2,249 research outputs found
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
Clustering Algorithms for Scale-free Networks and Applications to Cloud Resource Management
In this paper we introduce algorithms for the construction of scale-free
networks and for clustering around the nerve centers, nodes with a high
connectivity in a scale-free networks. We argue that such overlay networks
could support self-organization in a complex system like a cloud computing
infrastructure and allow the implementation of optimal resource management
policies.Comment: 14 pages, 8 Figurs, Journa
A framework for smart production-logistics systems based on CPS and industrial IoT
Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems
End-to-end elasticity control of cloud-network slices
The design of efficient elasticity control mechanisms for dynamic resource allocation is crucial to increase the efficiency of future cloud-network slice-defined systems. Current elasticity control mechanisms proposed for cloud- or network-slicing, only consider cloud- or network-type resources respectively. In this paper, we introduce the elaSticity in cLOud-neTwork Slices (SLOTS) which aims to extend the horizontal elasticity control to multi-providers scenarios in an end-to-end fashion, as well as to provide a novel vertical elasticity mechanism to deal with critical insufficiency of resources by harvesting underused resources on other slices. Finally, we present a preliminary assessment of the SLOTS prototype in a real testbed, revealing outcomes that suggest the viability of the proposal.Peer ReviewedPostprint (published version
ACHIEVING AUTONOMIC SERVICE ORIENTED ARCHITECTURE USING CASE BASED REASONING
Service-Oriented Architecture (SOA) enables composition of large and complex
computational units out of the available atomic services. However, implementation of
SOA, for its dynamic nature, could bring about challenges in terms of service
discovery, service interaction, service composition, robustness, etc. In the near future,
SOA will often need to dynamically re-configuring and re-organizing its topologies of
interactions between the web services because of some unpredictable events, such as
crashes or network problems, which will cause service unavailability. Complexity and
dynamism of the current and future global network system require service architecture
that is capable of autonomously changing its structure and functionality to meet
dynamic changes in the requirements and environment with little human intervention.
This then needs to motivate the research described throughout this thesis.
In this thesis, the idea of introducing autonomy and adapting case-based reasoning
into SOA in order to extend the intelligence and capability of SOA is contributed and
elaborated. It is conducted by proposing architecture of an autonomic SOA
framework based on case-based reasoning and the architectural considerations of
autonomic computing paradigm. It is then followed by developing and analyzing
formal models of the proposed architecture using Petri Net. The framework is also
tested and analyzed through case studies, simulation, and prototype development. The
case studies show feasibility to employing case-based reasoning and autonomic
computing into SOA domain and the simulation results show believability that it
would increase the intelligence, capability, usability and robustness of SOA. It was
shown that SOA can be improved to cope with dynamic environment and services
unavailability by incorporating case-based reasoning and autonomic computing
paradigm to monitor and analyze events and service requests, then to plan and execute
the appropriate actions using the knowledge stored in knowledge database
Autonomic Management of Cloud Neighbourhoods through Pulse Monitoring
Abstract-This paper reports on autonomic computing research, including the development of a self-* proof of concept, for a cloud based environment. It monitors administrative boundaries from within an autonomic manager, with each manager operating in a peer-to-peer mode and utilizing a pulse monitor. The prototype was developed in Java utilizing SNMP to demonstrate the manager's self-situation and environment-awareness of the current state of the whole neighborhood and proves the feasibility of communicating the health of the neighborhood to peer managers using an XML pulse concept. Each manager houses the functionality to enact changes to their neighborhood using SNMP based rules. This enables the capability to provide self-healing, self-configuring, self-optimizing and self-protection to network neighborhoods within cloud computing
A network approach for managing and processing big cancer data in clouds
Translational cancer research requires integrative analysis of multiple levels of big cancer data to identify and treat cancer. In order to address the issues that data is decentralised, growing and continually being updated, and the content living or archiving on different information sources partially overlaps creating redundancies as well as contradictions and inconsistencies, we develop a data network model and technology for constructing and managing big cancer data. To support our data network approach for data process and analysis, we employ a semantic content network approach and adopt the CELAR cloud platform. The prototype implementation shows that the CELAR cloud can satisfy the on-demanding needs of various data resources for management and process of big cancer data
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