Survey and analysis on Task scheduling in Cloud environment

Akilandeswari, P.; Srimathi, H.

doi:10.17485/ijst/2016/v9i37/102058

Cited by 23 publications

(9 citation statements)

References 13 publications

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“…There are different taskscheduling algorithms that can be adopted, but suitable to the situation. There were many surveys done on task scheduling www.ijacsa.thesai.org [24,25,26]. Selecting the best scheduling policy is the prime concern.…”

Section: Methodsmentioning

confidence: 99%

Cluster based Hybrid Approach to Task Scheduling in Cloud Environment

Raju¹,

Devarakonda²

2019

IJACSA

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Cloud computing technology enables sharing of computer system resources among users through internet. Many numbers of users may request for sharable resources from a cloud. The sharable resources must be effectively distributed among requested users with in a less amount of time. Task scheduling is one of the ways of handling the user requests effectively in a cloud environment. There were many existing biologically inspired optimization techniques worked with task scheduling problems. The proposed paper is aimed at clubbing clustering techniques with biologically inspired optimization algorithms for deriving better results. A new hybrid methodology KPSOW (K-means with PSO using weights) has been proposed in the paper, which makes use of the strengths of both the Kmeans and PSO algorithms with the inclusion of weights concept. The results have shown that KPSOW has made considerable changes in reducing the makespan and improves the utilization of computing resources in the cloud.

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Section: Methodsmentioning

confidence: 99%

Cluster based Hybrid Approach to Task Scheduling in Cloud Environment

Raju¹,

Devarakonda²

2019

IJACSA

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“…In [61], the authors discuss a system named RedEdge which is responsible to 1 https://www.cisco.com/c/en/us/products/cloud-systems-management/edge-fog-fabric/index.html. 2 https://segment.com/.…”

Section: Related Workmentioning

confidence: 99%

“…Task scheduling is a widely studied problem for computer systems [20]. In-depth studies on several task scheduling algorithms for Cloud computing have also been conducted [2,43]. In [58], the authors focus on the task scheduling not only at the edge but also at the Cloud.…”

Section: Related Workmentioning

confidence: 99%

A probabilistic model for assigning queries at the edge

Kolomvatsos

Anagnostopoulos

2019

Computing

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Data management at the edge of the network can increase the performance of applications as the processing is realized close to end users limiting the observed latency in the provision of responses. A typical data processing involves the execution of queries/tasks defined by users or applications asking for responses in the form of analytics. Query/task execution can be realized at the edge nodes that can undertake the responsibility of delivering the desired analytics to the interested users or applications. In this paper, we deal with the problem of allocating queries to a number of edge nodes. The aim is to support the goal of eliminating further the latency by allocating queries to nodes that exhibit a low load and high processing speed, thus, they can respond in the minimum time. Before any allocation, we propose a method for estimating the computational burden that a query/task will add to a node and, afterwards, we proceed with the final assignment. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query/task and a probabilistic decision making model. The proposed scheme matches the characteristics of the incoming queries and edge nodes trying to conclude the optimal allocation. We discuss our mechanism and through a large set of simulations and the adoption of benchmarking queries, we reveal the potentials of the proposed model supported by numerical results.

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“…Several algorithms for task scheduling are available in a computer system [31]. Indepth studies on several task scheduling algorithms for cloud computing have also been conducted [32,33]. Reference [34] proposes a task scheduling approach called HealthEdge, which sets various processing priorities for different tasks based on the collected human health data and determines whether a task must be operated on a local device or a remote cloud in order to reduce the total processing time as much as possible.…”

Section: Background and Related Workmentioning

confidence: 99%

Task Scheduling for Edge Computing with Agile VNFs On‐Demand Service Model toward 5G and Beyond

Tseng

Yang

et al. 2018

Wireless Communications and Mobile Computing

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The demand for satisfying service requests, effectively allocating computing resources, and providing service on-demand application continuously increases along with the rapid development of the Internet. Edge computing is used to satisfy the low latency, network connection, and local data processing requirements and to alleviate the workload in the cloud. This paper proposes a gateway-based edge computing service model to reduce the latency of data transmission and the network bandwidth from and to the cloud. An on-demand computing resource allocation can be achieved by adjusting the task schedule of the edge gateway via the lightweight virtualization technology, Docker. The edge gateway can also process the service requests in the local network. The proposed edge computing service model not only eliminates the computation burden of the traditional cloud service model but also improves the operation efficiency of the edge computing nodes. This model can also be used for various innovation applications in the cloud-edge computing environment for 5G and beyond.

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Survey and analysis on Task scheduling in Cloud environment

Cited by 23 publications

References 13 publications

Cluster based Hybrid Approach to Task Scheduling in Cloud Environment

Cluster based Hybrid Approach to Task Scheduling in Cloud Environment

A probabilistic model for assigning queries at the edge

Task Scheduling for Edge Computing with Agile VNFs On‐Demand Service Model toward 5G and Beyond

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