IPSS: A Hybrid Approach to Planning and Scheduling Integration

Recently, the areas of planning and scheduling in artificial intelligence (AI) have witnessed a big push toward their integration in order to solve complex problems. These problems require both reasoning on which actions are to be performed as well as their precedence constraints (planning) and the reasoning with respect to temporal constraints (e.g., duration, precedence, and deadline); those actions should satisfy the resources they use (scheduling). This paper describes IPSS (integrated planning and scheduling system), a domain independent solver that integrates an AI planner that synthesizes courses of actions with constraint-based techniques that reason based upon time and resources. IPSS is able to manage not only simple precedence constraints, but also more complex temporal requirements (as the Allen primitives) and multicapacity resource usage/consumption. The solver is evaluated against a set of problems characterized by the use of multiple agents (or multiple resources) that have to perform tasks with some temporal restrictions in the order of the tasks or some constraints in the availability of the resources. Experiments show how the integrated reasoning approach improves plan parallelism and gains better makespans than some state-of-the-art planners where multiple agents are represented as additional fluents in the problem operators. It also shows that IPSS is suitable for solving real domains (i.e., workflow problems) because it is able to impose temporal windows on the goals or set a maximum makespan, features that most of the planners do not yet incorporate     

A branch-and-price-and-check model for the vehicle routing problem with location congestion

This paper considers a vehicle routing problem with pickup and delivery, time windows and location congestion. Locations provide a number of cumulative resources that are utilized by vehicles either during service (e.g., forklifts) or for the entirety of their visit (e.g., parking bays). Locations can become congested if insufficient resources are available, upon which vehicles must wait until a resource becomes available before proceeding. The problem is challenging from a computational standpoint since it incorporates the vehicle routing problem and the resource-constrained project scheduling problem. The main contribution of this paper is a branch-and-price-and-check model that uses a branch-and-price algorithm that solves the underlying vehicle routing problem, and a constraint programming subproblem that checks the feasibility of the location resource constraints, and then adds combinatorial nogood cuts to the master problem if the resource constraints are violated. Experimental results show the benefits of the branch-and-price-and-check approach.     

A hybrid Branch-and-Bound and evolutionary approach for allocating strings of applications to heterogeneous distributed computing systems

Providing efficient workload management is an important issue for a large-scale heterogeneous distributed computing environment where a set of periodic applications is executed. The considered shipboard distributed system is expected to operate in an environment where the input workload is likely to change unpredictably, possibly invalidating a resource allocation that was based on the initial workload estimate. The tasks consist of multiple strings, each made up of an ordered sequence of applications. There is a quality of service (QoS) minimum throughput constraint that must be satisfied for each application in a string, and a maximum utilization constraint that must be satisfied on each of the hardware resources in the system. The challenge, therefore, is to efficiently and robustly manage both computation and communication resources in this unpredictable environment to achieve high performance while satisfying the imposed constraints. This work addresses the problem of finding a robust initial allocation of resources to strings of applications that is able to absorb some level of unknown input workload increase without rescheduling. The proposed hybrid two-stage method of finding a near-optimal allocation of resources incorporates two specially designed mapping techniques: (1) the Permutation Space Genitor-Based heuristic, and (2) the follow-up Branch-and-Bound heuristic based on an Integer Linear Programming (ILP) problem formulation. The performance of the proposed resource allocation method is evaluated under different simulation scenarios and compared to an iteratively computed upper bound.     

A theory of translation from relational queries to hierarchicalqueries

In a heterogeneous database system, a query for one type of database system (i.e., a source query) may have to be translated to an equivalent query (or queries) for execution in a different type of database system (i.e., a target query). Usually, for a given source query, there is more than one possible target query translation. Some of them can be executed more efficiently than others by the receiving database system. Developing a translation procedure for each type of database system is time-consuming and expensive. We abstract a generic hierarchical database system (GHDBS) which has properties common to database systems whose schema contains hierarchical structures (e.g., System 2000, IMS, and some object-oriented database systems). We develop principles of query translation with GHDBS as the receiving database system. Translation into any specific system can be accomplished by a translation into the general system with refinements to reflect the characteristics of the specific system. We develop rules that guarantee correctness of the target queries, where correctness means that the target query is equivalent to the source query. We also provide rules that can guarantee a minimum number of target queries in cases when one source query needs to be translated to multiple target queries. Since the minimum number of target queries implies the minimum number of times the underlying system is invoked, efficiency is taken into consideration     

On the Optimality of Randomized Deadlock Avoidance Policies

This paper revisits the problem of selecting an optimal deadlock resolution strategy, when the selection criterion is the maximization of the system throughput, and the system is Markovian in terms of its timing and routing characteristics. This problem was recently addressed in some of our previous work, that (i) provided an analytical formulation for it, (ii) introduced the notion of randomized deadlock avoidance as a generalization of the more traditional approaches of deadlock prevention/avoidance, and detection and recovery, and (iii) provided a methodology for selecting the optimal randomized deadlock avoidance policy for a given resource allocation system (RAS) configuration. An issue that remained open in the problem treatment of that past work, was whether the proposed policy randomization is essential, i.e., whether there exist any RAS configurations for which a randomized deadlock avoidance policy is superior to any other policy that does not employ randomization. The work presented in this paper establishes that for the basic problem formulation where the only concern is the (unconstrained) maximization of the system throughput—or the other typical performance objectives of minimizing the system work-in-process and mean sojourn time—randomization of the deadlock resolution strategy is not essential. However, it is also shown that, sometimes, it can offer an effective mechanism for accommodating additional operational constraints, like the requirement for production according to a specified product mix. Furthermore, the undertaken analysis provides an analytical characterization of the dependence of the aforementioned performance measures on the transition rates relating to the various events of the underlying state space, which can be useful for the broader problem of synthesizing efficient scheduling policies for the considered class of resource allocation systems.     

A fast exact algorithm for the allocation of seats for the EU Parliament

In this paper, we analyse the problem of allocation of seats for the EU Parliament. To solve it, we propose a fast exact algorithm which overwhelms limitations of the existing methods. It allows us to examine all feasible allocations of seats within few minutes. On this basis, an in-depth analysis of the problem is provided and some of its properties are revealed (e.g., the number of feasible allocations of seats holding the Treaty of Lisbon), which have never been presented in the scientific literature. Furthermore, the proposed algorithm is not limited to dealing with the problem of allocation of seats for the EU Parliament, but it can be applied in the expert system for any other similar problem, especially under degressive proportionality constraints.     

Cold-standby redundancy allocation problem with degrading components

Components in cold-standby state are usually assumed to be as good as new when they are activated. However, even in a standby environment, the components will suffer from performance degradation. This article presents a study of a redundancy allocation problem (RAP) for cold-standby systems with degrading components. The objective of the RAP is to determine an optimal design configuration of components to maximize system reliability subject to system resource constraints (e.g. cost, weight). As in most cases, it is not possible to obtain a closed-form expression for this problem, and hence, an approximated objective function is presented. A genetic algorithm with dual mutation is developed to solve such a constrained optimization problem. Finally, a numerical example is given to illustrate the proposed solution methodology.     

A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.     

Stochastic robustness metric and its use for static resource allocations

This research investigates the problem of robust static resource allocation for distributed computing systems operating under imposed Quality of Service (QoS) constraints. Often, such systems are expected to function in a physical environment replete with uncertainty, which causes the amount of processing required to fluctuate substantially over time. Determining a resource allocation that accounts for this uncertainty in a way that can provide a probabilistic guarantee that a given level of QoS is achieved is an important research problem. The stochastic robustness metric proposed in this research is based on a mathematical model where the relationship between uncertainty in system parameters and its impact on system performance are described stochastically.The utility of the established metric is then exploited in the design of optimization techniques based on greedy and iterative approaches that address the problem of resource allocation in a large class of distributed systems operating on periodically updated data sets. The performance results are presented for a simulated environment that replicates a heterogeneous cluster-based radar data processing center. A mathematical performance lower bound is presented for comparison analysis of the heuristic results. The lower bound is derived based on a relaxation of the Integer Linear Programming formulation for a given resource allocation problem.     

Rate control for heterogeneous wireless sensor networks: Characterization,algorithms and performance

This paper addresses the rate control and resource allocation problem for heterogeneous wireless sensor networks, which consist of diverse node types or modalities such as sensors and actuators, and different tasks or applications. The performance of these applications, either elastic traffic nature (e.g., typical data collection) or inelastic traffic nature (e.g., real-time monitoring and controlling), is modeled as a utility function of the sensor source rate. The traditional rate control approach, which requires the utility function to be strictly concave, is no longer applicable because of the involvement of inelastic traffic. Therefore, we develop a utility framework of rate control for heterogeneous wireless sensor networks with single- and multiple-path routing, and propose utility fair rate control algorithms, that are able to allocate the resources (wireless channel capacity and sensor node energy) efficiently and guarantee the application performance in a utility proportional or max–min fair manner. Furthermore, the optimization and convergence of the algorithm is investigated rigorously as well.     

Constraint-based reasoning for generating naval flying programmes

Abstract: A system for the generation of naval flying programmes is described. The task is typical of some resource allocation problems, and comprises both the allocation of airborne resources to naval activities whilst taking into account a number of constraints, and the re-allocation of these resources when circumstances change whilst retaining as much of the original plan as possible. Techniques for constraint-based reasoning and assumption-based reasoning are combined to solve the task. An algorithm is described, based around an Assumption-Based Truth Maintenance System (ATMS), that is able to generate an initial allocation, determine the knock-on effects of changing requirements or resources, to retain those parts of the plan that were unaffected, and to re-allocate those parts that were affected. An interactive graphical interface has been designed that allows the user and the system to cooperate in the creation of flying programmes that meet the constraints and fit the situation at hand.     

On the Application of Game-Theoretic Mechanism Design for Resource Allocation in Multimedia Systems

In this paper, we study the system-level computational resource allocation problem among multiple multimedia tasks. We consider the multimedia tasks to be autonomous, i.e., they are selfish and behave strategically. We propose a resource allocation framework based on mechanism design to prevent the tasks from behaving strategically and manipulating the available system resources. We apply two mechanisms in the framework and assess their advantages over proportional-share resource allocation algorithms, which are often used in multimedia systems. We show in the simulations that the incorporation of mechanism design for system resource allocation is a promising solution that achieves efficient, fair and robust allocation against manipulation from strategic applications.      

Static heuristics for robust resource allocation of continuously executing applications

We investigate two distinct issues related to resource allocation heuristics: robustness and failure rate. The target system consists of a number of sensors feeding a set of heterogeneous applications continuously executing on a set of heterogeneous machines connected together by high-speed heterogeneous links. There are two quality of service (QoS) constraints that must be satisfied: the maximum end-to-end latency and minimum throughput. A failure occurs if no allocation is found that allows the system to meet its QoS constraints. The system is expected to operate in an uncertain environment where the workload, i.e., the load presented by the set of sensors, is likely to change unpredictably, possibly resulting in a QoS violation. The focus of this paper is the design of a static heuristic that: (a) determines a robust resource allocation, i.e., a resource allocation that maximizes the allowable increase in workload until a run-time reallocation of resources is required to avoid a QoS violation, and (b) has a very low failure rate (i.e., the percentage of instances a heuristic fails). Two such heuristics proposed in this study are a genetic algorithm and a simulated annealing heuristic. Both were “seeded” by the best solution found by using a set of fast greedy heuristics.     

RAPS: a rule-based language for specifying resource allocation andtime-tabling problems

A general language for specifying resource allocation and time-tabling problems is presented. The language is based on an expert system paradigm that was developed previously by the authors and that enables the solution of resource allocation problems by using experts' knowledge and heuristics. The language enables the specification of a problem in terms of resources, activities, allocation rules, and constraints, and thus provides a convenient knowledge acquisition tool. The language syntax is powerful and allows the specification of rules and constraints that are very difficult to formulate with traditional approaches, and it also supports the specification of various control and backtracking strategies. We constructed a generalized inference engine that runs compiled resource allocation problem specification language (RAPS) programs and provides all necessary control structures. This engine acts as an expert system shell and is called expert system for resource allocation (ESRA). The performance of RAPS combined with ESRA is demonstrated by analyzing its solution of a typical resource allocation problem     

Distributed Resource Allocation via Accelerated Saddle Point Dynamics

In this paper, accelerated saddle point dynamics is proposed for distributed resource allocation over a multi-agent network, which enables a hyper-exponential convergence rate. Specifically, an inertial fast-slow dynamical system with vanishing damping is introduced, based on which the distributed saddle point algorithm is designed. The dual variables are updated in two time scales, i.e., the fast manifold and the slow manifold. In the fast manifold, the consensus of the Lagrangian multipliers and the tracking of the constraints are pursued by the consensus protocol. In the slow manifold, the updating of the Lagrangian multipliers is accelerated by inertial terms. Hyper-exponential stability is defined to characterize a faster convergence of our proposed algorithm in comparison with conventional primal-dual algorithms for distributed resource allocation. The simulation of the application in the energy dispatch problem verifies the result, which demonstrates the fast convergence of the proposed saddle point dynamics.      

基于鞍点法的自适应分布式资源分配算法

研究一类带有不等式约束为凸函数的多智能体系统分布式资源分配问题.在资源分配问题中,各智能体拥有仅自身可知的局部成本函数和局部凸不等式约束.分布式资源分配旨在如何利用智能体间的信息交互设计一种分布式优化算法,完成定量资源分配的同时还保证最小化全局成本函数.针对该问题,基于卡罗需-库恩-塔克条件和比例积分控制思想,首先提出一种自适应分布式优化算法,其中凸不等式约束的对偶变量可实现自适应获取;然后,为了降低系统的通信资源消耗,设计一种动态事件触发控制策略以实现离散时间通信的分布式资源分配算法;最后,通过数值仿真验证所设计算法的有效性.     

基于自适应精确罚函数的分布式资源分配算法

在多智能体系统中, 分布式资源分配问题是近年来研究热点之一. 分布式资源分配问题旨在通过智能体间信息交互实现资源最优配置. 其中智能体局部约束给算法设计带来巨大挑战. 首先, 针对一阶多智能体系统, 提出基于自适应精确罚函数的分布式资源分配算法, 其中各智能体利用距离函数实现局部约束求解. 此外, 自适应设计思想旨在避免算法对全局先验知识获取. 其次, 利用跟踪技术实现二阶多智能体系统算法设计. 并利用凸函数和非光滑分析法给出严谨的收敛性分析. 最后, 仿真结果验证了本文所设计优化算法对强凸分布式资源分配问题的有效性.     

Optimized Resource Allocation for Software Release Planning

Release planning for incremental software development assigns features to releases such that technical, resource, risk and budget constraints are met. Planning of software releases and allocation of resources cannot be handled in isolation. A feature can be offered as part of a release only if all its necessary tasks are done before the given release date. We assume a given pool of human resources with different degrees of productivity to perform different types of tasks. To address the inherent difficulty of this process, we propose a two-phased optimization approach that combines the strength of two existing solution methods. The industrial applicability of the approach is primarily directed towards mature organizations having systematic development and measurement processes in place. The expected practical benefit of the planning method is to provide release plan solutions that achieve a better overall business value (e.g., expressed by the degree of stakeholder satisfaction) by better allocation of resources. Without ignoring the importance of the human expert in this process, the contributions of the paper are seen in making the overall process more objective and the resulting decisions more transparent.     

An innovative system for sharing,integration and visualization of heterogeneous 4D-information

Treating the time dimension (i.e., the 4th dimension) of geospatial parameters, likewise the other dimensions is important for some applications to better understand the possible space-time relations among the physical parameters underlying the dynamics of complex phenomena. In this paper we present the potential of an innovative solution, named 4 Dimensions Environmental ObServation (4DEOS), which is a platform able to handle the 4th dimension in an asynchronous way (i.e., to visualize more signals, holding some of them fixed in the time domain while moving others over time). 4DEOS is based on a Client-Broker-Server architecture for the easy integration and visualization of heterogeneous, asynchronous, geospatial products. The prototype system was evaluated in the case of earthquake prediction studies where the asynchronous visualization of independent observations could allow a timely identification of the spatial correlations appearing at different time lags, which could be missed using other existing 4D Geographic Information System software.