220 research outputs found
Checkpointing as a Service in Heterogeneous Cloud Environments
A non-invasive, cloud-agnostic approach is demonstrated for extending
existing cloud platforms to include checkpoint-restart capability. Most cloud
platforms currently rely on each application to provide its own fault
tolerance. A uniform mechanism within the cloud itself serves two purposes: (a)
direct support for long-running jobs, which would otherwise require a custom
fault-tolerant mechanism for each application; and (b) the administrative
capability to manage an over-subscribed cloud by temporarily swapping out jobs
when higher priority jobs arrive. An advantage of this uniform approach is that
it also supports parallel and distributed computations, over both TCP and
InfiniBand, thus allowing traditional HPC applications to take advantage of an
existing cloud infrastructure. Additionally, an integrated health-monitoring
mechanism detects when long-running jobs either fail or incur exceptionally low
performance, perhaps due to resource starvation, and proactively suspends the
job. The cloud-agnostic feature is demonstrated by applying the implementation
to two very different cloud platforms: Snooze and OpenStack. The use of a
cloud-agnostic architecture also enables, for the first time, migration of
applications from one cloud platform to another.Comment: 20 pages, 11 figures, appears in CCGrid, 201
A STUDY ON CLOUD COMPUTING EFFICIENT JOB SCHEDULING ALGORITHMS
cloud computing is a general term used to depict another class of system based computing that happens over the web. The essential advantage of moving to Clouds is application versatility. Cloud computing is extremely advantageous for the application which are sharing their resources on various hubs. Scheduling the errand is a significant testing in cloud condition. Typically undertakings are planned by client prerequisites. New scheduling techniques should be proposed to defeat the issues proposed by organize properties amongst client and resources. New scheduling systems may utilize a portion of the customary scheduling ideas to consolidate them with some system mindful procedures to give answers for better and more effective employment scheduling. Scheduling technique is the key innovation in cloud computing. This paper gives the study on scheduling calculations. There working regarding the resource sharing. We systemize the scheduling issue in cloud computing, and present a cloud scheduling pecking order
LIBRA: An Economical Hybrid Approach for Cloud Applications with Strict SLAs
Function-as-a-Service (FaaS) has recently emerged to reduce the deployment
cost of running cloud applications compared to Infrastructure-as-a-Service
(IaaS). FaaS follows a serverless 'pay-as-you-go' computing model; it comes at
a higher cost per unit of execution time but typically application functions
experience lower provisioning time (startup delay). IaaS requires the
provisioning of Virtual Machines, which typically suffer from longer cold-start
delays that cause higher queuing delays and higher request drop rates. We
present LIBRA, a balanced (hybrid) approach that leverages both VM-based and
serverless resources to efficiently manage cloud resources for the
applications. LIBRA closely monitors the application demand and provisions
appropriate VM and serverless resources such that the running cost is minimized
and Service-Level Agreements are met. Unlike state of the art, LIBRA not only
hides VM cold-start delays, and hence reduces response time, by leveraging
serverless, but also directs a low-rate bursty portion of the demand to
serverless where it would be less costly than spinning up new VMs. We evaluate
LIBRA on real traces in a simulated environment as well as on the AWS
commercial cloud. Our results show that LIBRA outperforms other
resource-provisioning policies, including a recent hybrid approach - LIBRA
achieves more than 85% reduction in SLA violations and up to 53% cost savings
Detecting TCP SYN Flood Attack in the Cloud
In this paper, an approach to protecting virtual machines (VMs) against TCP SYN flood attack in a cloud environment is proposed. An open source cloud platform Eucalyptus is deployed and experimentation is carried out on this setup. We investigate attacks emanating from one VM to another in a multi-tenancy cloud environment. Various scenarios of the attack are executed on a webserver VM. To detect such attacks from a cloud providerâs perspective, a security mechanism involving a packet sniffer, feature extraction process, a classifier and an alerting component is proposed and implemented. We experiment with k-nearest neighbor and artificial neural network for classification of the attack. The dataset obtained from the attacks on the webserver VM is passed through the classifiers. The artificial neural network produced a F1 score of 1 with the test cases implying a 100% detection accuracy of the malicious attack traffic from legitimate traffic. The proposed security mechanism shows promising results in detecting TCP SYN flood attack behaviors in the cloud
A Cost-Aware Mechanism for Optimized Resource Provisioning in Cloud Computing
Due to the recent wide use of computational resources in cloud computing, new
resource provisioning challenges have been emerged. Resource provisioning
techniques must keep total costs to a minimum while meeting the requirements of
the requests. According to widely usage of cloud services, it seems more
challenging to develop effective schemes for provisioning services
cost-effectively; we have proposed a novel learning based resource provisioning
approach that achieves cost-reduction guarantees of demands. The contributions
of our optimized resource provisioning (ORP) approach are as follows. Firstly,
it is designed to provide a cost-effective method to efficiently handle the
provisioning of requested applications; while most of the existing models allow
only workflows in general which cares about the dependencies of the tasks, ORP
performs based on services of which applications comprised and cares about
their efficient provisioning totally. Secondly, it is a learning automata-based
approach which selects the most proper resources for hosting each service of
the demanded application; our approach considers both cost and service
requirements together for deploying applications. Thirdly, a comprehensive
evaluation is performed for three typical workloads: data-intensive,
process-intensive and normal applications. The experimental results show that
our method adapts most of the requirements efficiently, and furthermore the
resulting performance meets our design goals
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