You can use the following tools for simulating your scenario

CloudSim: The CloudSim toolkit supports both system and behavior modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited effort. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds

iCanCloud: is a simulation platform aimed to model and simulate cloud computing systems, which is targeted to those users who deal closely with those kinds of systems. The main objective of iCanCloud is to predict the trade-offs between cost and performance of a given set of applications executed in a specific hardware, and then provide to users useful information about such costs. However, iCanCloud can be used by a wide range of users, from basic active users to developers of large distributed applications.

Meghdoot: is free and open source solution to implement your cloud environment on your hardware. This is single middleware suite incorporates tools across all layers of cloud.

For cloud computing there is a range of simulators with different features. GreenCloud, CloudSIm are the most used. However, for mobile cloud computing, building a testbed is the better option as mobile devices are cheaper than servers.

The following article may help.

https://www.researchgate.net/publication/275339106_Context-Aware_Mobile_Cloud_Computing_and_Its_Challenges

Article Context-Aware Mobile Cloud Computing and Its Challenges

you can use the following cloud computing simulation tools:

1) CloudSim: it is a toolkit (library) for simulation of Cloud computing environments developed in the CLOUDS Laboratory at the Computer Science and Engineering Department of the University of Melbourne, Australia. It provides basic classes for describing data centers, virtual machines, applications, users, computational resources, and policies for management of diverse parts of the system (e.g., scheduling and provisioning). These components can be put together for users to evaluate new policies, scheduling algorithms, mapping etc. in Cloud. It is a complex simulation toolkit using which most of the Cloud scenarios can be built by simply extending or replacing the classes and coding the desired scenario.

2) SPECI:  (Simulation Program for Elastic Cloud Infrastructures), is a simulation tool which allows exploration of aspects of scaling as well as performance

properties of future data centres. Given the size and middleware design policy as the input, SPECI simulates the performance and behaviour of data centers.

3)  GreenCloud: GreenCloud, as proposed by Kliazovich, is a sophisticated

packet-level simulator for energy-aware cloud computing data centres with a focus on cloud communications. It offers a detailed fine-grained modelling of the energy consumed by the data centre IT equipment, such as computing servers, network switches, and communication links. It can be used to develop novel solutions in monitoring, resource allocation, workload scheduling as well as optimization of communication protocols and network infrastructures.

4) OCT ( Open Cloud Testbed ):  It is currently configured as a smaller-scale testbed. It is use to develop cloud computing software and infrastructure. Its main characteristics:

* Different cloud systems and services are installed, including Eucalyptus, Hadoop, CloudStore (KosmosFS), Sector/Sphere, and Thrift in order to make OCT easier to study interoperability and benchmarking, to develop network libraries, monitoring systems and benchmarking suites to support the development and experimental studies of cloud computing stacks.

* The architecture of OCT includes high-performance protocols, services, and infrastructure at all levels. It uses a high-performance 10Gb/s network based on extremely fast transport protocols supported by dedicated light paths instead of using the internet.

5) GroudSim: It provides a comprehensive set of features for complex simulation scenarios from simple job executions on leased computing resources to calculation of costs, and background load on resources. Simulations can be parameterised and are easily extendable by probability distribution packages for failures which normally occur in complex environments. It is mainly concentrated on the IaaS, but it is easily extendable to support additional models such as PaaS, DaaS and TaaS.

6) NetworkCloudSim:  it is an extension of CloudSim with a scalable network and generalized application model, which allows more accurate evaluation of scheduling and resource provisioning policies to optimize the performance

of a Cloud infrastructure.