Jeffery P. Hansen

A scalable solution to the multi-resource QoS problem

The problem of maximizing system utility by allocating a single finite resource to satisfy discrete Quality of Service (QoS) requirements of multiple applications along multiple QoS dimensions was studied previously. In this paper we consider the more complex problem of apportioning multiple finite resources to satisfy the QoS needs of multiple applications along multiple QoS dimensions. In other words, each application, such as video-conferencing, needs multiple resources to satisfy its QoS requirements. We evaluate and compare three strategies to solve this provably NP-hard problem. We show that dynamic programming and mixed integer programming compute optimal solutions to this problem but exhibit very long running times. We then adapt the mixed integer programming problem to yield near-optimal results with smaller running times. Finally, we present an approximation algorithm based on a local search technique that is less than 5% away from the optimal solution but which is more than two orders of magnitude faster. Perhaps more significantly, the local search technique turns out to be very scalable and robust as the number of resources required by each application increases.

Integrated resource management and scheduling with multi-resource constraints

Dynamic real-time systems such as phased-array radars must manage multiple resources, satisfy energy constraints and make frequent on-line scheduling decisions. These systems are hard to manage because task and system requirements change rapidly (e.g. in radar systems, the targets/tasks in the sky are moving continuously) and must satisfy a multitude of constraints. Their highly dynamic nature and stringent time constraints lead to complex cross-layer interactions in these systems. Therefore, the design of such systems has long been a conservative and/or unpredictable mixture of pre-computed schedules, pessimistic resource allocations, cautious energy usage and operator intuition. In this paper, we present an integrated approach that simultaneously maximizes overall system utility, performs task scheduling and satisfies multi-resource constraints. Using a phased-array radar system, we show that our approach can reconfigure settings of 100 tracks at every 0.7 sec in real-time, and performs within 0.1% of the achievable optimal solution.

Scalable resource allocation for multi-processor QoS optimization

We present scalable QoS optimization algorithms for allocating resources to tasks in a multi-processor environment. Given a set of tasks, each of which is capable of running at one of several different QoS levels, the algorithms can select a QoS operating point, the number of replicas for fault-tolerance and the processors on which to run the replicas so as to maximize overall system QoS. The algorithms are extensions of Q-RAM (QoS-based Resource Allocation Model) [5] and fix two deficiencies with the basic algorithm. The first is that the existing algorithm is weak in making resource trade-off decisions such as to which processor to map a task. The second was that it was not scalable to very large numbers of resources such as in a large multi-processor system. In this paper we present two new algorithms which significantly enhance the ability of Q-RAM to make resource tradeoff decisions. We also introduce a hierarchical decomposition scheme which enables QoS optimization to be performed on problems with thousands of resources and thousands of tasks.

Models for time coalescence in event logs

One heuristic for data reduction that is widely used in the literature is coalescence of events occurring close in time. The authors explore the validity of this heuristic by developing a model for the formation of the contents of an event log by multiple independent error processes. The probability of coalescing events from two or more error sources is formulated and compared with results from hand analysis of actual event logs taken from a Tandem TNS II system. Results indicate that the probability of coalescing events from more than one error source is a strong function of the time constant selected. The model can be used to select an appropriate time constant and also has implications in designing event logging systems.<<ETX>>

Integrated QoS-aware resource management and scheduling with multi-resource constraints

In dynamic real-time systems such as sensor networks, mobile ad hoc networking and autonomous systems, the mapping between level of service and resource requirements is often not fixed. Instead, the mapping depends on a combination of level of service and outside environmental factors over which the application has no direct control. An example of an application where environmental factors play a significant role is radar tracking. In radar systems, resources must be shared by a set of radar tasks including tracking, searching and target confirmation tasks. Environmental factors such as noise, heating constraints of the radar and the speed, distance and maneuverability of tracked targets dynamically affect the mapping between the level of service and resource requirements. The QoS manager in a radar system must be adaptive, responding to dynamic changes in the environment by efficiently reallocating resource to maintain an acceptable level of service. In this paper, we present an integrated QoS optimization and dwell scheduling scheme for a radar tracking application. QoS optimization is performed using the Q-RAM (Baugh, 1973, ghosh-et al.,2004a approach. Heuristics are used to achieve a two order magnitude of reduction in optimization time over the basic Q-RAM approach allowing QoS optimization and scheduling of a 100 task radar problem to be performed in as little as 700 ms with only a 0.1% QoS penality over Q-RAM alone.

Resource management of highly configurable tasks

Summary form only given. We present an extension to our QoS optimization algorithm, Q-RAM, that can improve optimization time by several orders of magnitude when managing highly configurable tasks. A highly configurable task is one with a large number of QoS dimensions and/or a large number of quality levels on those dimensions. For example, an application that has ten QoS dimensions with ten quality levels each will have 10/sup 10/ setpoints, or ways in which it can be configured. While the existing Q-RAM algorithm has been shown to be a very effective resource management tool, it must still explicitly perform computations on all of the setpoints for each task. For tasks with 10/sup 10/ setpoints or more, this is clearly impractical. The key idea presented here is a new approximation algorithm for the concave majorant step in Q-RAM. By using this algorithm in a filtering step, the best performing subset of the setpoints can be quickly found without explicitly examining all of the setpoints. The idea is validated using a phased array radar system as an example application.

Optimization of quality of service in dynamic systems

Resource allocation policies which signi cantly improve Quality of Service (QoS) while minimizing QoS instability are presented. In a dynamic system in which applications are continuously entering and departing the system, QoS instability occurs when the system over-commits resources and can not meet the resource demands of new admission requests. The system is forced to either reject the request or degrade one or more existing tasks. In this paper we introduce three admission control policies and compare their QoS performance/stability tradeo s. We show that by maintaining a small resource reserve modeled as a competing application in the QoS optimization framework, we can achieve QoS levels that are over 90% of the theoretical maximum while reducing instability by one to two orders of magnitude.

Optimal partitioning for quantized EDF scheduling

The quantized earliest deadline first (Q-EDF) scheduling policy assumes a limited number of priority levels available to express task (or packet) deadlines. This situation arises in computer and communication systems in which priority levels must be expressed using a limited number of bits. The range of possible deadlines is partitioned into bins, and a task whose deadline lies within the range of a bin is assigned a quantized deadline equal to the left endpoint of the bin. We assume soft real-time tasks arrive according to a renewal process, have random service requirements, and have random deadlines drawn from a probability distribution. Using real-time queueing theory, a general method for computing the long run fraction of tasks that miss their actual deadlines (or their quantized deadlines) is presented. Results are given for uniform deadline distributions, and the optimal bin partitioning is determined for this distribution. The theoretical results show excellent agreement with simulation results. Finally, assuming only the min, mean, and max task deadlines are specified, we determine the worst-case deadline distribution given any specified priority bin choice, then optimize the priority bin choice to minimize the worst-case lateness relative to EDF. For this Q-EDF partition, we determine the number of bins needed to achieve a given performance relative to EDF. We show that for practical cases with soft deadlines, 3 bits are sufficient to achieve nearly the same performance with Q-EDF as with pure EDF.

Synthesis by spectral translation using Boolean decision diagrams

Many logic synthesis systems are strongly influenced by the size of the SOP (Sum-of-Products) representation of the function being synthesized. Two-level PLA (Programmable Logic Array) synthesis and many multi-level synthesis systems perform poorly without a good SOP representation of the target function. In this paper, we propose a new spectral-based algorithm using BDDs (Boolean Decision Diagram) to transform the target function into a form that is easier to synthesize by using a linear filter on the inputs. Using the methods described in this paper, we were able to perform spectral translation on circuits with many more inputs and much larger cube sets then previously possible. This can result in a substantial decrease in delay and area for some classes of circuits.

Diff-EDF: a simple mechanism for differentiated EDF service

Many existing and emerging network applications such as voice-over-IP, videoconferencing and online gaming have end-to-end timing requirements. Despite the real-time demands of these applications, they are usually deployed on best-effort networks such as the Internet. This results in unpredictable and often unsatisfactory performance. In this paper we propose a simple and novel task (or packet) scheduling algorithm Diff-EDF (differentiated earliest deadline first) which can meet the real-time needs of these applications while continuing to provide best effort service to nonreal time traffic. In our system we consider each flow as having stochastic traffic characteristics, a stochastic deadline and a maximum allowable miss rate. The Diff-EDF service meets the flow miss rate requirements through the combination of an admission control test and a scheduling algorithm similar to EDF (earliest deadline first). However, unlike standard EDF scheduling each flow receives a deadline bias based on the flows miss rate requirement. Applying this bias allows the miss rate to be controlled on a flow-by-flow basis. Both the admission control test and the bias selection algorithms can be computed as a linear function of the flow traffic parameters and the logarithms of the miss rate requirements resulting in an efficient implementation. In this paper, we presented the proposed system structure, protocols, algorithms, analysis and experiments. Experiments with randomly generated and real-life data closely match values predicted by the theory.