A Penalty-Aware Cloud Monitoring System based on Blockchains

Today, traded cloud services are described by service level agreements that specify the obligations of providers such as availability or reliability. Violations of service level agreements lead to penalty payments. The recent development of prominent cloud platforms such as the re-design of Amazon's spot marketspace underpins a trend towards dynamic cloud markets where consumers migrate their services continuously to different marketspaces and providers to reach a cost-optimum. This leads to a heterogeneous IT infrastructure and consequently aggravates the monitoring of the delivered service quality. Hence, there is a need for a transparent penalty management system, which ensures that consumers automatically get penalty payments from providers in case of service violations. \newline In the paper at hand, we present a cloud monitoring system that is able to execute penalty payments autonomously. In this regard, we apply smart contracts hosted on blockchains, which continuously monitor cloud services and trigger penalty payments to consumers in case of service violations. For justification and evaluation we implement our approach by the IBM Hyperledger Fabric framework and create a use case with Amazon's cloud services as well as Azures cloud services to illustrate the universal design of the presented mechanism.

Intervention Planning in ITIL Context

In incident management and especially in after-sales services, customer interventions must be planned according to a priority order set by service level agreements as well as the availability of both technicians and clients. Despite the availability of incident management software solutions, intervention planning is still performed manually in most solutions because numerous constraints must be considered such as the synchronization of technician skills and customer requests, their availability, and the customer priorities. The intervention planning problem is considered as a difficult combinatorial optimization issue. Various approaches have been proposed in the literature including the transformation of this problem into a vehicle routing problem (VRP) or into a CSP in the context of ITIL framework. Yet, the resolution of this problem with a classical CSP solver is time consuming and must be optimized by proposing filtering rules or specific heuristics. This paper proposes the improved CSP and COP models for intervention planning problem with implementing filtering rules and techniques.

Challenges in the Cloud Computing Model of Resource Management Issues Such as Service Level Agreements and Services Models

Cloud computing has recently emerged as a new model for hosting and delivering services over the internet. Cloud computing has many advantages, such as the ability to increase capacity or add capabilities without the need to invest in new infrastructure. It can also fulfil technological requirements in a fast and automated manner. In recent years, cloud computing has changed the IT industry; in fact, it is one of the industry's fastest growing phenomena. However, as more information about people and businesses becomes available in the cloud, concerns about the safety of this environment will increase. In addition, some challenges to the use of this service exist. This paper presents the results of a survey about cloud computing and outlines the main concepts of the technology along with examples of appropriate usage. It also discusses resource management issues such as service level agreements and highlights the challenges faced by users when choosing cloud computing services.

Modeling And Analyzing Cloud Auto-Scaling Mechanism Using Stochastic Well-Formed Coloured Nets

Auto-scaling is one of the most important features in Cloud computing. This feature promises cloud computing customers the ability to best adapt the capacity of their systems to the load they are facing while maintaining the Quality of Service (QoS). This adaptation will be done automatically by increasing or decreasing the amount of resources being leveraged against the workload’s resource demands. There are two types and several techniques of auto-scaling proposed in the literature. However, regardless the type or technique of auto-scaling used, over-provisioning or under-provisioning problem is often observed. In this paper, we model the auto-scaling mechanism with the Stochastic Well-formed coloured Nets (SWN). The simulation of the SWN model allows us to find the state of the system (the number of requests to be dispatched, the idle times of the started resources) from which the auto-scaling mechanism must be operated in order to minimize the amount of used resources without violating the service-level agreements (SLA).

Scalable Blockchain-assisted Log Storage System for Cloud-generated Logs

During the normal operation of a Cloud solution, no one pays attention to the logs except the 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 opportunities. While tamper detection solution can be used to detect any changes in the logs, we argue that the critical nature of logs calls for immutability. In this thesis, 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.