Logchain: Blockchain-assisted Log Storage

During the normal operation of a Cloud solution, no one usually pays attention to the logs except technical department, which may periodically check them to ensure that the performance of the platform conforms to the Service Level Agreements. However, the moment the status of a component changes from acceptable to unacceptable, or a customer complains about accessibility or performance of a platform, the importance of logs increases significantly. Depending on the scope of the issue, all departments, including management, customer support, and even the actual customer, may turn to logs to find out what has happened, how it has happened, and who is responsible for the issue. The party at fault may be motivated to tamper the logs to hide their fault. Given the number of logs that are generated by the Cloud solutions, there are many tampering possibilities. While tamper detection solution can be used to detect any changes in the logs, we argue that critical nature of logs calls for immutability. In this work, we propose a blockchain-based log system, called Logchain, that collects the logs from different providers and avoids log tampering by sealing the logs cryptographically and adding them to a hierarchical ledger, hence, providing an immutable platform for log storage.Comment: 4 pages, 1 figur

Adaptive monitoring: A systematic mapping

Context: Adaptive monitoring is a method used in a variety of domains for responding to changing conditions. It has been applied in different ways, from monitoring systems’ customization to re-composition, in different application domains. However, to the best of our knowledge, there are no studies analyzing how adaptive monitoring differs or resembles among the existing approaches. Objective: To characterize the current state of the art on adaptive monitoring, specifically to: (a) identify the main concepts in the adaptive monitoring topic; (b) determine the demographic characteristics of the studies published in this topic; (c) identify how adaptive monitoring is conducted and evaluated by the different approaches; (d) identify patterns in the approaches supporting adaptive monitoring. Method: We have conducted a systematic mapping study of adaptive monitoring approaches following recommended practices. We have applied automatic search and snowballing sampling on different sources and used rigorous selection criteria to retrieve the final set of papers. Moreover, we have used an existing qualitative analysis method for extracting relevant data from studies. Finally, we have applied data mining techniques for identifying patterns in the solutions. Results: We have evaluated 110 studies organized in 81 approaches that support adaptive monitoring. By analyzing them, we have: (1) surveyed related terms and definitions of adaptive monitoring and proposed a generic one; (2) visualized studies’ demographic data and arranged the studies into approaches; (3) characterized the main approaches’ contributions; (4) determined how approaches conduct the adaptation process and evaluate their solutions. Conclusions This cross-domain overview of the current state of the art on adaptive monitoring may be a solid and comprehensive baseline for researchers and practitioners in the field. Especially, it may help in identifying opportunities of research; for instance, the need of proposing generic and flexible software engineering solutions for supporting adaptive monitoring in a variety of systems.Peer ReviewedPostprint (author's final draft

Quality-aware Tasking in Mobile Opportunistic Networks - Distributed Information Retrieval and Processing utilizing Opportunistic Heterogeneous Resources.

Advances in wireless technology have facilitated direct communication among mobile devices in recent years, enabling opportunistic networks. Opportunistic networking among mobile devices is often utilized to offload and save cellular network traffic and to maintain communication in case of impaired communication infrastructure, such as in emergency situations. With a plethora of built-in capabilities, such as built-in sensors and the ability to perform even intensive operations, mobile devices in such networks can be used to provide distributed applications for other devices upon opportunistic contact. However, ensuring quality requirements for such type of distributed applications is still challenging due to uncontrolled mobility and resource constraints of devices. Addressing this problem, in this thesis, we propose a tasking methodology, which allows for assigning tasks to capable mobile devices, considering quality requirements. To this end, we tackle two fundamental types of tasks required in a distributed application, i.e., information retrieval and distributed processing. Our first contribution is a decentralized tasking concept to obtain crowd collected data through built-in sensors of participating mobile devices. Based on the Named Data Networking paradigm, we propose a naming scheme to specify the quality requirements for crowd sensing tasks. With the proposed naming scheme, we design an adaptive self-organizing approach, in which the sensing tasks will be forwarded to the right devices, satisfying specified quality requirements for requested information. In our second contribution, we develop a tasking model for distributed processing in opportunistic networks. We design a task-oriented message template, which enhances the definition of a complex processing task, which requires multiple processing stages to accomplish a predefined goal. Our tasking concept enables distributed coordination and an autonomous decision of participating device to counter uncertainty caused by the mobility of devices in the network. Based on this proposed model, we develop computation handover strategies among mobile devices for achieving quality requirements of the distributed processing. Finally, as the third contribution and to enhance information retrieval, we integrate our proposed tasking concept for distributed processing into information retrieval. Thereby, the crowd-collected data can be processed by the devices during the forwarding process in the network. As a result, relevant information can be extracted from the crowd-collected data directly within the network without being offloaded to any remote computation entity. We show that the obtained information can be disseminated to the right information consumers, without over-utilizing the resource of participating devices in the network. Overall, we demonstrate that our contributions comprise a tasking methodology for leveraging resources of participating devices to ensure quality requirement of applications built upon an opportunistic network

QoS-aware Cloud Infrastructure Provisioning in Heterogeneous Environments

Over the last decades Information Technology (IT) has become an enabler for nearly all businesses from industrial production to finance. The IT resources required for these business activities are usually provided by local and remote data centers. Although most resources are still hosted in companies’ proprietary data centers, cloud computing initiated a paradigm shift in IT service provisioning from owning to leasing resources and services. Today, over 50% of German companies use cloud services while shifting services into the cloud has become an emerging trend. Cloud computing, which is often referred to as the fifth utility in addition to water, electricity, gas, and telephony, provides commoditized computation resources that are available any time on demand in the required quantity. However, in contrast to other commodities, a single quality level is insufficient for IT service provisioning. Instead, the required quality for a provided IT service depends on the various functional and non-functional requirements. For example, highly interactive applications such as cloud gaming require a high quality level in terms of latency. Providers of cloud services have to face a highly competitive market. Cost advantages in cloud computing are primarily achieved by utilizing large centralized data centers at low-cost locations. However, this kind of resource provisioning impacts the quality of service of different types of services such as the aforementioned interactive multimedia services that possess strict quality of service constraints. Hence, infrastructure providers have to face a trade-off between cost reduction and adherence to the required Quality of Service (QoS) attributes. Apart from how services are provisioned, the way of consuming IT services also changed substantially over the last years. Mobile devices have begun to replace locally installed desktop computers at an accelerated pace. By utilizing these mobile devices, service providers are confronted with two major challenges: (i) a cellular network connection, which potentially causes a higher and more fluctuating latency and (ii) severely limited resources compared to local Personal Computers (PCs). These two aspects restrict the utilization of multimedia services, e. g., cloud gaming. To address these challenges, we present two novel approaches for (i) resource planning on a global level for multiple services with heterogeneous QoS characteristics and (ii) the augmentation of the centralized cloud infrastructure with locally installed resources to provide viable multimedia services to mobile devices. As the first major contribution, we introduce the Cloud Data Center Selection Problem (CDCSP). This problem describes the data center placement and resource selection on a global scale. We consider the role of a cloud provider, who aims to dimension resources in a cost-minimal fashion under the consideration of multiple services with different QoS attributes. Based on a mathematical optimization model, we propose the exact solution approach CDCSP-EXA.KOM. Due to the high complexity and the resulting computational effort to find the optimal solution, we propose and analyze four heuristic approaches to identify the most appropriate one for the given problem. As a first heuristic, we propose an approach based on linear program relaxation, CDCSP-REL.KOM. Furthermore, to take the specific structure of the problem into consideration, we develop the custom tailored CDCSP-PBST.KOM approach, which is based on a prioritized processing of demands and supplies. To further improve the results, we combine multiple heuristics to a Best-of-Breed approach, named CDCSP-BoB. KOM. Finally, as a metaheuristic improvement procedure, we propose the tabu search approach CDCSP-TS.KOM. To assess the practical applicability and performance of these optimization approaches, we analyze them in detail and compare their performance in a quantitatively. The second major contribution of this work addresses the augmentation of the centralized cloud infrastructure with local resources to provide services to mobile devices. Therefore, we formulate the Dynamic Cloudlet Placement and Selection Problem (DCPSP), as a multi-period resource planning problem, which includes local characteristics, such as space for hosting resources and available network bandwidth. We focus on a cloud provider who aims to augment the centralized infrastructure using local resources to improve the QoS guarantees for mobile used applications. We formalize the problem as a mathematical optimization model and derive the exact solution approach DCPSP-EXA.KOM. Due to the high complexity that is caused by an optimization over many time slots, we propose the heuristic optimization approach DCPSP-HEU.KOM. We assess the performance of these two approaches by the means of quantitative evaluation. In summary, the contributions of this thesis provide the means for a cost-efficient and QoS-aware resource selection in cloud infrastructures. We contribute the formalization of the problems and algorithms to support the efficient planning of future cloud infrastructures in environments with a multitude of heterogeneous services on a global scale. Furthermore, to enable mobile users to consume multimedia cloud services, we propose an optimization model and algorithms to augment a global centralized infrastructure by local resource units

Untersuchungen zur Risikominimierungstechnik Stealth Computing für verteilte datenverarbeitende Software-Anwendungen mit nutzerkontrollierbar zusicherbaren Eigenschaften

Die Sicherheit und Zuverlässigkeit von Anwendungen, welche schutzwürdige Daten verarbeiten, lässt sich durch die geschützte Verlagerung in die Cloud mit einer Kombination aus zielgrößenabhängiger Datenkodierung, kontinuierlicher mehrfacher Dienstauswahl, dienstabhängiger optimierter Datenverteilung und kodierungsabhängiger Algorithmen deutlich erhöhen und anwenderseitig kontrollieren. Die Kombination der Verfahren zu einer anwendungsintegrierten Stealth-Schutzschicht ist eine notwendige Grundlage für die Konstruktion sicherer Anwendungen mit zusicherbaren Sicherheitseigenschaften im Rahmen eines darauf angepassten Softwareentwicklungsprozesses.:1 Problemdarstellung 1.1 Einführung 1.2 Grundlegende Betrachtungen 1.3 Problemdefinition 1.4 Einordnung und Abgrenzung 2 Vorgehensweise und Problemlösungsmethodik 2.1 Annahmen und Beiträge 2.2 Wissenschaftliche Methoden 2.3 Struktur der Arbeit 3 Stealth-Kodierung für die abgesicherte Datennutzung 3.1 Datenkodierung 3.2 Datenverteilung 3.3 Semantische Verknüpfung verteilter kodierter Daten 3.4 Verarbeitung verteilter kodierter Daten 3.5 Zusammenfassung der Beiträge 4 Stealth-Konzepte für zuverlässige Dienste und Anwendungen 4.1 Überblick über Plattformkonzepte und -dienste 4.2 Netzwerkmultiplexerschnittstelle 4.3 Dateispeicherschnittstelle 4.4 Datenbankschnittstelle 4.5 Stromspeicherdienstschnittstelle 4.6 Ereignisverarbeitungsschnittstelle 4.7 Dienstintegration 4.8 Entwicklung von Anwendungen 4.9 Plattformäquivalente Cloud-Integration sicherer Dienste und Anwendungen 4.10 Zusammenfassung der Beiträge 5 Szenarien und Anwendungsfelder 5.1 Online-Speicherung von Dateien mit Suchfunktion 5.2 Persönliche Datenanalyse 5.3 Mehrwertdienste für das Internet der Dinge 6 Validierung 6.1 Infrastruktur für Experimente 6.2 Experimentelle Validierung der Datenkodierung 6.3 Experimentelle Validierung der Datenverteilung 6.4 Experimentelle Validierung der Datenverarbeitung 6.5 Funktionstüchtigkeit und Eigenschaften der Speicherdienstanbindung 6.6 Funktionstüchtigkeit und Eigenschaften der Speicherdienstintegration 6.7 Funktionstüchtigkeit und Eigenschaften der Datenverwaltung 6.8 Funktionstüchtigkeit und Eigenschaften der Datenstromverarbeitung 6.9 Integriertes Szenario: Online-Speicherung von Dateien 6.10 Integriertes Szenario: Persönliche Datenanalyse 6.11 Integriertes Szenario: Mobile Anwendungen für das Internet der Dinge 7 Zusammenfassung 7.1 Zusammenfassung der Beiträge 7.2 Kritische Diskussion und Bewertung 7.3 Ausblick Verzeichnisse Tabellenverzeichnis Abbildungsverzeichnis Listings Literaturverzeichnis Symbole und Notationen Software-Beiträge für native Cloud-Anwendungen Repositorien mit ExperimentdatenThe security and reliability of applications processing sensitive data can be significantly increased and controlled by the user by a combination of techniques. These encompass a targeted data coding, continuous multiple service selection, service-specific optimal data distribution and coding-specific algorithms. The combination of the techniques towards an application-integrated stealth protection layer is a necessary precondition for the construction of safe applications with guaranteeable safety properties in the context of a custom software development process.:1 Problemdarstellung 1.1 Einführung 1.2 Grundlegende Betrachtungen 1.3 Problemdefinition 1.4 Einordnung und Abgrenzung 2 Vorgehensweise und Problemlösungsmethodik 2.1 Annahmen und Beiträge 2.2 Wissenschaftliche Methoden 2.3 Struktur der Arbeit 3 Stealth-Kodierung für die abgesicherte Datennutzung 3.1 Datenkodierung 3.2 Datenverteilung 3.3 Semantische Verknüpfung verteilter kodierter Daten 3.4 Verarbeitung verteilter kodierter Daten 3.5 Zusammenfassung der Beiträge 4 Stealth-Konzepte für zuverlässige Dienste und Anwendungen 4.1 Überblick über Plattformkonzepte und -dienste 4.2 Netzwerkmultiplexerschnittstelle 4.3 Dateispeicherschnittstelle 4.4 Datenbankschnittstelle 4.5 Stromspeicherdienstschnittstelle 4.6 Ereignisverarbeitungsschnittstelle 4.7 Dienstintegration 4.8 Entwicklung von Anwendungen 4.9 Plattformäquivalente Cloud-Integration sicherer Dienste und Anwendungen 4.10 Zusammenfassung der Beiträge 5 Szenarien und Anwendungsfelder 5.1 Online-Speicherung von Dateien mit Suchfunktion 5.2 Persönliche Datenanalyse 5.3 Mehrwertdienste für das Internet der Dinge 6 Validierung 6.1 Infrastruktur für Experimente 6.2 Experimentelle Validierung der Datenkodierung 6.3 Experimentelle Validierung der Datenverteilung 6.4 Experimentelle Validierung der Datenverarbeitung 6.5 Funktionstüchtigkeit und Eigenschaften der Speicherdienstanbindung 6.6 Funktionstüchtigkeit und Eigenschaften der Speicherdienstintegration 6.7 Funktionstüchtigkeit und Eigenschaften der Datenverwaltung 6.8 Funktionstüchtigkeit und Eigenschaften der Datenstromverarbeitung 6.9 Integriertes Szenario: Online-Speicherung von Dateien 6.10 Integriertes Szenario: Persönliche Datenanalyse 6.11 Integriertes Szenario: Mobile Anwendungen für das Internet der Dinge 7 Zusammenfassung 7.1 Zusammenfassung der Beiträge 7.2 Kritische Diskussion und Bewertung 7.3 Ausblick Verzeichnisse Tabellenverzeichnis Abbildungsverzeichnis Listings Literaturverzeichnis Symbole und Notationen Software-Beiträge für native Cloud-Anwendungen Repositorien mit Experimentdate

Developing a user-centric distributed middleware for SLA monitoring in SaaS cloud computing using RESTful services

One of the most important discussions in the cloud computing field is user satisfaction with the associated services. It is important to maintain trusted relationships between clients and providers, for customers who pay subscriptions to receive these services in a timely and accurate manner. Despite the overwhelming advantages of cloud services, clients sometimes have problems in service outage and resource failure. This is due to the failures that can happen in cloud servers, which cause outages to the received services. For example, the failure of Microsoft Office 365 on 18th of January 2016, caused email disruption which lasted for many days. New measures are needed to ensure that the contract signed between the two parties, known as a Service Level Agreement (SLA) has been adhered to. Measuring the quality of cloud computing provision from the client’s point of view is, therefore, essential in order to ensure that the service conforms to the level specified in the agreement; this is usually referred to as Quality of Experience. In recent years, there has been an increase shift in using Simple Object Access Protocol (SOAP) to Representational State Transfer (REST) technology as an alternative technology in cloud applications APIs development. However, there is a penchant in most of cloud monitoring solutions to use SOAP protocol in managing the monitoring process. This trend has drawn the attention to the need for using REST technology in transferring the monitored data between the provider side and the client side. This thesis addresses the problem of monitoring the quality of Software as a Service from the users’ perspective, and the need for developing a lightweight middleware for delivering the monitored data in Software as a Service cloud computing. The aim of this research is to propose a user centric approach for monitoring Software as a Service in cloud computing, and to reduce the overhead caused by the monitoring process. In order to achieve this aim, a user centric middleware capable of monitoring the Quality of Experience has been developed. The developed middleware is a Service Oriented middleware which uses RESTful web services and provides the monitoring process as an add-on service. A new approach was developed for embedding the SLA parameters in REST services through extending the HTTP messages and exploiting the HEAD and OPTIONS methods to transmit the monitored data and to send notifications about anySLA violations. This reduces the need to exchange extra monitoring messages between the two parties, and hence reduces the communication overhead. Furthermore, the estimation of the user satisfaction was implemented by developing a decision making approach to estimate the Quality of Experience value and to predict the effect of the SLA parameters and the Quality of Service (QoS) on the user satisfaction. Fuzzy logic techniques were employed in the decision making process.The developed middleware is called MonSLAR, for Monitoring SLA for Restful services in SaaS cloud computing environments. The middleware was implemented using the Java programming language, and tested successfully in a cloud environment to prove the proposed solution’s capability of transmitting the data using the REST methods, in addition to providing automated and real time feedback. MonSLAR uses a distributed monitoring architecture, which allows SLA parameters to be embedded in the requests and responses of the REST protocol. The proposed middleware was evaluated by measuring the overhead caused by using REST technology in terms of response time and message size and compared to existing techniques. The results revealed that the message size overhead of using REST is approximately five times less than the message size overhead caused by SOAP. Furthermore, the response time overhead of the monitoring process is comparable to the overhead caused by the available monitoring frameworks. To sum up, the proposed middleware will help to strengthen the relationship between the client and the provider by using real time notifications to the client about any degradation in the cloud services, using a lightweight middleware