and presents different kinds of software ecosystems. The
focus is on the development, tooling and analytics aspects of software
ecosystems, i.e., communities of software developers and the interconnected
software components (e.g., projects, libraries, packages, repositories,
plug-ins, apps) they are developing and maintaining. The technical and social
dependencies between these developers and software components form a
socio-technical dependency network, and the dynamics of this network change
over time. We classify and provide several examples of such ecosystems. The
chapter also introduces and clarifies the relevant terms needed to understand
and analyse these ecosystems, as well as the techniques and research methods
that can be used to analyse different aspects of these ecosystems.Comment: Preprint of chapter "An Introduction to Software Ecosystems" by Tom
Mens and Coen De Roover, published in the book "Software Ecosystems: Tooling
and Analytics" (eds. T. Mens, C. De Roover, A. Cleve), 2023, ISBN
978-3-031-36059-6, reproduced with permission of Springer. The final
authenticated version of the book and this chapter is available online at:
https://doi.org/10.1007/978-3-031-36060-
Decentralized SDN Control Plane for a Distributed Cloud-Edge Infrastructure: A Survey
International audienceTodayâs emerging needs (Internet of Things applications, Network Function Virtualization services, Mobile Edge computing, etc.) are challenging the classic approach of deploying a few large data centers to provide cloud services. A massively distributed Cloud-Edge architecture could better fit these new trendsâ requirements and constraints by deploying on-demand infrastructure services in Point-of-Presences within backbone networks. In this context, a key feature is establishing connectivity among several resource managers in charge of operating, each one a subset of the infrastructure. After explaining the networking management challenges related to distributed Cloud-Edge infrastructures, this article surveys and analyzes the characteristics and limitations of existing technologies in the Software Defined Network field that could be used to provide the intersite connectivity feature. We also introduce Kubernetes, the new de facto container orchestrator platform, and analyze its use in the proposed context. This survey is concluded by providing a discussion about some research directions in the field of SDN applied to distributed Cloud-Edge infrastructuresâ management
Understanding, Analysis, and Handling of Software Architecture Erosion
Architecture erosion occurs when a software system's implemented architecture diverges from the intended architecture over time. Studies show erosion impacts development, maintenance, and evolution since it accumulates imperceptibly. Identifying early symptoms like architectural smells enables managing erosion through refactoring. However, research lacks comprehensive understanding of erosion, unclear which symptoms are most common, and lacks detection methods. This thesis establishes an erosion landscape, investigates symptoms, and proposes identification approaches. A mapping study covers erosion definitions, symptoms, causes, and consequences. Key findings: 1) "Architecture erosion" is the most used term, with four perspectives on definitions and respective symptom types. 2) Technical and non-technical reasons contribute to erosion, negatively impacting quality attributes. Practitioners can advocate addressing erosion to prevent failures. 3) Detection and correction approaches are categorized, with consistency and evolution-based approaches commonly mentioned.An empirical study explores practitioner perspectives through communities, surveys, and interviews. Findings reveal associated practices like code review and tools identify symptoms, while collected measures address erosion during implementation. Studying code review comments analyzes erosion in practice. One study reveals architectural violations, duplicate functionality, and cyclic dependencies are most frequent. Symptoms decreased over time, indicating increased stability. Most were addressed after review. A second study explores violation symptoms in four projects, identifying 10 categories. Refactoring and removing code address most violations, while some are disregarded.Machine learning classifiers using pre-trained word embeddings identify violation symptoms from code reviews. Key findings: 1) SVM with word2vec achieved highest performance. 2) fastText embeddings worked well. 3) 200-dimensional embeddings outperformed 100/300-dimensional. 4) Ensemble classifier improved performance. 5) Practitioners found results valuable, confirming potential.An automated recommendation system identifies qualified reviewers for violations using similarity detection on file paths and comments. Experiments show common methods perform well, outperforming a baseline approach. Sampling techniques impact recommendation performance
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