Middleware-based Database Replication: The Gaps between Theory and Practice

The need for high availability and performance in data management systems has been fueling a long running interest in database replication from both academia and industry. However, academic groups often attack replication problems in isolation, overlooking the need for completeness in their solutions, while commercial teams take a holistic approach that often misses opportunities for fundamental innovation. This has created over time a gap between academic research and industrial practice. This paper aims to characterize the gap along three axes: performance, availability, and administration. We build on our own experience developing and deploying replication systems in commercial and academic settings, as well as on a large body of prior related work. We sift through representative examples from the last decade of open-source, academic, and commercial database replication systems and combine this material with case studies from real systems deployed at Fortune 500 customers. We propose two agendas, one for academic research and one for industrial R&D, which we believe can bridge the gap within 5-10 years. This way, we hope to both motivate and help researchers in making the theory and practice of middleware-based database replication more relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, June 200

DEPAS: A Decentralized Probabilistic Algorithm for Auto-Scaling

The dynamic provisioning of virtualized resources offered by cloud computing infrastructures allows applications deployed in a cloud environment to automatically increase and decrease the amount of used resources. This capability is called auto-scaling and its main purpose is to automatically adjust the scale of the system that is running the application to satisfy the varying workload with minimum resource utilization. The need for auto-scaling is particularly important during workload peaks, in which applications may need to scale up to extremely large-scale systems. Both the research community and the main cloud providers have already developed auto-scaling solutions. However, most research solutions are centralized and not suitable for managing large-scale systems, moreover cloud providers' solutions are bound to the limitations of a specific provider in terms of resource prices, availability, reliability, and connectivity. In this paper we propose DEPAS, a decentralized probabilistic auto-scaling algorithm integrated into a P2P architecture that is cloud provider independent, thus allowing the auto-scaling of services over multiple cloud infrastructures at the same time. Our simulations, which are based on real service traces, show that our approach is capable of: (i) keeping the overall utilization of all the instantiated cloud resources in a target range, (ii) maintaining service response times close to the ones obtained using optimal centralized auto-scaling approaches.Comment: Submitted to Springer Computin

Adaptable Service Oriented Infrastructure Provisioning with Lightweight Containers Virtualization Technology

Modern computing infrastructures should enable realization of converged provisioning and governance operations on virtualized computing, storage and network resources used on behalf of users' workloads. These workloads must have ensured sufficient access to the resources to satisfy required QoS. This requires flexible platforms providing functionality for construction, activation and governance of Runtime Infrastructure which can be realized according to Service Oriented Infrastructure (SOI) paradigm. Implementation of the SOI management framework requires definition of flexible architecture and utilization of advanced software engineering and policy-based techniques. The paper presents an Adaptable SOI Provisioning Platform which supports adaptable SOI provisioning with lightweight virtualization, compliant with the structured process model suitable for construction, activation and governance of IT environments. The requirements, architecture and implementation of the platform are all discussed. Practical usage of the platform is presented on the basis of a complex case study for provisioning JEE middleware on top of the Solaris 10 lightweight virtualization platform

Adaptive Mechanisms for Mobile Spatio-Temporal Applications

Mobile spatio-temporal applications play a key role in many mission critical fields, including Business Intelligence, Traffic Management and Disaster Management. They are characterized by high data volume, velocity and large and variable number of mobile users. The design and implementation of these applications should not only consider this variablility, but also support other quality requirements such as performance and cost. In this thesis we propose an architecture for mobile spatio-temporal applications, which enables multiple angles of adaptivity. We also introduce a two-level adaptation mechanism that ensures system performance while facilitating scalability and context-aware adaptivity. We validate the architecture and adaptation mechanisms by implementing a road quality assessment mobile application as a use case and by performing a series of experiments on cloud environment. We show that our proposed architecture can adapt at runtime and maintain service level objectives while offering cost-efficiency and robustness

Achieving Adaptation Through Live Virtual Machine Migration in Two-Tier Clouds

This thesis presents a model-driven approach for application deployment and management in two-tier heterogeneous cloud environments. For application deployment, we introduce the architecture, the services and the domain specific language that abstract common features of multi-cloud deployments. By leveraging the architecture and the language, application deployers author a deployment model that captures the high-level structure of the application. The deployment model is then translated into deployment workflows on specific clouds. As a use case, we introduce a live VM migration framework that maintains the application quality of services through VM migrations across two tier-clouds. The proposed framework can monitor the performance of the applications and their underlying infrastructure and plan and executes VM migrations to eliminate hotspots in a datacenter. We evaluate both the application deployment architecture and the live migration on public clouds

Requirements of the SALTY project

This document is the first external deliverable of the SALTY project (Self-Adaptive very Large disTributed sYstems), funded by the ANR under contract ANR-09-SEGI-012. It is the result of task 1.1 of the Work Package (WP) 1 : Requirements and Architecture. Its objective is to identify and collect requirements from use cases that are going to be developed in WP 4 (Use cases and Validation). Based on the study and classification of the use cases, requirements against the envisaged framework are then determined and organized in features. These features will aim at guide and control the advances in all work packages of the project. As a start, features are classified, briefly described and related scenarios in the defined use cases are pinpointed. In the following tasks and deliverables, these features will facilitate design by assigning priorities to them and defining success criteria at a finer grain as the project progresses. This report, as the first external document, has no dependency to any other external documents and serves as a reference to future external documents. As it has been built from the use cases studies that have been synthesized in two internal documents of the project, extracts from the two documents are made available as appendices (cf. appen- dices B and C)

On Implementing Autonomic Systems with a Serverless Computing Approach: The Case of Self-Partitioning Cloud Caches

The research community has made significant advances towards realizing self-tuning cloud caches; notwithstanding, existing products still require manual expert tuning to maximize performance. Cloud (software) caches are built to swiftly serve requests; thus, avoiding costly functionality additions not directly related to the request-serving control path is critical. We show that serverless computing cloud services can be leveraged to solve the complex optimization problems that arise during self-tuning loops and can be used to optimize cloud caches for free. To illustrate that our approach is feasible and useful, we implement SPREDS (Self-Partitioning REDiS), a modified version of Redis that optimizes memory management in the multi-instance Redis scenario. A cost analysis shows that the serverless computing approach can lead to significant cost savings: The cost of running the controller as a serverless microservice is 0.85% of the cost of the always-on alternative. Through this case study, we make a strong case for implementing the controller of autonomic systems using a serverless computing approach

Energy-QoS Tradeoffs in J2EE Hosting Centers

International audienceNowadays, hosting centres are widely used to host various kinds of applications e.g., web servers or scientific applications. Resource management is a major challenge for most organisations that run these infrastructures. Many studies show that clusters are not used at their full capacity which represents a significant source of waste. Autonomic management systems have been introduced in order to dynamically adapt software infrastructures according to runtime conditions. They provide support to deploy, configure, monitor, and repair applications in such environments. In this paper, we report our experiments in using an autonomic management system to provide resource aware management for a clustered application. We consider a standard replicated server infrastructure in which we dynamically adapt the degree of replication in order to ensure a given QoS while minimising energy consumption