Measuring the Relationships between Internet Geography and RTT

When designing distributed systems and Internet protocols, designers can benefit from statistical models of the Internet that can be used to estimate their performance. However, it is frequently impossible for these models to include every property of interest. In these cases, model builders have to select a reduced subset of network properties, and the rest will have to be estimated from those available. In this paper we present a technique for the analysis of Internet round trip times (RTT) and its relationship with other geographic and network properties. This technique is applied on a novel dataset comprising ∼19 million RTT measurements derived from ∼200 million RTT samples between ∼54 thousand DNS servers. Our main contribution is an information-theoretical analysis that allows us to determine the amount of information that a given subset of geographic or network variables (such as RTT or great circle distance between geolocated hosts) gives about other variables of interest. We then provide bounds on the error that can be expected when using statistical estimators for the variables of interest based on subsets of other variables

Taming Anycast in a Wild Internet

Anycast is a popular tool for deploying global, widely available systems, including DNS infrastructure and content delivery networks (CDNs). The optimization of these networks often focuses on the deployment and management of anycast sites. However, such approaches fail to consider one of the primary configurations of a large anycast network: the set of networks that receive anycast announcements at each site (i.e., an announcement configuration). Altering these configurations, even without the deployment of additional sites, can have profound impacts on both anycast site selection and round-trip times. In this study, we explore the operation and optimization of any-cast networks through the lens of deployments that have a large number of upstream service providers. We demonstrate that these many-provider anycast networks exhibit fundamentally different properties when interacting with the Internet, having a greater number of single AS hop paths and reduced dependency on each provider, compared with few-provider networks. We further examine the impact of announcement configuration changes, demonstrating that in nearly 30% of vantage point groups, round-trip time performance can be improved by more than 25%, solely by manipulating which providers receive anycast announcements. Finally, we propose DailyCatch, an empirical measurement methodology for testing and validating announcement configuration changes, and demonstrate its ability to influence user-experienced performance on a global anycast CDN

Characterizing the Role of Power Grids in Internet Resilience

Among critical infrastructures, power grids and communication infrastructure are identified as uniquely critical since they enable the operation of all other sectors. Due to their vital role, the research community has undertaken extensive efforts to understand the complex dynamics and resilience characteristics of these infrastructures, albeit independently. However, power and communication infrastructures are also interconnected, and the nature of the Internet's dependence on power grids is poorly understood. In this paper, we take the first step toward characterizing the role of power grids in Internet resilience by analyzing the overlap of global power and Internet infrastructures. We investigate the impact of power grid failures on Internet availability and find that nearly $65\%$ of the public Internet infrastructure components are concentrated in a few ($< 10$) power grid failure zones. More importantly, power grid dependencies severely limit the number of disjoint availability zones of cloud providers. When dependency on grids serving data center locations is taken into account, the number of isolated AWS Availability Zones reduces from 87 to 19. Building upon our findings, we develop NetWattZap, an Internet resilience analysis tool that generates power grid dependency-aware deployment suggestions for Internet infrastructure and application components, which can also take into account a wide variety of user requirements

Simulation Modelling of Cloud Mini and Mega Data Centers Using Cloud Analyst

Cloud Computing has now become a base technology for various other technologies including Internet of Things, Big Data Technologies and many other technologies, the responsibility of Cloud become critical in case of real time applications where the cloud services are required in real time. Delay in the response from Cloud may lead to serious consequences even loss of lives where the processes data from cloud must reach within predefined time interval. The performance of Cloud has experienced delays with the current infrastructure due to multiple issues in Traditional Cloud Network Model. The Paper suggests a proposed architecture Cloud Mini Data Centers simulated using Cloud Analyst to minimize the delays of Cloud Service delivery. The paper also simulate traditional cloud Network model using Cloud Analyst and provides a comparative study of both models