When optical switching is deployed in Data Center Networks (DCNs), the reconfiguration of the optical switching matrix leads to substantially longer overheads, posing a significant impact on the system performance. De...
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Heuristic algorithms for solving the task scheduling problem with moving executors to minimize the sum of completion times are considered. The corresponding combinatorial optimization problem is formulated. Three hybr...
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Heuristic algorithms for solving the task scheduling problem with moving executors to minimize the sum of completion times are considered. The corresponding combinatorial optimization problem is formulated. Three hybrid solution algorithms are introduced. As a basis an evolutionary algorithm is assumed that is combined with the procedure that uses simulated annealing metaheuristics. The results of simulation experiments are given in which the influence of parameters of the solution algorithms as well as of the number of tasks on the quality of scheduling and on the time of computation is investigated. (c) 2005 Elsevier Ltd. All rights reserved.
Distributed data stream processing system is NP-complete problem to assign tasks to any number of nodes handling the task scheduling. Even for substantially reducing scheduling scale, the problem still cannot be avoid...
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Distributed data stream processing system is NP-complete problem to assign tasks to any number of nodes handling the task scheduling. Even for substantially reducing scheduling scale, the problem still cannot be avoided. This paper takes advantage of the classical algorithm (ant colony optimization) of heuristic methods to simulate the global task scheduling problem of distributed system. Rational improvement on ant colony optimization path-finding for the memory and CPU usage of each node achieves load balancing in a short time. It gives the sub-optimal solution of the global task scheduling. The experiments show that the data stream processing system we proposed has good real-time characteristics and stability.
Resource Constrained Project scheduling Problem (RCPSP) is a fundamental scheduling problem that has attracted much attention from researchers for many years. Many variants of this problem have been modeled, and many ...
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With the rapid advancement of technology, intelligent applications increasingly require higher computing power, driving the evolution of the end-edge-cloud Collaborative scheduling (CS) paradigm. However, this paradig...
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Five scheduling problems are considered, concerning unit-length independent tasks and uniform machines. New improved optimal algorithms are presented that can solve these problems in at most O(n log n) time, where n i...
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Five scheduling problems are considered, concerning unit-length independent tasks and uniform machines. New improved optimal algorithms are presented that can solve these problems in at most O(n log n) time, where n is the number of tasks. The existing algorithms solve most of these problems in O(n3) time. Proofs of optimality of the present algorithms are included, and simple representative examples are provided that illustrate the type of results obtained.
This paper focuses on data-intensive workflows and addresses the problem of scheduling workflow ensembles under cost and deadline constraints in Infrastructure as a Service (IaaS) clouds. Previous research in this are...
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This paper focuses on data-intensive workflows and addresses the problem of scheduling workflow ensembles under cost and deadline constraints in Infrastructure as a Service (IaaS) clouds. Previous research in this area ignores file transfers between workflow tasks, which, as we show, often have a large impact on workflow ensemble execution. In this paper we propose and implement a simulation model for handling file transfers between tasks, featuring the ability to dynamically calculate bandwidth and supporting a configurable number of replicas, thus allowing us to simulate various levels of congestion. The resulting model is capable of representing a wide range of storage systems available on clouds: from in-memory caches (such as memcached), to distributed file systems (such as NFS servers) and cloud storage (such as Amazon S3 or Google Cloud Storage). We observe that file transfers may have a significant impact on ensemble execution;for some applications up to 90 % of the execution time is spent on file transfers. Next, we propose and evaluate a novel scheduling algorithm that minimizes the number of transfers by taking advantage of data caching and file locality. We find that for data-intensive applications it performs better than other scheduling algorithms. Additionally, we modify the original scheduling algorithms to effectively operate in environments where file transfers take non-zero time.
Existing power and bit scheduling algorithms mostly focus on open-loop system performance, i.e., improving estimation accuracy. This paper focuses on the scheduling methods for the closed-loop Markov jump systems in t...
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In this correspondence, we consider vehicular edge computing systems in which computing tasks arrive randomly to vehicles over time and are offloaded to one of the edge servers. Unlike previous work that assumes ideal...
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Most classical scheduling formulations assume a fixed and known duration for each activity. In this paper, we weaken this assumption, requiring instead that each duration can be represented by an independent random va...
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Most classical scheduling formulations assume a fixed and known duration for each activity. In this paper, we weaken this assumption, requiring instead that each duration can be represented by an independent random variable with a known mean and variance. The best solutions are ones which have a high probability of achieving a good makespan. We first create a theoretical framework, formally showing how Monte Carlo simulation can be combined with deterministic scheduling algorithms to solve this problem. We propose an associated deterministic scheduling problem whose solution is proved, under certain conditions, to be a lower bound for the probabilistic problem. We then propose and investigate a number of techniques for solving such problems based on combinations of Monte Carlo simulation, solutions to the associated deterministic problem, and either constraint programming or tabu search. Our empirical results demonstrate that a combination of the use of the associated deterministic problem and Monte Carlo simulation results in algorithms that scale best both in terms of problem size and uncertainty. Further experiments point to the correlation between the quality of the deterministic solution and the quality of the probabilistic solution as a major factor responsible for this success.
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