Abstract Group-Guaranteed Channel Capacity in Multimedia Storage Servers

One of the open questions in the design of multimedia storage servers is in what order to serve incoming requests. Given the capability provided by the disk layout and scheduling algorithms to serve multiple streams simultaneously, improved request scheduling algorithms can reduce customer waiting times. This results in better service and/or lower customer loss. In this paper we define a new class of request scheduling algorithms, called Group-Guaranteed Server Capacity (GGSC), that preassign server channel capacity to groups of objects. We also define a particular formal method for computing the assigned capacities to achieve a given performance objective. We observe that the FCFS policy can provide the precise time of service to incoming customer requests. Under this assumption, we compare the performance of one of the new GGSC algorithms, GGSCW-FCFS, against FCFS and against two other recently proposed scheduling algorithms: Maximum Factored Queue length (MFQ), and the FCFS-n algorithm that preassigns capacity only to each of the n most popular objects. The algorithms are compared for both competitive market and captured audience environments. Key findings of the algorithm comparisons are that: (1) FCFS-n has no advantage over FCFS if FCFS gives time of service guarantees to arriving customers, (2) FCFS and GGSCW-FCFS are superior to MFQ for both competitive and captive audience environments, (3) for competitive servers that are configured for customer loss less than 10%, FCFS is superior to all other algorithms examined in this paper, and (4) for captive audience environments that have objects with variable playback length, GGSCW-FCFS is the most promising of the policies considered in this paper. The conclusions for FCFS-n and MFQ differ from previous work because we focus on competitive environments with customer loss under 10%, we assume FCFS can provide time of service guarantees to all arriving customers, and we consider the distribution of customer waiting time as well as the average waiting time.

The Impact of Autocorrelation on Queuing Systems

The performance of single-server queues with independent interarrival intervals and service demands is well understood, and often analytically tractable. In particular, the M/M/1 queue has been thoroughly studied, due to its analytical tractability. Little is known, though, when autocorrelation is introduced into interarrival times or service demands, resulting in loss of analytical tractability. Even the simple case of an M/M/1 queue with autocorrelations does not appear to be well understood. Such autocorrelations do, in fact, abound in real-life systems, and worse, simplifying independence assumptions can lead to very poor estimates of performance measures. This paper reports the results of a simulation study of the impact of autocorrelation on performance in an FIFO queue. The study used two computer methods for generating autocorrelated random sequences, with different autocorrelation characteristics. The simulation results show that the injection of autocorrelation into interarrival times, and to a lesser extent into service demands, can have a dramatic impact on performance measures. From a performance viewpoint, these effects are generally deleterious, and their magnitude depends on the method used to generate the autocorrelated process. The paper discusses these empirical results and makes some recommendations to practitioners of performance analysis of queuing systems.autocorrelation, autocorrelated arrivals, autocorrelated services, queuing systems, TES processes, minification/maxification processes, waiting times