Reducing the complexity of the performance analysis of a multi- server facilities

Systems with multiple servers are common in many areas and their correct dimensioning is in general a difficult problem under realistic assumptions on the pattern of user arrivals and service time distribution. We present an approximate solution for the underlying Ph/Ph/c/N queueing model. Our approximation decomposes the solution of the Ph/Ph/c/N queue into solutions of simpler M/Ph/c/N and Ph/M/c/N queues. It is conceptually simple, easy to implement and produces generally accurate results for the mean number in the system, as well as the loss probability. A significant speed advantage compared to the numerical solution of the full Ph/Ph/c/N queue can be gained as the number of phases representing the arrival process and/or the number of servers increases

Evaluation des performances d'un atelier flexible avec panne

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Performance Evaluation of Cloud Computing Centers with General Arrivals and Service

International audienceCloud providers need to size their systems to determine the right amount of resources to allocate as a function of customer’s needs so as to meet their SLAs (Service Level Agreement), while at the same time minimizing their costs and energy use. Queueing theory based tools are a natural choice when dealing with performance aspects of the QoS (Quality of Service) part of the SLA and forecasting resource utilization. The characteristics of a cloud center lead to a queueing system with multiple servers (nodes) in which there is potentially a very large number of servers and both the arrival and service process can exhibit high variability. We propose to use a G/G/c-like model to represent a cloud system and assess expected performance indices. Given the potentially high number of servers in a cloud system, we present an efficient, fast and easy-to-implement approximate solution. We have extensively validated our approximation against discrete-event simulation for several QoS performance metrics such as task response time and blocking probability with excellent results. We apply our approach to examples of system sizing and our examples clearly demonstrate the importance of taking into account the variability of the tasks arrivals and thus expose the risk of under- or over-provisioning if one relies on a model with Poisson assumptions

A study of systems with multiple operating levels, probabilistic thresholds and hysteresis

International audienceCurrent architecture of many computer systems relies on dynamic allocation of a pool of resources according to workload conditions to meet specific performance objectives while minimizing cost (e.g., energy or billing). In such systems, different levels of operation may be defined, and switching between operating levels occurs at certain thresholds of system congestion. To avoid rapid oscillations between levels of service, " hysteresis " is introduced by using different thresholds for increasing and decreasing workload levels, respectively. We propose a model of such systems with general arrivals, arbitrary number of servers and operating levels where each higher operating level may correspond to an arbitrary number of additional servers and soft (i.e. non-deterministic) thresholds to account for " inertia " in switching between operating levels. In our model, request service times are assumed to be memoryless and server processing rates may be a function of the current operating level and of the number of requests (users) in the system. Additionally, we allow for delays in the activation of additional operating levels. We use simple mathematics to obtain a semi-numerical solution of our model. We illustrate the versatility of our model using several case study examples inspired by features of real systems. In particular, we explore optimal thresholds as a tradeoff between performance and energy consumptio

Reducing the complexity of the performance analysis of a multi- server facilities

Systems with multiple servers are common in many areas and their correct dimensioning is in general a difficult problem under realistic assumptions on the pattern of user arrivals and service time distribution. We present an approximate solution for the underlying Ph/Ph/c/N queueing model. Our approximation decomposes the solution of the Ph/Ph/c/N queue into solutions of simpler M/Ph/c/N and Ph/M/c/N queues. It is conceptually simple, easy to implement and produces generally accurate results for the mean number in the system, as well as the loss probability. A significant speed advantage compared to the numerical solution of the full Ph/Ph/c/N queue can be gained as the number of phases representing the arrival process and/or the number of servers increases

A Comparative Analysis of N-Nearest Neighbors (N3) and Binned Nearest Neighbors (BNN) Algorithms for Indoor Localization

In this study, performances of classification algorithms N-Nearest Neighbors (N3) and Binned Nearest Neighbor (BNN) are analyzed in terms of indoor localizations. Fingerprint method which is based on Received Signal Strength Indication (RSSI) is taken into consideration. RSSI is a measurement of the power present in a received radio signal from transmitter. In this method, the RSSI information is captured at the reference points and recorded for creating a signal map. The obtained signal map is knows as fingerprint signal map and in the second stage of algorithm is creating a positioning model to detect individual's position with the help of fingerprint signal map. In this work; N-Nearest Neighbors (N3) and Binned Nearest Neighbors (BNN) algorithms are used to create an indoor positioning model. For this purpose; two different signal maps are used to test the algorithms. UJIIndoorLoc includes multi-building and multi floor signal information while different from this RFKON includes a single-building single floor signal information. N-Nearest Neighbors (N3) and Binned Nearest Neighbors (BNN) algorithms are presented comparatively with respect to success of finding user position

A Study of Systems with Multiple Operating Levels, Soft Thresholds and Hysteresis

Current architecture of many computer systems relies on dynamic allocation of a pool of resources according to workload conditions to meet specific performance objectives while minimizing cost (e.g., energy or billing). In such systems, different levels of operation may be defined, and switching between operating levels occurs at certain thresholds of system congestion. To avoid rapid oscillations between levels of service, ”hysteresis” is introduced by using different thresholds for increasing and decreasing workload levels, respectively.We propose a model of such systems with non-Poisson arrivals, arbitrary number of servers and operating levels where each operating level may correspond to an arbitrary number of additional servers and soft (i.e. non-deterministic) thresholds to account for ”inertia” in switching between operating levels. Additionally, in our model server processing rates may be a function of the current operating level and of the number of requests (users) in the system. We also allow for delays in the activation of additional operating levels. We use simple mathematics to obtain a semi-numerical solution of our model. We illustrate the versatility of our model using several case study examples inspired by features of real systems. In particular, we explore optimal thresholds as a tradeoff between performance and energy consumption