The document discusses resource allocation and scheduling (RAS) in cloud computing. It identifies five major topics in cloud RAS: locality-aware task scheduling, reliability-aware scheduling, energy-aware RAS, Software as a Service (SaaS) layer RAS, and workflow scheduling. These topics are classified into three parts: performance-based RAS, cost-based RAS, and performance- and cost-based RAS. Existing RAS policies and algorithms are discussed for each topic in terms of parameters like execution efficiency, cost-effectiveness, reliability, and resource utilization.

1. PDC REVIEW 2
DONE BY
PRAFUL MITTAL
20BCE1577

2. ABSTRACT
Abstract:
Cloud computing is a new distributed commercial computing model that aims at providing computational
resources or services to users over a network in a low-cost manner. Resource allocation and scheduling
(RAS) is the key focus of cloud computing, and its policy and algorithm have a direct effect on cloud
performance and cost. This paper presents five major topics in cloud computing, namely locality-aware task
scheduling; reliability aware scheduling; energy-aware RAS; Software as a Service (SaaS) layer RAS; and
workflow scheduling. These five topics are then classified into three parts: performance-based RAS; cost-
based RAS; and performance- and cost-based RAS. A number of existing RAS policies and algorithms are
discussed in detail accordingly with regard to their given parameters. In addition, a comparative analysis of
five identified problems with their representative algorithms is performed. Finally, some future research
directions of cloud RAS are pointed out.

3. INTRODUCTION
As a new distributed commercial computing model, cloud computing aims at providing on-demand, highly reliable
and infinitely scalable computational resources or services to the remote users over a network in a low-cost manner.
In order to achieve this goal, data centre management, virtualization, resource allocation and scheduling (RAS),
quality of service (QoS) guarantee, security and privacy have become research hotspots, which are very challenging
in cloud computing. In particular, to improve resource utilization and execution efficiency, save energy, increase the
cloud provider’s profits and satisfy the QoS requirements of users, RAS plays an important role in cloud computing,
and its policy and algorithm have a direct effect on cloud performance and cost.
In the cloud computing environment, computational resources or services are provided by cloud providers (e.g.,
Google, Amazon and Microsoft), and are accessed as general utilities that can be leased and re-leased by users.
Cloud providers are responsible for resource management, storage and guarantee of reliability, while users only
need to pay for their use in executing tasks. To satisfy user requirements and maximize profit, cloud providers have
to take into account cloud performance and cost during RAS in cloud computing.

4. In solving the problem of RAS in cloud computing, researchers have performed significant numbers of studies from various
aspects.
We can sum up five hot topics of cloud RAS from these researches:
(1) locality aware task scheduling;
(2) reliability-aware scheduling;
(3) energy-aware RAS;
(4) SaaS layer RAS; and
(5) workflow scheduling.
Performance-based RAS policies and algorithms are classified into locality-aware task scheduling and reliability-aware RAS.
Most researches on locality-aware task scheduling focus on solving the problem 1.

5. Problem 1,and researches about reliability-aware RAS focus on solving the Problem.
Cost-based RAS policies and algorithms are classified into energy-aware RAS and SaaS-
layer RAS. Research in energy-aware RAS focuses on solving Problem, and research on
SaaS-layer RAS focuses on solving. Performance and cost-based RAS policies and
algorithms mainly relate to workflow scheduling. Research meta-heuristic methods have
been proposed for cloud workflow scheduling. The heuristic-based approach is to develop
a scheduling algorithm that fits only a particular type of problem. execution efficiency,
cost-effective, reliability, resource utilization and QoS. In addition, two examples of RAS
policies and algorithms are given for each problem during the comparison.

6. Problem 1 focuses on locality-aware task scheduling to optimize execution efficiency, and
takes the MaxCover-BalAssign algorithm proposed by Fischer et al. And the ADS
algorithm proposed by Jin et al. as comparative examples.
Problem 2 focuses on RAS to improve the reliability of cloud computing and improve
execution efficiency, and takes the SAMR.

7. I present five hot topics of RAS in cloud computing, and perform a detailed analysis and
discussion of various existing RAS policies and algorithms of the presented problems in terms of
different parameters. In addition, we make a comparative analysis of five problems with their
representative algorithms. Future research directions in the field of cloud RAS could be
summed up in the following points. First, except for considering data locality in the task
scheduling, we can also take into account code locality, which turns writing code into a
sequence of separate tasks where one task can be completed before the next is undertaken. The
problem of maximizing “code locality” is like affinity scheduling in multiprocessor operating
systems, where we attempt to pin a thread to a processor for as long as possible. Second, load
balancing is also a significant parameter to improve performance in RAS of cloud computing.
Considering load balancing helps to avoid hotspots and improve resource utility.

8. QRAS - EFFICIENT RESOURCE ALLOCATION FOR TASK
SCHEDULING IN CLOUD COMPUTING
Cloud resource allocation, a real-time problem can be dealt with efficaciously to reduce
execution cost and improve resource utilization. Resource usability can fulfil customers’
expectations if the allocation has performed according to demand constraint. Task Scheduling
is NP-hard problem where unsuitable matching leads to performance degradation and violation
of service level agreement (SLA). In this research paper, the workflow scheduling problem has
been conducted with objective of higher exploitation of resources. To overcome scheduling
optimization problem, the proposed QoS based resource allocation and scheduling has used
swarm-based ant colony optimization provide more predictable results. The experimentation of
proposed algorithms has been done in a simulated cloud environment. Further, the results of
the proposed algorithm have been compared with other policies, it performed better in terms of
Quality-of-Service parameters.

9. INTRODUCTION
Cloud computing is a Service Oriented Architecture (SOA), providing service in form of infrastructure as a service
(IaaS), platform as a service (PaaS), and software as a service (SaaS). Users can access cloud resources using
network services under the pay-per-use model. Thus, providers are doing efforts to optimize resources according to
customers’ requirements whilst managing the Service Level Agreement (SLA) violation. The existing literature
survey shows that cloud resource management problem has become difficult due to increase in demand of cloud
services [2]. Hence, the study in this research area motivated to develop a novel technique for optimal matching and
throughput utilization of resources. Further, the resource allocation policy should be able to identify tasks exact
requirements before placing on virtual machine (VM). Such a scheme will be managed resource availability,
facilitate to avoid SLA violations, and deadline of tasks. research work motivated from resource allocation and
scheduling problem in cloud environment to workload automatically. VM technology inspired to work on autonomic
resource allocation based on different tasks requirements. Therefore, cost aware VM scheduling is difficult process
whilst dealing with dynamic and heterogeneous environment. Further, optimal usage of resource energy was also
challenging work. Consequently, efficient task execution requires an expected execution cost (EEC) with specific
VM configurations. The proposed framework aims to reduce the energy consumption and to exploit the cloud
resources by optimization EEC

10. CONCLUSION
Resource allocation in cloud environment is challenging due to users’ dynamic requirements. It
has also complex process and impacts cost and resource utilization. This research paper
proposed a novel resource allocation technique for QoS based performance. ACO technique
used for identifying an optimal solution to reduce the execution cost and improve resource
utilization. Therefore, it proves in identification of task to resource suitability. Furthermore,
results and analysis show proposed algorithm outperformed against traditional algorithms. In
addition, the optimization mechanism can be adopted in optimal usage of advanced cloud
computing components such as internet of things. The future work in cloud computing resource
management would be more concentrating on renewable energy, automotive of resource
management and reading consumer behavior which accelerate the cloud services.

11. TASK SCHEDULING AND RESOURCE ALLOCATION OF
CLOUD COMPUTING ON QOS
With the enlargement of the scope of cloud computing application, the number of users
and types also increases accordingly, the special demand for cloud computing resources has
also
improved. Cloud computing task scheduling and resource allocation are key technologies,
mainly
responsible for assigning user jobs to the appropriate resources to perform. But the existing
scheduling algorithm is not fully consider the user demand for resources is different, and not
well
provided for different users to meet the requirements of its resources. As the demand for quality
of
service based on cloud computing and cloud computing original scheduling algorithm, the
computing power scheduling algorithm is proposed based on the QoS constraints to research
the
cloud computing task scheduling and resource allocation problems, improving the overall
efficiency

12. With the cloud proposed computing concept and its low cost and high efficiency of resource use
in
terms of outstanding advantages, it is esteemed the major IT companies. The high reliability of
cloud computing need to perfect safety management mechanism and resource monitoring
mechanism; Cloud computing high scalability needed resource management system to support
various heterogeneous resources; The low cost of cloud computing services to resource
management system effectively organize a large number of low-cost PC, and efficient
distribution
of resource scheduling strategy is needed to improve the use efficiency of system. So the
resource
management to large extent determines the quality of service provided by the cloud computing
platform, is highly efficient and one of the key problems in stable operation of the relationship
to
the cloud computing system

13. The resources matching and task scheduling is one of the cloud computing resources
management basic core functions. Effective task resource scheduling algorithm can reduce the
number of cloud computing system task completion time, increase the efficiency of the use of
computing resources in the system, thus improve the performance of the system and the quality
of
services is one of the most core function of this system. It is proposed in this paper, based on the
QoS constraints of computing power scheduling algorithm and the QoS parameters generated
by the
vector of resource and task matching, this will be able to distinguish between user service
quality
requirements, to provide users with in accordance with the requirements of resources.

14. it analyzes the cloud computing task scheduling and resource allocation management
system that should have the main function based on the cloud computing architecture. On the
basis
of in-depth analysis of the resource scheduling, it put forward the resource scheduling
algorithm
based on QoS, supporting QoS constraints resources task. At the same time, this paper analyzes
the
batch mode and online mode of two kinds of resource scheduling model design thought, puts
forward the task under the guidance of QoS load balancing resource scheduling algorithm, and
further analyzes the cloud computing platform and the research status of resource management
system, and cloud computing resource management subsystem was designed and implemented.