SALSA: QoS-aware load balancing for autonomous service brokering

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BRAHMA(+): A Framework for Resource Scaling of Streaming and ASAP Time-Varying Workflows

Automatic scaling of complex software-as-a-service application workflows is one of the most important problems concerning resource management in clouds. In this paper, we study the automatic workflow resource scaling problem for streaming and ASAP workflows, and its time-varying variant where the workflow resource requirements change over time. Service components of streaming workflows execute concurrently while those of ASAP workflows execute sequentially. We propose an intelligent framework, BRAHMA(+), which possesses the capability to learn the workflow behavior and construct a knowledge base that serves as its decision making engine. The proposed resource provisioning algorithms leverage this learned information curated in the knowledge base to perform informed and intelligent scaling decisions. Additionally, BRAHMA(+) employs the use of online-learning strategies to keep the knowledge base up-to-date, thereby accommodating the changes in the workflow resource requirements over time. We evaluate the proposed algorithms using CloudSim simulations. Results on streaming and ASAP workflows, with both static and time-varying resource requirements show that the proposed algorithms are effective and produce good cost-quality trade-offs. The proactive and hybrid algorithms meet the service level agreements and restrict deadline violations to a small fraction (3%-5% in the considered scenarios), while only suffering a marginal increase in average cost per component compared to the described baseline algorithms

Distributed service orchestration : eventually consistent cloud operation and integration

Both researchers and industry players are facing the same obstacles when entering the big data field. Deploying and testing distributed data technologies requires a big up-front investment of both time and knowledge. Existing cloud automation solutions are not well suited for managing complex distributed data solutions. This paper proposes a distributed service orchestration architecture to better handle the complex orchestration logic needed in these cases. A novel service-engine based approach is proposed to cope with the versatility of the individual components. A hybrid integration approach bridges the gap between cloud modeling languages, automation artifacts, image-based schedulers and PaaS solutions. This approach is integrated in the distributed data experimentation platform Tengu, making it more flexible and robust

Optimizing the integration of agent-based cloud archestrators and higher-level workloads

Part 5: Ph.D. Track: Autonomic and Self-Management SolutionsInternational audienceThe flexibility of cloud computing has put significant strain on operations teams. Manually installing and configuring applications in the cloud simply isn’t an option anymore. Configuration management automation solves the issue of getting a single application into a certain state automatically and reliably. However, the issue of automatic dependency management between multiple applications is still an “open, hard problem” according to researchers at Google. Agent-based modeling and orchestration tools like Juju solve the issue of getting from zero to a working set of correctly clustered and connected frameworks. The shortcomings of these state-of-the-art tools are that they don’t provide efficient ways to model and orchestrate workloads running on top of these frameworks. This paper presents a number of ways to deploy and orchestrate workloads with Juju, compares their performance and overhead, and suggests how this overhead can be minimized

Message correlation in web services choreographies: a 4-phase validation method

The majority of large companies are adopting Service Oriented Architectures, mainly to automate their business processes, both centralized and distributed. This paper will focus on distributed business processes. At the moment there are two interesting ways to implement a distributed business process, via orchestration or choreography. Whereas an orchestration can be thought of as a service composition with a single participant taking the lead, a choreography is a decentralized collaboration between different autonomous participants. One of the most prominent remaining issues, associated with both approaches, is the correlation problem, which is addressed in this paper. We will show that the abstract overall view, provided by a choreography description, makes it possible to determine (even at design time) whether its interactions can be unambiguously correlated. It is shown that this correlation validation is more feasible to realize in case of choreographies than with orchestrations, due to the orchestration's limited view on the overall business process