No abstract

Data center virtualization allows cost-effective server consolidation which can increase system throughput and reduce power consumption. Resource management of virtualized servers is an important and challenging task, especially when dealing with fluctuating workloads and complex multi-tier server applications. Recent results in control theory-based resource management have shown the potential benefits of adjusting allocations to match changing workloads.This paper presents a new resource management scheme that integrates the Kalman filter into feedback controllers to dynamically allocate CPU resources to virtual machines hosting server applications. The novelty of our approach is the use of the Kalman filter-the optimal filtering technique for state estimation in the sum of squares sense-to track the CPU utilizations and update the allocations accordingly. Our basic controllers continuously detect and self-adapt to unforeseen workload intensity changes.Our more advanced controller self-configures itself to any workload condition without any a priori information. Indicatively, it results in within 4.8% of the performance of workload-aware controllers under high intensity workload changes, and performs equally well under medium intensity traffic. In addition, our controllers are enhanced to deal with multi-tier server applications: by using the pair-wise resource coupling between application components, they provide a 3% on average server performance improvement when facing large unexpected workload increases when compared to controllers with no such resource-coupling mechanism. We evaluate our techniques by controlling a 3-tier Rubis benchmark web site deployed on a prototype Xen-virtualized cluster.

Abstract-The software engineering community has proposed numerous approaches for making software self-adaptive. These approaches take inspiration from machine learning and control theory, constructing software that monitors and modifies its own behavior to meet goals. Control theory, in particular, has received considerable attention as it represents a general methodology for creating adaptive systems. Control-theoretical software implementations, however, tend to be ad hoc. While such solutions often work in practice, it is difficult to understand and reason about the desired properties and behavior of the resulting adaptive software and its controller.This paper discusses a control design process for software systems which enables automatic analysis and synthesis of a controller that is guaranteed to have the desired properties and behavior. The paper documents the process and illustrates its use in an example that walks through all necessary steps for self-adaptive controller synthesis.

When users submit new queries to a distributed stream processing system (DSPS), a query planner must allocate physical resources, such as CPU cores, memory and network bandwidth, from a set of hosts to queries. Allocation decisions must provide the correct mix of resources required by queries, while achieving an efficient overall allocation to scale in the number of admitted queries. By exploiting overlap between queries and reusing partial results, a query planner can conserve resources but has to carry out more complex planning decisions.In this paper, we describe SQPR, a query planner that targets DSPSs in data centre environments with heterogeneous resources. SQPR models query admission, allocation and reuse as a single constrained optimisation problem and solves an approximate version to achieve scalability. It prevents individual resources from becoming bottlenecks by re-planning past allocation decisions and supports different allocation objectives. As our experimental evaluation in comparison with a state-of-the-art planner shows SQPR makes efficient resource allocation decisions, even with a high utilisation of resources, with acceptable overheads.

This is the accepted version of the paper.This version of the publication may differ from the final published version. Resource management of virtualized servers in data-centres has become a critical task, since it enables costeffective consolidation of server applications. Resource management is an important and challenging task, especially for multi-tier applications with unpredictable time-varying workloads. Work in resource management using control theory has shown clear benefits of dynamically adjusting resource allocations to match fluctuating workloads. However, little work has been done towards adaptive controllers for unknown workload types. This work presents a new resource management scheme that incorporates the Kalman filter into feedback controllers to dynamically allocate CPU resources to virtual machines hosting server applications. We present a set of controllers that continuously detect and self-adapt to unforeseen workload changes. Furthermore, our most advanced controller also self-configures itself without any a priori information and with a small 4.8% performance penalty in the case of high intensity workload changes. In addition, our controllers are enhanced to deal with multi-tier server applications: by using the pair-wise resource coupling between tiers, they improve server response to large workload increases as compared to controllers with no such resource-coupling mechanism. Our approaches are evaluated and their performance is illustrated on a 3-tier Rubis benchmark web-site deployed on a prototype Xen-virtualized cluster. Permanent repository link