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The appearance of Edge Computing with the possibility to bring powerful computation servers near the mobile device is a major stepping stone towards better user experience and resource consumption optimization. Due to the Internet of Things invasion that led to the constant demand for communication and computation resources, many issues were imposed in order to deliver a seamless service within an optimized cost of time and energy, since most of the applications nowadays require real response time and rely on a limited battery resource. Therefore, Mobile Edge Computing is the new reliable paradigm in terms of communication and computation consumption by the mobile devices. Mobile Edge Computing rely on computation offloading to surpass cloud-based technologies issues and break the limitations of mobile devices such as computing, storage and battery resources. However, computation offloading is not always the optimal choice to adopt, which makes the offloading decision a crucial part in which many parameters should be taken in consideration such as delegating the heavy tasks to the appropriate machine within the network by migrating the high-resource node to an edge server and lend these capabilities to the low-resources one. In this paper, we use an Edge Computing simulator to see how network delay can impact the delivery of a certain result, we also experiment computation offloading using a two-tier with Edge Orchestration architecture, which turns out to be efficient in terms of processing time.

Cloud computing provides on-demand access to a diverse set of remote IT services. It offers a number of advantages over traditional computing methods. These advantages include pay-as-you-go pricing, increased agility and on-demand scalability. It also reduces costs due to increased efficiency and better business continuity. The most significant barrier preventing many businesses from moving to the cloud is the security of crucial data maintained by the cloud provider. The cloud server must have complete access to the data to respond to a client request. That implies the decryption key must be sent to the cloud by the client, which may compromise the confidentiality of data stored in the cloud. One way to allow the cloud to use encrypted data without knowing or decrypting it is homomorphic encryption. In this paper, we focus on improving the Paillier cryptosystem, first by using two protocols that allow the cloud to perform the multiplication of encrypted data and then comparing the two protocols in terms of key size and time.