This paper presents the design, implementation, and evaluation of an adaptive architecture to provide relative delay guarantees for different service classes on web servers under HTTP 1.1. The first contribution of this paper is the architecture based on a feedback control loop that enforces desired relative delays among classes via dynamic connection scheduling and process reallocation. The second contribution is our use of feedback control theory to design the feedback loop with proven performance guarantees. In contrast with ad hoc approaches that often rely on laborious tuning and design iterations, our control theory approach enables us to systematically design an adaptive web server with established analytical methods. The design methodology includes using system identification to establish a dynamic model, and using the Root Locus method to design a feedback controller to satisfy performance specifications of a web server. The adaptive architecture has been implemented by modifying an Apache web server. Experimental results demonstrate that our adaptive server achieves robust relative delay guarantees even when workload varies significantly. Properties of our adaptive web server include guaranteed stability, and satisfactory efficiency and accuracy in achieving the desired relative delay differentiation. 1.

Policy-based management provides a means for IT systems to operate according to business needs. Unfortunately, there is often an “impedance mismatch ” between the policies administrators want and the controls they are given. Consider the Apache web server. Administrators want to control CPU and memory utilizations, but this must be done indirectly by manipulating tuning parameters such as MaxClients and KeepAlive. There has been much interest in using feedback control to bridge the impedance mismatch. However, these efforts have focused on a single metric that is manipulated by a single control and hence have not considered interactions between controls such as those that are common in computing systems. This paper shows how multiple-input, multiple-output (MIMO) control theory can be used to enforce policies for interrelated metrics. MIMO is used both to model the target system, Apache in our case, and to design feedback controllers. The MIMO model captures the interactions between KA and MC, and can be used to identify infeasible metric policies. In addition, MIMO control techniques can provide considerable benefit in handling trade-offs between speed of metric convergence and sensitivity to random fluctuations while enforcing the desired policies.

Controlling the timing performance of a network server is a challenging problem. This paper presents a Queueing Model Based Feedback Control approach to keep the timing performance of a network server close to the service level specification. We show that in an instrumented Apache server, combining feedback control with a queueing model leads to better tracking of QoS specifications than with feedback control alone or queueing model based feed forward control alone. Network based server systems, e.g., Web servers, have now become an integral part of our information services infrastructure. Controlling the timing performance of each individual connection to a network server is a challenging

We develop Feedback Control real-time Scheduling (FCS) as a unified framework to provide Quality of Service (QoS) guarantees in unpredictable environments (such as ebusiness servers on the Internet). FCS includes four major components. First, novel scheduling architectures provide performance control to a new category of QoS critical systems that cannot be addressed by traditional open loop scheduling paradigms. Second, we derive dynamic models for computing systems for the purpose of performance control. These models provide a theoretical foundation for adaptive performance control. Third, we apply established control methodology to design scheduling algorithms with proven performance guarantees, which is in contrast with existing heuristics-based solutions relying on laborious design/tuning/testing iterations. Fourth, a set of controlbased performance specifications characterizes the efficiency, accuracy, and robustness of QoS guarantees. The

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Abstract—The emerging class of adaptive, real-time, data-driven applications are a significant problem for today’s HPC systems. In general, it is extremely difficult for queuing-system-controlled HPC resources to make and guarantee a tightly-bounded prediction regarding the time at which a newly-submitted application will execute. While a reservation-based approach partially addresses the problem, it can create severe resource underutilization (unused reservations, necessary scheduled idle slots, underutilized reservations, etc.) that resource providers are eager to avoid. In contrast, this paper presents a fundamentally different approach to guarantee predictable execution. By creating a virtualized application layer called the performance container, and opportunistically multiplexing concurrent performance containers through the application of formal feedback control theory, we regulate the job’s progress such that the job meets its deadline without requiring exclusive access to resources even in the presence of a wide class of unexpected disturbances. Our evaluation using two widely-used applications, WRF and BLAST, on an 8-core server show our approach is predictable and meets deadlines with 3.4 % of errors on average while achieving high overall utilization. I.

Abstract- Self-managing solutions have recently attracted a lot of interest from the database community. The need for self-* properties is more evident in distributed applications comprising heterogeneous and autonomous databases and functionality providers. Such resources are typically exposed as Web Services (WSs), which encapsulate remote DBMSs and functions called from within database queries. In this setting, database queries are over WSs, and the data transfer cost becomes the main bottleneck. To reduce this cost, data is shipped to and from WSs in chunks; however the optimum chunk size is volatile, depending on both the resources ' runtime properties and the query. In this paper we propose a robust control theoretical solution to the problem of optimizing the data transfer in queries over WSs, by continuously tuning at runtime the block size and thus tracking the optimum point. Also, we develop online system identification mechanisms that are capable of estimating the optimum block size analytically. Both contributions are evaluated via both empirical experimentation in a real environment and simulations, and have been proved to be more effective and efficient than static solutions. I.

With the increasing popularity and diversity of computer systems, quality of services for computer applications becomes more and more important. In order to facilitate the theoretical studies on the computer performance management problem, an effective simulation environment is desired. In this paper, we propose a methodology of extracting a queueing model from the measurements of computer systems. In particular, a queueing simulator in the form of a closed queueing network is constructed from Lotus Notes log files with demonstrated accuracy. 1

close loop management, MBO, businessdriven management, design, redesign, SLA, policy In this paper we describe a model based approach to making resource allocation decisions driven by the value of those decisions to the business. We believe this enables a generic approach to realizing closeloop management. Our solution is centered on two technologies being developed at HP Labs: Quartermaster and Management by Business Objectives (MBO). Our approach was validated by a demonstrator built using these technologies, and other commercially available HP products.