An Enhanced Adaptive Learning System based on Microservice Architecture

This study aims to enhance Adaptive Learning Systems (ALS) in Petroleum Sector in Egypt by using the Microservice Architecture and measure the impact of enhancing ALS by participating ALS users through a statistical study and questionnaire directed to them if they accept to apply the Cloud Computing Service “Microservices” to enhance the ALS performance, quality and cost value or not. The study also aims to confirm that there is a statistically significant relationship between ALS and Cloud Computing Service “Microservices” and prove the impact of enhancing the ALS by using Microservices in the cloud in Adaptive Learning in the Egyptian Petroleum Sector. After developing and strengthening the ALS using the cloud computing with the benefits of using Function as a Services “FaaS”, the functions are start rapidly in order to allow handling of individual requests by using the Microservice Architecture. This study includes a description of the statistic field study approach (The study’s community and its sample. As well as used tools, methodologies, and their validity and reliability. It also includes used procedures for tools codification and their application. Finally, statistical processes that were relied upon in study analysis)

Providing Private and Fast Data Access for Cloud Systems

Cloud storage and computing systems have become the backbone of many applications such as streaming (Netflix, YouTube), storage (Dropbox, Google Drive), and computing (Amazon Elastic Computing, Microsoft Azure). To address the ever growing demand for storage and computing requirements of these applications, cloud services are typically im-plemented over a large-scale distributed data storage system. Cloud systems are expected to provide the following two pivotal services for the users: 1) private content access and 2) fast content access. The goal of this thesis is to understand and address some of the challenges that need to be overcome to provide these two services. The first part of this thesis focuses on private data access in distributed systems. In particular, we contribute to the areas of Private Information Retrieval (PIR) and Private Computation (PC). In the PIR problem, there is a user who wishes to privately retrieve a subset of files belonging to a database stored on a single or multiple remote server(s). In the PC problem, the user wants to privately compute functions of a subset of files in the database. The PIR and PC problems seek the most efficient solutions with the minimum download cost that enable the user to retrieve or compute what it wants privately. We establish fundamental bounds on the minimum download cost required for guaran-teeing the privacy requirement in some practical and realistic settings of the PIR and PC problems and develop novel and efficient privacy-preserving algorithms for these settings. In particular, we study the single-server and multi-server settings of PIR in which the user initially has a random linear combination of a subset of files in the database as side in-formation, referred to as PIR with coded side information. We also study the multi-server setting of the PC in which the user wants to privately compute multiple linear combinations of a subset of files in the database, referred to as Private Linear Transformation. The second part of this thesis focuses on fast content access in distributed systems. In particular, we study the use of erasure coding to handle data access requests in distributed storage and computing systems. Service rate region is an important performance metric for coded distributed systems, which expresses the set of all data access request rates that can be simultaneously served by the system. In this context, two classes of problems arise: 1) characterizing the service rate region of a given storage scheme and finding the optimal request allocation, and 2) designing the underlying erasure code to handle a given desired service rate region. As contributions along the first class of problems, we characterize the service rate region of systems with some common coding schemes such as Simplex codes and Reed-Muller codes by introducing two novel techniques: 1) fractional matching and vertex cover on graph representation of codes, and 2) geometric representations of codes. Moreover, along the second class of code design, we establish some lower bounds on the minimum storage required to handle a desired service rate region for a coded distributed system and in some regimes, we design efficient storage schemes that provide the desired service rate region while minimizing the storage requirements