Performance and scalability testing and measurements of cloud-based software services are necessary for future optimizations and growth of cloud computing. Scalability, elasticity, and efficiency are interrelated aspects of cloud-based software services’ performance requirements. In this work, we use a technical measurement of the scalability of cloud-based software services. Our technical scalability metrics are inspired by metrics of elasticity. We used two cloud-based systems to demonstrate the usefulness of our metrics and compare their scalability performance in two cloud platforms: Amazon EC2 and Microsoft Azure. Our experimental analysis considers three sets of comparisons: first we compare the same cloud-based software service hosted on two different public cloud platforms; second we compare two different cloud-based software services hosted on the same cloud platform; finally, we compare between the same cloud-based software service hosted on the same cloud platform with two different auto-scaling policies. We note that our technical scalability metrics can be integrated into a previously proposed utility oriented metric of scalability. We discuss the implications of our work.

/pdf/scalability-analysis-comparisons-of-cloud-based-software-2jnvu3hlr3.pdf

Scalability analysis comparisons of cloud-based software services

Anomaly detection of network-initiated LTE signaling traffic in wireless sensor and actuator networks based on a Hidden semi-Markov Model

Data-driven software defined network attack detection : State-of-the-art and perspectives

The emergence of cloud computing has made dynamic provisioning of elastic capacity to applications on-demand. Cloud data centers contain thousands of physical servers hosting orders of magnitude more virtual machines that can be allocated on demand to users in a pay-as-you-go model. However, not all systems are able to scale up by just adding more virtual machines. Therefore, it is essential, even for scalable systems, to project workloads in advance rather than using a purely reactive approach. Given the scale of modern cloud infrastructures generating real time monitoring information, along with all the information generated by operating systems and applications, this data poses the issues of volume, velocity, and variety that are addressed by Big Data approaches. In this paper, we investigate how utilization of Big Data analytics helps in enhancing the operation of cloud computing environments. We discuss diverse applications of Big Data analytics in clouds, open issues for enhancing cloud operations via Big Data analytics, and architecture for anomaly detection and prevention in clouds along with future research directions.

pdf/big-data-analytics-enhanced-cloud-computing-challenges-42w4unt7ke.pdf

Big Data Analytics-Enhanced Cloud Computing: Challenges, Architectural Elements, and Future Directions

Author(s): Mohammed, B; Kiran, M; Maiyama, KM; Kamala, MM; Awan, IU | Abstract: Cloud fault tolerance is an important issue in cloud computing platforms and applications. In the event of an unexpected system failure or malfunction, a robust fault-tolerant design may allow the cloud to continue functioning correctly possibly at a reduced level instead of failing completely. To ensure high availability of critical cloud services, the application execution, and hardware performance, various fault-tolerant techniques exist for building self-autonomous cloud systems. In comparison with current approaches, this paper proposes a more robust and reliable architecture using optimal checkpointing strategy to ensure high system availability and reduced system task service finish time. Using pass rates and virtualized mechanisms, the proposed smart failover strategy (SFS) scheme uses components such as cloud fault manager, cloud controller, cloud load balancer, and a selection mechanism, providing fault tolerance via redundancy, optimized selection, and checkpointing. In our approach, the cloud fault manager repairs faults generated before the task time deadline is reached, blocking unrecoverable faulty nodes as well as their virtual nodes. This scheme is also able to remove temporary software faults from recoverable faulty nodes, thereby making them available for future request. We argue that the proposed SFS algorithm makes the system highly fault tolerant by considering forward and backward recovery using diverse software tools. Compared with existing approaches, preliminary experiment of the SFS algorithm indicates an increase in pass rates and a consequent decrease in failure rates, showing an overall good performance in task allocations. We present these results using experimental validation tools with comparison with other techniques, laying a foundation for a fully fault-tolerant infrastructure as a service cloud environment. Copyright © 2017 John Wiley a Sons, Ltd.

/pdf/failover-strategy-for-fault-tolerance-in-cloud-computing-454haniq08.pdf

Failover strategy for fault tolerance in cloud computing environment

Cloud computing has rapidly been adopted in recent years by freeing users from the low-level task of setting up the hardware and managing the system software. Anomaly detection is an effective approach to enhancing availability and reliability of Cloud infrastructures. Anomaly detection solution for Cloud computing systems must operate at runtime and without the need for prior knowledge about normal or anomalous behaviors. In this paper, we propose an unsupervised online anomaly detection scheme based on hidden Markov Model. Our algorithm is basically distributed and runs locally on each computing machine on the Cloud in order to achieve high scalability. Experiments performed on real data sets validate the fact that our algorithm can achieve high detection accuracy for a range of system anomalies with low overhead to the infrastructure in Cloud. Copyright © 2015 John Wiley & Sons, Ltd.

DAC-Hmm: detecting anomaly in cloud systems with hidden Markov models

Cloud computing has become increasing popular by freeing users from the low-level task of setting up the hardware and managing the system software. Anomaly detection is an effective approach to enhancing availability and reliability of Cloud infrastructures. In this paper, we propose a supervised online anomaly detection scheme that analyses monitoring data within current sliding data window and judging the current working state of the monitored component in Cloud based on Hidden Markov Model (HMM). What makes our method different from existing anomaly detecting is that it determines current state in context of recent continuous monitoring data, instead of isolated data point. Besides, our algorithm is basically distributed and runs locally on each computing machine on the Cloud in order to achieve high scalability. Experiments performed on real data sets validate the fact that our algorithm can effectively detect performance anomalies while imposing low overhead to the infrastructure in Cloud.

Distributed State Monitoring for IaaS Cloud with Continuous Observation Sequence

Cloud computing becomes more and more popular for its on-demand services and pay-as-you-go model. Elasticity is the key feature of cloud computing technology, which can reduce and add resources flexibly to meet customers' need. Considering the important of elasticity of cloud computing platform, the objective of this paper is to study the evaluation elasticity of cloud computing platform. In this paper, we propose some important aspects of elasticity, such as definition and metrics. Then we address some different tools and frameworks for measuring and evaluating the elasticity of cloud computing platform. Furthermore, we discuss some open issues and challenges associated with evaluating elasticity of cloud computing platform.

A survey on evaluating elasticity of cloud computing platform

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DAC-Hmm: detecting anomaly in cloud systems with hidden Markov models

Distributed State Monitoring for IaaS Cloud with Continuous Observation Sequence

A survey on evaluating elasticity of cloud computing platform