10,923 research outputs found
Autonomic Cloud Computing: Open Challenges and Architectural Elements
As Clouds are complex, large-scale, and heterogeneous distributed systems,
management of their resources is a challenging task. They need automated and
integrated intelligent strategies for provisioning of resources to offer
services that are secure, reliable, and cost-efficient. Hence, effective
management of services becomes fundamental in software platforms that
constitute the fabric of computing Clouds. In this direction, this paper
identifies open issues in autonomic resource provisioning and presents
innovative management techniques for supporting SaaS applications hosted on
Clouds. We present a conceptual architecture and early results evidencing the
benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape
DEPAS: A Decentralized Probabilistic Algorithm for Auto-Scaling
The dynamic provisioning of virtualized resources offered by cloud computing
infrastructures allows applications deployed in a cloud environment to
automatically increase and decrease the amount of used resources. This
capability is called auto-scaling and its main purpose is to automatically
adjust the scale of the system that is running the application to satisfy the
varying workload with minimum resource utilization. The need for auto-scaling
is particularly important during workload peaks, in which applications may need
to scale up to extremely large-scale systems.
Both the research community and the main cloud providers have already
developed auto-scaling solutions. However, most research solutions are
centralized and not suitable for managing large-scale systems, moreover cloud
providers' solutions are bound to the limitations of a specific provider in
terms of resource prices, availability, reliability, and connectivity.
In this paper we propose DEPAS, a decentralized probabilistic auto-scaling
algorithm integrated into a P2P architecture that is cloud provider
independent, thus allowing the auto-scaling of services over multiple cloud
infrastructures at the same time. Our simulations, which are based on real
service traces, show that our approach is capable of: (i) keeping the overall
utilization of all the instantiated cloud resources in a target range, (ii)
maintaining service response times close to the ones obtained using optimal
centralized auto-scaling approaches.Comment: Submitted to Springer Computin
Microservice Transition and its Granularity Problem: A Systematic Mapping Study
Microservices have gained wide recognition and acceptance in software
industries as an emerging architectural style for autonomic, scalable, and more
reliable computing. The transition to microservices has been highly motivated
by the need for better alignment of technical design decisions with improving
value potentials of architectures. Despite microservices' popularity, research
still lacks disciplined understanding of transition and consensus on the
principles and activities underlying "micro-ing" architectures. In this paper,
we report on a systematic mapping study that consolidates various views,
approaches and activities that commonly assist in the transition to
microservices. The study aims to provide a better understanding of the
transition; it also contributes a working definition of the transition and
technical activities underlying it. We term the transition and technical
activities leading to microservice architectures as microservitization. We then
shed light on a fundamental problem of microservitization: microservice
granularity and reasoning about its adaptation as first-class entities. This
study reviews state-of-the-art and -practice related to reasoning about
microservice granularity; it reviews modelling approaches, aspects considered,
guidelines and processes used to reason about microservice granularity. This
study identifies opportunities for future research and development related to
reasoning about microservice granularity.Comment: 36 pages including references, 6 figures, and 3 table
Towards a Formal Model of Recursive Self-Reflection
Self-awareness holds the promise of better decision making based on a comprehensive assessment of a system\u27s own situation. Therefore it has been studied for more than ten years in a range of settings and applications. However, in the literature the term has been used in a variety of meanings and today there is no consensus on what features and properties it should include. In fact, researchers disagree on the relative benefits of a self-aware system compared to one that is very similar but lacks self-awareness.
We sketch a formal model, and thus a formal definition, of self-awareness. The model is based on dynamic dataflow semantics and includes self-assessment, a simulation and an abstraction as facilitating techniques, which are modeled by spawning new dataflow actors in the system. Most importantly, it has a method to focus on any of its parts to make it a subject of analysis by applying abstraction, self-assessment and simulation. In particular, it can apply this process to itself, which we call recursive self-reflection. There is no arbitrary limit to this self-scrutiny except resource constraints
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A survey on online monitoring approaches of computer-based systems
This report surveys forms of online data collection that are in current use (as well as being the subject of research to adapt them to changing technology and demands), and can be used as inputs to assessment of dependability and resilience, although they are not primarily meant for this use
Autonomic Management of Maintenance Scheduling in Chord
This paper experimentally evaluates the effects of applying autonomic
management to the scheduling of maintenance operations in a deployed Chord
network, for various membership churn and workload patterns. Two versions of an
autonomic management policy were compared with a static configuration. The
autonomic policies varied with respect to the aggressiveness with which they
responded to peer access error rates and to wasted maintenance operations. In
most experiments, significant improvements due to autonomic management were
observed in the performance of routing operations and the quantity of data
transmitted between network members. Of the autonomic policies, the more
aggressive version gave slightly better results
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