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

Passive Fault-Tolerance Management in Component-Based Embedded Systems

It is imperative to accept that failures can and will occur even in meticulously designed distributed systems and to design proper measures to counter those failures. Passive replication minimizes resource consumption by only activating redundant replicas in case of failures, as typically, providing and applying state updates is less resource demanding than requesting execution. However, most existing solutions for passive fault tolerance are usually designed and configured at design time, explicitly and statically identifying the most critical components and their number of replicas, lacking the needed flexibility to handle the runtime dynamics of distributed component-based embedded systems. This paper proposes a cost-effective adaptive fault tolerance solution with a significant lower overhead compared to a strict active redundancy-based approach, achieving a high error coverage with a minimum amount of redundancy. The activation of passive replicas is coordinated through a feedback-based coordination model that reduces the complexity of the needed interactions among components until a new collective global service solution is determined, hence improving the overall maintainability and robustness of the system

Modeling and Control of Server-based Systems

When deploying networked computing-based applications, proper resource management of the server-side resources is essential for maintaining quality of service and cost efficiency. The work presented in this thesis is based on six papers, all investigating problems that relate to resource management of server-based systems. Using a queueing system approach we model the performance of a database system being subjected to write-heavy traffic. We then evaluate the model using simulations and validate that it accurately mimics the behavior of a real test bed. In collaboration with Ericsson we model and design a per-request admission control scheme for a Mobile Service Support System (MSS). The model is then validated and the control scheme is evaluated in a test bed. Also, we investigate the feasibility to estimate the state of a server in an MSS using an event-based Extended Kalman Filter. In the brownout paradigm of server resource management, the amount of work required to serve a client is adjusted to compensate for temporary resource shortages. In this thesis we investigate how to perform load balancing over self-adaptive server instances. The load balancing schemes are evaluated in both simulations and test bed experiments. Further, we investigate how to employ delay-compensated feedback control to automatically adjust the amount of resources to deploy to a cloud application in the presence of a large, stochastic delay. The delay-compensated control scheme is evaluated in simulations and the conclusion is that it can be made fast and responsive compared to an industry-standard solution

Control Strategies for Improving Cloud Service Robustness

This thesis addresses challenges in increasing the robustness of cloud-deployed applications and services to unexpected events and dynamic workloads. Without precautions, hardware failures and unpredictable large traffic variations can quickly degrade the performance of an application due to mismatch between provisioned resources and capacity needs. Similarly, disasters, such as power outages and fire, are unexpected events on larger scale that threatens the integrity of the underlying infrastructure on which an application is deployed.First, the self-adaptive software concept of brownout is extended to replicated cloud applications. By monitoring the performance of each application replica, brownout is able to counteract temporary overload situations by reducing the computational complexity of jobs entering the system. To avoid existing load balancers interfering with the brownout functionality, brownout-aware load balancers are introduced. Simulation experiments show that the proposed load balancers outperform existing load balancers in providing a high quality of service to as many end users as possible. Experiments in a testbed environment further show how a replicated brownout-enabled application is able to maintain high performance during overloads as compared to its non-brownout equivalent.Next, a feedback controller for cloud autoscaling is introduced. Using a novel way of modeling the dynamics of typical cloud application, a mechanism similar to the classical Smith predictor to compensate for delays in reconfiguring resource provisioning is presented. Simulation experiments show that the feedback controller is able to achieve faster control of the response times of a cloud application as compared to a threshold-based controller.Finally, a solution for handling the trade-off between performance and disaster tolerance for geo-replicated cloud applications is introduced. An automated mechanism for differentiating application traffic and replication traffic, and dynamically managing their bandwidth allocations using an MPC controller is presented and evaluated in simulation. Comparisons with commonly used static approaches reveal that the proposed solution in overload situations provides increased flexibility in managing the trade-off between performance and data consistency