Cloud computing is a model of sharing computing resources over any communication network by using virtualization. Virtualization allows a server to be sliced in virtual machines. Each virtual machine has its own operating system/applications that rapidly adjust resource allocation. Cloud computing offers many benefits, one of them is elastic resource allocation. To fulfill the requirements of clients, cloud environment should be flexible in nature and can be achieve by efficient resource allocation. Resource allocation is the process of assigning available resources to clients over the internet and plays vital role in Infrastructure-as-aService (IaaS) model of cloud computing. Elastic resource allocation is required to optimize the allocation of resources, minimizing the response time and maximizing the throughput to improve the performance of cloud computing. Sufficient solutions have been proposed for cloud computing to improve the performance but for fog computing still efficient solution have to be found. Fog computing is the virtualized intermediate layer between clients and cloud. It is a highly virtualized technology which is similar to cloud and provide data, computation, storage, and networking services between end users and cloud servers. This paper presents an efficient architecture and algorithm for resources provisioning in fog computing environment by using virtualization technique.

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An Efficient Architecture and Algorithm for Resource Provisioning in Fog Computing

Fog computing concept is introduced to reduce the load on cloud and provide similar services as cloud However, fog covers small area rather than cloud by storing the data temporarily and sends data to cloud for permanent storage In this paper, an integrated fog and cloud based environment for effective energy management of buildings is proposed So, the load on cloud and fog should be balanced Various load balancing algorithms are used to manage the load among virtual machines (VMs) In this scenario, algorithm used for load balancing among VMs is round robin (RR) Service broker policies considered in this paper are; dynamically reconfigure with load (DR) and the proposed policy New dynamic service proximity (DSP) service broker policy is proposed for fog selection and results of DSP policy are compared with DR policy Therefore, a tradeoff is observed between cost and response time

Integration of Cloud and Fog based Environment for Effective Resource Distribution in Smart Buildings

Cloud computing is the big boom technology in IT industry infrastructure. Many people are moving to cloud computing because of dynamic allocation of resources and reduction in cost. Cloud computing delivers infrastructure, software, and platforms as a service to all consumers. But still, it has numerous issues related to performance unpredictability, resource sharing, security, storage capacity, availability of resources on each requirement, data confidentiality and many more. Load balancing and service brokering are the two main key areas, which ensures reliability, scalability, minimize response time, maximize throughput and cost in the cloud environment. These are the main things we have to focus to improve the performance of the computation. This survey paper presents a comparative and comprehensive study of various load balancing algorithm used in the load balancer and brokering policy used for each service and their scheduling types. The objectives of this survey is to (1) Determine, illustrate, compare and analyze newer methods developed for load balancing and service brokering (the most notable problem) by systematically reviewing papers from the year 2015 to 2018; (2) Classify and analyze techniques based on the key parameters in cloud computing techniques; (3) Ultimately set an updated, thorough and rigorous discussion on load balancing and service broker techniques so as to motivate and direct with valuable references for future research development and direction.

Cloud computing using load balancing and service broker policy for IT service: a taxonomy and survey

Dynamic resource allocation is the key objective of the paper motivated due to a large number of user’s service request and increasing network infrastructure complexity. Load balancing and Service Broker Policy are taken as two main key areas for the dynamic provision of resources to the cloud user in order to meet the QoS requirement. While provisioning the resources, the conventional approaches degrade due to QoS performance limits such as time delay, energy, etc. To overcome those problems, we proposed a new approach to provide dynamic provisioning of resources based on load balancing and service brokering. Initially, the Multi-agent Deep Reinforcement Learning-Dynamic Resource Allocation (MADRL-DRA) is used in the Local User Agent (LUA) to predict the environmental activities of user task and allocate the task to the Virtual Machine (VM) based on priority. Next, a Load balancing (LB) is performed in the VM, which increases the throughput and reduces the response time in the resource allocation task. Secondly, the Dynamic Optimal Load-Aware Service Broker (DOLASB) is used in the Global User Agent (GUA) for scheduling the task and provide the services to the users based on the available cloud brokers (CBs). In the global agent, cloud brokers are the mediators between users and providers. The optimization problem in Global Agent (GA) is formulated by the programming of mixed integers, and Bender decomposition algorithm. The result of our proposed method is better as compared with the conventional techniques in terms of Execution Time, Waiting Time, Energy Efficiency, Throughput, Resource Usage, and Makespan.

Dynamic provisioning of resources based on load balancing and service broker policy in cloud computing

The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud–fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

Cloud computing is one of the most promising computing field, which has given the new vision to the computing field. Cloud computing has opened a door as a new model for hosting and delivering services over the Internet. The main aim of cloud computing is to provide the resources as a services to the client. The new concept of Federated Cloud Computing in which multiple datacenters are distributed over different regions. Since the evolution of Cloud Computing: load balancing, energy management, VM migration, brokerage policies, cost modelling and security issues are popular research topics in the field. Deployment of real cloud environment for testing or for commercial use is very costly. Cloud simulators help to model various cloud applications and it is very easy to analyse. In this survey, two cloud simulators: CloudSim and CloudAnalyst, with their overview are presented so it can be easily decided which one is suitable for particular research topic. And also the survey on the service broker policy, its issues and available solutions are presented. Because there is always been the requirement to select appropriate datacenter so that further tasks for processing the request should be carried out with efficiency in least response time. So the issue of selecting appropriate datacenter which is known as service broker policy is kind of important.

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Hetal V. Patel

Papers

Cloud Analyst: An Insight of Service Broker Policy