Investigating order release dimensions of workload control mechanisms.

A variety of order release mechanisms have been developed for workload control. In this paper the effectiveness of order release mechanisms in a job shop environment is assessed by studying the influence of single order release dimensions, instead of comparing different mechanisms as a whole. In particular, the paper aims at improving the basis for selecting the workload accounting over time and the workload control strategies, through the understanding of its impact on the overall system performance. The robustness of these order release strategies to environmental perturbations is also assessed through a plan of experiments based on the Taguchi method. Simulation results provide important insights for the implementation of order release mechanisms

Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution

Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most cases, application developers in turn have limited knowledge of the cloud infrastructure. In this paper, we propose learning adaptation rules during runtime. To this end, we introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE learns and modifies fuzzy rules at runtime. The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to specify cloud controllers by simply adjusting weights representing priorities in system goals instead of specifying complex adaptation rules. The applicability of FQL4KE has been experimentally assessed as part of the cloud application framework ElasticBench. The experimental results indicate that FQL4KE outperforms our previously developed fuzzy controller without learning mechanisms and the native Azure auto-scaling

Performance of a connectionless protocol over ATM

Recent studies show the existence of a demand for a connectionless broadband service. In order to cope with this demand, a connectionless protocol for the B-ISDN needs to be designed. Such a protocol should make use of ATM and the ATM Adaptation Layer. It needs to specify destination and bandwidth of connections to the ATM network without advance knowledge of the traffic that has to be transferred over these connection. A possible mechanism which can cope with this problem, the 'On-demand Connection with Delayed Release' (OCDR) mechanism, is described. Its eficient operation is based on the assumption that there exists a certain correlation between subsequently arriving CL packets. Two different arrival processes are used to evaluate the performance of the OCDR mechanism: a Poisson arrival process, and a Markov Modulated Poisson Process (MMPP) which models a bursty trafic source. Markov models of the OCDR mechanism have been constructed for both arrival processes. For the madel with Poisson arrivals, a closed form solution is presented. The model with MMPP arrivals is solved numerically.\ud Compared to a 'Permanent Connection' mechanism significant bandwidth reductions can be obtained provided that the offered trafic has a bursty nature. Furthermore, the OCDR mechanism has the advantageous property that the obtained average node delay is not strongly related to the intensity and burstiness of the offered trafic

Efficient Batch Query Answering Under Differential Privacy

Differential privacy is a rigorous privacy condition achieved by randomizing query answers. This paper develops efficient algorithms for answering multiple queries under differential privacy with low error. We pursue this goal by advancing a recent approach called the matrix mechanism, which generalizes standard differentially private mechanisms. This new mechanism works by first answering a different set of queries (a strategy) and then inferring the answers to the desired workload of queries. Although a few strategies are known to work well on specific workloads, finding the strategy which minimizes error on an arbitrary workload is intractable. We prove a new lower bound on the optimal error of this mechanism, and we propose an efficient algorithm that approaches this bound for a wide range of workloads.Comment: 6 figues, 22 page