SaaS providers continuously aim to optimize the costefficiency, scalability and trustworthiness of their offerings. Traditionally, these concerns have been addressed by application-level middleware platforms that implement a multi-tenant architecture.However, the recent uprise and industry adoption of container technology such as Docker and Kubernetes, exactly for the purpose of improving the cost-efficiency, elasticity and resilience of cloud native services, triggers the unanswered question whether and how container technology may affect such multi-tenant architectures.To answer this question, we outline our ideas on a container-based multi-tenant architecture for SaaS applications. Subsequently, we make an assessment of the technical Strengths, Weaknesses, Opportunities, and Threats (SWOT) which should be taken into account by a SaaS provider when considering the adoption of such containerbased architecture.

No abstract

Many applications by design depend on costly trusted third-party auditors. One such example is the industrial application case of federated multi-disciplinary optimization (MDO), in which different organizations contribute to a complex engineering design effort. Although blockchain and distributed ledger technology (DLT) has strong potential in reducing the dependence on such intermediaries, the architectural complexity involved in designing a solution is daunting.In this paper, we analyze the architectural variants for decentralized private data sharing while guaranteeing auditability and non-repudiation of data access operations, as well availability of the shared data. The architectural variants analyzed focus on attaining: (i) confidential data exchange, (ii) governing access to the shared data, (iii) providing data access auditability, and (iv) data validation or conflict resolution. We systematically enumerate architectural decisions at the levels of: storage, policy-based file access control, data encryption methods, and auditability mechanisms for private data.The main contribution of this work is a comprehensive overview of architectural variants for decentralized control of private encrypted data, and the involved trade-offs in terms of performance, storage overhead, auditable trust and security. These findings are validated in the context of the aforementioned industry case that involves federated multi-disciplinary optimization (MDO).

A federated cloud storage setup which integrates and utilizes storage resources from multiple cloud storage providers has become an increasingly popular and attractive paradigm for the persistence tier in cloud-based applications (e.g., SaaS applications, IoT applications, etc). However, federated cloud storage setups are prone to run-time dynamicity: many dynamic properties impact the way such a setup is governed and evolved over time, e.g., storage providers enter or leave the market; QoS metrics and SLA guarantees may change over time; etc. In general, existing federated cloud systems are oblivious to dynamic properties of the underlying operational environment, resulting in both sub-optimal data management decisions and costly SLA violations. Additionally, due to the sheer complexity of cloud-based applications coupled with the heterogeneous and volatile nature of federated cloud setups, the complexity of building, maintaining, and expending such applications increases dramatically and therefore managing them manually is no longer simply an option. To address these concerns, we present SCOPE, a policy-based and autonomic middleware that provides self-adaptiveness for data management in federated clouds. We have validated SCOPE in the context of a realistic SaaS application, performed an extensive functional validation, and conducted a thorough experimental evaluation. The evaluation results demonstrate (i) the ability of the middleware to perform data management decisions that take into account the run-time dynamicity (i.e., dynamic properties) of a federated cloud storage setup to meet the promised SLAs, and (ii) the self-adaptive behavior of SCOPE without the need for operator intervention. In addition, our in-depth performance evaluation results indicate that the benefits are achieved with acceptable performance overhead, and as such highlight the applicability of the proposed middleware for real-world application cases.