ACOsched: A scheduling algorithm in a federated cloud infrastructure for bioinformatics applications

2013 ◽  
Author(s):  
Gabriel S. S. de Oliveira ◽  
Edward Ribeiro ◽  
Diogo A. Ferreira ◽  
Aleteia P. F. Araujo ◽  
Maristela T. Holanda ◽  
...  
Keyword(s):  
Scheduling Algorithm ◽  
Cloud Infrastructure

scholarly journals A STOCHASTIC DEVELOPMENT OF CLOUD COMPUTING BASED TASK SCHEDULING ALGORITHM

2019 ◽  
Vol 2019 (1) ◽  
pp. 41-48 ◽  
Author(s):  
Karunakaran V
Keyword(s):  
Virtual Machines ◽  
Cost Minimization ◽  
Scheduling Algorithm ◽  
Cloud Infrastructure ◽  
Solution Quality ◽  
Task Scheduling Algorithm ◽  
Simulation Results ◽  
Selection For ◽  
Stochastic Development ◽  
Data Centres

Due to diversity of services with respect to technology and resources, it is challenging to choose virtual machines (VM) from various data centres with varied features like cost minimization, reduced energy consumption, optimal response time and so on in cloud Infrastructure as a Service (IaaS) environment. The solutions available in the market are exhaustive computationally and aggregates multiple objectives to procure single trade-off that affects the solution quality inversely. This paper describes a hybrid algorithm that facilitates VM selection for scheduling applications based on Gravitational Search and Non-dominated Sorting Genetic Algorithm (GSA and NSGA). The efficiency of the proposed algorithm is verified by the simulation results.

Cost-Aware Ant Colony Optimization for Resource Allocation in Cloud Infrastructure

2020 ◽  
Vol 13 (3) ◽  
pp. 326-335
Author(s):  
Punit Gupta ◽  
Ujjwal Goyal ◽  
Vaishali Verma
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Fitness Function ◽  
Low Cost ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Ant Colony ◽  
Cloud Infrastructure ◽  
Network Cost

Background: Cloud Computing is a growing industry for secure and low cost pay per use resources. Efficient resource allocation is the challenging issue in cloud computing environment. Many task scheduling algorithms used to improve the performance of system. It includes ant colony, genetic algorithm & Round Robin improve the performance but these are not cost efficient at the same time. Objective: In early proven task scheduling algorithms network cost are not included but in this proposed ACO network overhead or cost is taken into consideration which thus improves the efficiency of the algorithm as compared to the previous algorithm. Proposed algorithm aims to improve in term of cost and execution time and reduces network cost. Methods: The proposed task scheduling algorithm in cloud uses ACO with network cost and execution cost as a fitness function. This work tries to improve the existing ACO that will give improved result in terms of performance and execution cost for cloud architecture. Our study includes a comparison between various other algorithms with our proposed ACO model. Results: Performance is measured using an optimization criteria tasks completion time and resource operational cost in the duration of execution. The network cost and user requests measures the performance of the proposed model. Conclusion: The simulation shows that the proposed cost and time aware technique outperforms using performance measurement parameters (average finish time, resource cost, network cost).

Using Value-Based Approach for Managing Cloud-Based Services

2017 ◽  
pp. 33-60
Author(s):  
Salah Eddin Murad ◽  
Salah Dowaji
Keyword(s):  
Response Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Software As A Service ◽  
Business Service ◽  
Cloud Infrastructure ◽  
Current Strategy ◽  
Target Value ◽  
Different Types ◽  
And Control

Software-as-a-Service (SaaS) providers are influenced by a variety of characteristics and capabilities of the available cloud infrastructure resources (IaaS). As a result, the decision made by business service owners to lease and use certain resources is an important one in order to achieve the planned outcome. This chapter uses value based approach to manage the SaaS service provided to the customers. Based on our approach, customer satisfaction is modeled not only based on the response time, but also based on the allotted budget. Using our model, the application owner is able to direct and control the decision of renting cloud resources as per the current strategy. This strategy is led by a set of defined key performance indicators. In addition, we present a scheduling algorithm that can bid for different types of virtual machines to achieve the target value. Furthermore, we proposed the required Ontology to semantically discover the needed IaaS resources. We conduct extensive simulations using different types of Amazon EC2 instances with dynamic prices.

scholarly journals The development of the system for arc nordugrid based grid-computing organization using virtual environments of the docker platform

2021 ◽  
Vol 6 (2 (114)) ◽  
pp. 117-124
Author(s):  
Olga Prila ◽  
Volodymyr Kazymyr ◽  
Volodymyr Bazylevych ◽  
Oleksandr Sysa
Keyword(s):  
Virtual Environments ◽  
Virtual Machines ◽  
Distributed Storage ◽  
Scheduling Algorithm ◽  
Simulation Models ◽  
Dynamic Programming Method ◽  
Cloud Infrastructure ◽  
Grid Environment ◽  
Urgent Task ◽  
Virtual Images

The study of modern frameworks and means of using virtualization in a grid environment confirmed the relevance of the task of automated configuration of the environment for performing tasks in a grid environment. Setting up a task execution environment using virtualization requires the implementation of appropriate algorithms for scheduling tasks and distributed storage of images of virtual environments in a grid environment. Existing cloud infrastructure solutions to optimize the process of deploying virtual machines on computing resources do not have integration with the Arc Nordugrid middleware, which is widely used in grid infrastructures. An urgent task is to develop tools for scheduling tasks and placing images of virtual machines on the resources of the grid environment, taking into account the use of virtualization tools. The results of the implementation of services of the framework are presented that allow to design and perform computational tasks in a grid environment based on ARC Nordugrid using the virtual environment of the Docker platform. The presented results of the implementation of services for scheduling tasks in a grid environment using a virtual computing environment are based on the use of a scheduling algorithm based on the dynamic programming method. Evaluations of the effectiveness of the solutions developed on the basis of a complex of simulation models showed that the use of the proposed algorithm for scheduling and replicating virtual images in a grid environment can reduce the execution time of a computational task by 88 %. Such estimates need further refinement; it is predicted that planning efficiency will increase over time with an increase in the number of running tasks due to the redistribution of the storage of virtual images

scholarly journals Makespan Optimisation in Cloudlet Scheduling with Improved DQN Algorithm in Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Amine Chraibi ◽  
Said Ben Alla ◽  
Abdellah Ezzati
Keyword(s):  
Execution Time ◽  
Work Performance ◽  
Virtual Machines ◽  
Service Providers ◽  
Scheduling Algorithm ◽  
Cloud Service ◽  
Cloud Infrastructure ◽  
Reward Function ◽  
Cloud Service Providers ◽  
Cloudlet Scheduling

Despite increased cloud service providers following advanced cloud infrastructure management, substantial execution time is lost due to minimal server usage. Given the importance of reducing total execution time (makespan) for cloud service providers (as a vital metric) during sustaining Quality-of-Service (QoS), this study established an enhanced scheduling algorithm for minimal cloudlet scheduling (CS) makespan with the deep Q-network (DQN) algorithm under MCS-DQN. A novel reward function was recommended to enhance the DQN model convergence. Additionally, an open-source simulator (CloudSim) was employed to assess the suggested work performance. Resultantly, the recommended MCS-DQN scheduler revealed optimal outcomes to minimise the makespan metric and other counterparts (task waiting period, resource usage of virtual machines, and the extent of incongruence against the algorithms).

scholarly journals AN INTEGRATED FRAMEWORK FOR SLA-AWARE MULTI-OBJECTIVE TASK SCHEDULING IN CLOUD COMPUTING

2021 ◽  
Vol 9 (2) ◽  
pp. 913-928
Author(s):  
Yadaiah Balagoni, Et. al.
Keyword(s):  
Energy Efficiency ◽  
State Of The Art ◽  
Scheduling Algorithm ◽  
Cloud Service ◽  
The State ◽  
Cloud Services ◽  
Hybrid Cloud ◽  
Cloud Environment ◽  
Cloud Infrastructure ◽  
Integrated Framework

Cloud services are offered to consumers based on Service Level Agreements (SLAs) signed between Cloud Service Provider (CSP) and consumer. Due to on-demand provisioning of resources there is exponential growth of cloud consumers. Job scheduling is one of the areas that has attracted researchers to improve performance of cloud management system. Along with the on premise infrastructure, Small and Medium Enterprises (SMEs) also depend on public cloud infrastructure (leading to hybrid cloud) for seamless continuity of their businesses. In this context, ensuring SLAs and effective management of hybrid cloud resources are major challenging issues to be considered. Hence, there is a need for an effective scheduling algorithm which considers multiple objective functions like SLA (deadline), cost and energy while making scheduling decisions. Most of the state of the art schedulers in hybrid cloud environment considered single objective function. However, in real world, it is inadequate for scheduling effectiveness. To overcome this problem, we proposed an integrated framework which ensures SLAs (deadline), cost effectiveness and energy efficiency with an underlying scheduling algorithm known as SCE-TS. This algorithm is evaluated with different workloads and SLAs using a cloud platform. The empirical study revealed that the proposed framework improves scheduling efficiency in terms of meeting SLAs, cost and energy efficiency. It is evaluated and compared with the state of the art and found to be effective in making scheduling decisions in cloud environment.

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