Scheduling

As a research and statistical consultant I have noted that there is a broad misunderstanding of the meaning, analysis, and interpretation of interactions. Unfortunately, some textbooks discourage the general researcher from learning more about interactions by placing too much emphasis on mathematical principles and equations. In contrast, however, this book by Leona Aiken and Stephen West is oriented toward applications and is a good addition to the regression literature for social scientists. The book provides the reader with a clear discussion of how to use multiple regression analysis to structure, test, and interpret interactions and higher order nonlinear relationships.

This article examines five common misunderstandings about case-study research: (a) theoretical knowledge is more valuable than practical knowledge; (b) one cannot generalize from a single case, therefore, the single-case study cannot contribute to scientific development; (c) the case study is most useful for generating hypotheses, whereas other methods are more suitable for hypotheses testing and theory building; (d) the case study contains a bias toward verification; and (e) it is often difficult to summarize specific case studies. This article explains and corrects these misunderstandings one by one and concludes with the Kuhnian insight that a scientific discipline without a large number of thoroughly executed case studies is a discipline without systematic production of exemplars, and a discipline without exemplars is an ineffective one. Social science may be strengthened by the execution of a greater number of good case studies.

The joint equihbrmm distribution of queue sizes in a network of queues containing N service centers and R classes of customers m derived The equilibrium state probabillUes have the general form P(S) = Cd(S) fl(xl)f2(x2) . fN(x~), where S is the state of the system, x, is the configuration of customers at the ~th service center, d(S) is a function of the state of the model, f, is a function that depends on the type of the zth service center, and C is a normalizing constant It is assumed that the eqmhbrlum probabfl~tles exmt and are unique Four types of service centers to model central processors, data channels, terminals, and routing delays are considered The queuemg dlSclphnes associated with these service centers include first-come-first-served, processor sharing, no queueing, and last-come-first-served Each customer belongs to a single class of customers while awaiting or receiving serwce at a service center, but may change classes and service centers according to fixed probabditms at the completion of a service request For open networks, state dependent arrival processes are considered Closed networks are those with no exogenous arrivals A network may be closed with respect to some classes of customers and open with respect to other classes of customers At three of the four types of serwce centers, the service times of customers are governed by probablhty dmtrlbutions hawng ratmnM Laplace transforms, different classes of customers hawng different distributions At first-come-first-served-type service centers, the service time distribution must be identical and exponentml for all classes of customers. Examples show how different classes of customers can affect models of computer systems.

We discuss formulations of integer programs with a huge number of variables and their solution by column generation methods, i.e., implicit pricing of nonbasic variables to generate new columns or to prove LP optimality at a node of the branchand-bound tree. We present classes of models for which t h i s a p p r o a c h decomposes the problem, provides tighter LP relaxations, and eliminates symmetry. W e t h e n discuss computational issues and implementation of column generation, branch-andbound algorithms, including special branching rules and e cient w ays to solve t h e LP relaxation.

Although the capacity of multiple-input/multipleoutput (MIMO) broadcast channels (BCs) can be achieved by dirty paper coding (DPC), it is difficult to implement in practical systems. This paper investigates if, for a large number of users, simpler schemes can achieve the same performance. Specifically, we show that a zero-forcing beamforming (ZFBF) strategy, while generally suboptimal, can achieve the same asymptotic sum capacity as that of DPC, as the number of users goes to infinity. In proving this asymptotic result, we provide an algorithm for determining which users should be active under ZFBF. These users are semiorthogonal to one another and can be grouped for simultaneous transmission to enhance the throughput of scheduling algorithms. Based on the user grouping, we propose and compare two fair scheduling schemes in round-robin ZFBF and proportional-fair ZFBF. We provide numerical results to confirm the optimality of ZFBF and to compare the performance of ZFBF and proposed fair scheduling schemes with that of various MIMO BC strategies.

Requirements are usually understood as stating what a system is supposed to do, as opposed to how it should do it. However, understanding the organizational context and rationales (the "Whys") that lead up to systems requirements can be just as important for the ongoing success of the system. Requirements modelling techniques can be used to help deal with the knowledge and reasoning needed in this earlier phase of requirements engineering. However, most existing requirements techniques are intended more for the later phase of requirements engineering, which focuses on completeness, consistency, and automated verification of requirements. In contrast, the early phase aims to model and analyze stakeholder interests and how they might be addressed, or compromised, by various system-and-environment alternatives. This paper argues, therefore, that a different kind of modelling and reasoning support is needed for the early phase. An outline of the $ % framework is given as an example of a step in this direction. Meeting scheduling is used as a domain example.

The energy usage of computer systems is becoming an important consideration, especially for battery-operated systems. Various methods for reducing energy consumption have been investigated, both at the circuit level and at the operating systems level. In this paper, we propose a simple model of job scheduling aimed at capturing some key aspects of energy minimization. In this model, each job is to be executed between its arrival time and deadline by a single processor with variable speed, under the assumption that energy usage per unit time, P, is a convex function, of the processor speed s. We give an off-line algorithm that computes, for any set of jobs, a minimum-energy schedule. We then consider some on-line algorithms and their competitive performance for the power function P(s)=sp where p⩾2. It is shown that one natural heuristic, called the Average Rate heuristic, uses at most a constant times the minimum energy required. The analysis involves bounding the largest eigenvalue in matrices of a special type

Systems engineering of products, processes, and organizations requires tools and techniques for system decomposition and integration. A design structure matrix (DSM) provides a simple, compact, and visual representation of a complex system that supports innovative solutions to decomposition and integration problems. The advantages of DSMs vis-à-vis alternative system representation and analysis techniques have led to their increasing use in a variety of contexts, including product development, project planning, project management, systems engineering, and organization design. This paper reviews two types of DSMs, static and time-based DSMs, and four DSM applications: 1)

Sleep and waking behaviors change significantly during the adolescent years. The objective of this study was to describe the relation between adolescents' sleep / wake habits, characteristics of students (age, sex, school), and daytime functioning (mood, school performance, and behavior). A Sleep Habits Survey was administered in homeroom classes to 3,120 high school students at 4 public high schools from 3 m o d e Island school districts.

This article presents results from the first statistically significant study of cost escalation in transportation infrastructure projects. Based on a sample of 258 transportation infrastructure projects worth US$90 billion and representing different project types, geographical regions, and historical periods, it is found with overwhelming statistical significance that the cost estimates used to decide whether such projects should be built are highly and systematically misleading. Underestimation cannot be explained by error and is best explained by strategic misrepresentation, that is, lying. The policy implications are clear: legislators, administrators, investors, media representatives, and members of the public who value honest numbers should not trust cost estimates and cost-benefit analyses produced by project promoters and their analysts.

The proliferation of applications that must reliably distribute bulk data to a large number of autonomous clients motivates the design of new multicast and broadcast protocols. We describe an ideal, fully scalable protocol for these applications that we call a digital fountain. A digital fountain allows any number of heterogeneous clients to acquire bulk data with optimal e ciency at times of their choosing. Moreover, no feedback channels are needed to ensure reliable delivery, even in the face of high loss rates. We develop a protocol that closely approximates a digital fountain using a new class of erasure codes that for large block sizes are orders of magnitude faster than standard erasure codes. We provide performance measurements that demonstrate the feasibility of our approach and discuss the design, implementation and performance of an experimental system. 1

This paper treats the problem of automatic fault diaging several hundred active components. While being nosis for systems with multiple faults. The system is decomposed undecomposable, these units would have the computainto n units ul, u2, , u,IU where a unit is a well-identifiable portion of the system which cannot be further decomposed for the purpose ofI diagnosis. By means of a given arrangement of testing links (connec-

The paper presents exact schedulability analyses for real-time systems scheduled at run-time with a static priority pre-emptive dispatcher. The tasks to be scheduled are allowed to experience internal blocking (from other tasks with which they share resources) and (with certain restrictions) release jitter -such as waiting for a message to arrive. The analysis presented is more general than that previously published, and subsumes, for example, techniques based on the Rate Monotonic approach. In addition to presenting the theory, an existing avionics case study is described and analysed. The predictions that follow from this analysis are seen to be in close agreement with the behaviour exhibited during simulation studies. † optimal is the sense that if a task set can be scheduled by any static priority algorithm it can also be scheduled by the Deadline Monotonic algorithm.

Back cover text: If the new fin de siècle marks a recurrence of the real, Bent Flyvbjerg’s Rationality and Power epitomizes that development and sets new standards for social and political inquiry. The Danish town of Aalborg is to Flyvbjerg what Florence was to Machiavelli: a laboratory for understanding the real workings of power, and for grasping what they mean to our more general concerns of social and political organization. Politics, administration, and planning are examined in ways that allow  a rare, in-depth understanding. The reader witnesses, firsthand, the classic and endless drama which defines what modernity and democracy are and can be.

Cloud computing aims to power the next generation data centers and enables application service providers to lease data center capabilities for deploying applications depending on user QoS (Quality of Service) requirements. Cloud applications have different composition, configuration, and deployment requirements. Quantifying the performance of resource allocation policies and application scheduling algorithms at finer details in Cloud computing environments for different application and service models under varying load, energy performance (power consumption, heat dissipation), and system size is a challenging problem to tackle. To simplify this process, in this paper we propose CloudSim: an extensible simulation toolkit that enables modelling and simulation of Cloud computing environments. The CloudSim toolkit supports modelling and creation of one or more virtual machines (VMs) on a simulated node of a Data Center, jobs, and their mapping to suitable VMs. It also allows simulation of multiple Data Centers to enable a study on federation and associated policies for migration of VMs for reliability and automatic scaling of applications.

The accelerated development in Peer-to-Peer (P2P) and Grid computing has positioned them as promising next generation computing platforms. They enable the creation of Virtual Enterprises (VE) for sharing resources distributed across the world. However, resource management, application development and usage models in these environments is a complex undertaking. This is due to the geographic distribution of resources that are owned by different organizations or peers. The resource owners of each of these resources have different usage or access policies and cost models, and varying loads and availability. In order to address complex resource management issues, we have proposed a computational economy framework for resource allocation and for regulating supply and demand in Grid computing environments. This framework provides mechanisms for optimizing resource provider and consumer objective functions through trading and brokering services. In a real world market, there exist various economic models for setting the price of services based on supply-and-demand and their value to the user. They include commodity market, posted price, tender and auction models. In this paper, we discuss the use of these models for interaction between Grid components to decide resource service value, and the necessary infrastructure to realize each model. In addition to usual services offered by Grid computing systems, we need an infrastructure to support interaction protocols, allocation mechanisms, currency, secure banking, and enforcement services. We briefly discuss existing technologies that provide some of these services and show their usage in developing the Nimrod-G grid resource broker. Furthermore, we demonstrate the effectiveness of some of the economic models in resource trading and scheduling using the Nimrod/G resource broker with deadline and cost constrained scheduling for two different optimization strategies on the World Wide Grid (WWG) testbed that has resources distributed across five continents.

Multi-objective genetic local search Self organizing maps Variable Neighborhood Search (VNS) Multi-objective evolutionary algorithm Learning Multi-reservoir operation management a b s t r a c t Genetic Algorithms (GAs) are population based global search methods that can escape from local optima traps and find the global optima regions. However, near the optimum set their intensification process is often inaccurate. This is because the search strategy of GAs is completely probabilistic. With a random search near the optimum sets, there is a small probability to improve current solution. Another drawback of the GAs is genetic drift. The GAs search process is a black box process and no one knows that which region is being searched by the algorithm and it is possible that GAs search only a small region in the feasible space. On the other hand, GAs usually do not use the existing information about the optimality regions in past iterations.

Optical burst switching (OBS) is a promising solution for building terabit optical routers and realizing IP over WDM. In this paper, we describe the basic concept of OBS and present a general architecture of optical core routers and electronic edge routers in the OBS network. The key design issues related to the OBS are also discussed, namely, burst assembly (burstification), channel scheduling, burst offset-time management, and some dimensioning rules. A nonperiodic time-interval burst assembly mechanism is described. A class of data channel scheduling algorithms with void filling is proposed for optical routers using fiber delay line buffer.

This article proposes a novel approach to assess the stability of linear systems with delayed and sampled-data inputs. The paper considers both asynchronous sampling and input delay based on an extension of existing results on the stability of sampled-data systems to the case where a delay is introduced in the control loop. The proposed method provides easy tractable sufficient conditions for asymptotic stability of sampled-data systems under asynchronous sampling and transmission delays. The period and delay-dependent conditions are expressed using computable linear matrix inequalities. Several examples show the efficiency of the stability criteria.

Cloud computing is an emerging commercial infrastructure paradigm that promises to eliminate the need for maintaining expensive computing facilities by companies and institutes alike. Through the use of virtualization and resource time sharing, clouds serve with a single set of physical resources a large user base with different needs. Thus, clouds have the potential to provide to their owners the benefits of an economy of scale and, at the same time, become an alternative for scientists to clusters, grids, and parallel production environments. However, the current commercial clouds have been built to support web and small database workloads, which are very different from typical scientific computing workloads. Moreover, the use of virtualization and resource time sharing may introduce significant performance penalties for the demanding scientific computing workloads. In this work, we analyze the performance of cloud computing services for scientific computing workloads. We quantify the presence in real scientific computing workloads of Many-Task Computing (MTC) users, that is, of users who employ loosely coupled applications comprising many tasks to achieve their scientific goals. Then, we perform an empirical evaluation of the performance of four commercial cloud computing services including Amazon EC2, which is currently the largest commercial cloud. Last, we compare through trace-based simulation the performance characteristics and cost models of clouds and other scientific computing platforms, for general and MTC-based scientific computing workloads. Our results indicate that the current clouds need an order of magnitude in performance improvement to be useful to the scientific community, and show which improvements should be considered first to address this discrepancy between offer and demand.

Effective utilization of communication resources is crucial for good overall performance in highly concurrent systems. In this paper, we address four different communication problems in Boolean n-cube configured multiprocessors: 1) oneto-all broadcasting: distribution of common data from a single source to all other nodes; 2) one-to-all personalized communication: a single nude sending unique data to all other nodes; 3) allto-all broadcasting: distribution of common data from each node to all other nodes; and 4) all-to-all personalized communication: each node sending a unique piece of information to every other node. Three new communication graphs for the Boolean n-cube are proposed for the routing, and scheduling disciplines provably optimum within a small constant factor are proposed. One of the new communication graphs consists of n edge-disjoint spanning binomial trees and offers optimal communication for case 1; a speedup with a factor of n over the spanning binomial tree for large data volumes. The other two new communication graphs are a balanced spanning tree and a graph composed of n rotated spanning binomial trees. With appropriate scheduling and concurrent communication on all ports of every processor, routings based on these two communication graphs offer a speedup of up to n/2,n/2, and O(&) over the routings based on the spanning binomial tree for cases 2, 3, and 4, respectively. All three new spanning graphs offer optimal communication times for cases 2, 3, and 4 and concurrent communication on all ports of every processor. The graph consisting of n edge-disjoint spanning trees offers graceful degradation of performance under faulty conditions. Timing models and complexity analysis have been verified by experiments on a Boolean cube configured multiprocessor.

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. The need for quality software solutions is acute for a number of reasons. In particular, it is very important to efficiently utilise time and effort, to evenly balance the workload among people and to attempt to satisfy personnel preferences. A high quality roster can lead to a more contented and thus more effective workforce. In this review, we discuss nurse rostering within the global personnel scheduling problem in healthcare. We begin by briefly discussing the review and overview papers that have appeared in the literature and by noting the role that nurse rostering plays within the wider context of longer term hospital personnel planning. The main body of the paper describes and critically evaluates solution approaches which span the interdisciplinary spectrum from operations research techniques to artificial intelligence methods. We conclude by drawing on the strengths and weaknesses of the literature to outline the key issues that need addressing in future nurse rostering research.

Spectrum sensing is the key enabling technology for cognitive radio networks. The main objective of spectrum sensing is to provide more spectrum access opportunities to cognitive radio users without interfering with the operations of the licensed network. Hence, recent research has been focused on the interference avoidance problem. Moreover, current radio frequency (RF) front-ends cannot perform sensing and transmission at the same time, which inevitably decreases their transmission opportunities, leading to the so-called sensing efficiency problem. In this paper, in order to solve both the interference avoidance and the spectrum efficiency problem, an optimal spectrum sensing framework is developed. More specifically, first a theoretical framework is developed to optimize the sensing parameters in such a way as to maximize the sensing efficiency subject to interference avoidance constraints. Second, in order to exploit multiple spectrum bands, spectrum selection and scheduling methods are proposed where the best spectrum bands for sensing are selected to maximize the sensing capacity. Finally, an adaptive and cooperative spectrum sensing method is proposed where the sensing parameters are optimized adaptively to the number of cooperating users. Simulation results show that the proposed sensing framework can achieve maximum sensing efficiency and opportunities in multi-user/multi-spectrum environments, satisfying interference constraints.

Dynamic mapping (matching and scheduling) heuristics for a class of independent tasks using heterogeneous distributed computing systems are studied. Two types of mapping heuristics are considered: immediate mode and batch mode heuristics. Three new heuristics, one for batch mode and two for immediate mode, are introduced as part of this research. Simulation studies are performed to compare these heuristics with some existing ones. In total, five immediate mode heuristics and three batch mode heuristics are examined. The immediate mode dynamic heuristics consider, to varying degrees and in different ways, task affinity for different machines and machine ready times. The batch mode dynamic heuristics consider these factors, as well as aging of tasks waiting to execute. The simulation results reveal that the choice of which dynamic mapping heuristic to use in a given heterogeneous environment depends on parameters such as: (a) the structure of the heterogeneity among tasks and machines, and (b) the arrival rate of the tasks.

This is a brief introduction to Java Remote Method Invocation (RMI). Java RMI is a mechanism that allows one to invoke a method on an object that exists in another address space. The other address space could be on the same machine or a different one. The RMI mechanism is basically an object-oriented RPC mechanism. CORBA is another objectoriented RPC mechanism. CORBA differs from Java RMI in a number of ways:

Many manufacturing facilities generate and update production schedules, which are plans that state when certain controllable activities (e.g., processing of jobs by resources) should take place. Production schedules help managers and supervisors coordinate activities to increase productivity and reduce operating costs. Because a manufacturing system is dynamic and unexpected events occur, rescheduling is necessary to update a production schedule when the state of the manufacturing system makes it infeasible. Rescheduling updates an existing production schedule in response to disruptions or other changes. Though many studies discuss rescheduling, there are no standard definitions or classification of the strategies, policies, and methods presented in the rescheduling literature. This paper presents definitions appropriate for most applications of rescheduling manufacturing systems and describes a framework for understanding rescheduling strategies, policies, and methods. This framework is based on a wide variety of experimental and practical approaches that have been described in the rescheduling literature. The paper also discusses studies that show how rescheduling affects the performance of a manufacturing system, and it concludes with a discussion of how understanding rescheduling can bring closer some aspects of scheduling theory and practice.

This paper presents HAS-QAP, a hybrid ant colony system coupled with a local search, applied to the quadratic assignment problem. HAS-QAP uses pheromone trail information to perform modifications on QAP solutions, unlike more traditional ant systems that use pheromone trail information to construct complete solutions. HAS-QAP is analysed and compared with some of the best heuristics available for the QAP: two versions of tabu search, namely, robust and reactive tabu search, hybrid genetic algorithm, and a simulated annealing method. Experimental results show that HAS-QAP and the hybrid genetic algorithm perform best on real world, irregular and structured problems due to their ability to find the structure of good solutions, while HAS-QAP performance is less competitive on random, regular and unstructured problems.

This article presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth U.S.$59 billion. The study shows with very high statistical significance that forecasters generally do a poor job of estimating the demand for transportation infrastructure projects. For 9 out of 10 rail projects, passenger forecasts are overestimated; the average overestimation is 106%. For half of all road projects, the difference between actual and forecasted traffic is more than ±20%. The result is substantial financial risks, which are typically ignored or downplayed by planners and decision makers to the detriment of social and economic welfare. Our data also show that forecasts have not become more accurate over the 30-year period studied, despite claims to the contrary by forecasters. The causes of inaccuracy in forecasts are different for rail and road projects, with political causes playing a larger role for rail than for road. The cure is transparency, accountability, and new forecasting methods. The challenge is to change the governance structures for forecasting and project development. Our article shows how planners may help achieve this.

We present a low complexity heuristic named the Dominant Sequence Clustering algorithm (DSC) for scheduling parallel tasks on an unbounded number of completely connected processors. The performance of DSC is comparable or even better on average than many other higher complexity algorithms. We assume no task duplication and nonzero communication overhead between processors. Finding the optimum solution for arbitrary directed acyclic task graphs (DAGs) is NP-complete. DSC nds optimal schedules for special classes of DAGs such as fork, join, coarse grain trees and some ne grain trees. It guarantees a performance within a factor of two of the optimum for general coarse grain DAGs. We compare DSC with three higher complexity general scheduling algorithms, the MD by Wu and Gajski [19], the ETF by Hwang, Chow, Anger and Lee [12] and Sarkar's clustering algorithm . We also give a sample of important practical applications where DSC has been found useful.

As wireless sensor networks have emerged as a exciting new area of research in Computer Science, many of the logistical challenges facing those who wish to develop, deploy, and debug applications on realistic large-scale sensor networks have gone unmet. Manually reprogramming nodes, deploying them into the physical environment, and instrumenting them for data gathering is tedious and time-consuming.

Among the most interesting and challenging theoretical and practical problems in describing pedestrians behavior are route choice and activity scheduling. Compared to other modes of transport, a characteristic feature of pedestrian route choice is that routes are continuous trajectories in time and space: since a pedestrian chooses a route from an infinite set of alternatives, dedicated theories and models describing pedestrian route choice are required. This article puts forward a new theory of pedestrian behavior under uncertainty based on the concept of utility maximization. The main behavioral assumption is that pedestrians optimize some predicted pedestrian-specific utility function, representing a trade-off between the utility gained from performing activities at a specific location, and the predicted cost of walking subject to the physical limitations of the pedestrians and the kinematics of the pedestrian. The uncertainty reflects the randomness of the experienced traffic conditions. Based on this normative theory, route choice, activity area choice, and activity scheduling are simultaneously optimized using dynamic programming for different traffic conditions and uncertainty levels. Throughout the article, the concepts are illustrated by examples.

In most real-world environments, scheduling is an ongoing reactive process where the presence of a variety of unexpected disruptions is usually inevitable, and continually forces reconsideration and revision of pre-established schedules. Many of the approaches developed to solve the problem of static scheduling are often impractical in real-world environments, and the near-optimal schedules with respect to the estimated data may become obsolete when they are released to the shop floor. This paper outlines the limitations of the static approaches to scheduling in the presence of realtime information and presents a number of issues that have come up in recent years on dynamic scheduling.