We leverage a novel telemetry source available in public clouds today: periodic summaries of every flow that enters or leaves any VM. A key aspect is that such telemetry can be collected transparently to customers and with minimal impact on their workloads. By consuming this telemetry, we show how one may realize complete and dynamic graphs of the communication inside cloud subscriptions. We describe novel analyses over these communication graphs with implications on network security and management.

We argue that network virtualization as experienced by many cloud tenants is overly complex and needs to be rethought. We propose that the goal for a new design should be to free cloud tenants entirely from having to build and operate virtual networks. Building on this philosophy, we propose that instead of low-level building blocks (virtual links, routers, firewalls), cloud networking should be exposed to tenants in a declarative and endpoint-centric manner.

The parliament, media, and citizens, are key stakeholders in any democracy. We study their priorities pertaining to four economic policies in India - Demonetization, Aadhaar, GST, and Farmers' Protests - by examining the content of questions asked by politicians in the parliament, news articles published in the mass media, and data from social media. We find that the mass media covers stories about different constituencies (the poor, middle-class, corporates, etc.) but shows biases in terms of which constituencies it chooses to focus on for each policy. The parliament tends to focus on procedural aspects of the policies. We also find that the social media simply echoes the trends of whatever is emphasized more in the mass media, without much deviation in the attention placed by social media users on different issues. We further find out that instead of selecting issues of importance based on feedback from citizens, the parliamentarians mostly indulge in partisanship, and shape their questions based on party goals that change depending on whether their party is in power or not. Overall, we are able to use this analysis to comment on how representative and responsive the Indian democracy is to its citizens.

Politicians, politically connected business persons, bureaucrats, celebrities, and highly placed government officials (collectively termed as the power elite in sociology literature) can influence national and regional policy for personal and organizational benefit, which may not always be in the best interests of the people. Media is a crucial tool to shape public opinion, and is used heavily by the power elite to bring legitimacy to their policy decisions. In this paper, we empirically analyze the coverage given to the power elite in mainstream media on Demonetization, a significant, recent policy event in India. We compare the extent of coverage given to the elite and non-elite, the policy slant expressed by them, and differences in coverage between seven of the largest news media organizations in India. We find that among the power elite, powerful politicians and political parties are given the maximum coverage in the media, with conspicuous negligence in coverage given to expert opinions. Sentiment analysis clearly reveals that opposing political factions express opposing views towards the policy, and there is variation across different news sources as well. We are applying our methods on other contentious policy events to be able to do a more systematic analysis of how media can aid the power elite to shape public opinion by giving them disproportionately large coverage and visibility.

Layer-4 load balancing is fundamental to creating scale-out web services. We designed and implemented Ananta, a scale-out layer-4 load balancer that runs on commodity hardware and meets the performance, reliability and operational requirements of multi-tenant cloud computing environments. Ananta combines existing techniques in routing and distributed systems in a unique way and splits the components of a load balancer into a consensus-based reliable control plane and a decentralized scale-out data plane. A key component of Ananta is an agent in every host that can take over the packet modification function from the load balancer, thereby enabling the load balancer to naturally scale with the size of the data center. Due to its distributed architecture, Ananta provides direct server return (DSR) and network address translation (NAT) capabilities across layer-2 boundaries. Multiple instances of Ananta have been deployed in the Windows Azure public cloud with combined bandwidth capacity exceeding 1Tbps. It is serving traffic needs of a diverse set of tenants, including the blob, table and relational storage services. With its scale-out data plane we can easily achieve more than 100Gbps throughput for a single public IP address. In this paper, we describe the requirements of a cloud-scale load balancer, the design of Ananta and lessons learnt from its implementation and operation in the Windows Azure public cloud.

In this paper, we define architecture of a system for managing network resources in a hierarchical OSPF routing domain supporting DiffServ, wherein resources in terms of bandwidth are dynamically allocated among data flows, each traversing the domain from an ingress edge router to an egress edge router. Specifically, we consider a bandwidth management system in a hierarchical routing domain, wherein each level of the hierarchy comprises a number of areas defined for that level. We propose two alternative approaches for bandwidth management in such a routing domain. In the first approach, the admission control function is centralized at a domain-level bandwidth management agent. In the second approach, bandwidth management functions are distributed among a number of bandwidth managers, and edge-to-edge session admission control is executed on an area-by-area basis.

This paper introduces and analyzes a class of nonlinear congestion control algorithms called binomial algorithms, motivated in part by the needs of streaming audio and video applications for which a drastic reduction in transmission rate upon each congestion indication (or loss) is problematic. Binomial algorithms generalize TCP-style additive-increase by increasing inversely proportional to a power k of the current window (for TCP, k=0); they generalize TCP-style multiplicative decrease by decreasing proportional to a power l of the current window (for TCP, l=1). We show that there are an infinite number of deployable TCP-compatible binomial algorithms, those which satisfy k+1=1, and that all binomial algorithms converge to fairness under a synchronized-feedback assumption provided k+l>0; k, l/spl ges/0. Our simulation results show that binomial algorithms interact well with TCP across a RED gateway. We focus on two particular algorithms, IIAD (k=1, l=0) and SQRT (k=l=0.5), showing that they are well-suited to applications that do not react well to large TCP-style window reductions.

The recently developed notion of TCP-compatibility has led to a number of proposals for alternative congestion control algorithms whose long-term throughput as a function of a steady-state loss rate is similar to that of TCP. Motivated by the needs of some streaming and multicast applications, these algorithms seem poised to take the current TCP-dominated Internet to an Internet where many congestion control algorithms co-exist. An important characteristic of these alternative algorithms is that they are slowly-responsive, refraining from reacting as drastically as TCP to a single packet loss.However, the TCP-compatibility criteria explored so far in the literature considers only the static condition of a fixed loss rate. This paper investigates the behavior of slowly-responsive, TCP-compatible congestion control algorithms under more realistic dynamic network conditions, addressing the fundamental question of whether these algorithms are safe to deploy in the public Internet. We study persistent loss rates, long- and short-term fairness properties, bottleneck link utilization, and smoothness of transmission rates.