388246
On the joint utility accrual model
Summary form only given. We extend Jensen's time/utility functions and utility accrual model with the concept of joint utility functions (or JUFs) that allow an activity's utility to be described as a function of the completion times of other activities and their progress. We also specify the concept of progressive utility that generalizes the previously studied imprecise computational model, by describing an activity's utility as a function of its progress. Given such an extended utility accrual model, we consider the scheduling criterion of maximizing the weighted sum of completion time, progressive, and joint utilities. We present an algorithm called the combined utility accrual algorithm (or CUA) for this criterion. Experimental measurements with an implementation of CUA on a POSIX RTOS illustrate the effectiveness of JUFs in a class of applications of interest to us.
6769302
A utility accrual scheduling algorithm for real-time activities with mutual exclusion resource constraints
This paper presents a uni-processor real-time scheduling algorithm called the generic utility scheduling algorithm (which we refer to simply as GUS). GUS solves a previously open real-time scheduling problem-scheduling application activities that have time constraints specified using arbitrarily shaped time/utility functions and have mutual exclusion resource constraints. A time/ utility function are a time constraint specification that describes an activity's utility to the system as a function of that activity's completion time. Given such time and resource constraints, we consider the scheduling objective of maximizing the total utility that is accrued by the completion of all activities. Since this problem is NP-hard, GUS heuristically computes schedules with a polynomial-time cost of O(n/sup 3/) at each scheduling event, where n is the number of activities in the ready queue. We evaluate the performance of GUS through simulation and by an actual implementation on a real-time POSIX operating system. Our simulation studies and implementation measurements reveal that GUS performs close to, if not better than, the existing algorithms for the cases that they apply. Furthermore, we analytically establish several properties of GUS.
246686
Time/utility function decomposition techniques for utility accrual scheduling algorithms in real-time distributed systems
We consider Real-Time CORBA 1.2's distributable threads (DTs), whose time constraints are specified using time/utility functions (TUFs), operating in legacy environments. In legacy environments, system node resources - both physical and logical - are shared among time-critical DTs and local applications that may also be time-critical. Hence, DTs that are scheduled using their propagated TUFs, as mandated by Real-Time CORBA 1.2's Case 2 approach, may suffer performance degradation, if a node utility accrual (UA) scheduler achieves higher locally accrued utility by giving higher eligibility to local threads than to DTs. To alleviate this, we consider decomposing TUFs of DTs into "sub-TUFs" for scheduling segments of DTs. We present five decomposition techniques, called UT, SCEQF, SCALL, OPTCON, and TUFS, which are specific to different classes of UA scheduling algorithms, such as those that use utility density and those that use deadline as their key decision metric. Our experimental studies identify the decomposition technique that performs best for each class of UA scheduling algorithms. In particular, our studies show that OPTCON and TUFS perform best for utility density-based UA algorithms, while SCEQF and SCALL perform best for deadline-based UA algorithms.
51883995
Energy-efficient, utility accrual scheduling under resource constraints for mobile embedded systems
We present an energy-efficient real-time scheduling algorithm called the Resource-constrained Energy-Efficient Utility Accrual Algorithm (or ReUA). ReUA considers an application model where activities are subject to time/utility function-time constraints, resource dependencies including mutual exclusion constraints, and statistical performance requirements including probabilistically satisfied, activity (timeliness) utility bounds. Further, ReUA targets mobile embedded systems where system-level energy consumption is a major concern. For such a model, we consider the scheduling objectives of (1) satisfying statistical performance requirements, and (2) maximizing system-level energy efficiency, while respecting resource dependencies. Since the problem is NP-hard, ReUA allocates resources using statistical properties of application cycle demands and heuristically computes schedules with a polynomial-time cost. We analytically establish several timeliness and non-timeliness properties of the algorithm. Further, our simulation experiments illustrate ReUA's effectiveness.
2876560
Utility Accrual Real-Time Scheduling under Variable Cost Functions
We present a utility accrual real-time scheduling algorithm called CIC-VCUA for tasks whose execution times are functions of their starting times (and, potentially, other factors). We model such variable execution times using variable cost functions (or VCFs). The algorithm considers application activities that are subject to time/utility function time constraints, execution times described using VCFs, and mutual exclusion constraints on concurrent sharing of non-CPU resources. We consider the twofold scheduling objective of 1) assuring that the maximum interval between any two consecutive, successful completions of job instances in an activity must not exceed the activity period (an application-specific objective) and 2) maximizing the system's total accrued utility while satisfying mutual exclusion resource constraints. Since the scheduling problem is intractable, CIC-VCUA is a polynomial-time heuristic algorithm. The algorithm statically computes worst-case task sojourn times, dynamically selects tasks for execution based on their potential utility density, and completes tasks at specific times. We establish that CIC-VCUA achieves optimal timeliness during underloads, and tightly upper bounds inter and intratask completion times. Our simulation experiments confirm the algorithm's effectiveness and superiority
10090646
On Multiprocessor Utility Accrual Real-Time Scheduling with Statistical Timing Assurances
We present the first Utility Accrual (or UA) real-time scheduling algorithm for multiprocessors, called gMUA. The algorithm considers an application model where real-time activities are subject to time/utility function time constraints, variable execution time demands, and resource overloads where the total activity utilization demand exceeds the total capacity of all processors. We establish several properties of gMUA including optimal total utility (for a special case), conditions under which individual activity utility lower bounds are satisfied, a lower bound on system-wide total accrued utility, and bounded sensitivity for assurances to variations in execution time demand estimates. Our simulation experiments confirm our analysis and illustrate the algorithm's effectiveness
17279570
Protocol Design and Optimization for Delay/Fault-Tolerant Mobile Sensor Networks
While extensive studies have been carried out in the past several years for many sensor applications, they cannot be applied to the network with extremely low and intermittent connectivity, dubbed the delay/fault-tolerant mobile sensor network (DFT-MSN). Without end-to-end connections due to sparse network density and sensor node mobility, routing in DFT-MSN becomes localized and ties closely to medium access control, which naturally calls for merging Layer 3 and Layer 2 protocols in order to reduce overhead and improve network efficiency. DFT-MSN is fundamentally an opportunistic network, where the communication links exist only with certain probabilities and become the scarcest resource. At the same time, the sensor nodes in DFT-MSN have very limited battery power like those in other sensor networks. Clearly, there is a tradeoff between link utilization and energy efficiency. To address this tradeoff, we develop a cross-layer data delivery protocol for DFT-MSN, which includes two phases, i.e., the asynchronous phase and the synchronous phase. In the first phase, the sender contacts its neighbors to identify a set of appropriate receivers. Since no central control exists, the communication in the first phase is contention-based. In the second phase, the sender gains channel control and multicasts its data message to the receivers. Furthermore, several optimization issues in these two phases are identified, with solutions provided to reduce the collision probability and to balance between link utilization and energy efficiency. Our results show that the proposed cross-layer data delivery protocol for DFT-MSN achieves a high message delivery ratio with low energy consumption and an acceptable delay.
26444739
Minimum entropy restoration of star field images
In this correspondence, we present an algorithm for restoration of star field images by incorporating both the minimum mean square error and the maximum varimax criteria. It is assumed that the point spread function of the distortion system can be well approximated by a Gaussian function. Simulated annealing (SA) is used to implement the optimization procedure. Simulation results for both Gaussian and square point spread functions with heavy additive independent white Gaussian noise are provided. Visual evaluation of the results indicate that the proposed algorithm performs better than the noncausal Wiener filtering method.
19790752
Classification of Battlefield Ground Vehicles Using Acoustic Features and Fuzzy Logic Rule-Based Classifiers
In this paper, we demonstrate, through the multicategory classification of battlefield ground vehicles using acoustic features, how it is straightforward to directly exploit the information inherent in a problem to determine the number of rules, and subsequently the architecture, of fuzzy logic rule-based classifiers (FLRBC). We propose three FLRBC architectures, one non-hierarchical and two hierarchical (HFLRBC), conduct experiments to evaluate the performances of these architectures, and compare them to a Bayesian classifier. Our experimental results show that: 1) for each classifier the performance in the adaptive mode that uses simple majority voting is much better than in the non-adaptive mode; 2) all FLRBCs perform substantially better than the Bayesian classifier; 3) interval type-2 (T2) FLRBCs perform better than their competing type-1 (T1) FLRBCs, although sometimes not by much; 4) the interval T2 nonhierarchical and HFLRBC-series architectures perform the best; and 5) all FLRBCs achieve higher than the acceptable 80% classification accuracy
31035436
Contractible Elements in Graphs and Matroids
Let G be a simple 3-connected graph with at least five vertices. Tutte [13] showed that G has at least one contractible edge. Thomassen [11] gave a simple proof of this fact and showed that contractible edges have many applications. In this paper, we show that there are at most $\frac{|V(G)|}{5}$ vertices that are not incident to contractible edges in a 3-connected graph G. This bound is best-possible. We also show that if a vertex v is not incident to any contractible edge in G, then v has at least four neighbours having degree three, and each such neighbour is incident to exactly two contractible edges. We give short proofs of several results on contractible edges in 3-connected graphs as well. We also study the contractible elements for k-connected matroids. We partially solve an open problem for regular matroids.
18504704
Instruction Scheduling with Timing Constraints on a Single RISC Processor with 0/1 Latencies
In this paper, We propose a faster algorithm for the following instruction scheduling problem: Given a set of UET (Unit Execution Time) instructions with precedence constraints in the form of a DAG(Directed Acyclic Graph), latency constraints where latencies between any two instructions are restricted to be either 0 or 1, timing constraints in the form of individual integer release times and deadlines and a single RISC processor, find a feasible schedule which satisfies all constraints. The time complexity of our algorithm is O(n2 log n) + min{O(ne), O(n2.376)}, where n is the number of instructions and e is the number of edges in the precedence graph. Our algorithm is faster than the existing algorithm which runs in O(n3α(n)) time, where α(n) is the inverse of Ackermann function. In addition, our algorithm can be used to solve the maximum lateness minimization problem in O(n2 log2 n + min{ne, n2.376}) time.