ISRASE First International Conference on Recent Advances in Science & Engineering -2014 (ISRASE-2014)
Survey on Models to Investigate Data Center
Performance and QoS in Cloud Computing
Infrastructure
Chandrakala
Department of Computer Science and Engineering
Srinivas School of Engineering, Mukka Mangalore, India
E-mail:[email protected]
Prof. Jyothi Shetty
Department of Computer Science and Engineering
Srinivas School of Engineering, Mukka Mangalore, India
E-mail:[email protected] ABSTRACT
Data center is a large group of networked computer servers typically used by an organizations for the remote processing or distribution of large amounts of data Cloud data center management is a key problem due to numerous and heterogeneous strategies that are need to be applied, ranging from virtual machine placement to federation with other cloud. This paper surveys various works and models those are used to investigating the data center performance and evaluation of quality of service in iaas cloud computing systems. This paper also discusses the comparison between all the works as well as the system performance in terms of utilization, availability, waiting time and responsiveness.
Index Terms—Data centers, cloud computing, cloud oriented performance metric, QOS. I. INTRODUCTION
Data is defined as a discreet element or calculation of content through the interaction between the applications or interaction between computing devices. Data center is a Centralized repository either physical or virtual, and dissemination of data and information organized around a particular body of knowledge or pertaining to a particular business [1]. Data centers are physical and virtual infrastructure used by enterprise to house computer, server and networking. Cloud computing is a term that describes the means of delivering ―any and all information technology from computing power to computing infrastructure, applications, business process and personal collaboration to end users as a service whenever and wherever they need it‖. cloud systems offer services at three different levels: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).
In particular, IaaS clouds provide users with computational resources in the form of virtual machine (VM) instances deployed in the provider data center, while PaaS and SaaS clouds offer services in terms of specific solution stacks and application software suites, respectively. In order to integrate business requirements and application level needs, in terms of Quality of Service (QoS), cloud service provisioning is regulated by Service Level Agreements (SLAs): contracts between clients and providers that express the price for a service, the QoS levels required during the service provisioning, and the penalties associated with the SLA violations. In such a context, performance evaluation plays a key role allowing system managers to evaluate the effects of different resource management strategies on the data center functioning and to predict the corresponding costs/benefits.
Data center image is shown in fig. 1. The internal structure of the data center contains the standards, services, workloads, virtualization and hardware‟ s. Workloads are mainly used to compare performances of two systems and virtualization is an abstraction layer that decouples the physical hardware from operating systems to deliver greater IT resource utilization and flexibility. Hardware‟ s are the physically visible parts these includes networks, servers, storage devices. Network is defined as a collection of collection of devices over the communication path. Whereas server is responsible for providing service to an end user. Storage devices are used for storing purpose. Some of the storage devices are disk subsystems. Virtualization includes kernel, operating systems and hypervisor.
Kernel is the core part of the operating systems it works under two modes First, user mode and second, processor mode. Whereas hypervisor is the foundation for
virtualization
II. QUALITY OF SERVICE DIMENSIONS 1. Availability
Service must be accessible when required for use. Service must be available to an end user wherever and whenever they need
it.
2. Performance
Performance dimensions consists of response time, throughput and timeliness. Response time specifies how long it takes to process a request.
3. Reliability
It is ability to keep operating over time without failure. Service must be able to satisfy the end user expectations. End users must feel the applications in easiest way.
4. Scalability
Scalability is an ability of SaaS Service to function well while service customer changes the size or volume of the consumed service resources.
5. Modifiability
It is the ability to make changes quickly and cost effectively. These changes include modification of three basic application layer data, logic and presentation while taking in to account multitenant nature of SaaS service.
6. Interoperability
It is the ability of communicating entities to share specific information and operate on it according to an agreed upon operational semantics.
7. Testability
Testability is a degree to which a service facilities the establishment of test criteria and the performance of tests to determine whether those criteria have been met.
8. Security
Security includes confidentiality, authorization, authenticity and integrity. Cloud has restrict access to its resources to the users that are eligible to access it. Cloud has „be aware‟ of the identity of the users that are interacting with it. Cloud provides security in terms of shared risks, multi-tenancy , staff security screening , policies, coding, data leakage, distributed data centers, security assessment and physical security.
ISRASE First International Conference on Recent Advances in Science & Engineering -2014 (ISRASE-2014)
on server. Virtual machine is server environment that physically does not exist but it can be created in another server. This virtual machine is called as a „guest‟ and the environment which it runs called the host. Cloud data centers consists of different services in that infrastructure as a service (iaas) is the provisions of hardware or the virtual computers where the organizations have control over the operating systems thereby allowing execution of arbitrary software‟ s. In IA as clouds provide users with computational resources in the form of virtual machine instances deployed in the provider data center [2]. Cloud systems differ from traditional distributed systems. First of all, they are characterized by a very large number of resources that can span different administrative domains.
It may require particular VM multiplexing or live virtual machine migration techniques. III. LITERATURE SURVEY
The following are the different works and models related to investigation of data center performances and quality of service in iaas cloud computing systems which are surveyed on.
A. Simulation
Simulation represents the operation of the system over time. Performance evaluation of computing infrastructure and application services requires the typical performance approach such as simulation by using the cloudsim toolkit. This approach is mainly focuses on the system design issues without getting concerned about low level details related to the cloud based infrastructure and services. Because of these reasons simulation does not allow to conduct comprehensive analysis of the system performance due to the greater number of parameters.
B. On-the-field experiments
On the field measurements are mainly focused on the offered quality of services these are based on a black box approach that makes difficult to correlate obtained data to the internal resource management strategies implemented by the system provider. C. Resiliency analysis of iaas cloud computing
Resiliency is defined as the capacity to rapidly adopt and respond to risks as well as opportunities. This maintains the cont inuous business operations. Resiliency is also defined as the persistence of service delivery that changes. IaaS cloud infrastructure may changes based on the increased workloads, system capacity, or from security attacks and accidents(disasters).This includes the two changes First, changes in client demand indicates job arrival rate. Second, change in system capacity indicates number of available physical machines. Physical machines are grouped into three server pools First, hot (running), Second, warm (turned on, but not ready), Third, cold (turned off).
D. Breaking down response time
Performance analysis of cloud computing centers by breaking down response time here data center is defined as a combination of web servers, database servers, directory servers and others after finishing the service the task leaves the center. It gives relation between the input buffer size and the numbers of servers available .It also gives the performance indicators like mean number of tasks in the system, task block probability and immediate service probability. Performance can be improved by breaking down the response time in the setup, execution, return and clean up time. This works can state that the complex clouds can be formed and analysis is possible by analyzing the results on the cloud. Uses a proper algorithm to accomplish the desired solution.
E. MMPP/G/m/m+r queuing system model
An M/G/m+r queuing system is the extension of the M/G/m queuing system. It adds r as the finite buffer size in the system. Hence the Capacity of the system is m+r. approximate analytical model based on an approximate Markov chain model for performance evaluation of a cloud computing center. Due to the nature of the cloud environment, it is based on queuing theory, MMPP task arrivals, a general service time for requests as well as large number of physical servers and a finite capacity. This makes the model more flexible in terms of scalability and diversity of service time. This model is used to evaluate the performance analysis of cloud server farms and it provides solution to obtain accurate estimation of the complete probability distribution of the request response time and other important performance indicators such as: the Mean number of Tasks in the System, the distribution of Waiting Time, the Probability of Immediate Service, the Blocking Probability and Buffer Size.
F. Server workload analysis
Server consolidation has emerged as a promising technique to reduce the energy cost of data center. It is used to analyze the enterprise workload. This analysis found significant potential for power savings if consolidation is performed using off-peak values for application demand. This works includes investigate a large number of characteristic relevant for medium (semi-static) to long-term (static) consolidation in order to save power. Server will provide the service to clients. Workloads are used to compare the performance of two systems.
G. Modeling with generalized stochastic petri nets
Stochastic means being or having a random variable and stochastic process is defined as a collection of random variables used to represent the random variables or to evaluate the system over time. This is based on the stochastic reward nets. Stochastic petri nets are the Graphical tool for the formal description of systems. Characterized by various concurrency, synchronization, mutual exclusion, and conflict, which are typical features of distributed environments.
H. Objective method
This method is based on open-source code for infrastructure clouds, and by the online bin-packing literature. Used to compare the virtual machine placement algorithms, in cloud computing, there are many strategies used for virtual machine placement. Objectives for virtual machine placement are to reduce the number of physical machines required, virtual machine allocation time and to reduce the resource and power wastage. Virtual placement algorithms like static server allocation problem, static server allocation problem with variable workload, Dynamic server allocation problem, Multi-objective
Ant Colony Optimization, Novel vector based approach for static virtual machine placement, Novel vector based approach for dynamic virtual machine placement and virtual machine scheduler algorithm.
ISRASE First International Conference on Recent Advances in Science & Engineering -2014 (ISRASE-2014)
IV. COMPARISON
Table shows the different works and models to evaluate the data center performance .
Works Focuses Results
Simulation QoS and system performance
Cloud computing infrastructure and application services uses the cloudsim tool kit.
On-the-field experiments
QoS based on black box approach
Makes some difficult to correlate the obtained data to the internal resource management strategies implemented by the system provider MMPP/G/m/m+r Queuing Based on Markov chain model Results system performance in terms of Task arrivals, service time for requests, waiting time, response time, buffer size.
Resiliency of IaaS cloud computing
Iaas cloud computing service
Identifies two changes First, change in client demand and Second. Change in system capacity Breaking down response time Based on cloud computing centers Performance in terms of Availability, reliability, response time, request time, Throughput.
Server Workload Based on Server
Used to power minimization and to Analysis consolidation reduce the energy costs of a data center. Modeling with
generalized stochastic petri nets
Graphical tool for the formal description of systems Characterized by various concurrency, synchronization, mutual exclusion. And conflict, which is typical, features of distributed
environments.
V. PROPOSE SOLUTIOIN
Different works and models which are done in earlier days have some limitations those are not much efficient to investigate the data center performance and quality of service in IaaS cloud computing systems because those works are not focused about the low level details of the cloud computing infrastructures. To overcome the drawbacks of models which are used in earlier days an analytical model is required. Propose model ―Stochastic model‖ is scalable and efficient for Evaluation of data center performance. Stochastic means being or having a random variable and stochastic process is a Collection of random variables used to evaluate the random variables or system over change. This model helps to predict and quantify the cost benefits of strategies portfolio and the corresponding quality of services experienced by users.
VI. CONCLUSION
Data center management is a key problem due to numerous and heterogeneous strategies that can be applied, ranging from virtual machine placement to federation with other clouds. Performance evaluation of data center is required to predict and quantifying the cost benefits of a strategy portfolio and the corresponding Quality of Service experienced by the users. Some works with different models of investigating data center performance are discussed in this paper, which improves the overall performances of servers in a data center.
REFERENCES
[1] Vladimir Stantchev, (2009) ―Performance Evaluation of Cloud Computing Offerings‖; Third International Con ference on Advanced Engineering Computing and Applications in Sciences IEEE.
[2] Kaiqi Xiong.(2009) ―Service Performance and Analysis in Cloud Computing‖; IEEE.
[3] Miguel G. Xavier & Marcelo V. Neves & Fabio D. Rossi, (2012), ―Performance Evaluation of Container-based Virtualization for High Performance Computing Environments‖, IEEE PDP.
[4] Marshall KT, Wolff RW. Customer average and time average queue lengths and waiting times. J Applied Probability.
[5] Khazaei H, Misic J, Misic VB. Performance analysis of cloud computing centers using M/G/m/m+r queuing systems. IEEE Transactions on parallel and distributed systems2012.
[6] Yang B, Tan F, Dai Y, Guo S. Performance evaluation of cloud service considering fault recovery. First Int‟ l Conference o n Cloud Computing; 2009.
[7] C. Hyser, B. McKee, R. Gardner and B. Watson, ―Autonomic Virtual Machine Placement in the Data Center‖, HP Laboratories, HPL-2007- 189, 11 pp.
[8] N. Bobroff, A. Kochut and K. Beaty, ―Dynamic Placement of Virtual Machines for Managing SLA Violations‖, Proceedings of the 10th
IFIP/IEEE Symposium on Integrated Network Management, 2007,119-128.
[9] D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper. Workload analysis and demand prediction of enterprise data center appli cations. In
IISWC, 2007.
[10] K. Xiong and H. Perros, ―Service Performance and Analysis in Cloud Computing‖, Proc. IEEE World Conf. Services, pp. 693 -700, 2009. [11] Bhadani, A., & Chaudhary, S., Performance evaluation of web servers using central load balancing policy over virtual machines on cloud. Proceedings of the Third Annual ACM Bangalore Conference on - COMPUTE ‟ 10, 1–4. doi:10.1145/1754288.1754304, (2010).
[12] Sharma, S., Singh, S., & Sharma, M. Performance Analysis of Load Balancing Algorithms, World Academy of Science, Engineering and Technology 269–272. (2008).
