Publications
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| 21. | Liu, Yunzhuo; Jiang, Bo; Guo, Tian; Sitaraman, Ramesh; Towsley, Don; Wang, Xinbing Grad: Learning for Overhead-aware Adaptive Video Streaming with Scalable Video Coding Proceedings Article In: ACM Multimedia (MM) Conference, 2020. @inproceedings{LiuJGSTW20, |
| 22. | Park, Jounsup; Shah, Yash; Rosenthal, Arielle; Wu, Mingyuan; Murray, John; Lee, Kuan-Ying; Spiteri, Kevin; Zink, Michael; Nahrstedt, Klara; Sitaraman, Ramesh Video 360 Content Navigation for Mobile HMD Devices Conference ACM Multimedia (MM) Conference, 2020. @conference{Parketal2020, |
| 23. | Schomp, Kyle; Bhardwaj, Onkar; Kurdoglu, Eymen; Muhaimen, Mashooq; Sitaraman, Ramesh K. Akamai DNS: Providing Authoritative Answers to the World’s Queries Proceedings Article In: Proceedings of the ACM SIGCOMM Conference, 2020. @inproceedings{SchompBKMS2020,We present Akamai DNS, one of the largest authoritative DNS infrastructures in the world, that supports the Akamai content de- livery network (CDN) as well as authoritative DNS hosting and DNS-based load balancing services for many enterprises. As the starting point for a significant fraction of the world’s Internet in- teractions, Akamai DNS serves millions of queries each second and must be resilient to avoid disrupting myriad online services, scalable to meet the ever increasing volume of DNS queries, per- formant to prevent user-perceivable performance degradation, and reconfigurable to react quickly to shifts in network conditions and attacks. We outline the design principles and architecture used to achieve Akamai DNS’s goals, relating the design choices to the system workload and quantifying the effectiveness of those designs. Further, we convey insights from operating the production system that are of value to the broader research community. |
| 24. | Sundarrajan, Aditya; Kasbekar, Mangesh; Sitaraman, Ramesh K; Shukla, Samta Midgress-aware traffic provisioning for content delivery Proceedings Article In: USENIX Annual Technical Conference (USENIX ATC 20), pp. 543–557, USENIX Association, 2020, ISBN: 978-1-939133-14-4. @inproceedings{254434,Content delivery networks (CDNs) cache and deliver hundreds of trillions of user requests each day from hundreds of thousands of servers around the world. The traffic served by CDNs can be partitioned into hundreds of traffic classes, each with different user access patterns, popularity distributions, object sizes, and performance requirements. Midgress is the cache miss traffic between the CDN's servers and the content provider origins. A major goal of a CDN is to minimize its midgress, since higher midgress translates to higher bandwidth costs and increased user-perceived latency. We propose algorithms that provision traffic classes to servers such that midgress is minimized. Using extensive traces from Akamai's CDN, we show that our midgress-aware traffic provisioning schemes can reduce midgress by nearly 20% in comparison with the midgress-unaware schemes currently in use. We also propose an efficient heuristic for traffic provisioning that achieves near-optimal midgress and is suitable for use in production settings. Further, we show how our algorithms can be extended to other settings that require minimum caching performance per traffic class and minimum content duplication for fault tolerance. Finally, our paper provides a strong case for implementing midgress-aware traffic provisioning in production CDNs. |
| 25. | Kirilin, Vadim; Sundarrajan, Aditya; Gorinsky, Sergey; Sitaraman, Ramesh K. RL-Cache: Learning-Based Cache Admission for Content Delivery Journal Article In: IEEE Journal on Selected Areas in Communications (JSAC), Special issue ssue on AI/ML for Networking and Communications, 2020. @article{KirilinSGS2020, |
| 26. | Aditya Sundarrajan Vadim Kirilin, Sergey Gorinsky; Sitaraman, Ramesh K. RL-Cache: Learning-Based Cache Admission for Content Delivery Journal Article In: IEEE Journal on Selected Areas of Communications (JSAC), 2020. @article{RLCacheJSAC2020,Content delivery networks (CDNs) distribute much of the Internet content by caching and serving the objects requested by users. A major goal of a CDN is to maximize the hit rates of its caches, thereby enabling faster content downloads to the users. Content caching involves two components: an admission algorithm to decide whether to cache an object and an eviction algorithm to determine which object to evict from the cache when it is full. In this paper, we focus on cache admission and propose a novel algorithm called RL-Cache that uses model-free reinforcement learning (RL) to decide whether or not to admit a requested object into the CDN’s cache. Unlike prior approaches that use a small set of criteria for decision making, RL-Cache weights a large set of features that include the object size, recency, and frequency of access. We develop a publicly available implementation of RL-Cache and perform an evaluation using production traces for the image, video, and web traffic classes from Akamai’s CDN. The evaluation shows that RL-Cache improves the hit rate in comparison with the state of the art and imposes only a modest resource overhead on the CDN servers. Further, RL-Cache is robust enough that it can be trained in one location and executed on request traces of the same or different traffic classes in other locations of the same geographic region. The paper also reports extensive analyses of the RL-Cache sensitivity to its features and hyperparameter values. The analyses validate the made design choices and reveal interesting insights into the RL-Cache behavior. |
| 27. | Rahul Urgaonkar Kevin Spiteri, Ramesh Sitaraman BOLA: Near-Optimal Bitrate Adaptation for Online Videos Journal Article In: IEEE/ACM Transactions on Networking, 2020. @article{BOLASUS2020, |
| 28. | Mohammad H. Hajiesmaili Lin Yang, Ramesh Sitaraman; Wong, Wing S. Online Linear Optimization with Inventory Management Constraints Journal Article In: Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 4, no. 1, pp. 29, 2020. @article{YangHSWMW20,This paper considers the problem of online linear optimization with inventory management constraints. Specifically, we consider an online scenario where a decision maker needs to satisfy her time-varying demand for some units of an asset, either from a market with a time-varying price or from her own inventory. In each time slot, the decision maker is presented a (linear) price and must immediately decide the amount to purchase for covering the demand and/or for storing in the inventory for future use. The inventory has a limited capacity and can be used to buy and store assets at low price and cover the demand when the price is high. The ultimate goal of the decision maker is to cover the demand at each time slot while minimizing the cost of buying assets from the market. We propose ARP, an online algorithm for linear programming with inventory constraints, and ARPRate, an extended version that handles rate constraints to/from the inventory. Both ARP and ARPRate achieve optimal competitive ratios, meaning that no other online algorithm can achieve a better theoretical guarantee. To illustrate the results, we use the proposed algorithms in a case study focused on energy procurement and storage management strategies for data centers. |
| 29. | Sabnis, Anirudh Anirudh; Sitaraman, Ramesh K; Towsley, Donald OCCAM: An optimization based approach to network inference Journal Article In: ACM SIGMETRICS Performance Evaluation Review, vol. 46, no. 2, pp. 36–38, 2019. @article{anirudh2019occam, |
| 30. | Spiteri, Kevin; Sitaraman, Ramesh; Sparacio, Daniel From theory to practice: Improving bitrate adaptation in the DASH reference player Journal Article In: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 15, no. 2s, pp. 67, 2019. @article{spiteri2019theory, |
| 31. | Zink, Michael; Sitaraman, Ramesh; Nahrstedt, Klara Scalable 360° Video Stream Delivery: Challenges, Solutions, and Opportunities Journal Article In: Proceedings of the IEEE, vol. 107, no. 4, pp. 639–650, 2019. @article{zink2019scalable, |
| 32. | Gupta, Vani; Shenoy, Prashant; Sitaraman, Ramesh K Combining Renewable Solar and Open Air Cooling for Greening Internet-Scale Distributed Networks Proceedings Article In: Proceedings of the Tenth ACM International Conference on Future Energy Systems, pp. 303–314, ACM 2019. @inproceedings{gupta2019combining, |
| 33. | Kumar, Dhruv; Li, Jian; Chandra, Abhishek; Sitaraman, Ramesh A TTL-based Approach for Data Aggregation in Geo-distributed Streaming Analytics Journal Article In: Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 3, no. 2, pp. 29, 2019. @article{kumar2019ttl, |
| 34. | Ahmad, Sohaib; Rosenthal, Arielle; Hajiesmaili, Mohammad H; Sitaraman, Ramesh K Learning from Optimal: Energy Procurement Strategies for Data Centers Proceedings Article In: Proceedings of the Tenth ACM International Conference on Future Energy Systems, pp. 326–330, ACM 2019. @inproceedings{ahmad2019learning, |
| 35. | Kirilin, Vadim; Sundarrajan, Aditya; Gorinsky, Sergey; Sitaraman, Ramesh K RL-Cache: Learning-Based Cache Admission for Content Delivery Proceedings Article In: Proceedings of the 2019 Workshop on Network Meets AI & ML, pp. 57–63, ACM 2019. @inproceedings{kirilin2019rl, |
| 36. | Paulos, Aaron; Dasgupta, Soura; Beal, Jacob; Mo, Yuanqiu; Hoang, Khoi; Bryan, Lyles J; Pal, Partha; Schantz, Richard; Schewe, Jon; Sitaraman, Ramesh; others, A framework for self-adaptive dispersal of computing services Proceedings Article In: 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS* W), pp. 98–103, IEEE 2019. @inproceedings{paulos2019framework, |
| 37. | Bonab, Hamed; Allan, James; Sitaraman, Ramesh Simulating CLIR translation resource scarcity using high-resource languages Proceedings Article In: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, pp. 129–136, ACM 2019. @inproceedings{bonab2019simulating, |
| 38. | Le, Tan N; Liang, Jie; Liu, Zhenhua; Sitaraman, Ramesh K; Nair, Jayakrishnan; Choi, Bong Jun Optimal Energy Procurement for Geo-distributed Data Centers in Multi-timescale Electricity Markets Journal Article In: ACM SIGMETRICS Performance Evaluation Review, vol. 45, no. 3, pp. 185–197, 2018. @article{le2018optimal, |
| 39. | Gupta, Vani; Shenoy, Prashant; Sitaraman, Ramesh K Efficient solar provisioning for net-zero internet-scale distributed networks Proceedings Article In: 2018 10th International Conference on Communication Systems & Networks (COMSNETS), pp. 372–379, IEEE 2018. @inproceedings{gupta2018efficient, |
| 40. | Dehghan, Mostafa; Jiang, Bo; Seetharam, Anand; He, Ting; Salonidis, Theodoros; Kurose, Jim; Towsley, Don; Sitaraman, Ramesh On the complexity of optimal request routing and content caching in heterogeneous cache networks Journal Article In: IEEE/ACM Transactions on Networking (TON), vol. 25, no. 3, pp. 1635–1648, 2017. @article{dehghan2017complexity, |