On incorporating differentiated levels of network service into GridSim

Abstract-Cloud computing became so important due to virtualization and IT systems in this decade. It has introduced as a distributed and heterogeneous computing pattern to sharing resources. Task Scheduling is necessary to make high performance heterogeneous computing. The optimization of related parameters, and using heuristic and meta-heuristic algorithms can lead to a reduction of the search space complexity and execution time. So, several studies have tried using a variety of algorithms to solve this issue and improve relative efficiency in their environments. This paper considered examines existing heuristic task scheduling algorithms. First, the concepts of scheduling, the layer of cloud computing, especially scheduling concept in the SaaS and PaaS layer, the main limits for improving the quality of service, evaluation methods of algorithms and applied tools for evaluating these ideas and practical experimental used methods were discussed and compared. Finally, future works in this area were also concluded and a summary of this article is presented in the form of a mind map.

Section: B Gridsimmentioning

confidence: 99%

Heuristic Algorithms for Task Scheduling in Cloud Computing: A Survey

Soltani¹,

Soleimani²,

Barekatain³

2017

IJCNIS

“…The GridSim toolkit [2,25] is one of the most widely used Grid simulation tools. It has been used for simulating and evaluating VO-based resource allocation [4], workflow scheduling [22], and dynamic resource provisioning techniques [23] in global Grids.…”

Section: The Gridsim Toolkitmentioning

confidence: 99%

“…It provides primitives for the creation of application tasks, mapping of tasks to resources, and their management so that resource schedulers can be simulated to study the involved scheduling algorithms. GridSim adopts a multilayered design architecture, as shown in Figure 2 [25].…”

Section: The Gridsim Toolkitmentioning

confidence: 99%

“…To address the above issues, we have incorporated failure detection and recovery scheme into GridSim [2,25]. We opted to work on GridSim because it has a complete set of features for simulating realistic Grid testbeds.…”

Section: Introductionmentioning

confidence: 99%

“…We opted to work on GridSim because it has a complete set of features for simulating realistic Grid testbeds. Such features are modeling heterogeneous computational resources of variable performance, scheduling jobs based on time-or spaced-shared policy, differentiated network service, and workload trace-based simulation from real supercomputers [25]. More importantly, GridSim allows the flexibility and extensibility to incorporate new components into its existing infrastructure.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Extending GridSim with an architecture for failure detection

Sulistio

2007 International Conference on Parallel and Distributed Systems

et al. 2007

Performance evaluation of an autonomic network‐aware metascheduler for Grids

Rana²,

Concurrency and Computation

et al. 2009

SUMMARYGrid technologies have enabled the aggregation of geographically distributed resources in the context of a particular application. The network remains an important requirement for any Grid application, as entities involved in a Grid system (such as users, services, and data) need to communicate with each other over a network. The performance of the network must therefore be considered when carrying out tasks such as scheduling, migration or monitoring of jobs. Surprisingly, many existing quality of service efforts ignore the network and focus instead on processor workload and disk access time. Making use of the network in an efficient and fault-tolerant manner is challenging. In a previous contribution, we proposed an autonomic network-aware scheduling architecture that is capable of adapting its behavior to the current status of the environment. Now, we present a performance evaluation in which our proposal is compared with a conventional scheduling strategy. We present simulation results that show the benefits of our approach.