In  this  study,  we  evaluate  the  performance  of  SQL  and  NoSQL  database  management  systems  namely; Cassandra, CouchDB, MongoDB, PostgreSQL, and RethinkDB.  We use a cluster of  four  nodes to run the database  systems,  with  external  load  generators.The  evaluation  is  conducted  using  data  from  Telenor Sverige,  a  telecommunication  company  that  operates in  Sweden.  The  experiments  are  conducted  using three  datasets  of  different  sizes.The  write  throughput  and  latency  as  well  as  the  read  throughput  and latency are evaluated for four queries; namely distance query, k-nearest neighbour query, range query, and region  query.  For  write  operations  Cassandra  has  the  highest  throughput  when  multiple  nodes  are  used, whereas  PostgreSQL  has  the  lowest  latency  and  the  highest  throughput  for  a  single  node.  For  read operations  MongoDB  has  the  lowest  latency  for  all  queries.  However,  Cassandra  has  the  highest throughput  for  reads.  The  throughput  decreasesas  the  dataset  size  increases  for  both  write  and  read,  for both  sequential  as  well  as  random  order  access.  However,  this  decrease  is  more  significant  for  random read and write. In this study, we present the experience we had with these different database management systems including setup and configuration complexity.

This paper quantifies the implications and trade-offs of three different architectures for voice driven web application, architectures are implemented as prototypes. The prototypes differ from each other by either using recording, or Text To Speech (TTS) as server based, or TTS as client based to process output speech. A typical application used in this paper, is the most dynamic weather information source which is presented as web feeds or Really Simple Syndication (RSS) feeds. The evaluated quality attributes are performance, maintainability, and development effort. The empirical results show that, each system's architecture has a different quality profile, for instance, one architecture has the lowest development time but the highest maintainability cost, and another has the lowest bandwidth requirements but the highest development cost. Finally, suggestions about optimal choice of system architecture according to the quality requirements of the final system are drawn.

This paper quantifies the performance of the core part of voice driven web using free and open source speech engine; the speech engine which is very high computation demanding, it consists of Automatic Speech Recognition (ASR) and Text To Speech (TTS). Two open source programs, Sphinx-4 and FreeTTS-1.2.2 are used for ASR and TTS respectively. These two programs are executed on 2 different hardware multicore processors with 4 hyperthreaded cores, and 8 cores respectively. The response time with respect to the load variance and the number of cores is measured and predicted using a linear regression model. The results show that, the response time is linear with respect to the input length, this property can be used to directly predict the response for any input length. Moreover, though the response time and the speed up increases as the number of cores increases, the regression coefficients and number of threads reveal that ASR benefits from multicore. The speedup factor for ASR is 1.56 for 8 cores. However for FreeTTS, though being sequential the speed up from the program itself is insignificant, there is about 1. 43 speedup for 8 cores, that comes from the system's contribution. Our findings show that the generalization of the results for multicore processor does not apply to hyperthreading. This paper presents the investigation that is useful for educators, researchers, and applications' developer in voice based applications 'domain.

    The use of virtualized systems is growing, and one would like to   benefit from   this   kind   of   systems   also   for   real-time applications  with  hard  deadlines.    There  are  two  levels  of  scheduling  in  real-time  applications  executing  in  a  virtualized  environment: traditional real-time scheduling of the tasks in the real-time  application  inside  a  Virtual  Machine  (VM),  and  scheduling   of   different   VMs   on   the   hypervisor   level.   Traditional real-time scheduling uses methods based on periods, deadlines and worst-case execution times of the real-time tasks.In   order   to   apply   the   existing   theory   also   to   virtualized   environments   we   must   obtain   periods   and   (worst-case) execution times for VMs containing real-time applications.   In this paper, we describe a technique for calculating periods and execution  times  and  utilization  for  VMs  containing  real-time applications with hard deadlines.  We show that when we look at  all  VMs  that  share  a  physical  processor  we  are  able  to  use  longer  (better)  periods.   Alternatively,  if  the  periods  are  the  same,  we  are  able  to  use  a  smaller  amount  of  the  processor  resource  for  the  VMs  and  more  tasks  become  schedulable  compared to when we look at each  VM  in  isolation.   We  also  introduce an overhead model that makes it possible to find VM periods that minimize the processor utilization.

We consider a real-time application that executes in a VM with multiple virtual cores. Tasks are scheduled globally using fixed-priority scheduling. In order to avoid Dhalls effect, we classify tasks into two priority classes: heavy and light. Heavy tasks have higher priority than light tasks. For light tasks we use rate monotonic priority assignment. We propose a utilization-based schedulability test. If the task set is schedulable, we provide an assignment of priorities to tasks. The input to the test is the task set, the number of cores in the VM, the period, deadline and blocking time for the VM. We evaluate how jitter, when scheduling VMs on the hypervisor level, affects the schedulability of the real-time tasks running in the VM. The schedulability of the real-time tasks in the VM decreases when the hypervisor jitter increases, but on the other hand the schedulability on the hypervisor level increases if we allow more jitter, i.e. there is a trade-off. Our results make it possible to evaluate this trade-off and take informed decisions when selecting scheduling parameters on the hypervisor level. Simulations show that the priority assignment used by our algorithm schedules more task sets than using rate monotonic priority assignment. © 2019 The British Computer Society. All rights reserved.