Cloud Computing and IT as a Service: Opportunities and Challenges

Lin, Geng; Dasmalchi, G.; Zhu, Jinzy

doi:10.1109/services-2.2008.50

Cited by 5 publications

(5 citation statements)

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“…Creating a stateful data drive is infeasible because the original data inside the drive is lost forever and cannot be replicated, even though the empty drive can be created by calling the relevant cloud API function. Reassigning a new IP address to a cloud instance is not feasible because this activity by cloud consumers is not allowed by public cloud platforms because of the limited visibility and indirect control of cloud . Hence, as long as the state transition does not involve data drive creation or IP address reassignment, it is feasible.…”

Section: Our Prototype Recovery Servicementioning

confidence: 99%

Runtime recovery actions selection for sporadic operations on public cloud

Zhu

Sun

et al. 2016

Softw. Pract. Exper.

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Summary Sporadic operations such as rolling upgrade or machine instance redeployment are prone to unpredictable failures in the public cloud largely because of the inherent high variability nature of public cloud. Previous dependability research has established several recovery methods for cloud failures. In this paper, we first propose eight recovery patterns for sporadic operations on public cloud. We then present the filtering process which filters applicable recovery patterns. We propose an automation mechanism to automatically generate recovery actions for those applicable recovery patterns based on our resource state transition algorithm. We also propose a methodology to evaluate the recovery actions generated for the applicable recovery patterns based on the recovery evaluation metrics of Recovery Time, Recovery Cost, and Recovery Impact. This quantitative evaluation will lead to selection of the acceptable recovery actions. We propose two recovery actions selection mechanisms: one is based on user constraints of the recovery evaluation metrics, and the other one is based on Pareto set searching algorithm. We implement a recovery service and illustrate its applicability by recovering from errors occurring in the rolling upgrade operation on AWS cloud.

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Section: Our Prototype Recovery Servicementioning

confidence: 99%

Runtime recovery actions selection for sporadic operations on public cloud

Zhu

Sun

et al. 2016

Softw. Pract. Exper.

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“…Conventional telecom service definition and design, each service is independent with specific data model, provision process and system interface; such design concept is called silo architecture [9]. Due to the reason, information is hard to be shared between systems and several stand-along service enabling processes cannot be linked smoothly for service integration.…”

Section: Problem Statementmentioning

confidence: 99%

Cloud service enabling architecture for multi-vendor environment

Lee

Sung

Wang

et al. 2015

2015 17th Asia-Pacific Network Operations and Management Symposium (APNOMS)

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In recent years, many Communication Service Providers (CSP) plan to gradually move from telecommunication companies to cloud service providers and many third party software vendors are invited as partners of CSPs. Without an agile cloud service enabling architecture, CSPs cannot integrate their own services with partners' efficiently due to some issues. First, silo operation support system (OSS) is difficult to share data during service enabling process. Second, in-house developers and outer partners cannot understand complete metadata of service because of lack catalog management mechanism, and it makes service mash up inefficiently and inappropriately. Finally, System APIs are customized for specific service, that is, APIs are not reusable. In this paper, CHT Cloud Service Enabling Architecture is introduced to overcome issues of silo system architecture and create capacity to enable integrated cloud service efficiently.

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“…In recent years, cloud computing [1,2] has become a hot topic of discussion. At present, IBM, Google, Amazon, Microsoft etc.…”

Section: Introductionmentioning

confidence: 99%

Improved Particle Optimization Algorithm Solving Hadoop Task Scheduling Problem

Jia¹,

Tang²

2015

Proceedings of the 2nd International Conference on Intelligent Computing and Cognitive Informatics

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Cloud computing to provide service for the user group is huge, so the number of cloud computer's tasks is enormous, the system handle large tasks all the time so that task scheduling is the key and difficult points in the cloud. This article make research on how to make full use of cloud resources for task efficiently scheduling. This paper proposes an Improved Particle Swarm-Estimation of Distribution optimization Algorithm (IPS-EDA) based on task allocation strategy. The task scheduling strategy is optimization strategy based on improved particle swarm algorithm, which introduce estimation of distribution algorithm (EDA) based probabilistic model and random sampling theory, the proposed algorithm does not fall into local optimum. The simulation results show that the performance of IPS-EDA has been greatly improved provides better load balancing and resource utilization.

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Cloud Computing and IT as a Service: Opportunities and Challenges

Cited by 5 publications

References 0 publications

Runtime recovery actions selection for sporadic operations on public cloud

Runtime recovery actions selection for sporadic operations on public cloud

Cloud service enabling architecture for multi-vendor environment

Improved Particle Optimization Algorithm Solving Hadoop Task Scheduling Problem

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