The Impacts of Global Inequality in Social Networks: Examined in Three Major Theories

The rapid growth of modern long-distance communication technologies both in term of quality and quantity and the consequent emergence of cyberspace in parallel with the real world, has led to new forms of inequality which can be interpreted in three different ways. Using the three-generation theory of social networks (Oral networks; Longdistance networks; and, Digital networks), one can make domestic comparisons, and find countries in which the majority of the population are within the third category or the digital network. On the other side of the extreme, are nations who are still under the limited conditions of the first and second categories of oral and long-distance networking. This paper presents a chart using a combination of different statistical indicators to illustrate the inequality in question. The focus of this paper has been on the two countries of Iran and the United States as its case study. The conclusion at the end suggests that tackling and reducing the inequality in question has to do with 'national will and national facilities' as well as 'individual will and individual facilities'

Extending Demand Response to Tenants in Cloud Data Centers via Non-intrusive Workload Flexibility Pricing

Participating in demand response programs is a promising tool for reducing energy costs in data centers by modulating energy consumption. Towards this end, data centers can employ a rich set of resource management knobs, such as workload shifting and dynamic server provisioning. Nonetheless, these knobs may not be readily available in a cloud data center (CDC) that serves cloud tenants/users, because workloads in CDCs are managed by tenants themselves who are typically charged based on a usage-based or flat-rate pricing and often have no incentive to cooperate with the CDC operator for demand response and cost saving. Towards breaking such "split incentive" hurdle, a few recent studies have tried market-based mechanisms, such as dynamic pricing, inside CDCs. However, such mechanisms often rely on complex designs that are hard to implement and difficult to cope with by tenants. To address this limitation, we propose a novel incentive mechanism that is not dynamic, i.e., it keeps pricing for cloud resources unchanged for a long period. While it charges tenants based on a Usage-based Pricing (UP) as used by today's major cloud operators, it rewards tenants proportionally based on the time length that tenants set as deadlines for completing their workloads. This new mechanism is called Usage-based Pricing with Monetary Reward (UPMR). We demonstrate the effectiveness of UPMR both analytically and empirically. We show that UPMR can reduce the CDC operator's energy cost by 12.9% while increasing its profit by 4.9%, compared to the state-of-the-art approaches used by today's CDC operators to charge their tenants