Pleasure versus Virtue Ethics in The Light of Aristotelians and the Utilitarianism of John Stuart Mills and Jeremy Bentham

The question of the end of morality is certainly as old as moral speculation itself. It is this question that prompted Aristotle speculating on moral or character virtue. Moral question is properly a human question since only human beings are expected to act in a given way and are subject to praise and reward or blame and punishment. We should remember that also God and angels are expected to act in a given way, but that would, strictly speaking, be the subject of moral theology and revelation, since without revelation depending only on reason, we cannot examine the acts of God and angels in order to determine how they should act. In short, it is only human beings who can be judged to act morally or immorally if we depend only on human reason, without the support of revelation. In the whole work, Stuart Mills and Jeremy Bentham stick on happiness, though each differ in approaches. Consequentialists are after the greatest happiness of the greatest number, by advocating on the struggle to that which may make man happy and avoid evil by all means. Aristotle on his side is on eudaimonism, where man is found to be happy but moral happy. Happiness for Aristotle should be reasonable, morally good and means should be maintained. This research is analytical by nature, where both qualitative and analytical methods have been implemented throughout the work. The work has been successful though some challenges could not be avoided. Finally, in doing or acting, man should observe virtue; and this is always doing good and avoiding evil.  

Performance Optimization and Traffic Management in Data Centers

In recent years, Web services and applications are gradually shifting into cloud environments. These ever-increasing cloud services are being hosted on large scale computation and storage infrastructures called data centers (DC). A data center contains a large number of servers interconnected by a specifically designed network, named data center network (DCN). Today’s data centers host a variety of different applications that exhibit unique traffic characteristics. Some applications exhibit many-to-one communication pattern, some others follow ON-OFF pattern for flow arrivals resulting in bursty traffic. The many-to-one communication is a frequent pattern in data centers and may cause buffer overflow at the bottleneck link which ultimately results in throughput degradation of the flows; this problem is known as TCP Incast. In addition, data centers exhibit heavy tailed distribution for flow sizes; more than 80% of the data center flows have size less than 10 KB, called short flows, but around 90% of the traffic volume, in data center networks, is produced by long flows. Short flows are usually latency-sensitive and long flows demand high throughput. Short flows suffer to quickly increase their sending rates due to the existing long flows occupying most of the available capacity; this results in inefficient flow completion time (FCT) of short flows which ultimately results in degradation of the application’s performance. Furthermore, data centers employ scale-out model for growth to keep up with the rapid increase in the cloud services; thus, multiple paths exist between each pair of end-hosts. Load balancing of the traffic over the multiple path is required to improve network utilization. In addition, requirements like: i) high performance for applications, ii) scalability, and iii) robustness against topology changes, makes the load balancing task more challenging. This research thesis proposes queue length based solutions to the performance issues of TCP Incast and flow completion time of short flows. In addition, a queue length based traffic load balancing scheme is proposed to improve network utilization and applications’ performance. The proposed solutions are validated through implementation in network simulator and compared with the existing schemes. The results show that the proposed solutions significantly improve the performance of applications in the data centers.