Multi objective genetic approach with Ranking
- 1. END TERM EVALUATION M.TECH DISSERTATION SUBMITTED BY: Namisha Goyal CSE DUAL 17MI550 SUBMITTED TO: PROFESSOR LALIT KUMAR AWASTHI AND DR. PRIYANKA RATHEE Workflow Scheduling by Multi Objective Genetic Approach with Ranking of Task in Cloud Environment
- 2. INTRODUCTION • Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. • The mapping of workflow to VM is done based on the availability of the VM in the cluster. • VM infrastructure provides the high availability resources with dynamic and on-demand configuration. • Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. • The proposed methodology supports VM clustering process to place and allocate VM based on the requesting task size with bandwidth level to enhance the efficiency and availability. • The VM clustering process uses different performance parameters like cost, energy and task execution time. • The main objective of the proposed VM clustering is that it maps the task with suitable VM with bandwidth for achieving high availability and reliability • It reduces task execution and allocated time when compared to existing algorithms.
- 3. LITERATURE REVIEW ● The Cao et al. proposed DCWS, a deadline constrained workflow scheduling algorithm for cost-effective execution of scientific workflows in clouds. DCWS is a list depends on scheduling algorithm that uses several strategies to reduce the monetary cost under deadline constraint: i) sub deadlines are assigned for individual tasks by considering the probabilities that tasks are placed together; ii) instance type upgrading and downgrading strategies are designed to accelerate workflow execution and reduce the total cost respectively; iii) task backfilling and sub-deadline violation penalizing are utilized to get better resource use as well as ensure that the sub-deadlines of the individual tasks are satisfied (Cao et al., 2019). Experimental results demonstrate that in comparison with two modern algorithms, DCWS is effective in reducing monetary cost under deadline constraint. ● Donyadari et al. presented a work scheduling strategy, called RRRSD (Relationship Aware of Round Robin Scheduling focused on Term Limitations). It applies the Round Robin algorithm together with the parameters for the deadline. The key purpose of this model is to simplify the assignment of actions to usable resources so that decrease the time period of experimental workflows as well as the failure rate (Donyadari et al., 2015).
- 4. LITERATURE REVIEW ● Wu et al. identified how to reduce the expense of execution of a workflow in clouds within a time limit and proposed a metaheuristic algorithm L-ACO as well as a basic heuristic ProLiS. ProLiS allocates the deadline by each assignment following a modern concept of probability upward level as well as applies a two-step scheduling methodology (Wu et al., 2017). It rates tasks as well as assign a provider that satisfies the sub-deadline and minimizes costs sequentially to each job. L-ACO utilizes ant colony optimization to conduct timely cost optimization. An ant constructs an organized task list as per the pheromone trail as well as probabilistic upward level, then utilizes the same timeline delivery then service selection techniques as ProLiS to create solutions. ● Malawski et al. provided a statistical model, which optimizes the expense of arranging workflows within a time period. It calls a multi-cloud system in which each provider provides a small range of heterogeneous VMs, and where intermediary data files are exchanged with global storage infrastructure (Malawski et al., 2015). Through formulating the scheduling difficulty as a Mixed Integer Program (MIP), their approach suggests regional assignment as well as data location optimization. Two distinct algorithm implementations are provided, one for coarse-grained workflows, in which actions have one hour of implementation time, as well as another one for fine-grained workflows with a lot of small tasks as well as less than one-hour deadlines.
- 5. LITERATURE REVIEW ● Verma & Kaushal proposed a Bi-Criteria Priority-based Particle Swarm Optimization (BPSO). The aim is to plan workflow activities over the available cloud resources to reduce execution costs and execution time while fulfilling deadlines and budget constraints (Verma and Kaushal, 2017). In addition, Rahman et al. proposed an efficient hybrid heuristic (AHH) for workflow scheduling in the hybrid cloud context. As well as being able to adjust to developments in the cloud, AHH is always able to follow the expenditure and timeline of customers. It is structured to first produce a task-to-resource mapping using GA (Genetic Algorithm) within the user's budget as well as timeframe with minimal execution expense (Rahman et al,. 2011). This basic timetable is then used to assign the expenditure and target at the workflow stage to the stages of the activities ● Vecchiola et al. described the timeline-based resource provisioning method using dynamic redistribution to carry out scientific workloads in a hybrid cloud environment via Aneka. This strategy simply increases the makespan of workloads when recognizing the quality of the capital to be performed (Vecchiola et al., 2012). ● Arabnejad et al. presented a first and required phase towards solving scheduling problems and proposed a new algorithm, Dynamic Workload Scheduler (DWS), which handles dynamics of multiple time-limited workflows that arrive unexpectedly or schedule such workflows with reduced costs in mind. The findings reveal that the DWS algorithm reaches an average performance rate of 10 percent higher in terms of reaching targets with various workloads and decreases operating costs by a total of 23 percent relative to the current comparable algorithm (Arabnejad et al., 2019).
- 6. COMPARISON OF RELATED WORKS
- 7. RESEARCH GAPS ● A number of optimization approaches depend on Random distribution of task. ● In research papers scheduling is depend on static configuration virtual machine, which is not a real condition. ● In existing approaches, initialization of optimization is random which take more time for convergence. ● In existing approaches, optimization use local (VM) or global (Data center).
- 8. PROBLEM STATEMENT ● In cloud computing, workflows based task computations have the main challenge as workflow scheduling in the cloud as single-objective makes unreal suppositions that doesn’t happen in many application scenarios. ● Because single objective analysis only take in consideration one parameter either time,energy or cost. ● Depending on the application, QoS levels may not always be high in cloud computing due to the fact that some applications require high dependability, high performance, short completion time. ● In proposed research work, we will be working on multiobjective approach taking time and cost in consideration and efficient utilization of resources of cloud and improve time and cost. ● These improvements with the constraint of deadline and budget of tasks with the help of dynamic ranking.
- 9. OBJECTIVES ● To study the existing task scheduling algorithm in the cloud environment and analyse different meta-heuristics scheduling algorithms. ● To propose task scheduling cloud framework to optimize the cost, time parameters with deadline as well as budget constraint by using meta-heuristic algorithms. ● To simulate and analyse the performance of the proposed meta-heuristic scheduling algorithm on standard workflows.
- 10. In Proposed flow chart and algorithm following steps: Step 1: In the proposed approach, we use workflows which are a combination of tasks with budget and deadlines. Step 2: It will parse and generate task according to deadlines and budget Step 3: Apply pareto distribution base ranking and map on VM Step 4: After mapping apply multi objective optimization with objective function Step 5: When it converges according to step 6 then analysis the parameters otherwise go to step6 Step 6: Initialize the genetic algorithm with learning value ɑ2 <-(Wi,θ) Step 7: Optimize the objective function and analysis of the parameters. PROPOSED ALGORITHM
- 11. PROPOSED FLOW:
- 12. RESULT ANALYSIS:
- 13. RESULT ANALYSIS:
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