858 research outputs found
TimeTrader: Exploiting Latency Tail to Save Datacenter Energy for On-line Data-Intensive Applications
Datacenters running on-line, data-intensive applications (OLDIs) consume
significant amounts of energy. However, reducing their energy is challenging
due to their tight response time requirements. A key aspect of OLDIs is that
each user query goes to all or many of the nodes in the cluster, so that the
overall time budget is dictated by the tail of the replies' latency
distribution; replies see latency variations both in the network and compute.
Previous work proposes to achieve load-proportional energy by slowing down the
computation at lower datacenter loads based directly on response times (i.e.,
at lower loads, the proposal exploits the average slack in the time budget
provisioned for the peak load). In contrast, we propose TimeTrader to reduce
energy by exploiting the latency slack in the sub- critical replies which
arrive before the deadline (e.g., 80% of replies are 3-4x faster than the
tail). This slack is present at all loads and subsumes the previous work's
load-related slack. While the previous work shifts the leaves' response time
distribution to consume the slack at lower loads, TimeTrader reshapes the
distribution at all loads by slowing down individual sub-critical nodes without
increasing missed deadlines. TimeTrader exploits slack in both the network and
compute budgets. Further, TimeTrader leverages Earliest Deadline First
scheduling to largely decouple critical requests from the queuing delays of
sub- critical requests which can then be slowed down without hurting critical
requests. A combination of real-system measurements and at-scale simulations
shows that without adding to missed deadlines, TimeTrader saves 15-19% and
41-49% energy at 90% and 30% loading, respectively, in a datacenter with 512
nodes, whereas previous work saves 0% and 31-37%.Comment: 13 page
Analysis of Multiple Flows using Different High Speed TCP protocols on a General Network
We develop analytical tools for performance analysis of multiple TCP flows
(which could be using TCP CUBIC, TCP Compound, TCP New Reno) passing through a
multi-hop network. We first compute average window size for a single TCP
connection (using CUBIC or Compound TCP) under random losses. We then consider
two techniques to compute steady state throughput for different TCP flows in a
multi-hop network. In the first technique, we approximate the queues as M/G/1
queues. In the second technique, we use an optimization program whose solution
approximates the steady state throughput of the different flows. Our results
match well with ns2 simulations.Comment: Submitted to Performance Evaluatio
Proportional Integrator with Short-lived flows Adjustment
The number of Web traffic flows dominates Internet traffic today and most Web interactions are short-lived HTTP connections handled by TCP. Most core Internet routers use Drop Tail queuing which produces bursts of packet drops that contribute to unfair service. This thesis introduces two new active queue management (AQM) algorithms, PISA (PI with Short-lived flows Adjustment) and PIMC (PI with Minimum Cwnd). These AQMs are built on top of the PI (Proportional Integrator). To evaluate the performance of PISA and PIMC, a new simple model of HTTP traffic was developed for the NS-2 simulation. TCP sources inform PISA and PIMC routers of their congestion window by embedding a source hint in the packet header. Using the congestion window, PISA drops packets from short-lived Web flows less than packets from long-lived flows. Using a congestion window, PIMC does not drop a packet when congestion window is below a fixed threshold. This study provides a series of NS-2 experiments to investigate the behavior of PISA and PIMC. The results show fewer drops for both PISA and PIMC that avoids timeouts and increases the rate at which Web objects are sent. PISA and PIMC improve the performance of HTTP flows significantly over PI. PISA performs slightly better than PIMC
Modeling and estimation techniques for understanding heterogeneous traffic behavior
The majority of current internet traffic is based on TCP. With the emergence of new applications, especially new multimedia applications, however, UDP-based traffic is expected to increase. Furthermore, multimedia applications have sparkled the development of protocols responding to congestion while behaving differently from TCP. As a result, network traffc is expected to become more and more diverse. The increasing link capacity further stimulates new applications utilizing higher bandwidths of future. Besides the traffic diversity, the network is also evolving around new technologies. These trends in the Internet motivate our research work. In this dissertation, modeling and estimation techniques of heterogeneous traffic at a router are presented. The idea of the presented techniques is that if the observed queue length and packet drop probability do not match the predictions from a model of responsive (TCP) traffic, then the error must come from non-responsive traffic; it can then be used for estimating the proportion of non-responsive traffic. The proposed scheme is based on the queue length history, packet drop history, expected TCP and queue dynamics. The effectiveness of the proposed techniques over a wide range of traffic scenarios is corroborated using NS-2 based simulations. Possible applications based on the estimation technique are discussed. The implementation of the estimation technique in the Linux kernel is presented in order to validate our estimation technique in a realistic network environment
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