Abstract:Due to its simplicity, regularity and suitability for VLSI implementation, the mesh topology for multiprocessors has drawn considerable attention. Several processor allocation strategies for mesh-connected multiprocessors have been proposed in recent years. In this paper; we present the results of a perform" study of all the proposed strategies known to authors. Originally each of these allocation strategies was proposed for use with First-Come-FirstServed job scheduling. In this paper we also propose and eval… Show more
“…Multi-level Queue Scheduling Algorithm: In this type of procedure; the initialized queue is demarcated into n number of queues; with different tasks in multi level scheduling [15]. The whole exercise involves the use of a simulator for assembling scheduling tasks and strategies.…”
One of the latest apt platform in today's trending technological scenario for imparting application based utilities and services rendered by various distributed resources situated remotely is the Cloud Computing paradigm. The routine task operations and services involve efficient energy utilization with minimum dissipation via load balancing and process allocation. A heuristics defined approach has been implemented in this research work for execution of task scheduling activities with optimal resource sharing. Here a robust scheduling technique is used by the scheduler in the heterogeneous cloud network for mapping the available resources to execute scalable tasks optimally. The research area takes into consideration various performance based parameters like Make-Span, Throughput, Average Response Time (ART) etc. for analysis and comparison with the standard scheduling procedures. There was a large energy loss using the previous scheduling and load balancing standard algorithms in reference to the above mentioned parameters. Hence a algorithm named Energy Efficient Multi layered Scheduling (EEMLS) is proposed that outperforms the earlier algorithms in common practice; viz. Max-Min, Round Robin, Opportunistic Load Balancing (OLB), Artificial Bee Colony (ABC) and Minimum Completion Time (MCT) by containing the energy loss considerably during the process work flow of task execution. The entire scenario is best implemented in cloud environment using the Cloudsim simulator for obtaining the results to show better performance with energy saving.
“…Multi-level Queue Scheduling Algorithm: In this type of procedure; the initialized queue is demarcated into n number of queues; with different tasks in multi level scheduling [15]. The whole exercise involves the use of a simulator for assembling scheduling tasks and strategies.…”
One of the latest apt platform in today's trending technological scenario for imparting application based utilities and services rendered by various distributed resources situated remotely is the Cloud Computing paradigm. The routine task operations and services involve efficient energy utilization with minimum dissipation via load balancing and process allocation. A heuristics defined approach has been implemented in this research work for execution of task scheduling activities with optimal resource sharing. Here a robust scheduling technique is used by the scheduler in the heterogeneous cloud network for mapping the available resources to execute scalable tasks optimally. The research area takes into consideration various performance based parameters like Make-Span, Throughput, Average Response Time (ART) etc. for analysis and comparison with the standard scheduling procedures. There was a large energy loss using the previous scheduling and load balancing standard algorithms in reference to the above mentioned parameters. Hence a algorithm named Energy Efficient Multi layered Scheduling (EEMLS) is proposed that outperforms the earlier algorithms in common practice; viz. Max-Min, Round Robin, Opportunistic Load Balancing (OLB), Artificial Bee Colony (ABC) and Minimum Completion Time (MCT) by containing the energy loss considerably during the process work flow of task execution. The entire scenario is best implemented in cloud environment using the Cloudsim simulator for obtaining the results to show better performance with energy saving.
“…This scheme is most appropriate for large, sparse, 2-D meshes where processor utilization requirement is low due to the nature of the task requests. For algorithmic details see [ 11, and see [2] for an enhancement to the method. In Section 3 we present a completely new and much more competitive buddy strategy.…”
Section: Free Submesh Strategiesmentioning
confidence: 99%
“…For the submesh allocations shown in Figure 3 and a request for a 3 x 4 submesh, there are two candidate submesh bases: < 2,3 > and < 3,3 >. With bV (2,3,4,6)=8 and bv (3,3,5,6)=9, the HBV will allocate submesh (3,3,5,6).…”
SUMMARYMultiple processor systems are an integral part of today's high-performance computing environment. Such systems are often configured as a two-dimensional grid of processors called a mesh. Tasks compete for rectangular submeshes of this mesh. The choice of submesh allocation strategy can significantly affect the level of processor utilization and a task's waiting time. In addition, the execution speed of various allocation algorithms varies widely, which can further affect system performance. This paper describes and categorizes several submesh allocation strategies, including a previously unreported method that is superior to other methods in terms of execution speed. The paper includes results of simulation studies used to compare the performance characteristics of the most efficient allocation strategies in each category.
“…Scan scheduling achieves this performance advantage by carefully ordering job executions in such a way that the hypercube is efficiently ''packed,'' reducing external fragmentation. Though this paper focuses on hypercube systems, Scan has been shown to have similar advantages for mesh-connected multiprocessors [1].An important weakness in these results is that, while they are based on the assumption that jobs have no scheduling constraints, such constraints are not uncommon in practice. As a result, a job scheduler may not have complete freedom to set job execution order, so it may have limited ability to affect performance.…”
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