QoS Based Efficient Resource Allocation and Scheduling in Cloud Computing

2019 ◽  
Vol 15 (4) ◽  
pp. 13-29
Author(s):  
Harvinder Chahal ◽  
Anshu Bhasin ◽  
Parag Ravikant Kaveri
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Provisioning ◽  
Cloud Environment ◽  
Allocation Decision ◽  
Research Article ◽  
Large Pool ◽  
Efficient Resource ◽  
Algorithm Implementation

The Cloud environment is a large pool of virtually available resources that perform thousands of computational operations in real time for resource provisioning. Allocation and scheduling are two major pillars of said provisioning with quality of service (QoS). This involves complex modules such as: identification of task requirement, availability of resource, allocation decision, and scheduling operation. In the present scenario, it is intricate to manage cloud resources, as Service provider aims to provide resources to users on productive cost and time. In proposed research article, an optimized technique for efficient resource allocation and scheduling is presented. The proposed policy used heuristic based, ant colony optimization (ACO) for well-ordered allocation. The suggested algorithm implementation done using simulation, shows better results in terms of cost, time and utilization as compared to other algorithms.

Application of Particle Swarm Optimisation-R Factor Optimisation Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment

2020 ◽  
Vol 17 (4) ◽  
pp. 1990-1998
Author(s):  
R. Valarmathi ◽  
T. Sheela
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Management ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Cost Optimization ◽  
Optimal Solution ◽  
Service Needs ◽  
Cloud Environment ◽  
Efficient Resource

Cloud computing is a powerful technology of computing which renders flexible services anywhere to the user. Resource management and task scheduling are essential perspectives of cloud computing. One of the main problems of cloud computing was task scheduling. Usually task scheduling and resource management in cloud is a tough optimization issue at the time of considering quality of service needs. Huge works under task scheduling focuses only on deadline issues and cost optimization and it avoids the significance of availability, robustness and reliability. The main purpose of this study is to develop an Optimized Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment. This study uses PSO and R factor algorithm. The main aim of PSO algorithm is that tasks are scheduled to VM (virtual machines) to reduce the time of waiting and throughput of system. PSO is a technique inspired by social and collective behavior of animal swarms in nature and wherein particles search the problem space to predict near optimal or optimal solution. A hybrid algorithm combining PSO and R-factor has been developed with the purpose of reducing the processing time, make span and cost of task execution simultaneously. The test results and simulation reveals that the proposed method offers better efficiency than the previously prevalent approaches.

scholarly journals Benchmarking and Performance Evaluations on Various Configurations of Virtual Machine and Containers for Cloud-Based Scientific Workloads

2021 ◽  
Vol 11 (3) ◽  
pp. 993
Author(s):  
Syed Asif Raza Shah ◽  
Ahmad Waqas ◽  
Moon-Hyun Kim ◽  
Tae-Hyung Kim ◽  
Heejun Yoon ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Scientific Community ◽  
Virtual Machines ◽  
Resource Provisioning ◽  
Performance Evaluations ◽  
Cloud Environment ◽  
Computational Power ◽  
Efficient Resource ◽  
Benchmark Suite ◽  
And Performance

Cloud computing manages system resources such as processing, storage, and networking by providing users with multiple virtual machines (VMs) as needed. It is one of the rapidly growing fields that come with huge computational power for scientific workloads. Currently, the scientific community is ready to work over the cloud as it is considered as a resource-rich paradigm. The traditional way of executing scientific workloads on cloud computing is by using virtual machines. However, the latest emerging concept of containerization is growing more rapidly and gained popularity because of its unique features. Containers are treated as lightweight as compared to virtual machines in cloud computing. In this regard, a few VMs/containers-associated problems of performance and throughput are encountered because of middleware technologies such as virtualization or containerization. In this paper, we introduce the configurations of VMs and containers for cloud-based scientific workloads in order to utilize the technologies to solve scientific problems and handle their workloads. This paper also tackles throughput and efficiency problems related to VMs and containers in the cloud environment and explores efficient resource provisioning by combining four unique methods: hyperthreading (HT), vCPU cores selection, vCPU affinity, and isolation of vCPUs. The HEPSCPEC06 benchmark suite is used to evaluate the throughput and efficiency of VMs and containers. The proposed solution is to implement four basic techniques to reduce the effect of virtualization and containerization. Additionally, these techniques are used to make virtual machines and containers more effective and powerful for scientific workloads. The results show that allowing hyperthreading, isolation of CPU cores, proper numbering, and allocation of vCPU cores can improve the throughput and performance of virtual machines and containers.

scholarly journals Energy Efficient Resource Allocation for Virtual Services Based on Heterogeneous Shared Hosting Platforms in Cloud Computing

2017 ◽  
Vol 17 (3) ◽  
pp. 47-58
Author(s):  
Nguyen Minh Nhut Pham ◽  
Van Son Le ◽  
Ha Huy Cuong Nguyen
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Service Providers ◽  
Resource Provisioning ◽  
Cloud Computing Service ◽  
Efficient Resource ◽  
Optimal Resource ◽  
Energy Efficient Resource Allocation ◽  
Virtual Services

Abstract This paper is an extended and updated version, presented at the INDIA 2017 conference. Optimal resource provisioning for virtual services in the Cloud computing is one of the concerns nowadays. For cloud computing service providers, reducing the number of physical machines providing resources for virtual services in cloud computing is one of the efficient ways to reduce the amount of energy consumption, which in turn enhances the performance of data centers. Multi-dimensional resource provisioning on a Heterogeneous Shared Hosting Platforms for virtual services is known as a NP-hard problem. Therefore, it is necessary to apply the metaheuristic algorithms for estimating the outcome of the problem. In this study, we propose the resource allocation problem for reducing the energy consumption. ECRA-SA algorithms were designed to solve it and were evaluated through CloudSim simulation tool compared with FFD algorithm. The experimental results show that the proposed ECRA-SA algorithm yields a better performance than FFD algorithm.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

scholarly journals Best Trade-Off Point Method for Efficient Resource Provisioning in Spark

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 190
Author(s):  
Peter Nghiem
Keyword(s):  
Resource Allocation ◽  
Cluster Computing ◽  
High Energy ◽  
Optimal Number ◽  
Resource Provisioning ◽  
Trade Off ◽  
Energy Efficient Computing ◽  
Hadoop Mapreduce ◽  
Efficient Resource ◽  
Mathematical Formulas

Considering the recent exponential growth in the amount of information processed in Big Data, the high energy consumed by data processing engines in datacenters has become a major issue, underlining the need for efficient resource allocation for more energy-efficient computing. We previously proposed the Best Trade-off Point (BToP) method, which provides a general approach and techniques based on an algorithm with mathematical formulas to find the best trade-off point on an elbow curve of performance vs. resources for efficient resource provisioning in Hadoop MapReduce. The BToP method is expected to work for any application or system which relies on a trade-off elbow curve, non-inverted or inverted, for making good decisions. In this paper, we apply the BToP method to the emerging cluster computing framework, Apache Spark, and show that its performance and energy consumption are better than Spark with its built-in dynamic resource allocation enabled. Our Spark-Bench tests confirm the effectiveness of using the BToP method with Spark to determine the optimal number of executors for any workload in production environments where job profiling for behavioral replication will lead to the most efficient resource provisioning.

Cloud Adaptive Resource Allocation Mechanism for Efficient Parallel Processing

2014 ◽  
Vol 4 (4) ◽  
pp. 1-6 ◽  
Author(s):  
Manisha Malhotra ◽  
Rahul Malhotra
Keyword(s):  
Resource Allocation ◽  
Parallel Processing ◽  
Virtual Machines ◽  
Resource Provisioning ◽  
Cloud Provider ◽  
Adaptive Resource Allocation ◽  
Cloud Environment ◽  
Allocation Mechanism ◽  
Adaptive Resource ◽  
Resource Allocation Mechanism

As cloud based services becomes more assorted, resource provisioning becomes more challenges. This is an important issue that how resource may be allocated. The cloud environment offered distinct types of virtual machines and cloud provider distribute those services. This is necessary to adjust the allocation of services with the demand of user. This paper presents an adaptive resource allocation mechanism for efficient parallel processing based on cloud. Using this mechanism the provider's job becomes easier and having the least chance for the wastage of resources and time.

IoT Based Agriculture as a Cloud and Big Data Service

2020 ◽  
pp. 1499-1521
Author(s):  
Sukhpal Singh Gill ◽  
Inderveer Chana ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cloud Services ◽  
Cloud Environment ◽  
Big Data Technologies ◽  
Sensor Networking ◽  
New Applications

Cloud computing has transpired as a new model for managing and delivering applications as services efficiently. Convergence of cloud computing with technologies such as wireless sensor networking, Internet of Things (IoT) and Big Data analytics offers new applications' of cloud services. This paper proposes a cloud-based autonomic information system for delivering Agriculture-as-a-Service (AaaS) through the use of cloud and big data technologies. The proposed system gathers information from various users through preconfigured devices and IoT sensors and processes it in cloud using big data analytics and provides the required information to users automatically. The performance of the proposed system has been evaluated in Cloud environment and experimental results show that the proposed system offers better service and the Quality of Service (QoS) is also better in terms of QoS parameters.

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