Srinivasan Seshan

Bio: Srinivasan Seshan is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: The Internet & Wireless network. The author has an hindex of 63, co-authored 197 publications receiving 17652 citations. Previous affiliations of Srinivasan Seshan include Wisconsin Alumni Research Foundation & IBM.

A comparison of mechanisms for improving TCP performance over wireless links

Abstract: Reliable transport protocols such as TCP are tuned to perform well in traditional networks where packet losses occur mostly because of congestion. However, networks with wireless and other lossy links also suffer from significant losses due to bit errors and handoffs. TCP responds to all losses by invoking congestion control and avoidance algorithms, resulting in degraded end-to end performance in wireless and lossy systems. We compare several schemes designed to improve the performance of TCP in such networks. We classify these schemes into three broad categories: end-to-end protocols, where loss recovery is performed by the sender; link-layer protocols that provide local reliability; and split-connection protocols that break the end-to-end connection into two parts at the base station. We present the results of several experiments performed in both LAN and WAN environments, using throughput and goodput as the metrics for comparison. Our results show that a reliable link-layer protocol that is TCP-aware provides very good performance. Furthermore, it is possible to achieve good performance without splitting the end-to-end connection at the base station. We also demonstrate that selective acknowledgments and explicit loss notifications result in significant performance improvements.

A case for end system multicast

Abstract: The conventional wisdom has been that Internet protocol (IP) is the natural protocol layer for implementing multicast related functionality. However, more than a decade after its initial proposal, IP multicast is still plagued with concerns pertaining to scalability, network management, deployment, and support for higher layer functionality such as error, flow, and congestion control. We explore an alternative architecture that we term end system multicast, where end systems implement all multicast related functionality including membership management and packet replication. This shifting of multicast support from routers to end systems has the potential to address most problems associated with IP multicast. However, the key concern is the performance penalty associated with such a model. In particular, end system multicast introduces duplicate packets on physical links and incurs larger end-to-end delays than IP multicast. We study these performance concerns in the context of the Narada protocol. In Narada, end systems self-organize into an overlay structure using a fully distributed protocol. Further, end systems attempt to optimize the efficiency of the overlay by adapting to network dynamics and by considering application level performance. We present details of Narada and evaluate it using both simulation and Internet experiments. Our results indicate that the performance penalties are low both from the application and the network perspectives. We believe the potential benefits of transferring multicast functionality from end systems to routers significantly outweigh the performance penalty incurred.

Improving TCP/IP performance over wireless networks

Abstract: TCP is a reliable transport protocol tuned to perform well in traditional networks made up of links with low bit-error rates. Networks with higher bit-error rates, such as those with wireless links and mobile hosts, violate many of the assumptions made by TCP, causing degraded end-to-end performance. In tbis paper, we describe the design and implementation of a simple protocol, called the snoop protocol, that improves TCP performance in wireless networks. The protocol modifies network-layer software mainly at a base station and preserves end-to-end TCP semantics. The main idea of the protocol is to cache packets at the base station and perform local retransmissions across the wireless link. We have implemented the snoop protocol on a wireless testbed consisting of IBM ThinkPad laptops and i486 base stations communicating over an ATT we have achieved throughput speedups of up to 20 times over regular TCP in our experiments with the protocol.

Improving reliable transport and handoff performance in cellular wireless networks

Abstract: TCP is a reliable transport protocol tuned to perform well in traditional networks where congestion is the primary cause of packet loss. However, networks with wireless links and mobile hosts incur significant losses due to bit-errors and hand-offs. This environment violates many of the assumptions made by TCP, causing degraded end-to-end performance. In this paper, we describe the additions and modifications to the standard Internet protocol stack (TCP/IP) to improve end-to-end reliable transport performance in mobile environments. The protocol changes are made to network-layer software at the base station and mobile host, and preserve the end-to-end semantics of TCP. One part of the modifications, called the snoop module, caches packets at the base station and performs local retransmissions across the wireless link to alleviate the problems caused by high bit-error rates. The second part is a routing protocol that enables low-latency handoff to occur with negligible data loss. We have implemented this new protocol stack on a wireless testbed. Our experiments show that this system is significantly more robust at dealing with unreliable wireless links than normal TCP; we have achieved throughput speedups of up to 20 times over regular TCP and handoff latencies over 10 times shorter than other mobile routing protocols.

Mercury: supporting scalable multi-attribute range queries

Abstract: This paper presents the design of Mercury, a scalable protocol for supporting multi-attribute range-based searches. Mercury differs from previous range-based query systems in that it supports multiple attributes as well as performs explicit load balancing. To guarantee efficient routing and load balancing, Mercury uses novel light-weight sampling mechanisms for uniformly sampling random nodes in a highly dynamic overlay network. Our evaluation shows that Mercury is able to achieve its goals of logarithmic-hop routing and near-uniform load balancing.We also show that Mercury can be used to solve a key problem for an important class of distributed applications: distributed state maintenance for distributed games. We show that the Mercury-based solution is easy to use, and that it reduces the game's messaging overheard significantly compared to a naive approach.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

A high-throughput path metric for multi-hop wireless routing

Abstract: This paper presents the expected transmission count metric (ETX), which finds high-throughput paths on multi-hop wireless networks. ETX minimizes the expected total number of packet transmissions (including retransmissions) required to successfully deliver a packet to the ultimate destination. The ETX metric incorporates the effects of link loss ratios, asymmetry in the loss ratios between the two directions of each link, and interference among the successive links of a path. In contrast, the minimum hop-count metric chooses arbitrarily among the different paths of the same minimum length, regardless of the often large differences in throughput among those paths, and ignoring the possibility that a longer path might offer higher throughput.This paper describes the design and implementation of ETX as a metric for the DSDV and DSR routing protocols, as well as modifications to DSDV and DSR which allow them to use ETX. Measurements taken from a 29-node 802.11b test-bed demonstrate the poor performance of minimum hop-count, illustrate the causes of that poor performance, and confirm that ETX improves performance. For long paths the throughput improvement is often a factor of two or more, suggesting that ETX will become more useful as networks grow larger and paths become longer.

Networking named content

Abstract: Network use has evolved to be dominated by content distribution and retrieval, while networking technology still speaks only of connections between hosts. Accessing content and services requires mapping from the what that users care about to the network's where. We present Content-Centric Networking (CCN) which treats content as a primitive - decoupling location from identity, security and access, and retrieving content by name. Using new approaches to routing named content, derived heavily from IP, we can simultaneously achieve scalability, security and performance. We implemented our architecture's basic features and demonstrate resilience and performance with secure file downloads and VoIP calls.

Networking named content

Abstract: Current network use is dominated by content distribution and retrieval yet current networking protocols are designed for conversations between hosts. Accessing content and services requires mapping from the what that users care about to the network's where. We present Content-Centric Networking (CCN) which uses content chunks as a primitive---decoupling location from identity, security and access, and retrieving chunks of content by name. Using new approaches to routing named content, derived from IP, CCN simultaneously achieves scalability, security, and performance. We describe our implementation of the architecture's basic features and demonstrate its performance and resilience with secure file downloads and VoIP calls.