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Scheduling in Cloud computing infrastructure contain several challenging issues like computation time, budget, load balancing etc. Out of them, load balancing is one the major challenges for Cloud platform. Load balancing basically balances the load to achieve higher throughput and better resource utilization. Since scheduling task is NP-complete problem, so heuristic and meta heuristic approaches...
One of the key methods for the optimization of parallel processing in a program is task scheduling. This intends to minimize implementation time for the entire program by determining schedule for optimally allocating tasks, which are processing units comprising the program, to available processor elements into pieces. Due to complexity of calculation and being a large-scale problem, the optimization...
Task scheduling is critical for obtaining a high performance schedule in heterogeneous computing systems (HCS) and searching an optimal scheduling solution has been shown to be NP-complete. In this paper, a hybrid heuristicgenetic algorithm with adaptive parameter (HGAAP) is proposed by combining a heuristic scheduling algorithm and a genetic algorithm. An existing common heuristic scheduling algorithm...
This paper put forward a novel scalability-aware scheduling optimization algorithm called Cloud Scalable Multi-Objective Cat Swarm Optimization Based Simulated Annealing (CSM-CSOSA) for solving task scheduling optimization problem in cloud computing environment. The novelty of the algorithm is based on the improvement of its local search procedure using improved simulated annealing optimization approach...
Cloud computing is an evolution of Distributed system that has been adopted by worldwide scientifically and commercially. For optimal use of cloud's potential power, effective and efficient algorithm are required, which will select best resources from available cloud resources for different applications. This allocation of user requests to the cloud resource can optimize various parameters like energy...
Cloud computing is the network of distributed remote servers to access the information anytime, anywhere. Thus, it also referred to as ubiquitous computing. Cloud computing offers the high performance environment that has great sharing of resources across the distributed servers. Though there are large numbers of tasks to be allocated to these resources, it becomes very necessary to efficiently assign...
Cloud Computing is a new emerging paradigm that provisions various computing resources to meet the developing computational needs. Scheduling the task poses many difficulties, because the cloud computing resources are complex, dynamic, heterogeneous, distributed in nature. Task Scheduling aims at minimising the makespan and maximising the resource utilisation. This paper gives an elaborate idea about...
In this paper, a T-LET planes measure is applied to manage the task scheduling for multiprocessors. First, a novel scheduling algorithm on T-LET planes is proposed, and it is based on the strategy that the biggest M tasks, within the current remaining execution time, are first selected. Secondly, the algorithm has proved as an optimal multiprocessor scheduling algorithm for assigning tasks and currently...
With the arrival of partial reconfiguration technology, modern FPGAs support tasks that can be loaded in (removed from) the FPGA individually without interrupting other tasks already running on the same FPGA. Many online task placement algorithms designed for such partially reconfigurable systems have been proposed to provide efficient and fast task placement. A new approach for online placement of...
Widely used computing systems are heterogeneous in nature, comprising of interconnected resources which differ in computational capability of processing nodes and network bandwidth. Due to this diversity, an efficient heuristic is required to achieve high performance in heterogeneous computing system. In our proposed scheduling algorithm, Heterogeneous Edge and Task Scheduling (HETS), we schedule...
Efficient task scheduling is a challenging aspect of achieving high performance in parallel programming. Aiming at the problem that many heuristics for this NP-hard problem were always developed based on homogeneous systems and ignored the heterogeneity of processors which are not met on real parallel systems, this paper presents a heuristic algorithm based on list and task duplication for scheduling...
Grids have been extensively deployed to handle various scientific and engineering applications that can be structured as bag-of-tasks (BoT). The scheduling of BoT applications on Grids is an important issue for achieving high performance. Grid scheduling involves a number of challenging issues, mainly due to the dynamic nature of the Grid. To deal with this dynamic nature, in this paper, we propose...
On the distributed or parallel heterogeneous computing systems, an application is usually decomposed into several independent and/or interdependent sets of cooperating subtasks and assigned to a set of available processors for execution. Heuristic-based task scheduling algorithms consist of the two typical phases of task prioritization and processor selection. However, heuristic-based task scheduling...
Task scheduling and task allocation, which are vital parts of mapping parallel programs to concurrent architectures, must take into account the interprocessor communication, whose overheads have emerged as the major performance limitation in parallel applications. Furthermore, its power consumption is an important research focus which must be addressed. Finding an optimal solution requires information...
Two traditional heuristic task scheduling algorithms (STFCMEF-MS algorithm and LTFCMEF-MS algorithm) are developed to solve a multi-task scheduling problem on multiple computers to reduce energy consumption and finish required tasks before a deadline. Two new green task scheduling algorithms (STFCMEF-SA algorithm and LTFCMEF-SA algorithm) are proposed to solve the same problem. Since the energy is...
To solve high real-time and complexity calculation problems such as feature extraction and pattern classification when wireless sensor network real-time diagnosis and equipment health record of the mine coal underground equipments monitoring, this paper purpose a optimal algorithm for task scheduling underground wireless monitoring network based on distributed computing, this method use the fast convergence...
It is widely believed that future simulation computing environment will consist of geographically distributed computing resources connected with diverse communication capacities, forming a so-called “Simulation Grid (SG)” environment. To construct the SG we try to resolve the issues of effective utilization, integration, and interoperability of numerous simulation resources in a large complex simulation...
The Ant Colony Optimization algorithms (ACO) are computational models inspired by the collective foraging behavior of ants. By looking at the strengths of ACO, they are the most appropriate for scheduling of tasks in soft real-time systems. In this paper, ACO based scheduling algorithm for real-time operating systems (RTOS) has been proposed. During simulation, results are obtained with periodic tasks,...
The dynamic feature is one of the most important differences between Grid and traditional heterogeneous distributed systems, thus the most significant challenge for task scheduling in Grid environment is how to relieve the resource performance dynamism effectively. However, the existing schedule algorithms usually suppose that computation or communication times are deterministic and static, thus they...
Increasing delay and power variation has become a major challenge to designing high performance Multiprocessor System-On-Chips (MPSoC) in deep sub-micron technologies. As a result, a paradigm shift from deterministic to statistical design methodology at all levels of the design hierarchy is inevitable. In this paper, we propose a static variation-aware task scheduling and power mode selection algorithm...
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