@article{iqbal_singh_shahzad_2022, title={Characterizing the Availability and Latency in AWS Network From the Perspective of Tenants}, ISSN={["1558-2566"]}, DOI={10.1109/TNET.2022.3148701}, abstractNote={Scalability and performance requirements are driving tenants to increasingly move their applications to public clouds. Unfortunately, cloud providers do not provide a view of their networking infrastructure to the tenants, rather only provide some generic service level agreements (SLAs). Tenants are, therefore, forced to plan the deployments of their applications based on these SLAs. This limits the performance that the tenants can achieve. Keeping this in view, we present a detailed network measurement study of the largest public cloud, Amazon Web Services (AWS). We collected network data to characterize the availability and latency of AWS over a period of 100 days and studied various temporal trends across several geographical locations of AWS throughout the world. We performed our study at all three levels of cloud hierarchy: inside availability zones (AZs), across AZs, and across regions. Our results show that network behavior varies significantly over time at different geographical locations, levels of hierarchy, and temporal granularities. For example, while we observed high availability at monthly granularity, it deteriorates at daily and hourly granularities. This and many other such observations that we present have significant implications for cloud tenants. We further implemented our measurement approach on Google Cloud Platform (GCP) to demonstrate that it can be deployed on any cloud platform and present some preliminary comparative observations from this implementation. Based on our observations, we present several recommendations that tenants can use to better deploy their applications.}, journal={IEEE-ACM TRANSACTIONS ON NETWORKING}, author={Iqbal, Hassan and Singh, Anand and Shahzad, Muhammad}, year={2022}, month={Feb} }
 @article{iqbal_khan_khan_shahzad_2022, title={Left or Right: A Peek into the Political Biases in Email Spam Filtering Algorithms During US Election 2020}, DOI={10.1145/3485447.3512121}, abstractNote={Email services use spam filtering algorithms (SFAs) to filter emails that are unwanted by the user. However, at times, the emails perceived by an SFA as unwanted may be important to the user. Such incorrect decisions can have significant implications if SFAs treat emails of user interest as spam on a large scale. This is particularly important during national elections. To study whether the SFAs of popular email services have any biases in treating the campaign emails, we conducted a large-scale study of the campaign emails of the US elections 2020 by subscribing to a large number of Presidential, Senate, and House candidates using over a hundred email accounts on Gmail, Outlook, and Yahoo. We analyzed the biases in the SFAs towards the left and the right candidates and further studied the impact of the interactions (such as reading or marking emails as spam) of email recipients on these biases. We observed that the SFAs of different email services indeed exhibit biases towards different political affiliations.}, journal={PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22)}, author={Iqbal, Hassan and Khan, Usman Mahmood and Khan, Hassan Ali and Shahzad, Muhammad}, year={2022}, pages={2491–2500} }
 @article{khan_iqbal_shahzad_jin_2022, title={RMS: Removing Barriers to Analyze the Availability and Surge Pricing of Ridesharing Services}, DOI={10.1145/3491102.3517464}, abstractNote={Ridesharing services do not make data of their availability (supply, utilization, idle time, and idle distance) and surge pricing publicly available. It limits the opportunities to study the spatiotemporal trends of the availability and surge pricing of these services. Only a few research studies conducted in North America analyzed these features for only Uber and Lyft. Despite the interesting observations, the results of prior works are not generalizable or reproducible because: i) the datasets collected in previous publications are spatiotemporally sensitive, i.e., previous works do not represent the current availability and surge pricing of ridesharing services in different parts of the world; and ii) the analyses presented in previous works are limited in scope (in terms of countries and ridesharing services they studied). Hence, prior works are not generally applicable to ridesharing services operating in different countries. This paper addresses the issue of ridesharing-data unavailability by presenting Ridesharing Measurement Suite (RMS). RMS removes the barrier of entry for analyzing the availability and surge pricing of ridesharing services for ridesharing users, researchers from various scientific domains, and regulators. RMS continuously collects the data of the availability and surge pricing of ridesharing services. It exposes real-time data of these services through i) graphical user interfaces and ii) public APIs to assist various stakeholders of these services and simplify the data collection and analysis process for future ridesharing research studies. To signify the utility of RMS, we deployed RMS to collect and analyze the availability and surge pricing data of 10 ridesharing services operating in nine countries for eight weeks in pre and during pandemic periods. Using the data collected and analyzed by RMS, we identify that previous articles miscalculated the utilization of ridesharing services as they did not count in the vehicles driving in multiple categories of the same service. We observe that during COVID-19, the supply of ridesharing services decreased by 54%, utilization of available vehicles increased by 6%, and a 5 × increase in the surge frequency of services. We also find that surge occurs in a small geographical region, and its intensity reduces by 50% in about 0.5 miles away from the location of a surge. We present several other interesting observations on ridesharing services’ availability and surge pricing.}, journal={PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22)}, author={Khan, Hassan Ali and Iqbal, Hassan and Shahzad, Muhammad and Jin, Guoliang}, year={2022} }
 @article{iqbal_khalid_shahzad_2021, title={Dissecting Cloud Gaming Performance with DECAF}, volume={5}, ISSN={["2476-1249"]}, DOI={10.1145/3491043}, abstractNote={Cloud gaming platforms have witnessed tremendous growth over the past two years with a number of large Internet companies including Amazon, Facebook, Google, Microsoft, and Nvidia publicly launching their own platforms. While cloud gaming platforms continue to grow, the visibility in their performance and relative comparison is lacking. This is largely due to absence of systematic measurement methodologies which can generally be applied. As such, in this paper, we implement DECAF, a methodology to systematically analyze and dissect the performance of cloud gaming platforms across different game genres and game platforms. DECAF is highly automated and requires minimum manual intervention. By applying DECAF, we measure the performance of three commercial cloud gaming platforms including Google Stadia, Amazon Luna, and Nvidia GeForceNow, and uncover a number of important findings. First, we find that processing delays in the cloud comprise majority of the total round trip delay experienced by users, accounting for as much as 73.54% of total user-perceived delay. Second, we find that video streams delivered by cloud gaming platforms are characterized by high variability of bitrate, frame rate, and resolution. Platforms struggle to consistently serve 1080p/60 frames per second streams across different game genres even when the available bandwidth is 8-20× that of platform's recommended settings. Finally, we show that game platforms exhibit performance cliffs by reacting poorly to packet losses, in some cases dramatically reducing the delivered bitrate by up to 6.6× when loss rates increase from 0.1% to 1%. Our work has important implications for cloud gaming platforms and opens the door for further research on comprehensive measurement methodologies for cloud gaming.}, number={3}, journal={PROCEEDINGS OF THE ACM ON MEASUREMENT AND ANALYSIS OF COMPUTING SYSTEMS}, author={Iqbal, Hassan and Khalid, Ayesha and Shahzad, Muhammad}, year={2021}, month={Dec} }