Serverless computing, or Function-as-a-Service (FaaS), is emerging as a popular alternative model to on-demand cloud computing. Function services are executed by a FaaS provider; a client no longer uses cloud infrastructure directly as in traditional cloud consumption. Is serverless computing a feasible and beneficial approach to big data processing, regarding performance, scalability, and cost effectiveness? In this paper, we explore this research question using matrix multiplication as example. We define requirements for the design of serverless big data applications, present a prototype for matrix multiplication using FaaS, and discuss and synthesize insights from results of extensive experimentation. We show that serverless big data processing can lower operational and infrastructure costs without compromising system qualities; serverless computing can even outperform cluster-based distributed compute frameworks regarding performance and scalability.

Distributed database system performance benchmarks are an important source of information for decision makers who must select the right technology for their data management problems. Since important decisions rely on trustworthy experimental data, it is necessary to reproduce experiments and verify the results. We reproduce performance and scalability benchmarking experiments of HBase and Cassandra that have been conducted by previous research and compare the results. The scope of our reproduced experiments is extended with a performance evaluation of Cassandra on different Amazon EC2 infrastructure configurations, and an evaluation of Cassandra and HBase elasticity by measuring scaling speed and performance impact while scaling.

Applications often have consistency requirements beyond those guaranteed by the underlying eventually consistent storage system. In this work, we present an approach that guarantees monotonic read consistency and read your writes consistency by running a special middleware component on the same server as the application. We evaluate our approach using both simulation and real world experiments on Cloud storage systems.

Compute clouds are pools of virtual machines that are shared in a multi-tenant environment by multiple users. The virtual machine images are stored in one or more repositories and are pre-configured with an operating system. Users of the compute cloud can upload their own images or install and configure additional software on top of existing basic virtual machines. Today, the Amazon Elastic Compute Cloud (EC2) counts more than 35,000 publicly available virtual machine images. We observe, however, that the meta-data that describes the virtual machine images is of poor quality and does not cover vital information such as operating system configurations or software package installations. The sprawl of poorly documented virtual machine images poses a hurdle to sharing and re-use among members of the compute cloud community. We present a method that allows collecting software-related meta-data in compute clouds through appliance introspection. Moreover, we show how applications in the domains of selection and configuration management benefit from rich meta-data and interact with the method. The method has been implemented as an automated tool, the crawler, that collects configuration data of virtual machine images in public compute clouds and evaluated our approach by crawling Amazon EC2.

Cost and scalability benefits of Cloud storage services are apparent. However, selecting a single storage service provider limits availability and scalability to the selected provider and may further cause a vendor lock-in effect. In this paper, we present MetaStorage, a federated Cloud storage system that can integrate diverse Cloud storage providers. MetaStorage is a highly available and scalable distributed hash table that replicates data on top of diverse storage services. MetaStorage reuses mechanisms from Amazon's Dynamo for cross-provider replication and hence introduces a novel approach to manage consistency-latency tradeoffs by extending the traditional quorum (N,R,W) configurations to an (N_P,R,W) scheme that includes different providers as an additional dimension. With MetaStorage, new means to control consistency-latency tradeoffs are introduced.

Building on compute and storage virtualization, Cloud Computing provides scalable, network-centric, abstracted IT infrastructure, platforms, and applications as on-demand services that are billed by consumption. Cloud Service Engineering is the application of a systematic approach to leverage Cloud Computing in the context of the Internet in its combined role as a platform for technical, economic, organizational and social networks. This tutorial introduces concepts and technology of Cloud Computing and Cloud Service Engineering, providing an overview of state-of-the-art in research and practice.

IT Service Continuity Management (ITSCM) delivers the recovery of IT services in the event of a disaster. ITSCM is widely perceived as an expensive challenge for enterprise-class IT operations. Cloud computing offers a model for dynamic, scalable infrastructure resource allocation on a pay-per-use basis. These attributes promise to bring cost-efficiency to ITSCM invocation and operation processes that only in the rare event of a rehearsal or an actual disaster need to allocate infrastructure resources. We propose to use the Web Service Business Process Execution Language (BPEL) in combination with Virtual Appliances to implement standardized, testable and executable ITSCM processes. The suggested solution is described and evaluated against collected data from manual recovery processes.