Search

SaaS application instances typically serve multiple tenants to improve cost-efficiency. This results in the need for adaptive performance isolation between tenants in order to guarantee custom service level objectives (SLOs) about request latency or throughput. Current solutions, which are based on request scheduling algorithms, suffer from SLO instability under globally varying workloads. This means that the configuration for an SLO has to be recalibrated when total workload patterns change such as an increase or decrease in the number of subscribed tenants, or the application becomes co-located with other types of resource-intensive applications. Lately container technology such as Docker and container orchestration frameworks like Kubernetes have been used to increase cost-efficiency, multi-tenancy and elasticity. This paper investigates if the problem of adaptive performance isolation can be mapped to resource management concepts of Kubernetes through a series of experiments. These experiments show that Kubernetes provides good support for QoS differentiation and adaptive resource allocation by grouping tenants according to their SLO class (e.g gold vs bronze) in different containers. Moreover, SLO instability does not occur when co-locating these containers with other container-based applications provided that a few interferences between CPU-, memory- and disk-io intensive applications are taken into account. However SLO instability does occur when the number of subscribed tenants changes. This latter problem is not caused by the replication and auto-scaling concepts of Kubernetes, but by a non-linear resource scaling phenomenon that is inherent when the goal is to meet multiple custom SLOs in a cost-optimal way.

User feedback is essential for understanding user needs. In this paper, we use free-text obtained from a survey on sleep-related issues to build a deep neural networks-based text classifier. However, to train the deep neural networks model, a lot of labelled data is needed. To reduce manual data labelling, we propose a method which is a combination of data augmentation and pseudo-labelling: data augmentation is applied to labelled data to increase the size of the initial train set and then the trained model is used to annotate unlabelled data with pseudo-labels. The result shows that the model with the data augmentation achieves macro-averaged f1 score of 65.2% while using 4,300 training data, whereas the model without data augmentation achieves macro-averaged f1 score of 68.2% with around 14,000 training data. Furthermore, with the combination of pseudo-labelling, the model achieves macro-averaged f1 score of 62.7% with only using 1,400 training data with labels. In other words, with the proposed method we can reduce the amount of labelled data for training while achieving relatively good performance.

As recent incidents have shown, weak passwords are a severe security risk for authenticating users and granting access to protected resources. Additionally, strong passwords score low on usability, especially on mobile devices. In this work, we present SmartAuth, a scalable context-aware authentication framework built on top of OpenAM, a state-of-practice identity and access management suite. It uses adaptive and dynamic context fingerprinting based on Hoeffding trees to continuously ascertain whether a user's identity is authentic or not, and it respects privacy preferences by adopting consent-driven use of context information. We assess our approach from both an offensive and defensive security perspective. Our results show that dynamic context fingerprinting has good potential for a zero-interaction authentication scheme, with a minimal performance overhead compared to traditional authentication schemes. ispartof: pages:2185-2191 ispartof: Proceedings of the 30th Annual ACM Symposium on Applied Computing 2015 vol:13-17-April-2015 pages:2185-2191 ispartof: Symposium on Applied Computing location:Salamanca, Spain date:13 Apr - 17 Apr 2015 status: published

Software-as-a-service (SaaS) has been a growing trend in cloud computing for several years. Moreover, SaaS providers are evolving to application-level multi-tenancy, in which all tenants share the application instances, platform and data store with the aim of maximizing resource sharing. For multi-tenant SaaS applications, access control often is the only application-level security mechanism. However, such access control is inherently complex because both the pro\-vider and all tenants should be able to specify their access rules for the application. Moreover, these rules must all be securely combined and correctly enforced in the shared multi-tenant application. To address this challenge, we present the Amusa access control middleware. Amusa enables both the provider and all its tenants to efficiently declare their access rules on the SaaS application. To achieve this, Amusa provides incremental three-layered management based on attribute-based tree-structured policies. Afterwards, Amusa securely combines the access rules of all parties and enforces them at run-time with low performance overhead. ispartof: pages:2141-2148 ispartof: Proceedings of the 30th Annual ACM Symposium on Applied Computing 2015. vol:13-17-April-2015 pages:2141-2148 ispartof: ACM Symposium on Applied Computing 2015 location:Salamanca, Spain date:13 Apr - 17 Apr 2015 status: published

Multi-view algorithms, such as co-training and co-EM, utilize unlabeled data when the available attributes can be split into independent and compatible subsets. Experiments have shown that multi-view learning is sometimes beneficial for problems for which the independence assumption is not satisfied. In practice, unfortunately, it is not possible to measure the dependency between two attribute sets; hence, there is no criterion which allows to decide whether multi-view learning is applicable. We conduct experiments with various text classification problems and investigate on the effectiveness of the co-trained SVM and the co-EM SVM under various conditions, including violations of the independence assumption. We identify the error correlation coefficient of the initial classifiers as an elaborate indicator of the expected benefit of multi-view learning. Multi-view algorithms, such as co-training and co-EM, utilize unlabeled data when the available attributes can be split into independent and compatible subsets. Experiments have shown that multi-view learning is sometimes beneficial for problems for which the independence assumption is not satisfied.In practice, unfortunately, it is not possible to measure the dependency between two attribute sets; hence, there is no criterion which allows to decide whether multi-view learning is applicable. We conduct experiments with various text classification problems and investigate on the effectiveness of the co-trained SVM and the co-EM SVM under various conditions, including violations of the independence assumption. We identify the error correlation coefficient of the initial classifiers as an elaborate indicator of the expected benefit of multi-view learning.