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Consider an airlift operation which consists of several routes, each having missions which are subject to given time schedules. The aircraft are manned with aircrews that are required to rest for a certain period of time after each leg of a mission. A mission may be continued whenever a rested aircrew is available at the location. Given the number of missions that are needed to be flown on the different routes, and given the schedule timetable that is associated with those missions, we consider the problems: (1) What is the minimum number of crews that are needed to maintain the operation? (2) How many aircrews are needed to be staged at each location? (3) If the number of available aircrews is less than the minimum needed, which legs of what missions may be delayed so that the minimum required number of aircrews is reduced? We will exclude from the analysis the trivial case where the rest period of the aircrews is always less than the period of time for which the aircrafts are delayed. If this is the case, then it is clear that the minimum number of aircrews is equal to the number of missions and all the aircrews must be staged initially at the home base.

We obtain fast parallel algorithms for several scheduling problems. Some of the problems considered are: scheduling to minimize the number of tardy jobs; job sequencing with deadlines; scheduling to minimize earliness and tardiness penalties; channel assignment; and minimizing the mean finish time. The shared memory model of parallel computers is used. (Author)

Motivated by applications in grid computing and project management, we study multiprocessor scheduling in scenarios where there is uncertainty in the successful execution of jobs when assigned to processors. We consider the problem of multiprocessor scheduling under uncertainty, in which we are given n unit-time jobs and m machines, a directed acyclic graph C giving the dependencies among the jobs, and for every job j and machine i, the probability p_{ij} of the successful completion of job j when scheduled on machine i in any given particular step. The goal of the problem is to find a schedule that minimizes the expected makespan, that is, the expected completion time of all the jobs. The problem of multiprocessor scheduling under uncertainty was introduced by Malewicz and was shown to be NP-hard even when all the jobs are independent. In this paper, we present polynomial-time approximation algorithms for the problem, for special cases of the dag C. We obtain an O(log(n))-approximation for the case of independent jobs, an O(log(m)log(n)log(n+m)/loglog(n+m))-approximation when C is a collection of disjoint chains, an O(log(m)log^2(n))-approximation when C is a collection of directed out- or in-trees, and an O(log(m)log^2(n)log(n+m)/loglog(n+m))-approximation when C is a directed forest.

Two efficient mapping algorithms for scheduling the robot inverse dynamics computation consisting of m computational modules with precedence relationship to be executed on a multiprocessor system consisting of p identical homogeneous processors with processor and communication costs to achieve minimum computation time are presented. An objective function is defined in terms of the sum of the processor finishing time and the interprocessor communication time. The minimax optimization is performed on the objective function to obtain the best mapping. This mapping problem can be formulated as a combination of the graph partitioning and the scheduling problems; both have been known to be NP-complete. Thus, to speed up the searching for a solution, two heuristic algorithms were proposed to obtain fast but suboptimal mapping solutions. The first algorithm utilizes the level and the communication intensity of the task modules to construct an ordered priority list of ready modules and the module assignment is performed by a weighted bipartite matching algorithm. For a near-optimal mapping solution, the problem can be solved by the heuristic algorithm with simulated annealing. These proposed optimization algorithms can solve various large-scale problems within a reasonable time. Computer simulations were performed to evaluate and verify the performance and the validity of the proposed mapping algorithms. Finally, experiments for computing the inverse dynamics of a six-jointed PUMA-like manipulator based on the Newton-Euler dynamic equations were implemented on an NCUBE/ten hypercube computer to verify the proposed mapping algorithms. Computer simulation and experimental results are compared and discussed.

One of the most important computer architecture innovations to appear in the market place during the last ten years is parallel processing on a shared memory multicomputer. This report presents new algorithms for a variety of network models along with empirical analysis on both sequential and parallel computers. An empirical study on the AT and T KORBX system is also presented. This system uses eight processors each of which has vector capability. Our research program objective is to develop and empirically test new parallel algorithms and software for a wide variety of optimization problems. The problems studied this past year include the shortest path problem, the assignment problem, the semi-assignment problem, the transportation problem, and the generalized network problem. Algorithms for all of these models have been developed and empirically tested on a variety of computers. In addition, we worked with the Military Airlift Command to test the AT&T KORBX system located at Scott Air Force Base. (kr)

In this thesis, we give efficient approximation algorithms for two classical combinatorial optimization problems: multicommodity flow problems and shop scheduling problem. The algorithms we develop for these problems yield solutions that are not necessarily optimal, but come with a provable performance guarantee; that is, we can guarantee that the solution found is within a certain percentage of the optimal solution. This type of algorithm is known as an approximation algorithm. Our results show that by allowing a small error in the solution of a problem, it is often possible to gain a significant reduction in the running time of an algorithm for that problem. In Chapter 2, we study the multicommodity flow problem. The multicommodity flow problem involves simultaneously shipping several different commodities from their respective sources to their sinks in a single network so that the total amount of flow going through each edge is no more than its capacity. Associated with each commodity is a demand, which is the amount of that commodity that we wish to ship. Given a multicommodity flow problem, one often wants to know if there is a feasible flow, i.e., if it is possible to find a flow that satisfies the demands and obeys the capacity constraints. More generally, we might wish to know the maximum percentage z such that at least z percent of each demand can be shipped without violating the capacity constraints. The latter problem is known as the concurrent flow problem. multicommodity flow, scheduling, combinatorial optimization, network algorithms, approximation algorithms, randomized algorithms.

The aim of power management policies is to reduce the amount of energy consumed by computer systems while maintaining satisfactory level of performance. One common method for saving energy is to simply suspend the system during the idle times. No energy is consumed in the suspend mode. However, the process of waking up the system itself requires a certain fixed amount of energy, and thus suspending the system is beneficial only if the idle time is long enough to compensate for this additional energy expenditure. In the specific problem studied in the paper, we have a set of jobs with release times and deadlines that need to be executed on a single processor. Preemptions are allowed. The processor requires energy L to be woken up and, when it is on, it uses one unit of energy per one unit of time. It has been an open problem whether a schedule minimizing the overall energy consumption can be computed in polynomial time. We solve this problem in positive, by providing an O(n^5)-time algorithm. In addition we provide an O(n^4)-time algorithm for computing the minimum energy schedule when all jobs have unit length.

This document presents a new serial and parallel algorithms for the on-to-one shortest problem. This is the current best algorithms for this problem and we believe that our software implementation is the world's fastest code. Other algorithms for various network models, including the pure network problem, the generalized problem, the multicommodity network problem with a piecewise linear convex cost function are also presented.

We obtain fast parallel algorithms for several scheduling problems. Some of the problems considered are: scheduling to minimize the number of tardy jobs; job sequencing with deadlines; scheduling to minimize earliness and tardiness penalties; channel assignment; and minimizing the mean finish time. The shared memory model of parallel computers is used. (Author)

A prototype system employing a genetic algorithm (GA) has been developed to support the scheduling of the Hubble Space Telescope. A non-standard knowledge structure is used and appropriate genetic operators have been created. Several different crossover styles (random point selection, evolving points, and smart point selection) are tested and the best GA is compared with a neural network (NN) based optimizer. The smart crossover operator produces the best results and the GA system is able to evolve complete schedules using it. The GA is not as time-efficient as the NN system and the NN solutions tend to be better.

Evolutionary algorithms have been frequently used for dynamic optimization problems. With this paper, we contribute to the theoretical understanding of this research area. We present the first computational complexity analysis of evolutionary algorithms for a dynamic variant of a classical combinatorial optimization problem, namely makespan scheduling. We study the model of a strong adversary which is allowed to change one job at regular intervals. Furthermore, we investigate the setting of random changes. Our results show that randomized local search and a simple evolutionary algorithm are very effective in dynamically tracking changes made to the problem instance.

A graph-theoretic design process and software tool is defined for selecting a multiprocessing scheduling solution for a class of computational problems. The problems of interest are those that can be described with a dataflow graph and are intended to be executed repetitively on a set of identical processors. Typical applications include signal processing and control law problems. Graph-search algorithms and analysis techniques are introduced and shown to effectively determine performance bounds, scheduling constraints, and resource requirements. The software tool applies the design process to a given problem and includes performance optimization through the inclusion of additional precedence constraints among the schedulable tasks.

This paper presents scheduling algorithms for procrastinators, where the speed that a procrastinator executes a job increases as the due date approaches. We give optimal off-line scheduling policies for linearly increasing speed functions. We then explain the computational/numerical issues involved in implementing this policy. We next explore the online setting, showing that there exist adversaries that force any online scheduling policy to miss due dates. This impossibility result motivates the problem of minimizing the maximum interval stretch of any job; the interval stretch of a job is the job's flow time divided by the job's due date minus release time. We show that several common scheduling strategies, including the "hit-the-highest-nail" strategy beloved by procrastinators, have arbitrarily large maximum interval stretch. Then we give the "thrashing" scheduling policy and show that it is a \Theta(1) approximation algorithm for the maximum interval stretch.

The United States Air Force has put an increased emphasis on the timely delivery of precision weapons. Part of this effort has been to us multiple bay aircraft such the B-1B Lancer and B-52 Stratofortress to provide Close Air Support and responsive strikes using 1760 weapons. In order to provide greater flexibility, the aircraft carry heterogeneous payloads which can require deconfiction in order to drop multiple different types of weapons. Current methods of deconfiction and weapon selection are highly crew dependant and work intensive. This research effort investigates the optimization of an algorithm for weapon release which allows the aircraft to perform deconfiction automatically. This reduces crew load and response time in order to deal with time-sensitive targets. The overall problem maps to the Job-Shop Scheduling problem. Optimization of the algorithm is done through the General Multiobjective Parallel Genetic Algorithm (GENMOP). We examine the results from pedagogical experiments and real-world test scenarios in the light of improving decision making. The results are encouraging in that the program proves capable of nding acceptable release schedules, however the solution space is such that applying the program to real world situations is unnecessary. We present visualizations of the schedules which demonstrate these conclusions.

This paper addresses scheduling algorithms for message transmissions over a satellite broadcast system. The system is expected to deliver messages of widely varied length. Our objective is to find a scheduling algorithm that exhibits good delay performance for messages of all sizes. We show that classical scheduling algorithms such as First-Come-First-Serve and Round-Robin perform poorly in this environment. We study two alternative schemes. The first gives preemptive priority to the message with the Shortest Remaining Processing Time (SRPT). This scheme is known to minimize overall average message delays, but results in disproportionately large delays for long messages. The second scheme serves messages based on a dynamic priority function, where the priority of a message varies based on how long the message has been in the system as well as its length. This scheme results in somewhat larger overall average delays, but it is more fair to long messages.

The Automated Scheduling and Planning Environment (ASPEN) software system, aspects of which have been reported in several previous NASA Tech Briefs articles, includes a subsystem that utilizes a portfolio of heuristic algorithms that work synergistically to solve problems. The nature of the synergy of the specific algorithms is that their likelihoods of success are negatively correlated: that is, when a combination of them is used to solve a problem, the probability that at least one of them will succeed is greater than the sum of probabilities of success of the individual algorithms operating independently of each other. In ASPEN, the portfolio of algorithms is used in a planning process of the iterative repair type, in which conflicts are detected and addressed one at a time until either no conflicts exist or a user-defined time limit has been exceeded. At each choice point (e.g., selection of conflict; selection of method of resolution of conflict; or choice of move, addition, or deletion) ASPEN makes a stochastic choice of a combination of algorithms from the portfolio. This approach makes it possible for the search to escape from looping and from solutions that are locally but not globally optimum.

We consider scheduling problems for unit jobs with release times, where the number or size of the gaps in the schedule is taken into consideration, either in the objective function or as a constraint. Except for a few papers on energy minimization, there is no work in the scheduling literature that uses performance metrics depending on the gap structure of a schedule. One of our objectives is to initiate the study of such scheduling problems with gaps. We show that such problems often lead to interesting algorithmic problems, with connections to other areas of algorithmics. We focus on the model with unit jobs. First we examine scheduling problems with deadlines, where we consider variants of minimum-gap scheduling, including maximizing throughput with a budget for gaps or minimizing the number of gaps with a throughput requirement. We then turn to other objective functions. For example, in some scenarios, gaps in a schedule may be actually desirable, leading to the problem of maximizing the number of gaps. Other versions we study include minimizing maximum gap or maximizing minimum gap. The second part of the paper examines the model without deadlines, where we focus on the tradeoff between the number of gaps and the total or maximum flow time. For all these problems we provide polynomial time algorithms, with running times ranging from $O(n \log n)$ for some problems, to $O(n^7)$ for other. The solutions involve a spectrum of algo- rithmic techniques, including different dynamic programming formulations, speed-up techniques based on searching Monge arrays, searching X + Y matrices, or implicit binary search.

We consider scheduling problems for unit jobs with release times, where the number or size of the gaps in the schedule is taken into consideration, either in the objective function or as a constraint. Except for a few papers on energy minimization, there is no work in the scheduling literature that uses performance metrics depending on the gap structure of a schedule. One of our objectives is to initiate the study of such scheduling problems with gaps. We show that such problems often lead to interesting algorithmic problems, with connections to other areas of algorithmics. We focus on the model with unit jobs. First we examine scheduling problems with deadlines, where we consider variants of minimum-gap scheduling, including maximizing throughput with a budget for gaps or minimizing the number of gaps with a throughput requirement. We then turn to other objective functions. For example, in some scenarios, gaps in a schedule may be actually desirable, leading to the problem of maximizing the number of gaps. Other versions we study include minimizing maximum gap or maximizing minimum gap. The second part of the paper examines the model without deadlines, where we focus on the tradeoff between the number of gaps and the total or maximum flow time. For all these problems we provide polynomial time algorithms, with running times ranging from $O(n \log n)$ for some problems, to $O(n^7)$ for other. The solutions involve a spectrum of algo- rithmic techniques, including different dynamic programming formulations, speed-up techniques based on searching Monge arrays, searching X + Y matrices, or implicit binary search.

The limited availability and high cost of crew time and scarce resources make optimization of space operations critical. Advances in computer technology coupled with new iterative search techniques permit the near optimization of complex scheduling problems that were previously considered computationally intractable. Described here is a class of search techniques called Intelligent Perturbation Algorithms. Several scheduling systems which use these algorithms to optimize the scheduling of space crew, payload, and resource operations are also discussed.

High-precision radial velocity planet searches have surveyed over ~2000 nearby stars and detected over ~200 planets. While these same stars likely harbor many additional planets, they will become increasingly challenging to detect, as they tend to have relatively small masses and/or relatively long orbital periods. Therefore, observers are increasing the precision of their observations, continuing to monitor stars over decade timescales, and also preparing to survey thousands more stars. Given the considerable amounts of telescope time required for such observing programs, it is important use the available resources as efficiently as possible. Previous studies have found that a wide range of predetermined scheduling algorithms result in planet searches with similar sensitivities. We have developed adaptive scheduling algorithms which have a solid basis in Bayesian inference and information theory and also are computationally feasible for modern planet searches. We have performed Monte Carlo simulations of plausible planet searches to test the power of adaptive scheduling algorithms. Our simulations demonstrate that planet searches performed with adaptive scheduling algorithms can simultaneously detect more planets, detect less massive planets, and measure orbital parameters more accurately than comparable surveys using a non-adaptive scheduling algorithm. We expect that these techniques will be particularly valuable for the N2K radial velocity planet search for short-period planets as well as future astrometric planet searches with the Space Interferometry Mission which aim to detect terrestrial mass planets.

The back-pressure algorithm is a well-known throughput-optimal algorithm. However, its delay performance may be quite poor even when the traffic load is not close to network capacity due to the following two reasons. First, each node has to maintain a separate queue for each commodity in the network, and only one queue is served at a time. Second, the back-pressure routing algorithm may route some packets along very long routes. In this paper, we present solutions to address both of the above issues, and hence, improve the delay performance of the back-pressure algorithm. One of the suggested solutions also decreases the complexity of the queueing data structures to be maintained at each node.

Gain scheduling. the traditional method of providing adaptive control to a nonlinear system, has long been an ad hoc design process. Until recently; little theoretical guidance directed this practitioners' art. For this reason a systematic study of this design process and its potential for optimization has never been accomplished. Additionally, the nonlinearities and the large search space involved in gain scheduling also precluded such an optimization study. Traditionally, the gain scheduling process has been some variation of a linear interpolation between discrete design points. By using powerful non-traditional optimization tools such as genetic algorithms there are ways of improving this design process. This thesis utilizes the power of genetic algorithms to optimally design a gain schedule. First, a design methodology is validated on a simple pole placement problem, then demonstrated for an F-18 Super-maneuverable Fighter. From this experience, a general gain scheduling design process is developed and presented.

Cloud computing is an essential tool for sharing resources across virtual machines, and it relies on scheduling and load balancing to ensure that tasks are assigned to the most appropriate resources. Multiple independent tasks need to be handled by cloud computing, and static and dynamic scheduling plays a crucial role in allocating tasks to the right resources. This is especially important in heterogeneous environments, where algorithms can improve load balancing and enhance cloud computing's efficiency. This paper aims to evaluate and discuss algorithms that can improve load balancing in cloud systems.

We have a set of processors (or agents) and a set of graph networks defined over some vertex set. Each processor can access a subset of the graph networks. Each processor has a demand specified as a pair of vertices $ $, along with a profit; the processor wishes to send data between $u$ and $v$. Towards that goal, the processor needs to select a graph network accessible to it and a path connecting $u$ and $v$ within the selected network. The processor requires exclusive access to the chosen path, in order to route the data. Thus, the processors are competing for routes/channels. A feasible solution selects a subset of demands and schedules each selected demand on a graph network accessible to the processor owning the demand; the solution also specifies the paths to use for this purpose. The requirement is that for any two demands scheduled on the same graph network, their chosen paths must be edge disjoint. The goal is to output a solution having the maximum aggregate profit. Prior work has addressed the above problem in a distibuted setting for the special case where all the graph networks are simply paths (i.e, line-networks). Distributed constant factor approximation algorithms are known for this case. The main contributions of this paper are twofold. First we design a distributed constant factor approximation algorithm for the more general case of tree-networks. The core component of our algorithm is a tree-decomposition technique, which may be of independent interest. Secondly, for the case of line-networks, we improve the known approximation guarantees by a factor of 5. Our algorithms can also handle the capacitated scenario, wherein the demands and edges have bandwidth requirements and capacities, respectively.

The allocation of radio resources is one of the most critical functions performed by the Radio Resource Management (RRM) mechanisms in the downlink Long Term Evolution-Advanced (LTE-Advanced). Packet scheduling concerns itself with allocation of these radio resources in an intelligent manner such that system throughput/capacity can be maximized whilst the required multimedia Quality of Service (QoS) is met. Majority of the previous studies of packet scheduling algorithms for LTE-Advanced did not take the effect of channel impairments into account. However, in real world the channel impairments cannot be obliterated completely and have a direct impact on the packet scheduling performance. As such, this work studies the impact of channel impairments on packet scheduling performance in a practical downlink LTE-Advanced. The simulation results obtained demonstrate the efficacy of RM2 scheduling algorithm over other scheduling algorithms in maximizing the system capacity and is more robust on the effect of the cellular channel impairments.

This article is from Sensors (Basel, Switzerland) , volume 11 . Abstract As a promising tool for monitoring the physical world, directional sensor networks (DSNs) consisting of a large number of directional sensors are attracting increasing attention. As directional sensors in DSNs have limited battery power and restricted angles of sensing range, maximizing the network lifetime while monitoring all the targets in a given area remains a challenge. A major technique to conserve the energy of directional sensors is to use a node wake-up scheduling protocol by which some sensors remain active to provide sensing services, while the others are inactive to conserve their energy. In this paper, we first address a Maximum Set Covers for DSNs (MSCD) problem, which is known to be NP-complete, and present a greedy algorithm-based target coverage scheduling scheme that can solve this problem by heuristics. This scheme is used as a baseline for comparison. We then propose a target coverage scheduling scheme based on a genetic algorithm that can find the optimal cover sets to extend the network lifetime while monitoring all targets by the evolutionary global search technique. To verify and evaluate these schemes, we conducted simulations and showed that the schemes can contribute to extending the network lifetime. Simulation results indicated that the genetic algorithm-based scheduling scheme had better performance than the greedy algorithm-based scheme in terms of maximizing network lifetime.

The problem of attaining energy efficiency in distributed systems is of importance, but a general, non-domain-specific theory of energy-minimal scheduling is far from developed. In this paper, we classify the problems of energy-minimal scheduling and present theoretical foundations of the same. We derive results concerning energy-minimal scheduling of independent jobs in a distributed system with functionally similar machines with different working and idle power ratings. The machines considered in our system can have identical as well as different speeds. If the jobs can be divided into arbitrary parts, we show that the minimum-energy schedule can be generated in linear time and give exact scheduling algorithms. For the cases where jobs are non-divisible, we prove that the scheduling problems are NP-hard and also give approximation algorithms for the same along with their bounds.

In a multiprogramming system, multiple processes exist concurrently in main memory. Each process alternates between using a processor and waiting for some event to occur, such as the completion of an I O operation. The processor or processors are kept busy by executing one process while the others wait. The key to multiprogramming is scheduling. CPU scheduling deals with the problem of deciding which of the processes in the ready queue is to be allocated the CPU. By switching the CPU among processor the operating system can make the computer more productive. Scheduling affectes the performance of the system because it determines which processes will wait and which will progress. In this paper, simulation of various scheduling algorithm First Come First Served FCFS , Round Robin RR , Shortest Process Next SPN and Shortest Remaining Time SRT is done over C Daw Khin Po "Simulation of Process Scheduling Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25124.pdf Paper URL: https://www.ijtsrd.com/computer-science/operating-system/25124/simulation-of-process-scheduling-algorithms/daw-khin-po

In a multiprogramming system, multiple processes exist concurrently in main memory. Each process alternates between using a processor and waiting for some event to occur, such as the completion of an I O operation. The processor or processors are kept busy by executing one process while the others wait. The key to multiprogramming is scheduling. CPU scheduling deals with the problem of deciding which of the processes in the ready queue is to be allocated the CPU. By switching the CPU among processor the operating system can make the computer more productive. Scheduling affectes the performance of the system because it determines which processes will wait and which will progress. In this paper, simulation of various scheduling algorithm First Come First Served FCFS , Round Robin RR , Shortest Process Next SPN and Shortest Remaining Time SRT is done over C Daw Khin Po "Simulation of Process Scheduling Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25124.pdf Paper URL: https://www.ijtsrd.com/computer-science/operating-system/25124/simulation-of-process-scheduling-algorithms/daw-khin-po

This paper examines the use of binary trees in the design of efficient parallel algorithms. Using binary trees, we develop efficient algorithms for several scheduling problems. The shared memory model for parallel computation is used. Our success in using binary trees for parallel computations, indicates that the binary tree is an important and useful design tool for parallel algorithms.

http://uf.catalog.fcla.edu/uf.jsp?st=UF024458560%26ix=pm%26I=0%26V=D%26pm=1

The recent advances in the area of wireless networking present novel opportunities for network operators to expand their services to infrastructure-less wireless systems. Such networks, often referred to as ad-hoc or multi-hop or peer-to-peer networks, require architectures which do not necessarily follow the cellular paradigm. They consist of entirely wireless nodes, mixed and/or mobile, that require multiple hops (and hence relaying by intermediate nodes) to transmit their messages to the desired destinations. The distinguishing features of such all-wireless network architectures give rise to new trade-offs between traditional concerns in wireless communications (such as spectral efficiency, and energy conservation) and the notions of routing, scheduling and resource allocation. The purpose of this work is to identify and study some of these novel issues, propose solutions in the context of network control and evaluate the usual network performance measures as functions of the new trade-offs. To these ends, we address first the problem of routing connection-oriented traffic with energy efficiency in all-wireless multi-hop networks. We take advantage of the flexibility of wireless nodes to transmit at different power levels and define a framework for formulating the problem of session routing from the perspective of energy expenditure. A set of heuristics are developed for determining end-to-end unicast paths with sufficient bandwidth and transceiver resources, in which nodes use local information in order to select their transmission power and bandwidth allocation. We propose a set of metrics that associate each link transmission with a cost and consider both the cases of plentiful and limited bandwidth resources, the latter jointly with a set of channel allocation algorithms.

We hypothesize that evolutionary algorithms can effectively schedule coordinated fleets of Earth observing satellites. The constraints are complex and the bottlenecks are not well understood, a condition where evolutionary algorithms are often effective. This is, in part, because evolutionary algorithms require only that one can represent solutions, modify solutions, and evaluate solution fitness. To test the hypothesis we have developed a representative set of problems, produced optimization software (in Java) to solve them, and run experiments comparing techniques. This paper presents initial results of a comparison of several evolutionary and other optimization techniques; namely the genetic algorithm, simulated annealing, squeaky wheel optimization, and stochastic hill climbing. We also compare separate satellite vs. integrated scheduling of a two satellite constellation. While the results are not definitive, tests to date suggest that simulated annealing is the best search technique and integrated scheduling is superior.

We obtain fast parallel algorithms for several scheduling problems. Some of the problems considered are: scheduling to minimize the number of tardy jobs; job sequencing with deadlines; scheduling to minimize earliness and tardiness penalties; channel assignment; and minimizing the mean finish time. The shared memory model of parallel computers is used. (Author)

We consider a discrete time stochastic queueing system where a controller makes a 2-stage decision every slot. The decision at the first stage reveals a hidden source of randomness with a control-dependent (but unknown) probability distribution. The decision at the second stage incurs a penalty vector that depends on this revealed randomness. The goal is to stabilize all queues and minimize a convex function of the time average penalty vector subject to an additional set of time average penalty constraints. This setting fits a wide class of stochastic optimization problems. This includes problems of opportunistic scheduling in wireless networks, where a 2-stage decision about channel measurement and packet transmission must be made every slot without knowledge of the underlying transmission success probabilities. We develop a simple max-weight algorithm that learns efficient behavior by averaging functionals of previous outcomes. The algorithm yields performance that can be pushed arbitrarily close to optimal, with a tradeoff in convergence time and delay.

We obtain fast parallel algorithms for several scheduling problems. Some of the problems considered are: scheduling to minimize the number of tardy jobs; job sequencing with deadlines; scheduling to minimize earliness and tardiness penalties; channel assignment; and minimizing the mean finish time. The shared memory model of parallel computers is used. (Author)

This work studies online scheduling algorithms for buffer management, develops new algorithms, and analyzes their performances. Packets arrive at a release time r, with a non-negative weight w and an integer deadline d. At each time step, at most one packet is scheduled. The modified greedy (MG) algorithm is 1.618-competitive for the objective of maximizing the sum of weights of packets sent, assuming agreeable deadlines. We analyze the empirical behavior of MG in a situation with arbitrary deadlines and demonstrate that it is at a disadvantage when frequently preferring maximum weight packets over early deadline ones. We develop the MLP algorithm, which remedies this problem whilst mimicking the behavior of the offline algorithm. Our comparative analysis shows that, although the competitive ratio of MLP is not as good as that of MG, it performs better in practice. We validate this by simulating the behavior of both algorithms under a spectrum of simulated parameter settings. Finally, we propose the design of three additional algorithms, which may help in improving performance in practice.

This paper considers an energy-efficient packet scheduling problem over quasi-static block fading channels. The goal is to minimize the total energy for transmitting a sequence of data packets under the first-in-first-out rule and strict delay constraints. Conventionally, such design problem is studied under the assumption that the packet transmission rate can be characterized by the classical Shannon capacity formula, which, however, may provide inaccurate energy consumption estimation, especially when the code blocklength is finite. In this paper, we formulate a new energy-efficient packet scheduling problem by adopting a recently developed channel capacity formula for finite blocklength codes. The newly formulated problem is fundamentally more challenging to solve than the traditional one because the transmission energy function under the new channel capacity formula neither can be expressed in closed form nor possesses desirable monotonicity and convexity in general. We analyze conditions on the code blocklength for which the transmission energy function is monotonic and convex. Based on these properties, we develop efficient offline packet scheduling algorithms as well as a rolling-window based online algorithm for real-time packet scheduling. Simulation results demonstrate not only the efficacy of the proposed algorithms but also the fact that the traditional design using the Shannon capacity formula can considerably underestimate the transmission energy for reliable communications.

The challenges of data processing, transmission scheduling and routing within a space network present a multi-criteria optimization problem. Long delays, intermittent connectivity, asymmetric data rates and potentially high error rates make traditional networking approaches unsuitable. The delay tolerant networking architecture and protocols attempt to mitigate many of these issues, yet transmission scheduling is largely manually configured and routes are determined by a static contact routing graph. A high level of variability exists among the requirements and environmental characteristics of different missions, some of which may allow for the use of more opportunistic routing methods. In all cases, resource allocation and constraints must be balanced with the optimization of data throughput and quality of service. Much work has been done researching routing techniques for terrestrial-based challenged networks in an attempt to optimize contact opportunities and resource usage. This paper examines several popular methods to determine their potential applicability to space networks.

The challenges of data processing, transmission scheduling and routing within a space network present a multi-criteria optimization problem. Long delays, intermittent connectivity, asymmetric data rates and potentially high error rates make traditional networking approaches unsuitable. The delay tolerant networking architecture and protocols attempt to mitigate many of these issues, yet transmission scheduling is largely manually configured and routes are determined by a static contact routing graph. A high level of variability exists among the requirements and environmental characteristics of different missions, some of which may allow for the use of more opportunistic routing methods. In all cases, resource allocation and constraints must be balanced with the optimization of data throughput and quality of service. Much work has been done researching routing techniques for terrestrial-based challenged networks in an attempt to optimize contact opportunities and resource usage. This paper examines several popular methods to determine their potential applicability to space networks.

CPU scheduling algorithms have a significant function in multiprogramming operating systems. When the CPU scheduling is effective a high rate of computation could be done correctly and also the system will maintain in a stable state. As well as, CPU scheduling algorithms are the main service in the operating systems that fulfill the maximum utilization of the CPU. This paper aims to compare the characteristics of the CPU scheduling algorithms towards which one is the best algorithm for gaining a higher CPU utilization. The comparison has been done between ten scheduling algorithms with presenting different parameters, such as performance, algorithm’s complexity, algorithm’s problem, average waiting times, algorithm’s advantages-disadvantages, allocation way, etc. The main purpose of the article is to analyze the CPU scheduler in such a way that suits the scheduling goals. However, knowing the algorithm type which is most suitable for a particular situation by showing its full properties.

Traffic congestion are among the most important issue that a country needs to confront due to increasing volume of vehicles around the world, particularly in the large urban areas. As a result, the requirement begins for modeling and improving traffic management procedures to improve the growing need. In order to address traffic problems in urban areas a smart traffic management method is the need of time. The solution in this paper is found through the dimensions of traffic mass on the roads. The core objective of this paper is to highlight latest techniques algorithm which has been used for scheduling traffic lights and a comparison based on achieved accuracy.

Traffic congestion are among the most important issue that a country needs to confront due to increasing volume of vehicles around the world, particularly in the large urban areas. As a result, the requirement begins for modeling and improving traffic management procedures to improve the growing need. In order to address traffic problems in urban areas a smart traffic management method is the need of time. The solution in this paper is found through the dimensions of traffic mass on the roads. The core objective of this paper is to highlight latest techniques algorithm which has been used for scheduling traffic lights and a comparison based on achieved accuracy.

We investigate dynamic algorithms for the interval scheduling problem. Our algorithm runs in amortised time $O(\log n)$ for query operation and $O(d\log^2 n)$ for insertion and removal operations, where $n$ and $d$ are the maximal numbers of intervals and pairwise overlapping intervals respectively. We also show that for a monotonic set, that is when no interval properly contains another interval, the amortised complexity is $O(\log n)$ for both query and update operations. We compare the two algorithms for the monotonic interval sets using experiments.

Cloud computing is one of the emerging fields in computer science due to its several advancements like on-demand processing, resource sharing, and pay per use. There are several cloud computing issues like security, quality of service (QoS) management, data center energy consumption, and scaling. Scheduling is one of the several challenging problems in cloud computing, where several tasks need to be assigned to resources to optimize the quality of service parameters. Scheduling is a well-known NP-hard problem in cloud computing. This will require a suitable scheduling algorithm. Several heuristics and meta-heuristics algorithms were proposed for scheduling the user's task to the resources available in cloud computing in an optimal way. Hybrid scheduling algorithms have become popular in cloud computing. In this paper, we reviewed the hybrid algorithms, which are the combinations of two or more algorithms, used for scheduling in cloud computing. The basic idea behind the hybridization of the algorithm is to take useful features of the used algorithms. This article also classifies the hybrid algorithms and analyzes their objectives, QoS parameters, and future directions for hybrid scheduling algorithms.

In any computing environment, the availability of resources is finite, so effective utilization of resources is very important. To achieve effective management of the available resources in a scalable environment optimal mechanism is required. In order to do this, choosing of scheduling algorithm is important. The scheduling techniques should beselected in such a way that the various user demands are satisfied. In this paper, an overview of different scheduling techniques like cost and energy aware scheduling, cloud based workflow scheduling, an improved ant colony algorithm in cloud computing environment are analysed. S. Saranya Devi | V. Venkatesa Kumar | M. Newlin Rajkumar"Scheduling Algorithms in Cloud Computing - An Extensive Survey" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5840.pdf  http://www.ijtsrd.com/engineering/electrical-engineering/5840/scheduling-algorithms-in-cloud-computing---an-extensive-survey/s-saranya-devi

Scheduling is referring to the process of allocating resources to User Equipment based on scheduling algorithms that is located at the LTE base station. Various algorithms have been proposed as the execution of scheduling algorithm, which represents an open issue in Long Term Evolution (LTE) standard. This paper makes an attempt to study and compare the performance of three well-known uplink schedulers namely, Maximum Throughput (MT), First Maximum Expansion (FME), and Round Robin (RR). The evaluation is considered for a single cell with interference for three flows such as Best effort, Video and VoIP in a pedestrian environment using the LTE-SIM network simulator. The performance evaluation is conducted in terms of system throughput, fairness index, delay and packet loss ratio (PLR). The simulations results show that RR algorithm always reaches the lowest PLR, delivering highest throughput for video and VoIP flows among all those strategies. Thus, RR is the most suitable scheduling algorithm for VoIP and video flows while MT and FME is appropriate for BE flows in LTE networks.

The Quality of Service (QoS) provided for different types of multimedia applications such as audio, video, etc. The IEEE 802.16 standard defines different service classes with their associated (QoS) parameters. The MAC scheduling algorithm is the main role performing in QoS provisioning over such broadband wireless access (BWA) network and it is important that the scheduling algorithm have a multi-dimensional objective of satisfying QoS requirements of the users, maximizing system utilization and ensuring fairness among users. In this paper, we present comparative analysis for different types of MAC scheduling algorithms for the uplink (UL) connection. The proposed scheduling algorithms are compared to several other MAC scheduling algorithms for UL traffic under different mixes of traffic and for various characteristics of the IEEE 802.16 MAC layer such as throughput & delay. Simulation results indicate that MAC scheduling algorithms are not suitable for the multi-class traffic in IEEE 802.16 since they do not explicitly incorporate the QoS parameters for the given standard.

Motivated by the Quality-of-Service (QoS) buffer management problem, we consider online scheduling of packets with hard deadlines in a finite capacity queue. At any time, a queue can store at most $b \in \mathbb Z^+$ packets. Packets arrive over time. Each packet is associated with a non-negative value and an integer deadline. In each time step, only one packet is allowed to be sent. Our objective is to maximize the total value gained by the packets sent by their deadlines in an online manner. Due to the Internet traffic's chaotic characteristics, no stochastic assumptions are made on the packet input sequences. This model is called a {\em finite-queue model}. We use competitive analysis to measure an online algorithm's performance versus an unrealizable optimal offline algorithm who constructs the worst possible input based on the knowledge of the online algorithm. For the finite-queue model, we first present a deterministic 3-competitive memoryless online algorithm. Then, we give a randomized ($\phi^2 = ((1 + \sqrt{5}) / 2)^2 \approx 2.618$)-competitive memoryless online algorithm. The algorithmic framework and its theoretical analysis include several interesting features. First, our algorithms use (possibly) modified characteristics of packets; these characteristics may not be same as those specified in the input sequence. Second, our analysis method is different from the classical potential function approach.

We examine a set of dynamic scheduling techniques for parallel adaptive algorithms in a distributed computational environment. We consider three basic scheduling approaches: centralized scheduling, which uses a master- slave model of computation; distributed scheduling, which uses local information about processor workload to determine when tasks should be requested from or sent to other processors; and a new approach that we refer to as centralized mediation, that uses aspects of both centralized and distributed scheduling. We use both distributed implementation and simulation to examine the performance and scalability of these three scheduling and simulation to examine the performance and scalability of these three scheduling approaches when applied to a parallel adaptive algorithm for solving the global optimization problem. In these experiments, the new centralized mediation approach appears to provide the best combination of robustness, efficiency, and ease of implementation.