Abstract. Steady state VM management in data centers should be network-aware so that VM migrations do not degrade network performance of other flows in the network, and if required, a VM migration can be intelligently orchestrated to decongest a network hotspot. Recent research in network-aware management of VMs has focused mainly on an optimal network-aware initial placement of VMs and has largely ignored steady state management. In this context, we present the design and implementation of Remedy. Remedy ranks target hosts for a VM migration based on the associated cost of migration, available bandwidth for migration and the network bandwidth balance achieved by a migration. It models the cost of migration in terms of additional network traffic generated during migration.We have implemented Remedy as an OpenFlow controller application that detects the most congested links in the network and migrates a set of VMs in a network-aware manner to decongest these links. Our choice of target hosts ensures that neither the migration traffic nor the flows that get rerouted as a result of migration cause congestion in any part of the network. We validate our cost of migration model on a virtual software testbed using real VM migrations. Our simulation results using real data center traffic data demonstrate that selective network aware VM migrations can help reduce unsatisfied bandwidth by up to 80-100%.

We propose a novel mechanism to infer topics of interest of individual users in the Twitter social network. We observe that in Twitter, a user generally follows experts on various topics of her interest in order to acquire information on those topics. We use a methodology based on social annotations (proposed earlier by us) to first deduce the topical expertise of popular Twitter users, and then transitively infer the interests of the users who follow them. This methodology is a sharp departure from the traditional techniques of inferring interests of a user from the tweets that she posts or receives. We show that the topics of interest inferred by the proposed methodology are far superior than the topics extracted by state-of-the-art techniques such as using topic models (Labeled LDA) on tweets. Based upon the proposed methodology, we build a system Who Likes What, which can infer the interests of millions of Twitter users. To our knowledge, this is the first system that can infer interests for Twitter users at such scale. Hence, this system would be particularly beneficial in developing personalized recommender services over the Twitter platform.

We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Even optimistic estimates suggest that most countries will likely take 6 to 24 months to vaccinate their citizens. These time estimates and the emergence of new viral strains urge us to find quick and effective ways to allocate the vaccines and contain the pandemic. While current approaches use combinations of age-based and occupation-based prioritizations, our strategy marks a departure from such largely aggregate vaccine allocation strategies. We propose a novel approach motivated by recent advances in (i) science of real-world networks that point to efficacy of certain vaccination strategies and (ii) digital technologies that improve our ability to estimate some of these structural properties. Using a realistic representation of a social contact network for the Commonwealth of Virginia, combined with accurate surveillance data on spatiotemporal cases and currently accepted models of within- and between-host disease dynamics, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals' degree (number of social contacts) and total social proximity time is significantly more effective than the currently used age-based allocation strategy in terms of number of infections, hospitalizations and deaths. Our results suggest that in just two months, by March 31, 2021, compared to age-based allocation, the proposed degree-based strategy can result in reducing an additional 56−110k infections, 3.2− 5.4k hospitalizations, and 700−900 deaths just in the Commonwealth of Virginia. Extrapolating these results for the entire US, this strategy can lead to 3−6 million fewer infections, 181−306k fewer hospitalizations, and 51−62k fewer deaths compared to age-based allocation. The overall strategy is robust even: (i) if the social contacts are not estimated correctly; (ii) if the vaccine efficacy is lower than expected or only a single dose is given; (iii) if there is a delay in vaccine production and deployment; and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed.

Many social networks are characterized by actors (nodes) holding quantitative opinions about movies, songs, sports, people, colleges, politicians, and so on. These opinions are influenced by network neighbors. Many models have been proposed for such opinion dynamics, but they have some limitations. Most consider the strength of edge influence as fixed. Some model a discrete decision or action on part of each actor, and an edge as causing an "infection" (that is often permanent or self-resolving). Others model edge influence as a stochastic matrix to reuse the mathematics of eigensystems. Actors' opinions are usually observed globally and synchronously. Analysis usually skirts transient effects and focuses on steady-state behavior. There is very little direct experimental validation of estimated influence models. Here we initiate an investigation into new models that seek to remove these limitations. Our main goal is to estimate, not assume, edge influence strengths from an observed series of opinion values at nodes. We adopt a linear (but not stochastic) influence model. We make no assumptions about system stability or convergence. Further, actors' opinions may be observed in an asynchronous and incomplete fashion, after missing several time steps when an actor changed its opinion based on neighbors' influence. We present novel algorithms to estimate edge influence strengths while tackling these aggressively realistic assumptions. Experiments with Reddit, Twitter, and three social games we conducted on volunteers establish the promise of our algorithms. Our opinion estimation errors are dramatically smaller than strong baselines like the DeGroot, flocking, voter, and biased voter models. Our experiments also lend qualitative insights into asynchronous opinion updates and aggregation.

We present a semantic methodology to identify topical groups in Twitter on a large number of topics, each consisting of users who are experts on or interested in a specific topic. Early studies investigating the nature of Twitter suggest that it is a social media platform consisting of a relatively small section of elite users, producing information on a few popular topics such as media, politics, and music, and the general population consuming it. We show that this characterization ignores a rich set of highly specialized topics, ranging from geology, neurology, to astrophysics and karate -each being discussed by their own topical groups. We present a detailed characterization of these topical groups based on their network structures and tweeting behaviors. Analyzing these groups on the backdrop of the common identity and bond theory in social sciences shows that these groups exhibit characteristics of topical-identity based groups, rather than socialbond based ones.

Modelling social phenomena in large-scale agent-based simulations has long been a challenge due to the computational cost of incorporating agents whose behaviors are determined by reasoning about their internal attitudes and external factors. However, COVID-19 has brought the urgency of doing this to the fore, as, in the absence of viable pharmaceutical interventions, the progression of the pandemic has primarily been driven by behaviors and behavioral interventions. In this paper, we address this problem by developing a large-scale data-driven agent-based simulation model where individual agents reason about their beliefs, objectives, trust in government, and the norms imposed by the government. These internal and external attitudes are based on actual data concerning daily activities of individuals, their political orientation, and norms being enforced in the US state of Virginia. Our model is calibrated using mobility and COVID-19 case data. We show the utility of our model by quantifying the benefits of the various behavioral interventions through counterfactual runs of our calibrated simulation.

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The COVID-19 global outbreak represents the most significant epidemic event since the 1918 influenza pandemic. Simulations have played a crucial role in supporting COVID-19 planning and response efforts. Developing scalable workflows to provide policymakers quick responses to important questions pertaining to logistics, resource allocation, epidemic forecasts and intervention analysis remains a challenging computational problem. In this work, we present scalable high performance computing-enabled workflows for COVID-19 pandemic planning and response. The scalability of our methodology allows us to run fine-grained simulations daily, and to generate county-level forecasts and other counterfactual analysis for each of the 50 states (and DC), 3140 counties across the USA. Our workflows use a hybrid cloud/cluster system utilizing a combination of local and remote cluster computing facilities, and using over 20,000 CPU cores running for 6-9 hours every day to meet this objective. Our state (Virginia), state hospital network, our university, the DOD and the CDC use our models to guide their COVID-19 planning and response efforts. We began executing these pipelines March 25, 2020, and have delivered and briefed weekly updates to these stakeholders for over 30 weeks without interruption.