TTSA: An Effective Scheduling Approach for Delay Bounded Tasks in Hybrid Clouds

Yuan, Haitao; Bi, Jing; Tan, Wei; Zhou, MengChu; Li, Bo Hu; Li, Jianqiang

doi:10.1109/tcyb.2016.2574766

Cited by 190 publications

(43 citation statements)

References 56 publications

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“…Hybrid algorithms can be classified into four categories as shown in Figure 9. Temporal Task Scheduling Algorithm (TTSA) is an example for optimizing the throughput by using hybrid methods [75] in cloud. The Table 19 summarizes the different hybrid methods in cloud resource and task management.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Review of the quality of service scheduling mechanisms in cloud

RemeshBabu¹,

Samuel²

2018

IJET

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Cloud computing provides on demand access to a large pool of heterogeneous computational and storage resources to users over the internet. Optimal scheduling mechanisms are needed for the efficient management of these heterogeneous resources. The optimal scheduler can improve the Quality of Services (QoS) as well as maintaining efficiency and fairness among these tasks. In large scale distributed systems, the performance of these scheduling algorithms is crucial for better efficiency. Now the cloud customers are charged based upon the amount of resources they are consumed or held in reserve. Comparing these scheduling algorithms from different perspectives is needed for further improvement. This paper provides a comparative study about different resource allocation, load balancing and virtual machine consolidation algorithms in cloud computing. These algorithms have been evaluated in terms of their ability to provide QoS for the tasks and Service Level Agreement (SLA) guarantee amongst the jobs served. This study identifies current and future research directions in this area for QoS enabled cloud scheduling.

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Section: Hybrid Methodsmentioning

confidence: 99%

Review of the quality of service scheduling mechanisms in cloud

RemeshBabu¹,

Samuel²

2018

IJET

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“…The population size is N . In order to obtain the sequence that a robot performs a set of tasks, the Simulated Annealing Algorithm [46] is used to design a shortest path and its number of iterations is L . Therefore, the evaluation process needs For example, in this application case, the number of robots is three at each station.…”

Section: E Application Casementioning

confidence: 99%

Multiple-Solution Optimization Strategy for Multirobot Task Allocation

Huang

Ding

Zhou

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Multiple solutions are often needed because of different kinds of uncertain failures in a plan execution process and scenarios for which precise mathematical models and constraints are difficult to obtain. This work proposes an optimization strategy for multi-robot task allocation (MRTA) problems and makes efforts on offering multiple solutions with same or similar quality for switching and selection. Since the mentioned problem can be regarded as a multimodal optimization one, this work presents a niching immune-based optimization algorithm based on Softmax regression (sNIOA) to handle it. A pre-judgment of population is done before entering an evaluation process to reduce the evaluation time and to avoid unnecessary computation. Furthermore, a guiding mutation operator inspired by the base pair in theory of gene mutation is introduced into sNIOA to strengthen its search ability. When a certain gene mutates, the others in the same gene group are more likely to mutate with a higher probability. Experimental results show the improvement of sNIOA on the aspect of accelerating computation speed with comparison to other heuristic algorithms. They also show the effectiveness of the proposed guiding mutation operator by comparing sNIOA with and without it. Two MRTA application cases are tested finally.Index Terms-Multi-robot task allocation, multimodal optimization, niching immune-based optimization algorithm, Softmax regression, guiding mutation operator.

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“…, tn) are considered in a cloud system. The primary objective of this research is to improve the following metrics for each user task: In [1], the temporal task scheduling algorithm (TTSA), which dispatches an entire task into a private CDC and public clouds, was used to schedule tasks. A hybrid simulated annealing-PSO is used to solve the cost minimization problem in each iteration of TTSA.…”

Section: Problem Definitionmentioning

confidence: 99%

“…A growing number of organizations and industrial companies deploy their applications in cloud data centers due to the economy of scale provided by cloud computing [1]. To support the increased user demand, cloud computing service providers support ubiquitous services with pay-as-you-go payment methods.…”

Section: Introductionmentioning

confidence: 99%

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

et al. 2019

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Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.

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TTSA: An Effective Scheduling Approach for Delay Bounded Tasks in Hybrid Clouds

Cited by 190 publications

References 56 publications

Review of the quality of service scheduling mechanisms in cloud

Review of the quality of service scheduling mechanisms in cloud

Multiple-Solution Optimization Strategy for Multirobot Task Allocation

Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

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