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In geo-replicated systems, the heterogeneous latencies of connections between replicas limit the system's ability to achieve consensus fast. State machine replication (SMR) protocols can be refined for their deployment in wide-area networks by using a weighting scheme for active replication that employs additional replicas and assigns higher voting power to faster replicas. Utilizing more variability in quorum formation allows replicas to swiftly proceed to subsequent protocol stages, thus decreasing consensus latency. However, if network conditions vary during the system's lifespan or faults occur, the system needs a solution to autonomously adjust to new conditions. We incorporate the idea of self-optimization into geographically distributed, weighted replication by introducing AWARE, an automated and dynamic voting weight tuning and leader positioning scheme. AWARE measures replica-to-replica latencies and uses a prediction model, thriving to minimize the system's consensus latency. In experiments using different Amazon EC2 regions, AWARE dynamically optimizes consensus latency by selfreliantly finding a fast weight configuration yielding latency gains observed by clients located across the globe.

In the last few years, research has been motivated to provide a categorization and classification of security concerns accompanying the growing adaptation of Infrastructure as a Service (IaaS) clouds. Studies have been motivated by the risks, threats and vulnerabilities imposed by the components within the environment and have provided general classifications of related attacks, as well as the respective detection and mitigation mechanisms. Virtual Machine Introspection (VMI) has been proven to be an effective tool for malware detection and analysis in virtualized environments. In this paper, we classify attacks in IaaS cloud that can be investigated using VMI-based mechanisms. This infers a special focus on attacks that directly involve Virtual Machines (VMs) deployed in an IaaS cloud. Our classification methodology takes into consideration the source, target, and direction of the attacks. As each actor in a cloud environment can be both source and target of attacks, the classification provides any cloud actor the necessary knowledge of the different attacks by which it can threaten or be threatened, and consequently deploy adapted VMI-based monitoring architectures. To highlight the relevance of attacks, we provide a statistical analysis of the reported vulnerabilities exploited by the classified attacks and their financial impact on actual business processes.

Proactive recovery of replicated services is a novel approach that allows tolerating a potentially unlimited number of malicious faults during system lifetime by periodically restarting replicas from a correct state. Recovering a stateful replica requires a time-consuming transfer and verification of the state. During this time, the replica usually is unable to handle client requests. Our VM-FIT architecture harnesses virtualization to significantly reduce this service unavailability. Our approach allows recovery in parallel with service execution, and uses copy-on-write techniques and provides efficient state transfer support between virtual replicas on a host.

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Abstract.Computer forensics involves the collection, analysis, and reporting of information about security incidents and computer-based criminal activity. Cloud computing causes new challenges for the forensics process. This paper addresses three challenges for network forensics in an Infrastructure-as-a-Service (IaaS) environment: First, network forensics needs a mechanism for analysing network traffic remotely in the cloud. This task is complicated by dynamic migration of virtual machines. Second, forensics needs to be targeted at the virtual resources of a specific cloud user. In a multi-tenancy environment, in which multiple cloud clients share physical resources, forensics must not infringe the privacy and security of other users. Third, forensic data should be processed directly in the cloud to avoid a costly transfer of huge amounts of data to external investigators. This paper presents a generic model for network forensics in the cloud and defines an architecture that addresses above challenges. We validate this architecture with a prototype implementation based on the OpenNebula platform and the Xplico analysis tool.

Abstract. This paper describes a novel approach to fault-tolerance in distributed object-based systems. It uses the fragmented-object model to integrate replication mechanisms into distributed applications. This approach enables the use of customised code on a per-object basis to access replica groups and to manage consistency. The addition of fault tolerance to the infrastructure has only little overhead, is fully transparent for clients, and does not require internal modifications to the existing middleware. Semantic annotations at the interface level allow the developer to customise the provision of fault tolerance. Operations can be marked as read-only to allow an execution with weaker ordering semantics or as parallelisable to allow true multithreaded execution. A code-generation tool is provided to automatically produce object-specific fragment code for client access and for replica consistency management, taking into account the annotations, the interface specification, and the non-replicated implementation. A further contribution of our code-generation approach is the support of deterministic multithreading in replicated objects.