A Spectrum of TCP-Friendly Window-Based Congestion Control Algorithms

The increased diversity of Internet application requirements has spurred recent interests in transport protocols with flexible transmission controls. In window-based congestion control schemes, increase rules determine how to probe available bandwidth, whereas decrease rules determine how to back off when losses due to congestion are detected. The parameterization of these control rules is done so as to ensure that the resulting protocol is TCP-friendly in terms of the relationship between throughput and loss rate. In this paper, we define a new spectrum of window-based congestion control algorithms that are TCP-friendly as well as TCP-compatible under RED. Contrary to previous memory-less controls, our algorithms utilize history information in their control rules. Our proposed algorithms have two salient features: (1) They enable a wider region of TCP-friendliness, and thus more flexibility in trading off among smoothness, aggressiveness, and responsiveness; and (2) they ensure a faster convergence to fairness under a wide range of system conditions. We demonstrate analytically and through extensive ns simulations the steady-state and transient behaviors of several instances of this new spectrum of algorithms. In particular, SIMD is one instance in which the congestion window is increased super-linearly with time since the detection of the last loss. Compared to recently proposed TCP-friendly AIMD and binomial algorithms, we demonstrate the superiority of SIMD in: (1) adapting to sudden increases in available bandwidth, while maintaining competitive smoothness and responsiveness; and (2) rapidly converging to fairness and efficiency.

A Spectrum of TCP-friendly Window-Based Congestion Control Algorithms

The increasing diversity of Internet application requirements has spurred recent interest in transport protocols with flexible transmission controls. In window-based congestion control schemes, increase rules determine how to probe available bandwidth, whereas decrease rules determine how to back off when losses due to congestion are detected. The control rules are parameterized so as to ensure that the resulting protocol is TCP-friendly in terms of the relationship between throughput and loss rate. This paper presents a comprehensive study of a new spectrum of window-based congestion controls, which are TCP-friendly as well as TCP-compatible under RED. Our controls utilize history information in their control rules. By doing so, they improve the transient behavior, compared to recently proposed slowly-responsive congestion controls such as general AIMD and binomial controls. Our controls can achieve better tradeoffs among smoothness, aggressiveness, and responsiveness, and they can achieve faster convergence. We demonstrate analytically and through extensive ns simulations the steady-state and transient behavior of several instances of this new spectrum.

Multi-version Speculative Concurrency Control with Delayed Commit

This paper presents an algorithm which extends the relatively new notion of speculative concurrency control by delaying the commitment of transactions, thus allowing other conflicting transactions to continue execution and commit rather than restart. This algorithm propagates uncommitted data to other outstanding transactions thus allowing more speculative schedules to be considered. The algorithm is shown always to find a serializable schedule, and to avoid cascading aborts. Like speculative concurrency control, it considers strictly more schedules than traditional concurrency control algorithms. Further work is needed to determine which of these speculative methods performs better on actual transaction loads.

Speculative Concurrency Control for Real-Time Databases

In this paper, we propose a new class of Concurrency Control Algorithms that is especially suited for real-time database applications. Our approach relies on the use of (potentially) redundant computations to ensure that serializable schedules are found and executed as early as possible, thus, increasing the chances of a timely commitment of transactions with strict timing constraints. Due to its nature, we term our concurrency control algorithms Speculative. The aforementioned description encompasses many algorithms that we call collectively Speculative Concurrency Control (SCC) algorithms. SCC algorithms combine the advantages of both Pessimistic and Optimistic Concurrency Control (PCC and OCC) algorithms, while avoiding their disadvantages. On the one hand, SCC resembles PCC in that conflicts are detected as early as possible, thus making alternative schedules available in a timely fashion in case they are needed. On the other hand, SCC resembles OCC in that it allows conflicting transactions to proceed concurrently, thus avoiding unnecessary delays that may jeopardize their timely commitment.

Value-cognizant Speculative Concurrency Control

A problem with Speculative Concurrency Control algorithms and other common concurrency control schemes using forward validation is that committing a transaction as soon as it finishes validating, may result in a value loss to the system. Haritsa showed that by making a lower priority transaction wait after it is validated, the number of transactions meeting their deadlines is increased, which may result in a higher value-added to the system. SCC-based protocols can benefit from the introduction of such delays by giving optimistic shadows with high value-added to the system more time to execute and commit instead of being aborted in favor of other validating transactions, whose value-added to the system is lower. In this paper we present and evaluate an extension to SCC algorithms that allows for commit deferments.

Timeliness via Speculation for Real-Time Databases

Various concurrency control algorithms differ in the time when conflicts are detected, and in the way they are resolved. In that respect, the Pessimistic and Optimistic Concurrency Control (PCC and OCC) alternatives represent two extremes. PCC locking protocols detect conflicts as soon as they occur and resolve them using blocking. OCC protocols detect conflicts at transaction commit time and resolve them using rollbacks (restarts). For real-time databases, blockages and rollbacks are hazards that increase the likelihood of transactions missing their deadlines. We propose a Speculative Concurrency Control (SCC) technique that minimizes the impact of blockages and rollbacks. SCC relies on the use of added system resources to speculate on potential serialization orders and to ensure that if such serialization orders materialize, the hazards of blockages and roll-backs are minimized. We present a number of SCC-based algorithms that differ in the level of speculation they introduce, and the amount of system resources (mainly memory) they require. We show the performance gains (in terms of number of satisfied timing constraints) to be expected when a representative SCC algorithm (SCC-2S) is adopted.

Performance Evaluation of Two-Shadow Speculative Concurrency Control

Speculative Concurrency Control (SCC) [Best92a] is a new concurrency control approach especially suited for real-time database applications. It relies on the use of redundancy to ensure that serializable schedules are discovered and adopted as early as possible, thus increasing the likelihood of the timely commitment of transactions with strict timing constraints. In [Best92b], SCC-nS, a generic algorithm that characterizes a family of SCC-based algorithms was described, and its correctness established by showing that it only admits serializable histories. In this paper, we evaluate the performance of the Two-Shadow SCC algorithm (SCC-2S), a member of the SCC-nS family, which is notable for its minimal use of redundancy. In particular, we show that SCC-2S (as a representative of SCC-based algorithms) provides significant performance gains over the widely used Optimistic Concurrency Control with Broadcast Commit (OCC-BC), under a variety of operating conditions and workloads.

Mapping Parallel Iterative Algorithms onto Workstation Networks

For communication-intensive parallel applications, the maximum degree of concurrency achievable is limited by the communication throughput made available by the network. In previous work [HPS94], we showed experimentally that the performance of certain parallel applications running on a workstation network can be improved significantly if a congestion control protocol is used to enhance network performance. In this paper, we characterize and analyze the communication requirements of a large class of supercomputing applications that fall under the category of fixed-point problems, amenable to solution by parallel iterative methods. This results in a set of interface and architectural features sufficient for the efficient implementation of the applications over a large-scale distributed system. In particular, we propose a direct link between the application and network layer, supporting congestion control actions at both ends. This in turn enhances the system's responsiveness to network congestion, improving performance. Measurements are given showing the efficacy of our scheme to support large-scale parallel computations.

TCP-friendly SIMD Congestion Control and Its Convergence Behavior

The increased diversity of Internet application requirements has spurred recent interests in flexible congestion control mechanisms. Window-based congestion control schemes use increase rules to probe available bandwidth, and decrease rules to back off when congestion is detected. The parameterization of these control rules is done so as to ensure that the resulting protocol is TCP-friendly in terms of the relationship between throughput and packet loss rate. In this paper, we propose a novel window-based congestion control algorithm called SIMD (Square-Increase/Multiplicative-Decrease). Contrary to previous memory-less controls, SIMD utilizes history information in its control rules. It uses multiplicative decrease but the increase in window size is in proportion to the square of the time elapsed since the detection of the last loss event. Thus, SIMD can efficiently probe available bandwidth. Nevertheless, SIMD is TCP-friendly as well as TCP-compatible under RED, and it has much better convergence behavior than TCP-friendly AIMD and binomial algorithms proposed recently.

Differentiated Control of Web Traffic: A Numerical Analysis

Internet measurements show that the size distribution of Web-based transactions is usually very skewed; a few large requests constitute most of the total traffic. Motivated by the advantages of scheduling algorithms which favor short jobs, we propose to perform differentiated control over Web-based transactions to give preferential service to short web requests. The control is realized through service semantics provided by Internet Traffic Managers, a Diffserv-like architecture. To evaluate the performance of such a control system, it is necessary to have a fast but accurate analytical method. To this end, we model the Internet as a time-shared system and propose a numerical approach which utilizes Kleinrock's conservation law to solve the model. The numerical results are shown to match well those obtained by packet-level simulation, which runs orders of magnitude slower than our numerical method.

Slack Stealing Job Admission Control Scheduling

In this paper, we present Slack Stealing Job Admission Control (SSJAC)---a methodology for scheduling periodic firm-deadline tasks with variable resource requirements, subject to controllable Quality of Service (QoS) constraints. In a system that uses Rate Monotonic Scheduling, SSJAC augments the slack stealing algorithm of Thuel et al with an admission control policy to manage the variability in the resource requirements of the periodic tasks. This enables SSJAC to take advantage of the 31\% of utilization that RMS cannot use, as well as any utilization unclaimed by jobs that are not admitted into the system. Using SSJAC, each task in the system is assigned a resource utilization threshold that guarantees the minimal acceptable QoS for that task (expressed as an upper bound on the rate of missed deadlines). Job admission control is used to ensure that (1) only those jobs that will complete by their deadlines are admitted, and (2) tasks do not interfere with each other, thus a job can only monopolize the slack in the system, but not the time guaranteed to jobs of other tasks. We have evaluated SSJAC against RMS and Statistical RMS (SRMS). Ignoring overhead issues, SSJAC consistently provides better performance than RMS in overload, and, in certain conditions, better performance than SRMS. In addition, to evaluate optimality of SSJAC in an absolute sense, we have characterized the performance of SSJAC by comparing it to an inefficient, yet optimal scheduler for task sets with harmonic periods.

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.