Unexpected increases in demand and most of all flash crowds are considered the bane of every web application as they may cause intolerable delays or even service unavailability. Proper quality of service policies must guarantee rapid reactivity and responsiveness even in such critical situations. Previous solutions fail to meet common performance requirements when the system has to face sudden and unpredictable surges of traffic. Indeed they often rely on a proper setting of key parameters which requires laborious manual tuning, preventing a fast adaptation of the control policies. We contribute an original Self-* Overload Control (SOC) policy. This allows the system to self-configure a dynamic constraint on the rate of admitted sessions in order to respect service level agreements and maximize the resource utilization at the same time. Our policy does not require any prior information on the incoming traffic or manual configuration of key parameters. We ran extensive simulations under a wide range of operating conditions, showing that SOC rapidly adapts to time varying traffic and self-optimizes the resource utilization. It admits as many new sessions as possible in observance of the agreements, even under intense workload variations. We compared our algorithm to previously proposed approaches highlighting a more stable behavior and a better performance.
The authors of @cite_18 also address a very important decision problem in the design of the monitoring module: the timing of performance control. They propose to adapt the time interval between successive decisions to the size of workload dependent system parameters, such as the processor queue length. The dynamic adjustment of this interval is of primary importance for threshold based policies for which a constant time interval between decisions may lead to an oscillatory behavior in high load scenarios as we show in Section . Simulations reveal that our algorithm is not subject to oscillations and shows a very little dependence on the time interval between decisions.
@cite_18
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We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users. We consider optimal load balancing in a distributed computing environment consisting of homogeneous unreliable processors. Each processor receives its own sequence of tasks from outside users, some of which can be redirected to the other processors. Processing times are independent and identically distributed with an arbitrary distribution. The arrival sequence of outside tasks to each processor may be arbitrary as long as it is independent of the state of the system. Processors may fail, with arbitrary failure and repair processes that are also independent of the state of the system. The only information available to a processor is the history of its decisions for routing work to other processors, and the arrival times of its own arrival sequence. We prove the optimality of the round-robin policy, in which each processor sends all the tasks that can be redirected to each of the other processors in turn. We show that, among all policies that balance workload, round robin stochastically minimizes the nth task completion time for all n, and minimizes response times and queue lengths in a separable increasing convex sense for the entire system. We also show that if there is a single centralized controller, round-robin is the optimal policy, and a single controller using round-robin routing is better than the optimal distributed system in which each processor routes its own arrivals. Again "optimal" and "better" are in the sense of stochastically minimizing task completion times, and minimizing response time and queue lengths in the separable increasing convex sense. 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. > Recent trends in System-on-a-Chip show that an increasing number of special-purpose processors are being added to improve the efficiency of common operations. Unfortunately, the use of these processors may introduce suspension delays incurred by communication, synchronization and external I O operations. When these processors are used in real-time systems, conventional schedulability analyses incorporate these delays in the worst-case execution response time, hence significantly reducing the schedulable utilization. In this paper, we provide schedulability analyses and propose segment-fixed priority scheduling for self-suspending tasks. We model the tasks as segments of execution separated by suspensions. We start from providing response-time analyses for self-suspending tasks under Rate Monotonic Scheduling (RMS). While RMS is shown to not be optimal, it can be used effectively in some special cases that we have identified. We then derive a utilization bound for the cases as a function of the ratio of the suspension duration to the period of the tasks. For general cases, we develop a segment-fixed priority scheduling scheme. Our scheme assigns individual segments different priorities and phase offsets that are used for phase enforcement to control the unexpected self-suspending nature. With the exact schedulability analysis designed for our scheme, our experiments show that the proposed scheme provides up to 40 times more schedulable utilization than RMS.