Predictive Data Center Selection Scheme for Response Time Optimization in Cloud Computing

Cloud computing has become a practical solution for processing big data. Cloud service providers have heterogeneous resources and offer a wide range of services with various processing capabilities. Typically, cloud users set preferences when working on a cloud platform. Some users tend to prefer the cheapest services for the given tasks, whereas other users prefer solutions that ensure the shortest response time or seek solutions that produce services ensuring an acceptable response time at a reasonable cost. The main responsibility of the cloud service broker is identifying the best data centre to be used for processing user requests. Therefore, to maintain a high level of quality of service, it is necessity to develop a service broker policy that is capable of selecting the best data centre, taking into consideration user preferences (e.g. cost, response time). This paper proposes an efficient and cost-effective plan for a service broker policy in a cloud environment based on the concept of VIKOR. The proposed solution relies on a multi-criteria decision-making technique aimed at generating an optimized solution that incorporates user preferences. The simulation results show that the proposed policy outperforms most recent policies designed for the cloud environment in many aspects, including processing time, response time, and processing cost. KEYWORDS Cloud computing, data centre selection, service broker, VIKOR, user priorities

The development of Cloud computing applications has initiated a substantial influence on the data center expansion around the world. This expansion has led to an increase in energy consumption within the different data centers. Aware of the environmental and financial impact of the energy efficiency, several research studies have tackled the problem of the energy efficiency at the level of the hardware as well as scheduling algorithms. However, the majority of the proposed solutions such as Dynamic Voltage and Frequency Scaling (DVFS) have demonstrated a negative effect on response time where most Cloud applications required a high responsiveness. This paper suggests an energy efficiency solution for Cloud computing by applying a fast and smart algorithm. Firstly, the paper provides an evaluation of the energy efficiency within the Cloud environment through the Taguchi experience plan. Secondly, the article introduces a bee colony scheduling algorithm which aims to optimize the energy efficiency while guaranteeing an optimum response time. The validation results acquired from the GreenCloud simulator emphasize the effectiveness of the suggested scheduling analysis methodology.

Cloud computing has become biggest buzz in the computer era these days. It runs entire operating systems on the cloud and do everything on cloud to store data off-site. Cloud computing is primarily based on grid computing, but it’s a new computational model. Cloud computing has emerged into a new opportunity to further enhance way of hosting data centre and provide services. The primary substance of cloud computing is to deal the computing power, storage, different sort of stages and services which assigned to the external users on demand through the internet. Task scheduling in cloud computing is vital role optimisation and effective dynamic resource allocation for load balancing. In cloud, the issue focused is under utilisation and over utilisation of the resources to distribute workload of multiple network links for example, when cloud clients try to access and send request to the same cloud server while the other cloud server remain idle at that moment, leads to the unbalanced of workload on cloud data centers. Thus, load balancing is to assign tasks to the individual cloud data centers of the shared system so that no single cloud data centers is overloaded or under loaded. A Hybrid approach of Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm is combined in order to get effective response time. The proposed hybrid algorithm has been experimented by using CloudSim simulator. The result shows that the hybrid load balancing algorithm improves the cloud system performance by reducing the response time compared to the Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm. Â

In the past few years, cloud computing has emerged as a vigorous technology in the era of internet. Its economic model fecilitate the client to use software, infrastructure, applications as a service by just paying according to the usage without possessing them. The development of cloud computing needs to balance the load among different replicated services offered by cloud environment. In terms of responsiveness and availability, load balancing is one major aspect to deal with. Load balancing ensures that the load is equally spread over the cloud servers. Efficient load balancing algorithm leads to decrease in maintenance costs and increase in resource availability. There are various factors that affect the efficiency of load balancing algorithm, two of such factors are addressed in the proposed algorithm. The download of large data files is handled by dual direction technique. This technique make use of replicated servers and download the partitions of a file simultaneously from multiple replica servers. Another factor that is taken into consideration is handling of high priority tasks. There are various parameters to define the effectiveness of a load balancing algorithm such as response time, data center processing time etc. In this present work a load balancing algorithm, based on honey bee optimization algorithm is proposed which has shown better response time. Experimental studies on standard data sets showed that the proposed request balanced honey bee algorithm compared to the original honey bee algorithm, has performed 5.2% to 9.7% better with respect to the response time and 13.8% to 68.2% better with respect to the processing time, which is a significant improvement.

When running mission-critical web-facing applications (e.g., electronic commerce) in cloud environments, predictable response time, e.g., specified as service level agreements (SLA), is a major performance reliability requirement. Through extensive measurements of n-tier application benchmarks in a cloud environment, we study three factors that significantly impact the application response time predictability: bursty workloads (typical of web-facing applications), soft resource management strategies (e.g., global thread pool or local thread pool), and bursts in system software consumption of hardware resources (e.g., Java Virtual Machine garbage collection). Using a set of profit-based performance criteria derived from typical SLAs, we show that response time reliability is brittle, with large response time variations (order of several seconds) depending on each one of those factors. For example, for the same workload and hardware platform, modest increases in workload burstiness may result in profit drops of more than 50%. Our results show that profitbased performance criteria may contribute significantly to the successful delimitation of performance unreliability boundaries and thus support effective management of clouds.

A Variable Service Broker Routing Policy for data center selection in cloud analyst

With the introduction of cloud computing, many organizations have reduced the costs and burdens of maintaining an on-site data center, migrating their data and applications to the cloud. The accessibility to companies’ data and applications via the Internet raises concerns about response time, service interruptions and security. To help address these concerns, intermediate data centers, also referred to as fog computing, are developed and implemented, between the end user (EU) and the cloud data center (DC). The purpose of this fog layer is to bring the processing operation closer to the edge of the network and to reduce response time for the EU. To address the concerns of latency and response time, the current paper proposes the implementation of a multilevel layer architecture, often called cloudlets, in these intermediate data centers.

Cloud computing and big data: Technologies and applications

This thesis presents a comprehensive study of tailored semiconductor devices that bridge conventional photodetection with neuromorphic photomemory applications through strategic engineering of response time characteristics. The fundamental insight underlying this work is that response time represents the critical parameter determining whether an optoelectronic device functions as a conventional photodetector or enables synaptic functionality for brain-inspired computing systems. The research demonstrates that the same physical mechanisms, such as photon absorption, carrier generation, charge transport, and recombination, can be optimized for dramatically different temporal characteristics, enabling devices that adapt their functionality based on operational requirements. Fast response times in the microsecond to millisecond range enable high-speed photodetection for telecommunications and sensing applications, while prolonged response times through persistent photoconductivity (PPC) spanning seconds to days enable the memory and learning functions essential for artificial synapses. The research contributions are organized into four interconnected studies that collectively demonstrate this dual functionality principle. Chapters 2 and 3 focus on optimizing devices for fast photodetection applications. Chapter 2 presents flexible germanium/titanium nitride (Ge/TiN) photodetectors that achieve enhanced near infrared responsivity through plasmonic enhancement and strain engineering, demonstrating how structural modifications can improve conventional photodetection performance while maintaining fast response characteristics. Chapter 3 investigates self powered silicon metal-semiconductor-metal (MSM) photodetectors utilizing asymmetric electrode configurations, achieving efficient photodetection without external bias through engineered Schottky barrier heights. Chapters 4 and 5 shift focus on exploiting extended response times for neuromorphic applications. Chapter 4 demonstrates silicon/indium gallium zinc oxide (Si/IGZO) photomemory devices with persistent photoconductivity exceeding nine days, achieved through substrate conductivity engineering that optimizes band alignment to trap photocarriers and prevent recombination. Chapter 5 presents transparent quartz/IGZO tracking devices that combine optical transparency with neuromorphic functionality, enabling covert trajectory monitoring through synaptic plasticity behaviors. The connection between these studies lies in the systematic exploration of how response time engineering enables the same semiconductor materials to serve two different purposes. The Ge/TiN and Si photodetectors in Chapters 2-3 are optimized for response times in the millisecond range, suitable for high-speed optical communication and imaging applications. In contrast, the IGZO-based devices in Chapters 4-5 exploit response times spanning minutes to days, enabling synaptic behaviors including PPF, learning-forgetting-relearning cycles, and long-term memory retention. All four device systems are based on MSM-type architectures that share the same fundamental band-to band excitation and carrier transport principles, and flexibility, self-powered operation, photomemory, and trajectory tracking are employed as representative manifestations of fast and slow response-time engineering within this common framework. The research provides new insights into the mechanisms controlling persistent photoconductivity, demonstrates practical strategies for substrate and interface engineering, and establishes design principles for next-generation neuromorphic optoelectronic systems. The significance of this research extends beyond individual device performance improvements to establish a fundamental framework for engineering semiconductor devices with tunable temporal characteristics. By demonstrating that the same materials and fabrication processes can yield devices optimized for either fast photodetection or extended photomemory functions, this work opens new possibilities for semiconductor devices with tunable temporal characteristics and design-level flexibility in their functional role.

Now a day, every field is consuming services of Cloud Computing (CC) because everyone is not able to deploy his own data centers. Due to the large number of requests on central place concept of Fog for the distributed processing of requests. Fog is placed between user and cloud to reduce the Response Time (RT). Smart Grid (SG) is also integrated cloud architecture for efficient management of energy. In this paper Fog Based architecture for SG is presented. Due to this there is no chance of a peak time generation at any time. A three-tier architecture is used for request processing. In SG environment there is need of low latency and high RT for providing uninterpretable services to the end user. For this purpose, Fog layer is used between cloud and SM (Smart Meter) and it acts as intermediate layer. A model is presented in this paper for efficient distribution of load among all available Virtual Machines (VMs). A load balancing technique is implemented for load distribution. Two regions are taken into account for experimental results and each region is divided into six group/cluster of building. Three Fogs are placed in each region for better RT and it produce optimal results as shown in simulations and discussion section.

With the rapid development of national economy, our demand for power resources has also increased rapidly. The traditional power grid can not be achieved operational status of equipment for effective monitoring and control, we will not be able to ensure the quality and quality of electricity service. Cloudbased build smart grid information platform enables gathering the parameter of grid operation state, greatly increasing the economic efficiency of power enterprises. Firstly, cloud computing and smart grid briefly described, and then pointed out that cloud computing applications in the smart grid information platform, and specialized of cloud-based smart grid information platform, in order for the power grid enterprises to enhance their level of service and provide effective reference. KEYWORD: cloud computing; smart power grids; information platform; Application 4th International Conference on Mechanical Materials and Manufacturing Engineering (MMME 2016) © 2016. The authors Published by Atlantis Press 202 3 THE MAIN PROBLEM IS STILL THE SMART GRID INFORMATION PLATFORM FACING The transition to a cloud computing data center. Our data center because of the different regions and the degree of information presents different characteristics, some data centers already have a basic cloud computing capabilities, but most data centers still remain in the initial level, to ensure the normal operation of the existing data center power under the premise, gradual transition to cloud computing data center in order to meet future requirements of the development of smart grid. Restricting data transmission problem. State Grid Corporation between provincial branches and there are a lot of data needs to be transmitted, will bring massive data transmission efficiency decreases, taking into account the load balancing device between the cloud node, thus building a wide area network channels to resolve data transmission problem. Security of data transmission. Data Center for Cloud Computing power companies to build internal private cloud, capable of self-maintenance and management, system wide area network data transmission services, according to the third security zone defined security partition, and install proprietary equipment production area and external public network isolation . 4 CLOUD COMPUTING APPLICATIONS IN THE SMART GRID INFORMATION POLICY PLATFORM Integrate and optimize the management of heterogeneous resources. Among the smart grid systems, data resources, information systems between various platforms and mutually dispersed, severe heterogeneity, thus leading to the integration of the quality of information sharing to increase difficulty, unable to effectively improve network management efficiency for the power distribution system electrical work quality management and marketing operations system maintenance upgrade will not be able to effectively meet demand (Jie Yang, 2014). Cloud computing technology enables the power system to effectively integrate the various business data and computing resources on the basis of information for business collaboration platform built on effective, able to satisfy the huge demand for smart grid information resource sharing. Cloud computing network virtualization and application virtualization technology, the server virtualization technology and other resources to achieve each service abstraction, and then realize the difference between the system and the software effectively eliminated by the appropriate method to achieve the effective integration of information resources, and then to optimize the management, greatly improving the efficiency of heterogeneous resources management applications. Distributed storage and management of large volume of data. After application of smart grid information platform construction, customer service requests varied, huge data volume information brought it to the storage and management of data provides a great challenge. Cloud computing technology in general face amount of data each smart grid system and unit collected using forms distributed storage of data to achieve the effective storage, greatly to ensure the security and reliability of data storage. GFS effective use of technology with each node, the master server is responsible for the effective management of the whole file system, the file system and complete the work of the metadata stored in the data block is the main server for data storage block sliced, and by saving three or more redundant backup storage. At the same time, client access and complete access to the main block server information. Fast Power System Parallel Computation and Treatment. Smart grids and information platform for the large volume of data to provide stable operation of the power grid data base, but more critical is the analysis and design of each system operating data, planning and decision-making, and even failures and transient stability, state estimation and intelligent a variety of computing and content analysis and decision-making process, its huge computing needs, general computing system can not reach its goal (Wang, Mengxue, 2015). Cloud computing technology through parallel computing technology has greatly enhanced parallel processing capability of the power system, which provides parallel programming model makes more simple and efficient parallel algorithms. MapReduce as Google made an important parallel programming model, and its role in large data sets in parallel computing plays a very prominent. Parallel processing job results by the client to achieve effective user submitted, via decomposition automated master role, the corresponding task scheduling to working nodes and nodes work completed by the specific calculations and perform the appropriate task. 5 SMART GRID INFORMATION PLATFORM CLOUD COMPUTING ANALYSIS Cloud Computing Smart Grid Information Platform Architecture. Smart grid technology information platform architecture cloud computing include the following several levels and aspects (Yan Cui, 2015): First, that the platform infrastructure layer is the integration of the various hardware resources and related management capabilities of virtualized using virtual technology to achieve the corresponding hardware devices such as computers and network storage devices abstraction, and then complete the automation and optimization of resource data and load management; secondly, the main platform for the integration of the various layers of software

Cloud Computing can be described as any where any time storage and it is able to provide on demand services like server, storage, software etc. to the users over internet. Cloud consists of multiple virtual machines which includes several facilities like computation and storage. The foremost aim of cloud computing is to provide systematic and well regulated access to distant and geographical resources. The efficient discharge of task scheduling computing is one of the primary factors. This paper deals with the subject of dynamic load balancing for task scheduling in cloud environment. Satisfactory results can be obtained by implementing the proposed algorithm by considering distribution of tasks on the basis of types (nature), processing efficiency and load efficiency factor. In this paper, algorithm has been implemented by taking into account cloud VM groups, configuration and nature of tasks which determines that resources have been utilized efficiently. The result has been compared and shows better results in terms of average turnaround time, average response time, average waiting time and cost efficiency ratio than existing load balancing algorithms [1, 2].

Goal: Load balancing policies often map workloads on virtual machines, and are being sought to achieve their goals by creating an almost equal level of workload on any virtual machine. In this research, a hybrid load balancing algorithm is proposed with the aim of reducing response time and processing time.
 Design / Methodology / Approach: The proposed algorithm performs load balancing using a table including the status indicators of virtual machines and the task list allocated to each virtual machine. The evaluation results of response time and processing time in data centers from four algorithms, ESCE, Throttled, Round Robin and the proposed algorithm is done.
 Results: The overall response time and data processing time in the proposed algorithm data center are shorter than other algorithms and improve the response time and data processing time in the data center. The results of the overall response time for all algorithms show that the response time of the proposed algorithm is 12.28%, compared to the Round Robin algorithm, 9.1% compared to the Throttled algorithm, and 4.86% of the ESCE algorithm.
 Limitations of the investigation: Due to time and technical limitations, load balancing has not been achieved with more goals, such as lowering costs and increasing productivity.
 Practical implications: The implementation of a hybrid load factor policy can improve the response time and processing time. The use of load balancing will cause the traffic load between virtual machines to be properly distributed and prevent bottlenecks. This will be effective in increasing customer responsiveness. And finally, improving response time increases the satisfaction of cloud users and increases the productivity of computing resources.
 Originality/Value: This research can be effective in optimizing the existing algorithms and will take a step towards further research in this regard.

Cloud computing is a revolutionary cloud or Internet-based computing that provides services to customers with their on-demand requirements. The service providers who use cloud computing can provide their services to the users simultaneously. Also, cloud computing helps the users to use it like pay-as-you-go service over the internet providing them with uncertainty, dynamism, and elasticity-based services. It is being widely used by many clients in the fields of education, government services, telecommunication, and financial services. Due to the rapid growth of demand for cloud computing services and the heterogeneous nature of the resources for cloud computing, resource scheduling has become a major issue. In recent times, it can be observed that the increase of cloud services is to increase the workload in the cloud environment. The algorithms used for scheduling also need to optimize the main performance indicating parameters such as makespan time, response time, cost, reliability, energy consumption, availability, and resource utilization. Many state-of-the-art scheduling algorithms are proposed in the past to fulfill these requirements. These algorithms are based on heuristic, metaheuristic, and hybrid techniques. In this paper, systematic reviews of these scheduling techniques including the proposed scheduling techniques are provided with their classification.

Cloud computing is the emerging domain in academia and IT Industry. It is a business framework for delivering the services and computing power on-demand basis. Cloud users have to pay the service providers based on their usage. For enterprises, cloud computing is the worthy of consideration and they try to build business systems with lower costs, higher profits and quality-of-service. Considering cost optmization, service provider may initially try to use less number of CPU cores and data centers. For that reason, this paper deals with CloudSim simulation tool which has been utilized for evaluating the number of CPU cores and execution time. Minimization of waiting time is also a considerable issue. When a large number of jobs are requested, they have to wait for getting allocated to the servers which in turn may increase the queue length and also waiting time. This paper also deals with queuing model with multi-server and finite capacity to reduce the waiting time and queue length.