A simulation model to implement multiple client class server-client ...

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Workloads of N client class Classifier Performance measurements Queue - 1 Queue – 2 ... Scheduler Queue – N Resource allocation decisions Shared computing resources Shared resource environment Figure 1: Conceptual structure of multi-client class system 3. Ability to validate the correctness of the simulation model. 4. Accurate measurements of the system outputs (e.g. response times) of each client class. 5. Valid implementation of the resource allocation decisions. 6. Accurate average statistics of the required system parameters. 7. Ability to simulate variable workload rates over the period of simulation. 8. Modifiability, extendibility and scalability. 9. Fast and efficient execution. 3. Simulation environment One of the main tools available to us to build a simulation environments is discrete event simulation [1]. Discrete event simulation is widely used to test and analyze new systems, policies before they are been implemented as a production system. Discrete event simulation environments can be implemented by general purpose programming languages (e.g., Java, C#.Net) or commercial simulation tools. As a consequence, in this work we build a Discrete event simulation model to simulate a multi-client class environment, while achieving the requirements mentioned in Section 2. 3.1. Brief introduction to discrete event simulation A discrete event simulation (DES) is defined as ”Modeling of systems in which the state variables change only at a discrete set of points in time” in [1]. A DES model consists of entities (e.g., clients, queues, and resources), attributes, events (e.g., client arrival and departure), and activities (operation invocations, statistic collection). For a given time instance, DES model has a snapshot of the system, which is updated based on the events that is scheduled to happen in that time instance. Hence, a time advance algorithm is there to keep track of the events that suppose to take place in a given time instance chronologically. These events trigger activities in the system that may in turn produce new events that needs to be executed in a future time instance or update the state variables of the system. After, these events have taken place, the clock is advanced to the next time instance and the same process 2
is continued till the simulation end condition is reached. During the simulation or at the end of the simulation statistics are gathered to analyze the results of the simulation. Generally, DES model can be designed in an event-oriented and a process-oriented point of view. In the eventoriented technique the DES model designer takes the events of the system and how they affect the system state variables of the model as major concerns. On the other hand, process oriented point of view enables to model the entities, their processes and how the inter-process communicates take place. The event-oriented design produces simulations that can execute faster compared to process-oriented design, however, modularity extendibility and the understandability of the system is a trade-off. Both of these design techniques can be used, however process-oriented design is popular among the commercial simulation products [1]. Further, a DES simulation can be designed with deterministic and stochastic inputs and variables. For instance, a resource utilization time is precisely 5 seconds for any request is deterministic, where as the utilization time is determined by a probabilistic distribution is stochastic. 3.2. DES model of a multi-client class system In this section, we provide implementation details of the simulation environments developed following the guidelines provided in [1]. Here, we have taken the process oriented design technique because it provides modularity, extendibility and convenience to design using general purpose object-orient programming languages like Java and C#.Net. Further, we use stochastic inputs and variables in this simulation to represent the variability in multi-client class systems. The DES simulation model constructed has following entities (components) in the architecture corresponding to the characteristic architecture of Figure 1. MasterClock: This component keeps track of the current time instance of the system and advances the time after all events and activities specific to the current time instance have taken place. It triggers events on the tick (smallest time unit) and major tick (which is 1000 ticks). Request: This represents a client request flowing through the simulation model. It has the properties of client class Id, start time, end time and processing time. The processing time is determined by a probabilistic distribution specified by the designer. ClientClassWorkloadGenerator: This component generates workloads for a specific client class. It needs a client class id, workload script and the corresponding queue instance at the initialization. Then the process of this component is at each tick the workload script is analyzed and generates the required number of requests that have to be sent to the system. Then, the requests are initialized with the class id and the start time and enqueued to the corresponding queue. Currently it can simulate deterministic time varying and stochastic (e.g., Poisson process) workloads. Queue: In a multi-client class system there is a corresponding queue to each client class (see Figure 1). The Queue component is used for this purpose. It is a container of the requests generated by the ClientClassWorkloadGenerator and ordered in a first-come-first-out fashion. The simulation model needs N Queue instances to represent N queues. ResourceUnit: The ResourceUnit entity is an abstraction of a resource unit in a multi-client class system. It simulates the time period a resource is reserved/occupied/provisioned to serve a request of a client class. It has the currently served request, serviced client class, status (idle or working) as attributes. The process of this entity is at each tick, it simulates the processing time specified on the request it is serving. When the request has utilized the resource for the specified period of time it is assumed to be sent back to the client after stamping the end time. However, in this simulation the copy of the served request is also sent to the statistical analysis component to compute measurements such as response times. 3
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