Modeling Strategies to Improve the Dependability of Cloud Infrastructures

Cloud computing presents some challenges that need to be overcome, such as planning infrastructures that maintain availability when failure events and repair activities occur. Cloud infrastructure planning that addresses the dependability aspects is an essential activity because it ensures business continuity and client satisfaction. Redundancy mechanisms cold standby, warm standby and hot standby can be allocated to components of the cloud infrastructure to maintain the availability levels agreed in service level agreement (SLAs). Mathematical formalisms based on state space such as stochastic Petri nets and based on combinatorial as reliability block diagrams can be adopted to evaluate the dependability of cloud infrastructures considering the allocation of different redundancy mechanisms to its components. This chapter shows the adoption of the mathematical formalisms stochastic Petri nets and reliability block diagrams to dependability evaluation of cloud infrastructures with different redundancy mechanisms

Proactive cloud management for highly heterogeneous multi-cloud infrastructures

Various literature studies demonstrated that the cloud computing paradigm can help to improve availability and performance of applications subject to the problem of software anomalies. Indeed, the cloud resource provisioning model enables users to rapidly access new processing resources, even distributed over different geographical regions, that can be promptly used in the case of, e.g., crashes or hangs of running machines, as well as to balance the load in the case of overloaded machines. Nevertheless, managing a complex geographically-distributed cloud deploy could be a complex and time-consuming task. Autonomic Cloud Manager (ACM) Framework is an autonomic framework for supporting proactive management of applications deployed over multiple cloud regions. It uses machine learning models to predict failures of virtual machines and to proactively redirect the load to healthy machines/cloud regions. In this paper, we study different policies to perform efficient proactive load balancing across cloud regions in order to mitigate the effect of software anomalies. These policies use predictions about the mean time to failure of virtual machines. We consider the case of heterogeneous cloud regions, i.e regions with different amount of resources, and we provide an experimental assessment of these policies in the context of ACM Framework

The case for cloud service trustmarks and assurance-as-a-service

Cloud computing represents a significant economic opportunity for Europe. However, this growth is threatened by adoption barriers largely related to trust. This position paper examines trust and confidence issues in cloud computing and advances a case for addressing them through the implementation of a novel trustmark scheme for cloud service providers. The proposed trustmark would be both active and dynamic featuring multi-modal information about the performance of the underlying cloud service. The trustmarks would be informed by live performance data from the cloud service provider, or ideally an independent third-party accountability and assurance service that would communicate up-to-date information relating to service performance and dependability. By combining assurance measures with a remediation scheme, cloud service providers could both signal dependability to customers and the wider marketplace and provide customers, auditors and regulators with a mechanism for determining accountability in the event of failure or non-compliance. As a result, the trustmarks would convey to consumers of cloud services and other stakeholders that strong assurance and accountability measures are in place for the service in question and thereby address trust and confidence issues in cloud computing

Attack-Surface Metrics, OSSTMM and Common Criteria Based Approach to “Composable Security” in Complex Systems

In recent studies on Complex Systems and Systems-of-Systems theory, a huge effort has been put to cope with behavioral problems, i.e. the possibility of controlling a desired overall or end-to-end behavior by acting on the individual elements that constitute the system itself. This problem is particularly important in the “SMART” environments, where the huge number of devices, their significant computational capabilities as well as their tight interconnection produce a complex architecture for which it is difficult to predict (and control) a desired behavior; furthermore, if the scenario is allowed to dynamically evolve through the modification of both topology and subsystems composition, then the control problem becomes a real challenge. In this perspective, the purpose of this paper is to cope with a specific class of control problems in complex systems, the “composability of security functionalities”, recently introduced by the European Funded research through the pSHIELD and nSHIELD projects (ARTEMIS-JU programme). In a nutshell, the objective of this research is to define a control framework that, given a target security level for a specific application scenario, is able to i) discover the system elements, ii) quantify the security level of each element as well as its contribution to the security of the overall system, and iii) compute the control action to be applied on such elements to reach the security target. The main innovations proposed by the authors are: i) the definition of a comprehensive methodology to quantify the security of a generic system independently from the technology and the environment and ii) the integration of the derived metrics into a closed-loop scheme that allows real-time control of the system. The solution described in this work moves from the proof-of-concepts performed in the early phase of the pSHIELD research and enrich es it through an innovative metric with a sound foundation, able to potentially cope with any kind of pplication scenarios (railways, automotive, manufacturing, ...)

10292 Abstracts Collection and Summary -- Resilience Assessment and Evaluation

From July 18 to July 23, 2010 the Dagstuhl Seminar 10292 ``Resilience Assessment and Evaluation \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations