A Comprehensive Survey on SLA Compliant Energy Aware Resource Allocation in Cloud Datacenters

Author(s):  
Anitha R ◽  
C Vidya Raj

Cloud Computing has achieved immense popularity due to its unmatched benefits and characteristics. With its increasing popularity and round the clock demand, cloud based data centers often suffer with problems due to over-usage of resources or under-usage of capable servers that ultimately leads to wastage of energy and overall elevated cost of operation. Virtualization plays a key role in providing cost effective solution to service users. But on datacenters, load balancing and scheduling techniques remain inevitable to provide better Quality of Service to the service users and maintenance of energy efficient operations in datacenters. Energy-Aware resource allocation and job scheduling mechanisms in VMs has helped datacenter providers to reduce their cost incurrence through predictive job scheduling and load balancing. But it is quite difficult for any SLA oriented systems to maintain equilibrium between QoS and cost incurrence while considering their legal assurance of quality, as there should not be any violations in their service agreement. This paper presents some state-of-the-art works by various researchers and experts in the arena of cloud computing systems and particularly emphasizes on energy aware resource allocations, job scheduling techniques, load balancing and price prediction methods. Comparisons are made to demonstrate usefulness of the mechanisms in different scenarios.

2016 ◽  
Vol 15 (4) ◽  
pp. 6681-6685
Author(s):  
Parveen Kaur ◽  
Monika Sachdeva

Now a days every organization is migrating towards  cloud computing as cloud computing is considered being more flexible and scalable as compared to other technologies. The technology simply means to provide the computing resources and services through a network. This paper discusses the existing approaches for scheduling algorithms that can maintain the load balancing and provides better improved strategies through efficient job scheduling and modified resource allocation techniques. The load can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of a distributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that all the processor in the system or every node in the network does approximately the equal amount of work at any instant of time. 


2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Endang Wahyu Pamungkas ◽  
Divi Galih Prasetyo Putri

Recently cloud computing technology has been implemented by many companies. This technology requires a really high reliability that closely related to hardware specification and management resource quality used. Adequate hardware would make resource allocation easier. On the other hand, resource allocation will be harder if the resources are limited. This is a common condition in a developing cloud service provider. In this paper, a load balancing algorithm to allocate resources in cloud computing environment that has limited resources has been proposed. This algorithm is developed by taking the advantages of the existing algorithms, Equally Spread Current Execution and Throttled. We merge those algorithm without losing the advantages and we try to eliminate the shortcoming of each algorithm. The result shows that this algorithm is able to give a significant improvement in the limited resources environment. In addition, the algorithm also able to compete with the other algorithm in the more adequate resource environment. Based on the consistent results, this algorithm is expected to be more adaptive in different resources environment.


2015 ◽  
Vol 15 (4) ◽  
pp. 138-148 ◽  
Author(s):  
B. Mallikarjuna ◽  
P. Venkata Krishna

Abstract Load balancing is treated as one of the important mechanisms for efficient resource allocation in cloud computing. In future there will appear a necessity of fully autonomic distributed systems to address the load balancing issues. With reference to this, we proposed a load balancing mechanism called Osmosis Load Balancing (OLB). OLB works on the principle of osmosis to reschedule the tasks in virtual machines. The solution is based on the Distributed Hash Table (DHT) with a chord overlay mechanism. The Chord overlay is used for managing bio inspired agents and status of the cloud. By simulation analysis, the proposed algorithm has shown better performance in different scenarios, both in heterogeneous and homogeneous clouds.


Author(s):  
K. Balaji, Et. al.

The evolution of IT led Cloud computing technology emerge as a new prototype in providing the services to its users on rented basis at any time or place. Considering the flexibility of cloud services, innumerable organizations switched their businesses to the cloud technology by setting up more data centers. Nevertheless, it has become mandatory to provide profitable execution of tasks and appropriate  resource utilization. A few approaches were outlined in literature to enhance performance, job scheduling, storage resources, QoS and load distribution. Load balancing concept permits data centers to avert over-loading or under-loading in virtual machines that as such is an issue in cloud computing domain. Consequently, it necessitate the researchers to layout and apply a proper load balancer for cloud environment. The respective study represents a view of problems and threats faced by the current load balancing techniques and make the researchers find more efficient algorithms.


2019 ◽  
Vol 8 (4) ◽  
pp. 9388-9394 ◽  

Cloud Computing is Internet based computing where one can store and access their personal resources from any computer through Internet. Cloud Computing is a simple pay-per-utilize consumer-provider service model. Cloud is nothing but large pool of easily accessible and usable virtual resources. Task (Job) scheduling is always a noteworthy issue in any computing paradigm. Due to the availability of finite resources and time variant nature of incoming tasks it is very challenging to schedule a new task accurately and assign requested resources to cloud user. Traditional task scheduling techniques are improper for cloud computing as cloud computing is based on virtualization technology with disseminated nature. Cloud computing brings in new challenges for task scheduling due to heterogeneity in hardware capabilities, on-demand service model, pay-per-utilize model and guarantee to meet Quality of Service (QoS). This has motivated us to generate multi-objective methods for task scheduling. In this research paper we have presented multi-objective prediction based task scheduling method in cloud computing to improve load balancing in order to satisfy cloud consumers dynamically changing needs and also to benefit cloud providers for effective resource management. Basically our method gives low probability value for not capable and overloaded nodes. To achieve the same we have used sigmoid function and Euclidean distance. Our major goal is to predict optimal node for task scheduling which satisfies objectives like resource utilization and load balancing with accuracy.


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