Hybrid Cloud Workload Monitoring as a Service

Cloud computing and cloud-based hosting has become embedded in our daily lives. It is imperative for cloud providers to make sure all services used by both enterprises and consumers have high availability and elasticity to prevent any downtime, which impacts negatively for any business. To ensure cloud infrastructures are working reliably, cloud monitoring becomes an essential need for both businesses, the provider and the consumer. This thesis project reports on the need of efficient scalable monitoring, enumerating the necessary types of metrics of interest to be collected. Current understanding of various architectures designed to collect, store and process monitoring data to provide useful insight is surveyed. The pros and cons of each architecture and when such architecture should be used, based on deployment style and strategy, is also reported in the survey. Finally, the essential characteristics of a cloud monitoring system, primarily the features they host to operationalize an efficient monitoring framework, are provided as part of this review. While its apparent that embedded and decentralized architectures are the current favorite in the industry, service-oriented architectures are gaining traction. This project aims to build a light-weight, scalable, embedded monitoring tool which collects metrics at different layers of the cloud stack and aims at achieving correlation in resource-consumption between layers. Future research can be conducted on efficient machine learning models used on the monitoring data to predict resource usage spikes pre-emptively

Enabling IoT in Manufacturing: from device to the cloud

Industrial automation platforms are experiencing a paradigm shift. With the new technol-ogies and strategies that are being applied to enable a synchronization of the digital and real world, including real-time access to sensorial information and advanced networking capabilities to actively cooperate and form a nervous system within the enterprise, the amount of data that can be collected from real world and processed at digital level is growing at an exponential rate. Indeed, in modern industry, a huge amount of data is coming through sensorial networks em-bedded in the production line, allowing to manage the production in real-time. This dissertation proposes a data collection framework for continuously collecting data from the device to the cloud, enabling resources at manufacturing industries shop floors to be handled seamlessly. The framework envisions to provide a robust solution that besides collecting, transforming and man-aging data through an IoT model, facilitates the detection of patterns using collected historical sensor data. Industrial usage of this framework, accomplished in the frame of the EU C2NET project, supports and automates collaborative business opportunities and real-time monitoring of the production lines

Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

Security for Cloud Environment through Information Flow Properties Formalization with a First-Order Temporal Logic

The main slowdown of Cloud activity comes from the lack of reliable security. The on-demand security concept aims at delivering and enforcing the client's security requirements. In this paper, we present an approach, Information Flow Past Linear Time Logic (IF-PLTL), to specify how a system can support a large range of security properties. We present in this paper how to control those information flows from lower system events. We give complete details over IF-PLTL syntax and semantics. Furthermore, that logic enables to formalize a large set of security policies. Our approach is exemplified with the Chinese Wall commercial-related policy. Finally, we discuss the extension of IF-PLTL with dynamic relabeling to encompass more realistic situations through the dynamic domains isolation policy.La principale cause de ralentissement de l'adoption du Cloud est le manque de sécurité fiable. Le concept de sécurité à la demande est de déployer et d'appliquer les demandes de sécurité d'un client. Dans ce papier, nous présentons une approche, Information Flow Past Linear Time Logic (IF-PLTL), qui permet de spécifier comment un système peut supporter un large ensemble de propriétés de sécurité. Nous présentons dans ce papier comment ces flux d'information peuvent être contrôler en utilisant les événements systèmes de bas niveau. Nous donnons une description compléte de la syntaxe de IF-PLTL ainsi que sa sémantique. De plus, cette logique permet de formaliser un large ensemble de politiques de sécurité. Notre approche est illustrée par la politique de sécurité de la muraille de Chine orienté vers le monde commercial. Finalement, nous montrons comment nous avons étendu notre langage pour supporter la relabélisation dynamique qui permet de supporter la dynamicité inhérante des systèmes. Nous illustrons cette extension par la formalisation d'une propriété de sécurité pour l'isolation dynamique de domaines

CloudMon: a resource-efficient IaaS cloud monitoring system based on networked intrusion detection system virtual appliances

The networked intrusion detection system virtual appliance (NIDS-VA), also known as virtualized NIDS, plays an important role in the protection and safeguard of IaaS cloud environments. However, it is nontrivial to guarantee both of the performance of NIDS-VA and the resource efficiency of cloud applications because both are sharing computing resources in the same cloud environment. To overcome this challenge and trade-off, we propose a novel system, named CloudMon, which enables dynamic resource provision and live placement for NIDS-VAs in IaaS cloud environments. CloudMon provides two techniques to maintain high resource efficiency of IaaS cloud environments without degrading the performance of NIDS-VAs and other virtual machines (VMs). The first technique is a virtual machine monitor based resource provision mechanism, which can minimize the resource usage of a NIDS-VA with given performance guarantee. It uses a fuzzy model to characterize the complex relationship between performance and resource demands of a NIDS-VA and develops an online fuzzy controller to adaptively control the resource allocation for NIDS-VAs under varying network traffic. The second one is a global resource scheduling approach for optimizing the resource efficiency of the entire cloud environments. It leverages VM migration to dynamically place NIDS-VAs and VMs. An online VM mapping algorithm is designed to maximize the resource utilization of the entire cloud environment. Our virtual machine monitor based resource provision mechanism has been evaluated by conducting comprehensive experiments based on Xen hypervisor and Snort NIDS in a real cloud environment. The results show that the proposed mechanism can allocate resources for a NIDS-VA on demand while still satisfying its performance requirements. We also verify the effectiveness of our global resource scheduling approach by comparing it with two classic vector packing algorithms, and the results show that our approach improved the resource utilization of cloud environments and reduced the number of in-use NIDS-VAs and physical hosts.The authors gratefully acknowledge the anonymous reviewers for their helpful suggestions and insightful comments to improve the quality of the paper. The work reported in this paper has been partially supported by National Nature Science Foundation of China (No. 61202424, 61272165, 91118008), China 863 program (No. 2011AA01A202), Natural Science Foundation of Jiangsu Province of China (BK20130528) and China 973 Fundamental R&D Program (2011CB302600)

Design, Implementation and Experiments for Moving Target Defense Framework

The traditional defensive security strategy for distributed systems employs well-established defensive techniques such as; redundancy/replications, firewalls, and encryption to prevent attackers from taking control of the system. However, given sufficient time and resources, all these methods can be defeated, especially when dealing with sophisticated attacks from advanced adversaries that leverage zero-day exploits

On the feasibility of collaborative green data center ecosystems

The increasing awareness of the impact of the IT sector on the environment, together with economic factors, have fueled many research efforts to reduce the energy expenditure of data centers. Recent work proposes to achieve additional energy savings by exploiting, in concert with customers, service workloads and to reduce data centers’ carbon footprints by adopting demand-response mechanisms between data centers and their energy providers. In this paper, we debate about the incentives that customers and data centers can have to adopt such measures and propose a new service type and pricing scheme that is economically attractive and technically realizable. Simulation results based on real measurements confirm that our scheme can achieve additional energy savings while preserving service performance and the interests of data centers and customers.Peer ReviewedPostprint (author's final draft

Contributions to Edge Computing

Efforts related to Internet of Things (IoT), Cyber-Physical Systems (CPS), Machine to Machine (M2M) technologies, Industrial Internet, and Smart Cities aim to improve society through the coordination of distributed devices and analysis of resulting data. By the year 2020 there will be an estimated 50 billion network connected devices globally and 43 trillion gigabytes of electronic data. Current practices of moving data directly from end-devices to remote and potentially distant cloud computing services will not be sufficient to manage future device and data growth. Edge Computing is the migration of computational functionality to sources of data generation. The importance of edge computing increases with the size and complexity of devices and resulting data. In addition, the coordination of global edge-to-edge communications, shared resources, high-level application scheduling, monitoring, measurement, and Quality of Service (QoS) enforcement will be critical to address the rapid growth of connected devices and associated data. We present a new distributed agent-based framework designed to address the challenges of edge computing. This actor-model framework implementation is designed to manage large numbers of geographically distributed services, comprised from heterogeneous resources and communication protocols, in support of low-latency real-time streaming applications. As part of this framework, an application description language was developed and implemented. Using the application description language a number of high-order management modules were implemented including solutions for resource and workload comparison, performance observation, scheduling, and provisioning. A number of hypothetical and real-world use cases are described to support the framework implementation