Optimal Assignment Algorithm Research Articles

Optimal Task Assignment in Mobile Cloud Computing by Queue Based Ant-Bee Algorithm

Mobile cloud computing (MCC) broadens the mobile devices capability by offloading tasks to the ‘cloud’. Hence, offloading numerous tasks simultaneously increases the ‘cloudlets’ load and augments the average completion duration of the offloaded tasks. To withstand this issue, we propose a hybrid Queue Ant Colony-Artificial Bee Colony Optimization (Ant-Bee) algorithm for optimal assignment of tasks in MCC environment. The proposed algorithm works on a two-way MCC model with offloading technique, that considers of both the ‘cloudlets’ and the public ‘cloud’. The ‘cloud’ and the ‘cloudlets’ are designed on the basis of queue model for the estimation of clients waiting time in the limitation of resources. The major concern of the proposed algorithm is to offload the tasks by identifying the accurate place preferably in a ‘cloud/cloudlet’. The ‘cloud/cloudlet’ is encompassed by a queue model with the end goal to minimize the drop rate by permitting the tasks to wait in the queue. It also aims for the optimal assignment of tasks to manage the ‘cloudlets’ load and to minimize the entire tasks average completion time. The performance of the proposed algorithm is analyzed with few Queue based conventional algorithms such as, “Round Robin”, “Weighted Round Robin” and “Random”. From the simulation result, it is analyzed that our proposed algorithm outperforms in the power consumption of the mobile devices, the average completion time of tasks, and drop rate. Also, to ensure the efficiency of our proposed hybrid QAnt-Bee algorithm, it is contrasted with the “HACAS” application scheduling algorithm, which fails to consider queue in the ‘cloudlets’.

Realizing Exchanged Crossed Cube Communication Patterns on Linear Array WDM Optical Networks

The exchanged crossed cube, denoted by [Formula: see text], is a novel interconnection network with fewer edges and smaller diameter compared to other variations of the corresponding hypercube. The linear array, denoted by [Formula: see text], is one of the most popular topologies in optical networks. This paper addresses the routing and wavelength assignment for realizing [Formula: see text] communication pattern on wavelength division multiplexing (WDM) optical network [Formula: see text], where [Formula: see text]. We prove that the congestion for [Formula: see text] on [Formula: see text] is equal to [Formula: see text], which is the lower bound of the minimum number of required wavelengths. In addition, an embedding scheme and an optimal wavelength assignment algorithm that achieve this bound are also proposed.

Efficient genetic algorithms for optimal assignment of tasks to teams of agents

There has always been a need to solve real-life large-scale problems, suchas efficiently allocating limited resources, and other complex and conflicting situations related to combinatorial optimization genre. A class of combinato- rial optimization problems is NP-hard and, among many well-known, several of them are assignment, planning and rescheduling problems. Assignment problems can deal with optimal assignment of teams of collaborating agents; planning problems can be effects-based planning that search for promising plans to get desired end states with minimal cost; rescheduling problems can be multi-criteria optimization of rescheduling resources that modify existing original schedule. These large scale optimization problems are complex with intractable and highly complex search spaces. Currently, there are no known algorithms with polynomial time complexity, which can solve these problems. Genetic Algorithms have been successfully applied to solve many complex optimization problems but not to the specific problems mentioned above.The aim of the research, presented in this thesis, is to use Genetic Algo- rithms for large scale optimization of assignment, planning and rescheduling problems. More specifically, the contributions of the thesis are to: (i) adapt existing and develop new efficient Genetic Algorithms to solve large scale as- signment problems, and (ii) adapt existing Genetic Algorithms to solve large scale effects-based planning, and multi-objective rescheduling optimization problems.In case of assignment, we solve a team assignment problem and investigate specific regions in a solution space for assignment problems with huge search spaces.For the team assignment, an existing Genetic Algorithm is adapted and applied for optimal assignment of tasks to teams of collaborating agents. The algorithm is scalable, stable, robust and produces a near optimal solution. The results of the team assignment problem show that the existing Genetic Algorithms are not efficient for optimal assignment of tasks to teams of agents. Hence, to solve larger instances of the problem efficiently, new Genetic Algo- rithms are developed with emphasis on the construction of crossover opera- tors. Since teams assignment can be multi-criteria, a multi-objective model is constructed and two widely used multi-objective evolutionary algorithms are applied. Further, for the assignment problems with huge search spaces, an existing Genetic Algorithm is adapted to extract possible combinations of input parameters from a specified solution space region. To solve the large scale effects-based planning, a multi-objective optimization problem is formu- lated for the evaluation of operational plans and a multi-objective Genetic Algorithm is adapted and applied to the problem. The results show that the suggested algorithm is much more efficient than A*. For the rescheduling problem, a multi-objective optimization model for rescheduling of resources is proposed and a multi-objective Genetic Algorithm is adapted and applied to obtain the Pareto-optimal solutions.The research presented in this thesis confirms that Genetic Algorithms can be used for large scale assignment, planning and rescheduling problems since they have shown to be suitable in solving these problems efficiently.

A path marginal cost approximation algorithm for system optimal quasi-dynamic traffic assignment

This study introduces an efficient path-based System-Optimal Quasi-Dynamic Traffic Assignment (SOQDTA) framework that benefits from the computational efficiency of static traffic assignment models, yet captures the realism of traffic flow, with less complexity and a lower computational burden, compared to dynamic traffic assignment models.To solve the proposed SOQDTA problem, we have developed a novel Path Marginal Cost (PMC) approximation algorithm, based on a Quasi-Dynamic Network Loading (QDNL) procedure (Bliemer et al., 2014), that incorporates a first order node model, and thus produces realistic path travel times consistent with queuing theory, and similar to those of dynamic network loading models, but at a lower computational cost. The model considers capacity constrained static flows, residual vertical/point queues and no spillback.The proposed SOQDTA model is applied to the test network of Sioux Falls and is demonstrated to result in system optimal traffic flow patterns that improve total system travel times compared to the user equilibrium solution. In the case study experiment, the convergence of the algorithm is demonstrated using a relative gap function. A sensitivity analysis is performed to realize the impact of perturbation size on the solution quality, and a discussion is presented on the selection of perturbation size for general network applications.

Optimal channel allocation for multi-PU and multi-SU pairs in underlay cognitive radio networks

In the underlay cognitive radio networks, this paper defines the joint channel and power allocation problem, which aims to optimise the max-total and max-min throughputs of secondary users (SUs), with the constraints of interference on primary receivers. For the max-total problem, we formulate the problem as a bipartite matching and derive a maximum weighted matching-based sum throughput maximisation algorithm (STMA) to solve this problem. For the max-min problem, on the basis of the optimal relay assignment (ORA) algorithm, we derive a polynomial time optimal channel assignment algorithm (OCAA) to iteratively assign channels to each SU pair under the power constraint. Simulation results demonstrate the effectiveness of our algorithms when compared with random method.

An exact schedulability test for fixed-priority preemptive mixed-criticality real-time systems

The current literature of fixed-priority scheduling algorithms relies on sufficient tests to determine if a set of mixed-criticality sporadic tasks is schedulable on a single processor. The drawback of these safe tests is their pessimism, a matter that could be solved if an exact schedulability analysis is used. However, because of the non-deterministic behavior of tasks in the mentioned setups, exact quantification of worst-case response times, needed for the test, is a difficult problem; more precisely, such a quantification needs evaluation of enormous sequences of job executions. The core problem is thus to merge such sequences to make the analysis practical. This paper, for the first time, gives an algorithm for exact worst-case response time characterization of mixed-criticality sporadic real-time tasks executing according to a given fixed-priority scheduler. We use a set of techniques which carefully consider the task properties and their relation to the worst scenarios to prune the analysis state space. We also show an interesting result that if an exact schedulability test is used, the Audsley's optimal priority assignment algorithm is not applicable to the mixed-criticality case. Accordingly, we need new priority assignment algorithms to work with the exact test; we give a simple task priority assignment algorithm to this aim. The performance of the proposed exact test (in terms of time complexity) is examined and the effectiveness of some heuristic priority assignment algorithms using the test (in terms of the ratio of task sets which are deemed schedulable) are compared.

Optimal Monitor Assignment for Preferential Link Tomography in Communication Networks

Inferring fine-grained link metrics by using aggregated path measurements, known as network tomography, is an effective and efficient way to facilitate various network operations, such as network monitoring, load balancing, and failure diagnosis. Given the network topology and a set of interesting links, we study the problem of calculating the link metrics of these links by end-to-end cycle-free path measurements among selected monitors, i.e., preferential link tomography. Since assigning nodes as monitors usually requires non-negligible operational cost, we focus on assigning a minimum number of monitors to identify these interesting links. We propose an optimal monitor assignment (OMA) algorithm for preferential link tomography in communication networks. OMA first partitions the graph representing the network topology into multiple graph components. Then, OMA carefully assigns monitors inside each graph component and at the boundaries of multiple graph components. We theoretically prove the optimality of OMA by proving: 1) the monitors assigned by OMA are able to identify all interesting links and 2) the number of monitors assigned by OMA is minimal. We also implement OMA and evaluate it through extensive simulations based on both real topologies and synthetic topologies. Compared with two baseline approaches, OMA reduces the number of monitors assigned significantly in various network settings.

Fixed priority scheduling with pre-emption thresholds and cache-related pre-emption delays: integrated analysis and evaluation

Commercial off-the-shelf programmable platforms for real-time systems typically contain a cache to bridge the gap between the processor speed and main memory speed. Because cache-related pre-emption delays (CRPD) can have a significant influence on the computation times of tasks, CRPD have been integrated in the response time analysis for fixed-priority pre-emptive scheduling (FPPS). This paper presents CRPD aware response-time analysis of sporadic tasks with arbitrary deadlines for fixed-priority pre-emption threshold scheduling (FPTS), generalizing earlier work. The analysis is complemented by an optimal (pre-emption) threshold assignment algorithm, assuming the priorities of tasks are given. We further improve upon these results by presenting an algorithm that searches for a layout of tasks in memory that makes a task set schedulable. The paper includes an extensive comparative evaluation of the schedulability ratios of FPPS and FPTS, taking CRPD into account. The practical relevance of our work stems from FPTS support in AUTOSAR, a standardized development model for the automotive industry. [(This paper forms an extended version of Bril et al. (in Proceedings of 35th IEEE real-time systems symposium (RTSS), 2014). The main extensions are described in Sect. 1.2.]

New Mathematical Model and Parallel Hybrid Genetic Algorithm for the Optimal Assignment of Strike packages to Targets

New Mathematical Model and Parallel Hybrid Genetic Algorithm for the Optimal Assignment of Strike packages to Targets

Tracking particles with large displacements using energy minimization.

We describe a method to track particles undergoing large displacements. Starting with a list of particle positions sampled at different time points, we assign particle identities by minimizing the sum across all particles of the trace of the square of the strain tensor. This method of tracking corresponds to minimizing the stored energy in an elastic solid or the dissipated energy in a viscous fluid. Our energy-minimizing approach extends the advantages of particle tracking to situations where particle imaging velocimetry and digital imaging correlation are typically required. This approach is much more reliable than the standard squared-displacement minimizing approach for spatially-correlated displacements that are larger than the typical interparticle spacing. Thus, it is suitable for particles embedded in a material undergoing large deformations. On the other hand, squared-displacement minimization is more effective for particles undergoing uncorrelated random motion. In the ESI, we include a flexible MATLAB particle tracker that implements either approach with a robust optimal assignment algorithm. This implementation returns an estimation of the strain tensor for each particle, in addition to its identification.

Optimal QoS-Aware Channel Assignment in D2D Communications With Partial CSI

In this paper, we propose effective channel assignment algorithms for network utility maximization in a cellular network with underlaying device-to-device (D2D) communications. A major innovation is the consideration of partial channel state information (CSI), i.e., the base station (BS) is assumed to be able to acquire `partial' instantaneous CSI of the cellular and D2D links, as well as, the interference links. In contrast to existing works, multiple D2D links are allowed to share the same channel, and the quality of service (QoS) requirements for both the cellular and D2D links are enforced. We first develop an optimal channel assignment algorithm based on dynamic programming (DP), which enjoys a much lower complexity compared to exhaustive search and will serve as a performance benchmark. To further reduce complexity, we propose a cluster-based sub-optimal channel assignment algorithm. New closed-form expressions for the expected weighted sum-rate and the successful transmission probabilities are also derived. Simulation results verify the effectiveness of the proposed algorithms. Moreover, by comparing different partial CSI scenarios, we observe that the CSI of the D2D communication links and the interference links from the D2D transmitters to the BS significantly affects the network performance, while the CSI of the interference links from the BS to the D2D receivers only has a negligible impact.

Doubly Stochastic Sequential Assignment Problem

AbstractThis article introduces the Doubly Stochastic Sequential Assignment Problem (DSSAP), an extension of the Sequential Stochastic Assignment Problem (SSAP), where sequentially arriving tasks are assigned to workers with random success rates. A given number of tasks arrive sequentially, each with a random value coming from a known distribution. On a task arrival, it must be assigned to one of the available workers, each with a random success rate coming from a known distribution. Optimal assignment policies are proposed for DSSAP under various assumptions on the random success rates. The optimal assignment algorithm for the general case of DSSAP, where workers have distinct success rate distribution, has an exponential running time. An approximation algorithm that achieves a fraction of the maximum total expected reward in a polynomial time is proposed. The results are illustrated by several numerical experiments. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 124–137, 2016

One-Sided Optimal Assignment and Swap Algorithm for Two-Sided Optimization of Assignment Problem

Generally, the optimal solution of assignment problem can be obtained by Hungarian algorithm of two-sided optimization with time complexity . This paper suggests one-sided optimal assignment and swap optimization algorithm with time complexity can be achieve the goal of two-sided optimization. This algorithm selects the minimum cost for each row, and reassigns over-assigned to under-assigned cell. Next, that verifies the existence of swap optimization candidates, and swap optimizes with . For 27 experimental data, the swap-optimization performs only 22% of data, and 78% of data can be get the two-sided optimal result through one-sided optimal result. Also, that can be improves on the solution of best known solution for partial problems.

On the compatibility of exact schedulability tests for global fixed priority pre-emptive scheduling with Audsley’s optimal priority assignment algorithm

Audsley's optimal priority assignment (OPA) algorithm can be applied to multiprocessor scheduling provided that three conditions hold with respect to the schedulability tests used. In this short paper, we prove that no exact test for global fixed priority pre-emptive scheduling of sporadic tasks can be compatible with Audsley's algorithm, and hence the OPA algorithm cannot be used to obtain an optimal priority assignment for such systems.

Task partitioning and priority assignment for distributed hard real-time systems

In this paper, we propose the Distributed using Optimal Priority Assignment (DOPA) heuristic that finds a feasible partitioning and priority assignment for distributed applications based on the linear transactional model. DOPA partitions the tasks and messages in the distributed system, and makes use of the Optimal Priority Assignment (OPA) algorithm known as Audsley's algorithm, to find the priorities for that partition. The experimental results show how the use of the OPA algorithm increases in average the number of schedulable tasks and messages in a distributed system when compared to the use of Deadline Monotonic (DM) usually favoured in other works. Afterwards, we extend these results to the assignment of Parallel/Distributed applications and present a second heuristic named Parallel-DOPA (P-DOPA). In that case, we show how the partitioning process can be simplified by using the Distributed Stretch Transformation (DST), a parallel transaction transformation algorithm introduced in [1].

Resource sharing among prioritized real-time applications on multiprocessors

In this paper, we propose a new protocol for handling resource sharing among prioritized real-time applications composed on a multiprocessor platform. We propose an optimal priority assignment algorithm which assigns unique priorities to the applications based on information in their interfaces. We have performed experimental evaluations to compare the proposed protocol (called MSOS-Priority) to the current state of the art locking protocols under multiprocessor partitioned scheduling, i.e., MPCP, MSRP, FMLP, MSOS, and OMLP. The evaluations show that MSOS-Priority mostly performs significantly better than alternative approaches.

Guaranteeing end-to-end deadlines for AUTOSAR-based automotive software

Automotive electrical/electronic (E/E) architectures are evolving towards a complex software intensive distributed system. However, current technology and practice in the automotive industry do not adequately address the increasing complexity of software, which hinders accomplishing reliable, maintainable, high-quality software within time and budget constraints. Although automotive open system architecture (AUTOSAR) addresses many software issues such as system software services, application interfaces, and communication middleware, it largely focuses on the implementation aspect without sufficiently addressing the design aspect. In this paper, we present a novel approach to guaranteeing end-to-end deadlines for AUTOSAR-based automotive systems in the early design stage. Our approach, we call zero slack priority assignment (ZSPA), decomposes end-to-end deadlines into local per-task deadlines and finds a feasible scheduling solution leveraging the Audsley’s optimal priority assignment algorithm. Our simulations show that ZSPA outperforms existing methods, heuristic optimized priority assignment (HOPA) (Garcia and Harbour, 1995) and the genetic algorithm (GA) (Azketa et al., 2011). Specifically, ZSPA shows up to 20% higher success rate in finding feasible solutions than HOPA and GA. The computational complexity of ZSPA is O(n2), whereas HOPA and GA have unbounded running time.

Punctured interval broadcast encryption scheme with free riders

In Broadcast Encryption (BE) schemes, the problem is to encrypt a content for a group of dynamically changing privileged (subscriber) subset within a receiver population. A popular approach is to carefully distribute a group of keys to several, carefully designed subsets of the receivers beforehand, and later use a precise subset of keys so that only intended users have those keys, thus decrypt the content. This approach is known as the subset cover framework. In the subset cover framework, one concern is the number of copies that must be encrypted, which affects the bandwidth requirement. This problem can be relaxed by allowing a limited number of free riders so that, by sacrificing some coverage accuracy, the transmission cost is reduced. Several BE schemes are proposed since 90s, and one of the most efficient schemes so far is the punctured interval BE scheme (Cheon et al., 2008). In this paper, we attack the problem of how to assign a given number of free riders effectively in the punctured interval BE scheme. We give the optimal free rider assignment algorithm which runs in poly(n) time, where n is the number of all users in the system, and we provide a heuristic which performs slightly worse than the optimal algorithm in terms of transmission cost reduction but is much faster, i.e., linear in terms of n. We also propose a hybrid approach which employs the core ideas of both optimal and heuristic methods in order to achieve a trade-off between speed and accuracy.

Optimized Assignment Algorithm to Maximize the Reduction for Backup System with Multiple Deduplication Methods Installed

SUMMARYDeduplication backup technology removes redundant data segments in a system in order to reduce the capacity usage in the target backup storage. This technique also provides better performance, less resource utilization, less energy consumption, and TCO. This paper describes an optimization method for deduplication backup in an IT system in which multiple deduplication processes are simultaneously installed and activated. The method provides an algorithm for assignment of backup target files to installed deduplication processes to maximize the aggregate deduplication ratio while maintaining the predefined system requirements, such as backup‐windows limitation and resource utilization limitation. In a real‐world system, the time used to perform deduplication in each process is not constant, but variable due to operational resources contention, waiting time in queues, and data characteristics. The proposed method points out that these time parameters can be simulated as following a normal distribution, formularizes discrete assignment programming, then defines combinatory approaches of integer linear programming to incorporate the maximum deduplication ratio and the binary adjustment of parameters so as to ensure that the variance is not exceeded. By utilizing this method, the system can achieve the maximal deduplication ratio while respecting time requirements within a predefined tolerance. The effectiveness of the method is demonstrated through simulations.

Assigning Priorities for Fixed Priority Preemption Threshold Scheduling.

Preemption threshold scheduling (PTS) enhances real-time schedulability by controlling preemptiveness of tasks. This benefit of PTS highly depends on a proper algorithm that assigns each task feasible scheduling attributes, which are priority and preemption threshold. Due to the existence of an efficient optimal preemption threshold assignment algorithm that works with fully assigned priority orderings, we need an optimal priority assignment algorithm for PTS. This paper analyzes the inefficiency or nonoptimality of the previously proposed optimal priority assignment algorithms for PTS. We develop theorems for exhaustively but safely pruning infeasible priority orderings while assigning priorities to tasks for PTS. Based on the developed theorems, we correct the previously proposed optimal priority assignment algorithm for PTS. We also propose a performance improved optimal priority assignment algorithm for PTS proving its optimality. The empirical evaluation results clearly show the effectiveness of the proposed algorithm.