In many public and private Cloud systems, users need to specify a limit for the amount of resources (CPU cores and RAM) to provision for their workloads. A job that exceeds its limits might be throttled or killed, resulting in delaying or dropping end-user requests, so human operators naturally err on the side of caution and request a larger limit than the job needs. At scale, this results in massive aggregate resource wastage.

This paper is concerned with the performance analysis of a slotted downlink channel in a wireless code division multiple access (CDMA) communication system with integrated packet voice/data transmission. The system model consists of a base station (BS) and mobile terminals (MT), each of which is able to receive voice and/or data packets. Packets of accepted voice calls are transmitted immediately while accepted multipacket data messages are initially buffered in first in, first out (FIFO) queues created separately for each destination. The BS distinguishes between silence and talkspurt periods of voice sources, so that packets of accepted data messages can use their own codes for transmission during silent time slots. To fulfill QoS requirements for both traffic types, the number of simultaneous packet transmissions over the downlink channel must be limited. To perform this task, a fair, single-priority multiqueueing scheduling scheme is employed. Discrete-time Markov processes are used to model the system operation. Statistical dependence between queues is the main difficulty which arises during the analysis. This dependence leads to serious computational complexity. The aim of this paper is to present an approximate analytical method which enables one to evaluate system performance despite the dependence. Therefore, it is assumed that the system is heavily loaded with data traffic, and a heuristic assumption is made that makes the queueing analysis computationally tractable. Typical system performance measures (i.e., the data message blocking probability, the average data throughput and delay) are evaluated, however, due to the accepted heuristic assumption, the analysis is approximate and that is why computer simulation is used to validate it.

This paper studies timed token protocols with respect to real-time packet traffic in local area networks (LANs), such as FDDI and token bus, employed in distributed control systems. Typically, in such systems, three classes of packet traffic are encountered. The first class consists of packets cyclically generated by data acquisition tasks. The second traffic class is represented by packets generated in a random manner by control tasks and sporadic events. Finally, the third traffic class represents nonreal-time packet streams such as, for example, file transfers. To evaluate protocol performance, three performance measures are taken into account with respect to randomly generated real-time traffic: the mean waiting time, the blocking probability, and the probability that accepted packets will wait for service no longer than a specified time limit. In order to determine the last performance measure, a two-moment approximation of the waiting time distribution is applied. All three performance measures are evaluated at the beginning of the heavy network load region. Two examples of numerical calculations compared with computer simulations done for FDDI-II and token bus networks are given.

The problem of functional dualism of local flow controllers used in packet networks is investigated. First a method for examining networks using local congestion controllers for exposure to store-and-forward deadlocks is presented. Then a simple analytical approach to performance evaluation of hop-dependent controllers is described that prevents store-and-forward deadlocks. Numerical examples show that in some cases typical local controllers are able to simultaneously perform both of the main flow control functions, i.e. deadlock prevention and congestion prevention.>

This paper introduces a unified queueing model of store-and-forward computer communication networks with random routing and a finite number of buffers at each node. This model has been based on a two-level classification of messages passing through the network. Using it we can analyze a wide class of local flow control mechanisms which have been proposed or used in practice. Some examples illustrate the usefulness of the presented approach. Numerical results are also given.