Multi objective genetic approach with Ranking

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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