Capacity and Fairness of Distributed Antenna Systems in Multi-Cell Environments with User Scheduling, Power Control and Imperfect CSI

Distributed antenna systems (DASs) have attractedlots of attention as a method to improve the performance of futurewireless networks. Capacity analysis and optimum power allocationfor the physical layer of DASs have been extensively exploredin the literature. However, the study of cross-layer issues, suchas channel-aware scheduling and fairness evaluation, is relativelyscarce. This paper partially fills this gap by addressing the downlinkcapacity and fairness analysis of a DAS assisted by jointuser scheduling and transmit power control. The algorithm isevaluated in a multi-cell environment assuming imperfect channelstate information. The algorithm exploits the spatial diversityprovided by the distributed antennas in order to schedule over thesame frequency band a different user attached to each one of thedistributed nodes. The objective is to optimize the power levelsto control the interference created between the transmissionsof the selected users, thereby multiplexing as many of them aspossible while maximizing capacity. To achieve this goal, a sumratecapacity optimization with respect to the power levels ishere proposed by using a gradient descent iterative technique.The result is the set of optimum user-antenna pairs to bescheduled and their optimum power levels. Inter-cell interferenceis calculated by reusing the results of previous simulation runsin the transmission parameters of outer-cells, thereby efficientlyreplicating system-level behavior. The algorithm is also evaluatedin terms of fairness by using the spatial distribution of the usercapacity. Capacity and fairness of the algorithm considerablyoutperform previous solutions, particularly in scenarios withgood line-of-sight and optimum node location

A framework for achieving the required degree of multitenancy isolation for deploying components of a cloud-hosted service.

Multitenancy allows multiple tenants to access a single instance of a cloud offering. While several approaches exist for implementing multitenancy, little attention has been paid to implementing the required degree of isolation since there are varying degrees of isolation that can be implemented for each tenant. This paper presents a framework for achieving the required degree of isolation between tenants accessing a cloud offering so that the required performance, resource utilisation and access privilege of one tenant does not affect other tenants when there are workload changes. The framework is composed of two main constituents: 1) component-based approach to multitenancy isolation through request re-routing (COMITRE); 2) an optimisation model for providing optimal solutions for deploying components of a cloud-hosted service. We demonstrate using a case study of: 1) a cloud-hosted bug tracking system; 2) a synthetic dataset, the possibility of providing the required degree of isolation and optimal deployment solutions

PROPOSED FRAMEWORK FOR TAILORING AGILE-BASED SOFTWARE DEVELOPMENT PROCESSES FOR SMALL AND MEDIUM SIZED COMPANIES

High risks are involved as well as a large number of resources are required to construct the software development processes from scratch. Most of the software development companies follow ad-hoc approaches in informal ways to tailor an existing software development process according to their requirements. Instead of devising new tailoring strategies, these approaches describe and compare the similar tailoring operations at very superficial level and mainly focus on the large sized software development companies

Applying Genetic Algorithms for Software Design and Project Planning

Today's software systems are growing in size and complexity. This means not only increased complexity in developing software systems, but also increase in the budget and completion time. This trend will lead to a situation where traditional manual software engineering practices are not sufficient to develop and evolve software systems in an economic and timely manner. Automated support can aid software engineers in reducing the time-to-market and improving the quality of the software. This thesis work explores the application of genetic algorithms for automated software architecture design and project planning.Software architecture design and project planning are non-trivial and challenging tasks. This thesis applies genetic algorithms to introduce automation into these tasks. The proposed genetic algorithm exploits reusable solutions, such as design patterns, architecture styles and application specific solutions for transforming a given initial rudimentary model into detailed design. The architectures are evaluated using multiple quality attributes, such as modifiability, efficiency and complexity. The fitness function encompasses the knowledge required for evaluating the architectures according to multiple quality attributes. The output from the genetic algorithm is an architecture proposal optimized with respect to multiple quality attributes.A genetic algorithm has also been devised for assigning work across teams located in distributed sites. The genetic algorithm takes information about the target system and the development organization as input and produces a set of work distribution and schedule plans optimized with respect to cost and duration objectives. The fitness function considers the differences in teams and barriers created by global dispersion into account in evaluating the work assignment. In addition, the genetic algorithm also takes solutions that ease or hamper distributed development into account in allocating the work. The genetic algorithm has been further extended with Pareto optimality to find a set of suitable work distribution proposals in a tradeoff between project cost and duration. In the experiments, an electronic home control system was developed by a set of different organizations structures. The results demonstrate that the proposed genetic algorithm can create reasonable work distribution proposals that conform to the general assumptions about the nature of cost and project completion time, i.e., cost of the project can be reduced at the expense of project completion time and vice-versa.In addition, variations have been made to the genetic algorithm approach to software architecture design. To accelerate the genetic algorithm towards multi-objective solutions, a quality farms approach has been developed. The approach uses the idea of cross breeding, where different individuals that are good with respect to one quality objective are combined for producing software architecture proposals that are good in multiple objectives. Also, to explore the suitability of other methods for software architecture synthesis, a constraint satisfaction approach has been developed. The approach models the software architecture design problem as a constraint satisfaction and optimization problem and solves it using constraint satisfaction techniques. This approach can provide rationale about why certain decisions are chosen in the proposed architecture proposals.Tool support for genetic algorithm-based architecture design and work planning approaches has been proposed. It facilitates an end user to give input, view and analyze the results of the developed genetic algorithm based approaches. The tool also provides support for semi-automated architecture design, where a human architect can guide the genetic algorithm towards optimal solutions. An empirical study has also been performed. It suggests that the quality of the proposals produced through semiautomated architecture design is roughly at the level of senior software engineering students. Furthermore, the project manager can interact with the tool and perform whatif analysis for choosing the suitable work distribution for the project at hand

Applying global software development approaches to building high-performing software teams

The rapid progress of communication technologies combined with the growing competition for talents and knowledge has made it necessary to reassess the potential of distributed development which has significantly changed the landscape of the IT industry introducing a variety of cooperation models and making notable changes to the software team work environment. Along with this, enterprises pay more attention to teams’ performance improvement, employing emerging management tools for building up efficient software teams, and trying to get the most out of understanding factors which significantly impact a team’s overall performance. The objective of the research is to systematize factors characterizing high-performing software teams; indicate the benefits of global software development (GSD) models positively influencing software teams’ development performance; and study how companies’ strategies can benefit from distributed development approaches in building high-performing software teams. The thesis is designed as a combination of a systematic literature review followed by qualitative research in the form of semi-structured interviews to validate the findings regarding classification of GSD models’ benefits and their influence on the development of high-performing software teams. At a literature review stage, the research (1) introduces a team performance factors’ model reflecting the aspects which impact the effectiveness of development teams; (2) suggests the classification of GSD models based on organizational, legal, and temporal characteristics, and (3) describes the benefits of GSD models which influence the performance of software development teams. Within the empirical part of the study, we refine the classification of GSD models’ benefits based on the qualitative analysis results of semi-structured interviews with practitioners from IT industry, form a comparison table of GSD benefits depending on the model in question, and introduce recommendations for company and team management regarding the application of GSD in building high-performing software teams. IT corporations, to achieve their strategic goals, can enrich their range of available tools for managing high-performing teams by considering the peculiarities of different GSD models. Company and team management should evaluate the advantages of the distributed operational models, and use the potential and benefits of available configurations to increase teams’ performance and build high-performing software teams