A Cost Efficient Service Broker Policy for Data Center Allocation in IaaS Cloud Model

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

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 aim of the paper is to analyze different service broker policies that can be used with cloud and are been used in cloud analyst. With the advancement in the cloud computing technologies cloud is becoming more popular these days. With the increase in the number of clients and servers there is also arising needs of service broker. Broker is been used to assign a server to the client on clients request. Here I will first discuss about cloud computing, and then cloud analyst and the different broker policies embedded in it under round robin load balancing policy. Then with the help of simulation results will propose the best broker policy on the basis of performance.

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.

Internet is a great marvel of science. Internet has changed the face of world. It is most essential and revolutionary creation in this world. Cloud computing is associated with internet computing. Cloud computing has undoubtedly benefited both service provider and clients in great extent. There is rapid increase in cloud's customers constantly. Although, the cloud data centers comprised of tremendous power but due to expeditious requests of users there is sudden need of balancing load. However, load balancing emerged as the conspicuous issue in the cloud heterogeneous environment. This study highlights the performance analysis of load balancing policies which are taken in a combination with service broker policy. In Fact, this study addresses that there can be reduction in response time and data center request processing time by using efficient load balancing policies. These all evaluations and results are carried out using cloud analyst simulation tool.

Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers

Increasing demand for Cloud infrastructure and services leads to the challenges for management and maintenance of large data Center. Data center is fully equipped with huge number of resources. Those resources consumes energy in spite of their partial or full utilization. As a result data center consumes lots of energy, which in turn increases the total cost of operation and carbon footprint in environment. These concern leads to “Green Computing”, i.e. to reduce total operational cost, Carbon Footprint in environment and efficient usage of the computing resources. In data center main processing element is virtual machine (VM), which is an instance of computing and storage resources, handles computational processes. Hence, it is important to reduce energy consumed by VM. As the workload distribution is varying in data Center as per the need, the number of VMs configured in the host are uneven, but host consumes maximum energy every time, irrespective of the workload. This leads to wastage of computational resources. This paper is intended to analyze such issues and specifically prove an algorithm which, significantly reduces energy consumption in data center, while ensuring SLA, when VM is in migration from one host to another in the data center.

Cloud federation is introduced to eliminate the resource limitation problem of individual Cloud Service Provider (CSP). In a federation, one CSP can outsource their overhead requests by hiring unused resources from other CSPs in the federation and CSPs cash greater revenue. In this paper, we propose a system that maximizes this revenue while putting some factor constraints in satisfactory standard. Factors include response time of instances that is analogous to Quality of Service (QoS), and profit that maintains a certain reputation. Response time is reduced by considering geographical location of the user and the data center of desired resources. In this system, CSPs choose suitable resources from resource pool of cloud federation that maximizes the profit while preserving Quality of Service (QoS) high and minimizing the rejection rate of user requests. We map factor constraints to MMKP (Multi-choice Multidimensional Knapsack Problem) algorithm and define objective function for revenue maximization. We calculate response time by Cloud Analyst to measure the QoS. Experimental results show that the proposed system can effectively increases QoS level by reducing response time and rejection requests in a cloud federation that eventually increases the revenue of the CSP.

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.

The ever-increasing demand for cloud services results in large electricity costs to cloud providers and causes significant impact on the environment. This has pushed cloud providers to power their data centers with renewable energy sources more than ever. Among the different ways of adopting renewable energy sources, on-site power generation using wind and solar energy has gained considerable attention by large companies and proved its potential to reduce data centers' carbon footprint and energy costs. Efficient utilization of renewable energy sources is challenging due to their intermittency and unpredictability. Cloud providers with multiple Geo-distributed data centers in a region can exploit the temporal variations in on-site power and grid power price by routing the load to a suitable data center in order to reduce cost and increase renewable energy utilization. To achieve this goal, we propose a fuzzy logic-based load balancing algorithm that acts with no knowledge of future. We conduct extensive experiments using a case study based on real world traces obtained from National Renewable Energy Laboratory (NREL) and Energy Information Administration (EIA) in the US, and Google cluster-usage. Compared to other benchmark algorithms, our method is able to significantly reduce the cost without a priori knowledge of the future electricity price, renewable energy availability, and workloads.

Many large organizations collect massive volumes of data each day in a geographically distributed fashion, at data centers around the globe. Despite their geographically diverse origin the data must be processed and analyzed as a whole to extract insight. We call the problem of supporting large-scale geo-distributed analytics Wide-Area Big Data (WABD). To the best of our knowledge, WABD is currently addressed by copying all the data to a central data center where the analytics are run. This approach consumes expensive cross-data center bandwidth and is incompatible with data sovereignty restrictions that are starting to take shape. We instead propose WANalytics, a system that solves the WABD problem by orchestrating distributed query execution and adjusting data replication across data centers in order to minimize bandwidth usage, while respecting sovereignty requirements. WANalytics achieves an up to 360x reduction in data transfer cost when compared to the centralized approach on both real Microsoft production workloads and standard synthetic benchmarks, including TPC-CH and Berkeley Big-Data. In this demonstration, attendees will interact with a live geo-scale multi-data center deployment of WANalytics, allowing them to experience the data transfer reduction our system achieves, and to explore how it dynamically adapts execution strategy in response to changes in the workload and environment.

Cloud computing represents a new way to deploy computing technology to give users the ability to access, work on, share, and store information using the Internet. Developments in Cloud computing gain rapid attention of the researcher to maximize the utilization of computing resources such as storage, CPUs, and network bandwidth as service-by-service providers at less cost. The optimization models aims to optimize both resource centric such as utilization, availability, reliability and user centric like response time, budget spent fairness, and since the broker is responsible of route the user request to the most appropriate datacenter in the cloud system, choosing the right broker policy affect directly the optimization of the resources, in this paper we explore various number of existing broker policy and compare them in different scenarios in order to show the advantages and limitations of each one of them due there impact on the response time and the load distribution over the system.

Cloud data centers play a significant role in providing services needed by users in a quick way. Recent studies show that, traffic patterns in data centers have a special importance to be improved, since they have significant effects on various aspects such as congestion, overall energy consumption and service response time. The traffic patterns inside a cloud data center have two categories: North-South and East-West. The former one is the outside-inside and inside-outside traffic, Whereas, the latter is the traffic among Virtual Machines (VMs) within data centers. Previous studies have shown that the East-West traffic pattern is multiple times larger than the North-South one. This leads data centers to experience congestion and packet loss in the core layer of their topology. Common cause of large traffic patterns is that, VMs of service chains are scattered within the data center in different racks, so that, it causes lots of packet injection into the data center. In this paper, we propose a heuristic algorithm to place VMs of a service chain in a closer proximity of each other to improve the East-West traffic pattern by reducing response time of services and also data centers' overall energy consumption. The simulation results compared to the state-of-the-art method demonstrate about 18% improvement in response time for users' requests and 10% of total energy consumption reduction in the data center.

A large amount of data are stored in geographically distributed data centers interconnected by the Internet. The power consumption for running the servers storing the data and inter-data center network transport for data update among the multiple data copies stored in different data centers impose a significant operational cost on cloud service providers. Few research places data replicas in the data centers by jointly taking both electricity consumption and network transport into account under the user access latency requirement constraints. However, there is an intrinsic tradeoff between the electricity consumption of the data centers and the network transport cost for data update. In this paper, we tackle the problem of data placement in data centers with the aim to minimize the operational cost under user access latency requirements, assuming all the data have K copies. We propose an effective algorithm, Latency-aware and operational Cost minimization Data Placement (LCDP), which partitions the data into multiple data groups according to the data access rates and greedily selects K data centers incurring the minimum cost for each data in each data group. We prove that algorithm LCDP is \(\frac{1}{2} \ln |\mathrm {U}|\)-approximation to the data placement problem, where \(|\mathrm {U}|\) is the number of users. Our simulation results demonstrate that the proposed algorithm can effectively reduce the power consumption cost, the network transport cost, and the operational cost of the data centers.

Data Center reliability and the soaring cost of infrastructure are of major concern to businesses and Telcos. Traditionally data centers have been UPS-powered however on reliability grounds UPS are considered the "poor cousin" to their DC counterpart. This paper presents an analysis of UPS systems to show that a reliability matching DC systems is readily achievable. The paper will also discuss design options that are not required to achieve "DC-grade" reliability and which add substantially to the cost of a data center. The analysis was conducted to determine a set of configurations for use in a national standard by a leading Australian Telco. Configurations graded according to reliability enable a value comparison to be made between different UPS systems. A detailed reliability assessment was made of each configuration using Monte Carlo power system reliability modeling software. Traditionally DC tele power systems have been the cornerstone of reliable telecommunications power. As telecommunications advances rapidly towards an IP-centric network, the role and importance of Data Centers in telecommunications has surged. Data Centers however are traditionally AC powered and accordingly the importance of UPS systems to Telcos has increased dramatically. Emerging telecommunications platforms do not always have a DC powering option therefore highly-dependable UPS systems are needed. The problem is that UPS systems typically provide an order of magnitude lower reliability. High-end DC systems can readily attain an MTBF of 200+ years while UPS systems typically achieve as little as -10-30 years MTBF. With the cost of modern data centers soaring, the paper will show how such costs can be contained while designing UPS systems with "DC-grade" reliability.