Many enterprises are currently exploring the possibility of migrating some or all of their IT functionalities to public clouds with the objective of reducing their overall IT service costs or to open new business frontiers. Unfortunately, making such a decision is not a straightforward task; it requires a vigorous evaluation of the various benefits, risks and costs associated with the migration of their diverse business processes that comprise of their current IT services. Yet, this problem has received very little attention in the literature, mainly due to its interdisciplinary nature. This thesis aims at filling this gap by aiding the enterprises during the phase of making their cloud migration decision. The contributions of this work are twofold. First, a novel cloud-migration framework is introduced to guide the enterprises through a sequence of well-defined recommended analysis steps. These steps culminate with the formulation of the migration decision problem as a mathematical optimization one. The second contribution is a decision engine that efficiently solves this optimization problem. More precisely, the proposed framework gradually guides the enterprise to first identify the various business processes that are related to their IT services and then to determine the relationship and the communication needed among those processes. The identified inter-process communication represents an indicator of how tightly coupled these business processes are to each other. When outsourcing business processes, tightly coupled processes add a high communication cost and may introduce service latency if they are not co-located. As such, inter-process communication becomes an important input parameter that affects the migration decision. Enterprises can then determine to partially or completely migrate IT services to clouds. Furthermore, multiple vendors can be used for different services. However, when different vendors are involved, the communication cost between different processes increases. The objective is to maximize profit for an organization which includes lowering IT expenses in the long term without compromising data integrity or security. An optimization formula is finally constructed to help the enterprise determine which services to migrate given input parameters of the cost of doing business in-house, cost of outsourcing, and communication costs. Finally, a case study is utilized to demonstrate the performance of the proposed work by analyzing the process of migrating the services to clouds for an IPTV service provider. More specifically, the case study focuses on the content delivery network (CDN) within the IPTV provider’s infrastructure which is responsible for delivering contents to viewers. The CDN network can use the proposed profit-optimization formula to determine whether to utilize a cloud service or to use its internal resource to deliver the content. A performance evaluation from a simulation is presented to demonstrate the proposed profit-optimization formula can return a set of optimal mix of both internal and external services to maximize profits.

With the exponential growth of virtualization and cloud computing over the last decade, many companies in the telecommunications sector have started their journey towards cloud migration by exchanging a lot of specialized hardware for virtualized solutions. With more and more applications running in a cloud environment, it became essential to run these applications on heterogeneous systems with shared underlying hardware and software resources. However, running these applications in a heterogeneous cloud environment often leads to  unpredictable and non-deterministic performance, as all the applications compete for the shared resources to improve their individual performance. This becomes a problem when the interference from the co-hosted applications starts affecting the performance of the critical applications running on the same server. Ericsson is therefore investigating a solution to dynamically manage the low-level hardware and software resources to get deterministic performance on applications deployed using Kubernetes.  In this thesis, the Intent Driven Orchestration (IDO) model developed by Intel is used as the baseline model. This model was then extended by adding another tool to the setup called Container Runtime Interface-Resource Manager (CRI-RM), which is used to manipulate low-level software and hardware resources managed by a Kubernetes cluster at runtime. The results achieved in this thesis suggest that it is possible to get deterministic performance for an application deployed using Kubernetes, by identifying and isolating the CPU cores in the cluster on which the application is running.