This paper presents a novel methodology to develop scheduling algorithms. The scheduling problem is phrased as a control problem, and control-theoretical techniques are used to design a scheduling algorithm that meets specific requirements. Unlike most approaches to feedback scheduling, where a controller integrates a "basic" scheduling algorithm and dynamically tunes its parameters and hence its performances, our methodology essentially reduces the design of a scheduling algorithm to the synthesis of a controller that closes the feedback loop. This approach allows the re-use of control-theoretical techniques to design efficient scheduling algorithms; it frames and solves the scheduling problem in a general setting; and it can naturally tackle certain peculiar requirements such as robustness and dynamic performance tuning. A few experiments demonstrate the feasibility of the approach on a real-time benchmark.
For example, in @cite_8 , the controller adjusts the reservation time (i.e., the time the scheduler assigns to each task) with the purpose of keeping the system utilization below a specified upper bound. The plant is instead a switching system with two different states, according to whether the system can satisfy the total amount of CPU requests or not. Some tests with a real-time Linux kernel show that the adaptation mechanism proposed is useful for to improve quality-of-service measurements. Continuing in the same line of work, Palopoli and Abeni @cite_10 combine a reservation-based scheduler and a feedback-based adaptation mechanism to identify the best parameter set for a given workload. pursue a similar approach @cite_17 where they integrate feedback models with optimization techniques.
@cite_10 @cite_17 @cite_8
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When executing soft real-time tasks in a shared processor, it is important to properly allocate the computational resources such that the quality of service requirements of each task are satisfied. In this paper we propose Adaptive Reservations, based on applying a feedback scheme to a reservation based scheduler After providing a precise mathematical model of the scheduler, we describe how this model can be used for synthesising the controller by applying results from control theory. Finally, we show the effectiveness of our method by simulation and by experiments with an MPEG player running on a modified Linux kernel. In this paper, we study the problem of designing new mechanisms for selling reserved instances (also referred to as virtual machines) in cloud computing. Unlike the practice in today's clouds in which users only have a few predefined options to reserve instances (i.e., either 1-year reservation or 3-year reservation), we allow users to reserve resources for any length and from any time point in the future. Our goal is to maximize the social welfare. We propose two mechanisms, one for the case where all the jobs are tight (their lengths are exactly their reservation time intervals), and the other for the more general case where jobs are delayable and have some flexibility on their reservations. Both of the mechanisms are prompt in the sense that the acceptance and the payment for a job is determined at the very moment of its arrival. We use competitive analysis to evaluate the performance of our mechanisms, and show that both of the mechanisms have a competitive ratio of O(ln(kT)) under some mild assumption, where k (res. T) is the maximum ratio between per-instance-hour valuation (res. length) of any two jobs. We then prove that no algorithm can achieve a competitive ratio better than ln(2kT) under the same assumption. Therefore, our mechanisms are optimal within a constant factor. In this paper, we develop an adaptive scheduling framework for changing the processor shares of tasks - a process called reweighting - on real-time multiprocessor platforms. Our particular focus is adaptive frameworks that are deployed in environments in which tasks may frequently require significant share changes. Prior work on enabling real-time adaptivity on multiprocessors has focused exclusively on scheduling algorithms that can enact needed adaptations. The algorithm proposed in this paper uses both feedback and optimization techniques to determine at runtime which adaptations are needed. A framework is given for discussing how to adjust load in order to handle periodic processes whose timing parameters vary with time. The schedulability of adjustable periodic processes by a preemptive fixed priority scheduler is formulated in terms of a configuration selection problem. Specifically, two process transformations are introduced for the purpose of deriving a bound for the achievable utilization factor of processes whose periods are related by harmonics. This result is then generalized so that the bound is applicable to any process set and an efficient algorithm to calculate the bound is provided. When the list of allowable configurations is implicitly given by a set of scalable periodic processes, the corresponding period assignment problem is shown to be NP-complete. The authors present an approximation algorithm for the period assignment problem for which some encouraging experimental results are included. >