58,376 research outputs found
Real-time agreement and fulfilment of SLAs in Cloud Computing environments
A Cloud Computing system must readjust its resources by taking into account the demand for its services. This raises the need for
designing protocols that provide the individual components of the Cloud architecture with the ability to self-adapt and to reach
agreements in order to deal with changes in the services demand. Furthermore, if the Cloud provider has signed a Service Level
Agreement (SLA) with the clients of the services that it offers, the appropriate agreement mechanism has to ensure the provision
of the service contracted within a specified time. This paper introduces real-time mechanisms for the agreement and fulfilment
of SLAs in Cloud Computing environments. On the one hand, it presents a negotiation protocol inspired by the standard WSAgreement
used in web services to manage the interactions between the client and the Cloud provider to agree the terms of
the SLA of a service. On the other hand, it proposes the application of a real-time argumentation framework for redistributing
resources and ensuring the fulfilment of these SLAs during peaks in the service demand.This work is supported by the Spanish government Grants CONSOLIDER-INGENIO 2010 CSD2007-00022, TIN2011-27652-C03-01, TIN2012-36586-C03-01 and TIN2012-36586-C03-03.De La Prieta, F.; Heras Barberá, SM.; Palanca Cámara, J.; Rodríguez, S.; Bajo, J.; Julian Inglada, VJ. (2014). Real-time agreement and fulfilment of SLAs in Cloud Computing environments. AI Communications. 1-24. doi:10.3233/AIC-140626S124[1]V. Aleven and K.D. Ashley, Teaching case-based argumentation through a model and examples, empirical evaluation of an intelligent learning environment, in: Artificial Intelligence in Education, AIED-97, Frontiers in Artificial Intelligence and Applications, Vol. 39, IOS Press, 1997, pp. 87–94.[2]M. Alhamad, W. Perth, T. Dillon and E. 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The Knowledge Engineering Review, 24(4), 327-352. doi:10.1017/s0269888909990178Heras, S., Jordán, J., Botti, V., & Julián, V. (2013). Argue to agree: A case-based argumentation approach. International Journal of Approximate Reasoning, 54(1), 82-108. doi:10.1016/j.ijar.2012.06.005[24]M. Jensen, J. Schwenk, N. Gruschka and L. Iacono, On technical security issues in cloud computing, in: IEEE International Conference on Cloud Computing, IEEE Press, 2009, pp. 109–116.Kakas, A., Maudet, N., & Moraitis, P. (2005). Modular Representation of Agent Interaction Rules through Argumentation. Autonomous Agents and Multi-Agent Systems, 11(2), 189-206. doi:10.1007/s10458-005-2176-4[26]M.J. Kim, H.G. Yoon and H.K. Lee, MAV: An intelligent Multi-agent model based on Cloud computing for resource virtualization, in: Computers, Networks, Systems, and Industrial Engineering, Studies in Computational Intelligence, Vol. 365, Springer, 2011, pp. 99–111.Kraus, S., Sycara, K., & Evenchik, A. (1998). 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Preparing for utility computing: The role of IT architecture and relationship management. IBM Systems Journal, 43(1), 5-19. doi:10.1147/sj.431.0005Schaffer, H. E. (2009). X as a Service, Cloud Computing, and the Need for Good Judgment. IT Professional, 11(5), 4-5. doi:10.1109/mitp.2009.112[39]K.M. Sim, Agent-based cloud commerce, in: IEEE International Conference on Industrial Engineering and Engineering Management, IEEE Press, 2009, pp. 717–721.Soh, L.-K., & Tsatsoulis, C. (2005). A Real-Time Negotiation Model and A Multi-Agent Sensor Network Implementation. Autonomous Agents and Multi-Agent Systems, 11(3), 215-271. doi:10.1007/s10458-005-0539-5Talia, D. (2012). Clouds Meet Agents: Toward Intelligent Cloud Services. IEEE Internet Computing, 16(2), 78-81. doi:10.1109/mic.2012.28Tolchinsky, P., Modgil, S., Atkinson, K., McBurney, P., & Cortés, U. (2011). Deliberation dialogues for reasoning about safety critical actions. 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Coady, Dynamic resource allocation in computing clouds using distributed multiple criteria decision analysis, in: IEEE 3rd International Conference on Cloud Computing (CLOUD), IEEE Computer Society, 2010, pp. 91–98.[49]Y. Yu, S. Ren, N. Chen and X. Wang, Profit and penalty aware (pp-aware) scheduling for tasks with variable task execution time, in: ACM Symposium on Applied Computing, SAC’10, ACM, 2010, pp. 334–339
Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities
Edge computing is a promising paradigm that enhances the capabilities of cloud computing. In order to continue patronizing the computing services, it is essential to conserve a good atmosphere free from all kinds of security and privacy breaches. The security and privacy issues associated with the edge computing environment have narrowed the overall acceptance of the technology as a reliable paradigm. Many researchers have reviewed security and privacy issues in edge computing, but not all have fully investigated the security and privacy requirements. Security and privacy requirements are the objectives that indicate the capabilities as well as functions a system performs in eliminating certain security and privacy vulnerabilities. The paper aims to substantially review the security and privacy requirements of the edge computing and the various technological methods employed by the techniques used in curbing the threats, with the aim of helping future researchers in identifying research opportunities. This paper investigate the current studies and highlights the following: (1) the classification of security and privacy requirements in edge computing, (2) the state of the art techniques deployed in curbing the security and privacy threats, (3) the trends of technological methods employed by the techniques, (4) the metrics used for evaluating the performance of the techniques, (5) the taxonomy of attacks affecting the edge network, and the corresponding technological trend employed in mitigating the attacks, and, (6) research opportunities for future researchers in the area of edge computing security and privacy
Efficient Optimal Algorithm of Task Scheduling in Cloud Computing Environment
Cloud computing is an emerging technology in distributed computing which
facilitates pay per model as per user demand and requirement.Cloud consist of a
collection of virtual machine which includes both computational and storage
facility. The primary aim of cloud computing is to provide efficient access to
remote and geographically distributed resources. Cloud is developing day by day
and faces many challenges, one of them is scheduling. Scheduling refers to a
set of policies to control the order of work to be performed by a computer
system. A good scheduler adapts its scheduling strategy according to the
changing environment and the type of task. In this research paper we presented
a Generalized Priority algorithm for efficient execution of task and comparison
with FCFS and Round Robin Scheduling. Algorithm should be tested in cloud Sim
toolkit and result shows that it gives better performance compared to other
traditional scheduling algorithm.Comment: 6,1. published in IJCTT 2014 mARC
Factors Influencing Job Rejections in Cloud Environment
The IT organizations invests heavy capital by consuming large scale
infrastructure and advanced operating platforms. The advances in technology has
resulted in emergence of cloud computing, which is promising technology to
achieve the aforementioned objective. At the peak hours, the jobs arriving to
the cloud system are normally high demanding efficient execution and dispatch.
An observation that has been carried out in this paper by capturing a job
arriving pattern from a monitoring system explains that most of the jobs get
rejected because of lack of efficient technology. The job rejections can be
controlled by certain factors such as job scheduling and load balancing.
Therefore, in this paper the efficiency of Round Robin (RR) scheduling strategy
used for job scheduling and Shortest Job First Scheduling (SJFS) technique used
for load balancing in reducing the job rejections are analyzed. Further, a
proposal for an effective load balancing approach to avoid deadlocks has been
discussed.Comment: 6 Pages, 5 Figures, 8 Table
Context-Awareness Enhances 5G Multi-Access Edge Computing Reliability
The fifth generation (5G) mobile telecommunication network is expected to
support Multi- Access Edge Computing (MEC), which intends to distribute
computation tasks and services from the central cloud to the edge clouds.
Towards ultra-responsive, ultra-reliable and ultra-low-latency MEC services,
the current mobile network security architecture should enable a more
decentralized approach for authentication and authorization processes. This
paper proposes a novel decentralized authentication architecture that supports
flexible and low-cost local authentication with the awareness of context
information of network elements such as user equipment and virtual network
functions. Based on a Markov model for backhaul link quality, as well as a
random walk mobility model with mixed mobility classes and traffic scenarios,
numerical simulations have demonstrated that the proposed approach is able to
achieve a flexible balance between the network operating cost and the MEC
reliability.Comment: Accepted by IEEE Access on Feb. 02, 201
Accelerating R-based Analytics on the Cloud
This paper addresses how the benefits of cloud-based infrastructure can be
harnessed for analytical workloads. Often the software handling analytical
workloads is not developed by a professional programmer, but on an ad hoc basis
by Analysts in high-level programming environments such as R or Matlab. The
goal of this research is to allow Analysts to take an analytical job that
executes on their personal workstations, and with minimum effort execute it on
cloud infrastructure and manage both the resources and the data required by the
job. If this can be facilitated gracefully, then the Analyst benefits from
on-demand resources, low maintenance cost and scalability of computing
resources, all of which are offered by the cloud. In this paper, a Platform for
Parallel R-based Analytics on the Cloud (P2RAC) that is placed between an
Analyst and a cloud infrastructure is proposed and implemented. P2RAC offers a
set of command-line tools for managing the resources, such as instances and
clusters, the data and the execution of the software on the Amazon Elastic
Computing Cloud infrastructure. Experimental studies are pursued using two
parallel problems and the results obtained confirm the feasibility of employing
P2RAC for solving large-scale analytical problems on the cloud.Comment: Concurrency and Computation, 201
Energy Efficient Resource Allocation in Vehicular Cloud based Architecture
The increasing availability of on-board processing units in vehicles has led
to a new promising mobile edge computing (MEC) concept which integrates
desirable features of clouds and VANETs under the concept of vehicular clouds
(VC). In this paper we propose an architecture that integrates VC with metro
fog nodes and the central cloud to ensure service continuity. We tackle the
problem of energy efficient resource allocation in this architecture by
developing a Mixed Integer Linear Programming (MILP) model to minimize power
consumption by optimizing the assignment of different tasks to the available
resources in this architecture. We study service provisioning considering
different assignment strategies under varying application demands and analyze
the impact of these strategies on the utilization of the VC resources and
therefore, the overall power consumption. The results show that traffic demands
have a higher impact on the power consumption, compared to the impact of the
processing demands. Integrating metro fog nodes and vehicle edge nodes in the
cloud-based architecture can save power, with an average power saving up to
54%. The power savings can increase by 12% by distributing the task assignment
among multiple vehicles in the VC level, compared to assigning the whole task
to a single processing node.Comment: 6 pages, 4 figures, ICTON 201
A Service Broker Model for Cloud based Render Farm Selection
Cloud computing is gaining popularity in the 3D Animation industry for
rendering the 3D images. Rendering is an inevitable task in creating the 3d
animated scenes. It is a process where the scene files to be animated is read
and converted into 3D photorealistic images automatically. Since it is a
computationally intensive task, this process consumes the majority of the time
taken for 3D images production. As the scene files could be processed in
parallel, clusters of computers called render farms can be used to speed up the
rendering process. The advantage of using Cloud based render farms is that it
is scalable and can be availed on demand. One of the important challenges faced
by the 3D studios is the comparison and selection of the cloud based render
farm service provider who could satisfy their functional and the non functional
Quality of Service (QoS) requirements. In this paper we propose, a frame work
for Cloud Service Broker (CSB) responsible for the selection and provision of
the cloud based render farm. The Cloud Service Broker matches the functional
and the non functional Quality of Service requirements (QoS) of the user with
the service offerings of the render farm service providers and helps the user
in selecting the right service provider using an aggregate utility function.
The CSB also facilitates the process of Service Level Agreement (SLA)
negotiation and monitoring by the third party monitoring services
A Comparative Taxonomy and Survey of Public Cloud Infrastructure Vendors
An increasing number of technology enterprises are adopting cloud-native
architectures to offer their web-based products, by moving away from
privately-owned data-centers and relying exclusively on cloud service
providers. As a result, cloud vendors have lately increased, along with the
estimated annual revenue they share. However, in the process of selecting a
provider's cloud service over the competition, we observe a lack of universal
common ground in terms of terminology, functionality of services and billing
models. This is an important gap especially under the new reality of the
industry where each cloud provider has moved towards his own service taxonomy,
while the number of specialized services has grown exponentially. This work
discusses cloud services offered by four dominant, in terms of their current
market share, cloud vendors. We provide a taxonomy of their services and
sub-services that designates major service families namely computing, storage,
databases, analytics, data pipelines, machine learning, and networking. The aim
of such clustering is to indicate similarities, common design approaches and
functional differences of the offered services. The outcomes are essential both
for individual researchers, and bigger enterprises in their attempt to identify
the set of cloud services that will utterly meet their needs without
compromises. While we acknowledge the fact that this is a dynamic industry,
where new services arise constantly, and old ones experience important updates,
this study paints a solid image of the current offerings and gives prominence
to the directions that cloud service providers are following
Dynamic resource management in Cloud datacenters for Server consolidation
Cloud resource management has been a key factor for the cloud datacenters
development. Many cloud datacenters have problems in understanding and
implementing the techniques to manage, allocate and migrate the resources in
their premises. The consequences of improper resource management may result
into underutilized and wastage of resources which may also result into poor
service delivery in these datacenters. Resources like, CPU, memory, Hard disk
and servers need to be well identified and managed. In this Paper, Dynamic
Resource Management Algorithm(DRMA) shall limit itself in the management of CPU
and memory as the resources in cloud datacenters. The target is to save those
resources which may be underutilized at a particular period of time. It can be
achieved through Implementation of suitable algorithms. Here, Bin packing
algorithm can be used whereby the best fit algorithm is deployed to obtain
results and compared to select suitable algorithm for efficient use of
resources.Comment: 8 pages, 4 figure
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