Dr J Jegadeesan, Karuppaiah V. Ph.D, HOD-CSE Department, SRM University, Chennai M.Tech Student, SRM University, Chennai
|
|
- Annabel Moore
- 7 years ago
- Views:
Transcription
1 QUALITY OF SERVICE MONITORING AND PREDICTION IN CLOUD COMPUTING ENVIRONMENTS Dr J Jegadeesan, Karuppaiah V Ph.D, HOD-CSE Department, SRM University, Chennai M.Tech Student, SRM University, Chennai Abstract Cloud computing provides on demand access to affordable hardware and software platforms where resources are provided in a self managing manner based on predefined customers requirements. A Service Level Agreement (SLA) which is established between a cloud provider and a customer specifies these requirements. As cloud provider platforms have diverse characteristics requiring extensive monitoring benchmarking mechanisms to ensure run-time Quality of Service (QoS). A Current challenge in Cloud environments is to detect any possible SLA violation and to timely react upon it to avoid paying penalties as well as managing resources more efficiently. In this paper we analyze and discuss the properties of a monitoring system for Cloud including techniques and algorithms for QoS metrics and prediction of performance. Index Terms QoS, Prediction, Metrics, Cloud Manager, Aggregate, Monitor Agent, Tools service adaptation by replacing the current working services with the corresponding candidate services in response to unexpected QoS changes. For cloud application, the working services are frequently invoked. Thus their QoS values can be collected via monitoring. Monitoring of cloud is a task of paramount importance for both providers and consumers. On the one side, it is a key tool for controlling and managing hardware and software infrastructures; on the other side it provides information and key performance indicators for both platform and application. Cloud monitoring phase structure is depicted below I. INTRODUCTION Cloud computing has gained increasing prevalence in recent years for providing a promising paradigm to host and deliver various online applications over the internet. However, as these applications scale up, for example, spanning across multiple geographically distributed data centers, a significant challenge their application designers is how to engineer their applications with self-adaption capabilities in response to the constantly changing operational environments, whereby the quality of service(qos) can be guaranteed. Many cloud applications have employed service-oriented architecture (SOA) as a mechanism for achieving selfadaptation, where component services are composed in a loosely-coupled way to fulfill complex application logic. For example, Amazon s e-commerce platform is built on SOA by composing hundreds of components services hosted worldwide to deliver functionalities ranging from item recommendation to order fulfillment to fraud detection. The features of SOA such as loose coupling and dynamic binding enable applications to switch component services without going offline, and thus make it particularly amenable to the introduction of service adaptation. On the other hand, with the proliferation of cloud computing, many service providers begin to offer more and more services in the cloud that provide equivalent functionalities through a well-defined interface. Such redundant services can thus be utilized for From cloud providers point of view it is very necessary to fulfill SLA to return their customers. Therefore it is on high priority to monitor QoS parameters so that there is no violation of SLA. If cloud provider provides sufficient resources, QoS assured automatically. Resources should be provided in balanced way as neither over provisioned nor under provisioned. QoS prediction approaches to accurately estimate the QoS values of candidate services without requiring direct invocations, which is exactly the goal of our work. In particular, effective QoS prediction on candidate services needs to fulfill the following requirements 41
2 Online: The changing and evolving cloud environment introduces a high degree of variability and uncertainly on user-perceived service quality. For instance, due to the impact of dynamic network conditions and varying server workload, the QoS values may vary significantly during different time periods. Therefore, in order to identify high-quality candidate services for service adaption, QoS prediction needs to be performed in an online fashion. Accurate: Ensuring the accuracy of QoS prediction is fundamental for service adaptation. Inaccurate predictions may lead to the execution of improper adaptations or missed adaptation opportunities. For example, a working service may be wrongly replaced by a low-quality service. Consequently, we need accurate QoS prediction approaches, as well as proper metrics to evaluate the prediction accuracy. Scalable: In the dynamic cloud environment, new services with different QoS may become available, and existing services may be discontinued by their providers. Likewise, service users and services, QoS prediction approaches need to scale well to new services and users, and perform robustly to make accurate predictions We also focus on CPU cycles as resource and monitoring CPU usage to predict performance degradation. We consider web applications are hosted on cloud to limit our scope. Teletraffic model is used for predicting extreme CPU usage. In this work we analyze QoS monitoring metrics model including monitoring tools and propose novel approach for predicting performance degradation due to insufficient CPU. II. RELATED WORK Usually Cloud environments consist of cloud elements represented by physical machines running one or several VMs, which serve as a platform for running customer's application. These elements consists of the following 3 layers -Physical layer with physical machines - System layer with VMs - Application layer - Manager employs a QoS data collection schema to store QoS statistics collected from monitoring agent 42 - Monitoring Agent resides in the VM running the application and collects and sends QoS values as requested by the manager. Cloud Monitoring: Concepts Cloud monitoring is needed to continuously measure and assess infrastructure or application behaviors in terms of performance, reliability, and power usage, ability to meet SLA, to perform business analytics, for improving the operation of systems and application and for several other activities. In this section we introduce a number of concepts at the base Cloud monitoring. According to the work of the Cloud Security Alliance, a Cloud can be modeled in seven layers: facility, network, hardware, OS, middleware, application and the user. Considering the role, these layers can be controlled by either a Cloud Service Provider or a Cloud Service Consumer. They are detailed in the following: Facility: at this layer we consider the physical infrastructure comprising the data centers that host the computing networking equipment. Network: at this layer we consider the network links and paths both in the Cloud and between the Cloud and the user. Hardware: at this layer we consider the physical components of the computing and networking equipment. Operating System (OS): at this layer we consider the software components forming the operating system of both the host (the OS running on physical machine) and the user (the OS running in the virtual machine). Middleware: at this layer we consider the software layer between the OS and the user application. It is typically present only in the Cloud systems offering SaaS an PaaS service models Application: at this layer we consider the application run by the user of the Cloud system User: at this layer we consider the final user of the Cloud system and the applications that run outside the Cloud (e.g. web browser running on a host at the user s premise) The difficulties of performing monitoring and metering in the Cloud- There are different ways of collecting monitoring data, for instance: 1. Obtain information from the Operating System or the Virtual Machine Unit (VMU) in relation to the CPU usage, the network interfaces and the storage connected to the machine. 2. Simple Network Management Protocol (SNMP) is used mostly on network system, and can be used to monitor the status of the network and detect error events (SNMP traps enable an agent to notify the management station of significant events by way of an unsolicited SNMP message). Besides SNMP there are other protocols provided by network vendors to monitor and gather usage statistics, for instance NetFlow is a network protocol developed by Cisco Systems to run
3 on Cisco IOS-enabled equipment for collecting IP traffic information 3. Extract the information from the application log files, searching for specific patterns that provide information about interesting events in the application, for instance: error in the interaction with the client or number of client interactions performed. 4. Specific ad-hoc monitoring mechanism. Applications that do not provide standard mechanisms (like SNMP) to deliver monitoring data can implement a private API for monitoring. This mechanism could be a set proprietary function or more generically a SOAP interface with an XML specific message format. The following steps are required for monitoring and link the raw monitoring data to the behavior of the application 1. Collect and correlate to reveal service performance: One of the problems to perform correctly this correlation of information from the different source is time synchronization. On a virtualized environment where the physical resources are shared by different applications and the infrastructure has the ability of reallocate the resources depending on the needs in each specific moment, one portion of a network or one server is not longer associated to a single service or a application. The monitoring system will be unable to associate the corresponding data of the different systems to the application unless a precise time synchronization mechanism is in place 2. Interpret the business impact: It is not trivial to deduce from the monitoring data collected from the different systems the performance of the application, and infer if the application is performing correctly. From one side, a complete outage in one segment of the network affecting some servers inside the application architecture could have no effect in the performance perceived by the user, for instance during low usage period where the rest of the service platform is able to provide the requested performance to the user. On the other side, a completely functional infrastructure could be sufficient to provide the QoS requested by the user.\ 3. Resolve quickly; prevent when possible: The ideal situation is the provision of the user demand so that the provider can anticipate the requirements and adapt the application dynamically to support this demand. This is the promise of the cloud, the easy adaptation of the application to the changing environment that affects it. To support this, the application must be designed in advance to allow the dynamic deployment of application components, and the reconfiguration of the application to support these changes in the internal architecture of the application. System Model- Metrics Individual metric classes (delay, performance, security) are described below 43 Performance Delay metrics: The performance QoS metrics are additive in the numerical sense. This is also dependent on the infrastructure components used to provide the service. Hence we must include the component-induced performance degradation. The delay metric can be represented as D sos = p 1.D p + p 2.D s + p 3.D i Where each p is a parameter dependent on the infra system; D - is the delay experienced in each layer Throughput: The throughput at every level is a function of the throughput at the lower level T i = a * Transaction throughput T s = b * T i T p = c * T s Here a, b and c are the number of transactions at the lower domain needed to complete a transaction in the higher domain. Additionally, at each level, throughput is additive in nature. For example, at the software layer, if there are p operations independent of each other (which may or may not require services from the infrastructure layer), then the throughput of the software layer is the sum of the number of operations completed per unit time. Security: Security can be thought of as a functional requirement of the system. It comprises authentication an authorization using certificates and accreditation. The authentication QoS metric is the logical conjunction. The user access to the system ceases at the level authentication fails. Hence authentication is a logical AND of the authentication. Security can be view as a top down metric, i.e., A sos = A p ^ A s ^ A i Authorization, however, is a bottom-up metric and is applicable at each level. User access to the service at any layer is a subject to authorization. The authorization is such that the least privilege is granted sufficient to accomplish the operation. Authorization at the IaaS level can be represented as Auth j = min {n P i } i set of actions P i is the permission to perform action i at the IaaS/PaaS/SaaS level Monitoring in Cloud Platform - Amazon Amazon CloudWatch is a web service that provides monitoring Amazon cloud resources, starting with Amazon EC2. It provides customers with visibility into resource utilization, operational performance and overall demand patterns - including metrics such as CPU utilization, disk reads, writes and network traffic. Amazon CloudWatch provides through APIs query and SOAP API to collect programmatically monitoring information. The
4 set of metrics that can be collected from EC2 shown below service are CloudWatch gathers several kinds of monitoring information and it stores them for two weeks. On these data, users can build plots, statistics, indicators, temporal behaviors, thresholds, alarms, etc. CloudWatch mainly focuses on Timeliness, Extensibility and Elasticity. III. PROPSED SYSTEM In this paper, we take a service perspective and initiate a quality model named CLOUDQUAL for cloud services. A quality model for cloud services, called CLOUDQUAL, which specifies six quality dimensions and five quality metrics i.e., usability, availability, reliability, responsiveness, security and elasticity. It is a model with quality dimensions and metrics that targets general cloud services. A case study involving three real-world storage clouds: Our experimental results show that CLOUDQUAL can evaluate their quality, which demonstrates its effectiveness. A method to formally validate a quality model using standard criteria, namely correlation, consistency, and discriminative power: We show that CLOUDQUAL can differentiate service quality, which demonstrates its soundness. Proposed system will address the following problem statements From Cloud provider point of view, it is necessary to fulfill SLA to retain their customers. Frequent violation of SLA may cause loss in terms of penalties and also reputation of provider Therefore it is beneficial to predict performance degradation which will lead to QoS degradation. So that there is no violation of SLA is ensured Contains Aggregate Manager, Usage Monitor and Prediction Manager using Generalized Pareto Distribution model (GPD) to predict performance degradation System Modules Cloud Manager: Responsible for interaction with customers and understanding their application needs. It collects all their requirements and performs discovery and ranking of suitable services using other components. 44 Monitoring: Discovers Cloud services that can satisfy user s essential QoS requirements. Then it monitors the performance of the Cloud services, for example for IaaS it monitors the speed of VMs, memory, scaling latency, storage performance, network latency and available bandwidth. It also keeps track of how SLA requirements of previous customers are being satisfied by the Cloud provider. Stability: Defines the variability in the performance of a service. For storage, it is the variances in the average read and write time Reliability: Reflects how a service operates without failure during a given time and condition. Therefore, it is defined based on the mean time to failure promised by the Cloud provider and previous failures experienced by the users Throughput: Throughput and efficiency are important measures to evaluate the performance of infrastructure services provided by Clouds. Throughput is the number of tasks completed by the Cloud service per unit of time. It is slightly different from the Service Response Time metric, which measures how fast the service is provided. Throughput depends on several factors that can affect execution of a task. These works again focused on comparing the low level performance of Cloud services such as CPU and network throughput. In our work we use performance data to measure various QoS attributes and evaluate the relative ranking of Cloud services. Prediction: Used to evaluate and model short term extreme values of CPU usage. We observed that CPU usage values are proportional to bandwidth used in web application. Since bandwidth used increases as there is increase in computation. So from analysis, we can easily apply GPD model used for performance degradation using CPU usage values. Generalized Pareto Distribution GPD is a tool which helps to evaluate and model extreme values over short period like hourly or daily extreme events. For that GPD uses technique called threshold excess. In this technique excess values are calculated above some predefined threshold to quantify extreme observations. G(x) = ( + k/(x+ )) (1+x/ ) -k e - x and x are shape parameters for x>0. ;, k The proposed system uses GPD model as Tele traffic model. GPD is used to evaluate and model short term extreme values. Model will predict extreme CPU usage using Pareto distribution so that it can predict performance degradation, if extreme value goes above assigned usage limit. Proposed system consists of cloud manager in which we integrate prediction system so that we are able to predict performance degradation. "Prediction Manager" and Aggregate Manager will be addon with the existing Cloud Monitoring system. The details are
5 elaborated below Prediction Model: In this module GPD model builder is implemented which takes excess values from aggregate manager. Aggregate Manager: This manager calculate excess values using CPU usage values given by Monitoring Agent. It also calculates threshold usage above which excess values are calculated. Threshold usage value should not give too many or too less excess value. Prediction Manager: This manager predicts whether there is performance degradation of any VM. If prediction model predicts excess values which are above usage limit i.e max CPU utilization then that VM put into critical pool. Monitoring Agent: It collects and sends QoS values as requested by the manager. It takes CPU usage given by monitoring tool and forwards values to Aggregate Manager. Extreme DB: This database stores excess values calculated by Aggregate Manager. This value can be used in decision making and in predicting usage behavior of virtual machine. Proposed system will address the Cloud service user requirements as Apply QoS for Cloud services to find stability, reliability and throughput. Evaluates efficiency to determine result of best cloud service QoS results consists of Performance and Security metrics in graphical representation and exposes service APIs Governance as a Service Monitoring global events; monitor and share policies and processes; monitor SOA components and data System scenario is described below IV. CONCLUSION In this paper we have provided a detailed analysis of the state of the art of the field of Cloud Monitoring. To contextualize and study Cloud monitoring, we have provided background and definitions for key concepts. We have also derived the main properties that Cloud monitoring systems should have and described one of the main commercial platform and services for Cloud monitoring- CloudWatch. Also proposed a novel system to predict performance degradation. This model will predict excess CPU usage of VMs. In future we will analyze and compare in detailed way of Cloud monitoring platform and services - Commercial and open source tools with the other prediction properties such as data, user growth and network bandwidth. REFERENCES In cloud there are n numbers of virtual machines in total hosted on m numbers of physical machines (PM). On one of the VM, there is cloud manager is running along with our system integrated with our system produces output which is use by Cloud manager to pace request of virtual machines and load balancer. And also generate notification for cloud administrator. So that necessary action can be taken. System architecture and flow of system are depicted below [1] [10] T. Gwo-Hshiung, G. H. Tzeng, and J.-J. Huang, Multiple Attribute Decision Making: Methods and Applications. Boca Raton, FL, USA: CRC Press, Jun [2] L. Zeng, B. Benatallah, A. H. H. Ngu, M. Dumas, J. Kalagnanam, and H. Chang, QoS-aware middleware for web services composition, IEEE Trans. Softw. Eng., vol. 30, no. 5, pp , May [3] T. L. Saaty, Multicriteria Decision Making: The Analytic Hierarchy Process: Planning, Priority Setting Resource Allocation. Pittsburgh, PA, USA: RWS Publications, [4] A. S. Prasad and S. Rao, A mechanism design approach to resource procurement in cloud computing, IEEE Trans. Comput., vol. 63, no. 1, pp , Jan [5] M. F. Mithani and S. Rao, Improving resource allocation in multi-tier cloud systems, in Proc. 6th Annu. IEEE Int. SysCon, Vancouver, BC, Canada, Mar. 2012, pp [6] G. A. Lewis, E. Morris, P. Place, S. Simanta, and D. B. Smith, Requirements engineering for systems of systems, in Proc. 3rd Annu. IEEE Int. SysCon, Mar. 2009, pp [7] S. M. White, Modeling a system of systems to analyze requirements, in Proc. 3rd Annu. IEEE Int. SysCon, Mar. 2009, pp [8] Defense Acquisition Guidebook (DAG), Jan [Online]. Available: [9] Systems Engineering Guide for Systems of Systems, Version 1.0, ser. OUSD (A & T) SSE, Aug [10] P. Hershey and D. Runyon, SOA monitoring for enterprise computing systems, in Proc. 11th Int. IEEE EDOC Conf., Oct. 2007, pp
6 [11] P. J. Denning and J. P. Buzen, The operational analysis of queueing network models, ACM Comput. Surv., vol. 10, no. 3, pp , Sep [12] DoD instruction : DoD Information Technology Security Certification and Accreditation Process, Dec [Online]. Available: [13] V. Emeakaroha, I. Brandic, M. Maurer, and S. Dustdar, Low level metrics to high level SLAs LoM2HiS framework: Bridging the gap between monitored metrics and SLA parameters in cloud environments, in Proc Int. Conf. HPCS, 2010, pp [14] DoD instruction : Security of DoD Installations and Resources, [Online]. Available: p.pdf [15] IETF, A one-way delay metric for IPPM, Fremont, CA, USA, RFC2679.[Online]. Available: [16] J.-Y. Girard, Linear logic, Theoretical Comput. Sci., vol. 50, no. 1, pp , [17] P. Hershey and C. Silio, Procedure for detection of and response to distributed denial of service cyber attacks on complex enterprise systems, in Proc. 6th Annu. IEEE Int. SysCon, Mar. 2012, pp [18] Network Infrastructure Technology Overview. Version 8, Release 5, Apr. 27, 2012, (ArchiveEntry=U_Network_V8R5_Overview.pdf). [Online]. Available: [19] Enclave Security Technical Implementation Guide, Jan. 3, 2011, (ArchiveEntry=U_Enclave_V4R3_STIG.pdf). [Online]. Available: [20] A. Iyengar, M. Squillante, and L. Zhang, Analysis and characterization of large-scale web server access patterns and performance, World Wide Web, vol. 2, no. 1/2, pp , Jun
Comparison of Request Admission Based Performance Isolation Approaches in Multi-tenant SaaS Applications
Comparison of Request Admission Based Performance Isolation Approaches in Multi-tenant SaaS Applications Rouven Kreb 1 and Manuel Loesch 2 1 SAP AG, Walldorf, Germany 2 FZI Research Center for Information
More informationEnsuring Cost-Optimal SLA Conformance for Composite Service Providers
Ensuring Cost-Optimal SLA Conformance for Composite Service Providers Philipp Leitner Supervised by: Schahram Dustdar Distributed Systems Group Vienna University of Technology Argentinierstrasse 8/184-1
More informationCloud Computing Architectures and Design Issues
Cloud Computing Architectures and Design Issues Ozalp Babaoglu, Stefano Ferretti, Moreno Marzolla, Fabio Panzieri {babaoglu, sferrett, marzolla, panzieri}@cs.unibo.it Outline What is Cloud Computing? A
More informationSupply Chain Platform as a Service: a Cloud Perspective on Business Collaboration
Supply Chain Platform as a Service: a Cloud Perspective on Business Collaboration Guopeng Zhao 1, 2 and Zhiqi Shen 1 1 Nanyang Technological University, Singapore 639798 2 HP Labs Singapore, Singapore
More information1.1.1 Introduction to Cloud Computing
1 CHAPTER 1 INTRODUCTION 1.1 CLOUD COMPUTING 1.1.1 Introduction to Cloud Computing Computing as a service has seen a phenomenal growth in recent years. The primary motivation for this growth has been the
More informationDigital Advisory Services Professional Service Description Network Assessment
Digital Advisory Services Professional Service Description Network Assessment 1. Description of Services. 1.1. Network Assessment. Verizon will perform Network Assessment services for the Customer Network,
More informationIaaS Cloud Architectures: Virtualized Data Centers to Federated Cloud Infrastructures
IaaS Cloud Architectures: Virtualized Data Centers to Federated Cloud Infrastructures Dr. Sanjay P. Ahuja, Ph.D. 2010-14 FIS Distinguished Professor of Computer Science School of Computing, UNF Introduction
More informationA Study on Service Oriented Network Virtualization convergence of Cloud Computing
A Study on Service Oriented Network Virtualization convergence of Cloud Computing 1 Kajjam Vinay Kumar, 2 SANTHOSH BODDUPALLI 1 Scholar(M.Tech),Department of Computer Science Engineering, Brilliant Institute
More informationDynamic Resource management with VM layer and Resource prediction algorithms in Cloud Architecture
Dynamic Resource management with VM layer and Resource prediction algorithms in Cloud Architecture 1 Shaik Fayaz, 2 Dr.V.N.Srinivasu, 3 Tata Venkateswarlu #1 M.Tech (CSE) from P.N.C & Vijai Institute of
More informationLecture 02a Cloud Computing I
Mobile Cloud Computing Lecture 02a Cloud Computing I 吳 秀 陽 Shiow-yang Wu What is Cloud Computing? Computing with cloud? Mobile Cloud Computing Cloud Computing I 2 Note 1 What is Cloud Computing? Walking
More informationSMICloud: A Framework for Comparing and Ranking Cloud Services
2011 Fourth IEEE International Conference on Utility and Cloud Computing SMICloud: A Framework for Comparing and Ranking Cloud Services Saurabh Kumar Garg, Steve Versteeg and Rajkumar Buyya Cloud Computing
More informationCloud deployment model and cost analysis in Multicloud
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) ISSN: 2278-2834, ISBN: 2278-8735. Volume 4, Issue 3 (Nov-Dec. 2012), PP 25-31 Cloud deployment model and cost analysis in Multicloud
More informationTowards Management of SLA-Aware Business Processes Based on Key Performance Indicators
Towards Management of SLA-Aware Business Processes Based on Key Performance Indicators Branimir Wetzstein, Dimka Karastoyanova, Frank Leymann Institute of Architecture of Application Systems, University
More informationTrademark Notice. General Disclaimer
Trademark Notice General Disclaimer Intelligent Management, Centralized Operation & Maintenance Huawei Data Center Network Management Solution A data center is an integrated IT application environment
More informationSurvey on Models to Investigate Data Center Performance and QoS in Cloud Computing Infrastructure
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,
More informationInternational Journal of Computer & Organization Trends Volume21 Number1 June 2015 A Study on Load Balancing in Cloud Computing
A Study on Load Balancing in Cloud Computing * Parveen Kumar * Er.Mandeep Kaur Guru kashi University,Talwandi Sabo Guru kashi University,Talwandi Sabo Abstract: Load Balancing is a computer networking
More informationLoad Testing and Monitoring Web Applications in a Windows Environment
OpenDemand Systems, Inc. Load Testing and Monitoring Web Applications in a Windows Environment Introduction An often overlooked step in the development and deployment of Web applications on the Windows
More informationG DATA TechPaper #0275. G DATA Network Monitoring
G DATA TechPaper #0275 G DATA Network Monitoring G DATA Software AG Application Development May 2016 Contents Introduction... 3 1. The benefits of network monitoring... 3 1.1. Availability... 3 1.2. Migration
More informationInternational Journal of Scientific & Engineering Research, Volume 6, Issue 5, May-2015 1681 ISSN 2229-5518
International Journal of Scientific & Engineering Research, Volume 6, Issue 5, May-2015 1681 Software as a Model for Security in Cloud over Virtual Environments S.Vengadesan, B.Muthulakshmi PG Student,
More informationOptimal Service Pricing for a Cloud Cache
Optimal Service Pricing for a Cloud Cache K.SRAVANTHI Department of Computer Science & Engineering (M.Tech.) Sindura College of Engineering and Technology Ramagundam,Telangana G.LAKSHMI Asst. Professor,
More informationMeeting the Five Key Needs of Next-Generation Cloud Computing Networks with 10 GbE
White Paper Meeting the Five Key Needs of Next-Generation Cloud Computing Networks Cloud computing promises to bring scalable processing capacity to a wide range of applications in a cost-effective manner.
More informationDynamic Composition of Web Service Based on Cloud Computing
, pp.389-398 http://dx.doi.org/10.14257/ijhit.2013.6.6.35 Dynamic Composition of Web Service Based on Cloud Computing WU Nai-zhong Information Center, Changzhou Institute of Engineering Technology, Changzhou
More informationOptimizing Service Levels in Public Cloud Deployments
WHITE PAPER OCTOBER 2014 Optimizing Service Levels in Public Cloud Deployments Keys to Effective Service Management 2 WHITE PAPER: OPTIMIZING SERVICE LEVELS IN PUBLIC CLOUD DEPLOYMENTS ca.com Table of
More informationMonitoring Performances of Quality of Service in Cloud with System of Systems
Monitoring Performances of Quality of Service in Cloud with System of Systems Helen Anderson Akpan 1, M. R. Sudha 2 1 MSc Student, Department of Information Technology, 2 Assistant Professor, Department
More informationWHITE PAPER. Five Steps to Better Application Monitoring and Troubleshooting
WHITE PAPER Five Steps to Better Application Monitoring and Troubleshooting There is no doubt that application monitoring and troubleshooting will evolve with the shift to modern applications. The only
More informationAchieve Better Ranking Accuracy Using CloudRank Framework for Cloud Services
Achieve Better Ranking Accuracy Using CloudRank Framework for Cloud Services Ms. M. Subha #1, Mr. K. Saravanan *2 # Student, * Assistant Professor Department of Computer Science and Engineering Regional
More informationLogical Data Models for Cloud Computing Architectures
Logical Data Models for Cloud Computing Architectures Augustine (Gus) Samba, Kent State University Describing generic logical data models for two existing cloud computing architectures, the author helps
More informationImpact of Service Oriented Architecture on ERP Implementations in Technical Education
Impact of Service Oriented Architecture on ERP Implementations in Technical Education Swati Verma Department of Computer Science & Engg, B.T. Kumaon Institute of Technology, Dwarahat, 263653, India. E-mail:
More informationCloud Models and Platforms
Cloud Models and Platforms Dr. Sanjay P. Ahuja, Ph.D. 2010-14 FIS Distinguished Professor of Computer Science School of Computing, UNF A Working Definition of Cloud Computing Cloud computing is a model
More informationNetwork Management and Monitoring Software
Page 1 of 7 Network Management and Monitoring Software Many products on the market today provide analytical information to those who are responsible for the management of networked systems or what the
More informationOptimizing Data Center Networks for Cloud Computing
PRAMAK 1 Optimizing Data Center Networks for Cloud Computing Data Center networks have evolved over time as the nature of computing changed. They evolved to handle the computing models based on main-frames,
More informationCONDIS. IT Service Management and CMDB
CONDIS IT Service and CMDB 2/17 Table of contents 1. Executive Summary... 3 2. ITIL Overview... 4 2.1 How CONDIS supports ITIL processes... 5 2.1.1 Incident... 5 2.1.2 Problem... 5 2.1.3 Configuration...
More informationHow To Understand Cloud Computing
Overview of Cloud Computing (ENCS 691K Chapter 1) Roch Glitho, PhD Associate Professor and Canada Research Chair My URL - http://users.encs.concordia.ca/~glitho/ Overview of Cloud Computing Towards a definition
More informationBusiness applications:
Consorzio COMETA - Progetto PI2S2 UNIONE EUROPEA Business applications: the COMETA approach Prof. Antonio Puliafito University of Messina Open Grid Forum (OGF25) Catania, 2-6.03.2009 www.consorzio-cometa.it
More informationFigure 1. The cloud scales: Amazon EC2 growth [2].
- Chung-Cheng Li and Kuochen Wang Department of Computer Science National Chiao Tung University Hsinchu, Taiwan 300 shinji10343@hotmail.com, kwang@cs.nctu.edu.tw Abstract One of the most important issues
More informationA Proposed Framework for Ranking and Reservation of Cloud Services Based on Quality of Service
II,III A Proposed Framework for Ranking and Reservation of Cloud Services Based on Quality of Service I Samir.m.zaid, II Hazem.m.elbakry, III Islam.m.abdelhady I Dept. of Geology, Faculty of Sciences,
More informationDISTRIBUTED SYSTEMS [COMP9243] Lecture 9a: Cloud Computing WHAT IS CLOUD COMPUTING? 2
DISTRIBUTED SYSTEMS [COMP9243] Lecture 9a: Cloud Computing Slide 1 Slide 3 A style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet.
More informationHow Route Analytics Enables Virtualization and Cloud Computing
How Route Analytics Enables Virtualization and Cloud Computing By Jim Metzler, Ashton Metzler & Associates Distinguished Research Fellow and Co-Founder, Webtorials Editorial/Analyst Division Introduction
More informationPERFORMANCE ANALYSIS OF PaaS CLOUD COMPUTING SYSTEM
PERFORMANCE ANALYSIS OF PaaS CLOUD COMPUTING SYSTEM Akmal Basha 1 Krishna Sagar 2 1 PG Student,Department of Computer Science and Engineering, Madanapalle Institute of Technology & Science, India. 2 Associate
More informationQoS Probing Of Real-World Web Services
QoS Probing Of Real-World Web Services SathishKumar 1, Anantharaj 2, Chendhavarayan. R 3 Student 1, HOD 2, Assistant Professor 3 Computer Science Engineering, Thiruvalluvar College of Engineering and Technology,
More informationA Review On SLA And Various Approaches For Efficient Cloud Service Provider Selection Shreyas G. Patel Student of M.E, CSE Department, PIET Limda
A Review On SLA And Various Approaches For Efficient Cloud Service Provider Selection Shreyas G. Patel Student of M.E, CSE Department, PIET Limda Prof. Gordhan B. Jethava Head & Assistant Professor, Information
More informationCloud computing - Architecting in the cloud
Cloud computing - Architecting in the cloud anna.ruokonen@tut.fi 1 Outline Cloud computing What is? Levels of cloud computing: IaaS, PaaS, SaaS Moving to the cloud? Architecting in the cloud Best practices
More informationA Framework for the Design of Cloud Based Collaborative Virtual Environment Architecture
, March 12-14, 2014, Hong Kong A Framework for the Design of Cloud Based Collaborative Virtual Environment Architecture Abdulsalam Ya u Gital, Abdul Samad Ismail, Min Chen, and Haruna Chiroma, Member,
More informationWhite Paper. NEC Invariant Analyzer with Oracle Enterprise Manager
NEC Invariant Analyzer with Oracle Enterprise Manager Table of Contents Introduction... 3 Proactive Performance Analysis... 3 Overview of Oracle Enterprise Manager... 4 Oracle Enterprise Manager Cloud
More informationSystem of Systems to Provide Quality of Service Monitoring, Management and Response in Cloud Computing Environments
System of Systems to Provide Quality of Service Monitoring, Management and Response in Cloud Computing Environments July 16-19, 2012 Paul C. Hershey 1 Shrisha Rao 2 Charles B. Silio, Jr. 3 Akshay Narayan
More informationBlackboard Collaborate Web Conferencing Hosted Environment Technical Infrastructure and Security
Overview Blackboard Collaborate Web Conferencing Hosted Environment Technical Infrastructure and Security Blackboard Collaborate web conferencing is available in a hosted environment and this document
More informationSecure Attack Measure Selection and Intrusion Detection in Virtual Cloud Networks. Karnataka. www.ijreat.org
Secure Attack Measure Selection and Intrusion Detection in Virtual Cloud Networks Kruthika S G 1, VenkataRavana Nayak 2, Sunanda Allur 3 1, 2, 3 Department of Computer Science, Visvesvaraya Technological
More informationLogentries Insights: The State of Log Management & Analytics for AWS
Logentries Insights: The State of Log Management & Analytics for AWS Trevor Parsons Ph.D Co-founder & Chief Scientist Logentries 1 1. Introduction The Log Management industry was traditionally driven by
More informationLecture 02b Cloud Computing II
Mobile Cloud Computing Lecture 02b Cloud Computing II 吳 秀 陽 Shiow-yang Wu T. Sridhar. Cloud Computing A Primer, Part 2: Infrastructure and Implementation Topics. The Internet Protocol Journal, Volume 12,
More informationASCETiC Whitepaper. Motivation. ASCETiC Toolbox Business Goals. Approach
ASCETiC Whitepaper Motivation The increased usage of ICT, together with growing energy costs and the need to reduce greenhouse gases emissions call for energy-efficient technologies that decrease the overall
More informationIntroduction to Service Oriented Architectures (SOA)
Introduction to Service Oriented Architectures (SOA) Responsible Institutions: ETHZ (Concept) ETHZ (Overall) ETHZ (Revision) http://www.eu-orchestra.org - Version from: 26.10.2007 1 Content 1. Introduction
More informationEvaluation Methodology of Converged Cloud Environments
Krzysztof Zieliński Marcin Jarząb Sławomir Zieliński Karol Grzegorczyk Maciej Malawski Mariusz Zyśk Evaluation Methodology of Converged Cloud Environments Cloud Computing Cloud Computing enables convenient,
More informationPerformance Management for Cloudbased STC 2012
Performance Management for Cloudbased Applications STC 2012 1 Agenda Context Problem Statement Cloud Architecture Need for Performance in Cloud Performance Challenges in Cloud Generic IaaS / PaaS / SaaS
More informationBig Data Mining Services and Knowledge Discovery Applications on Clouds
Big Data Mining Services and Knowledge Discovery Applications on Clouds Domenico Talia DIMES, Università della Calabria & DtoK Lab Italy talia@dimes.unical.it Data Availability or Data Deluge? Some decades
More informationIBM 000-281 EXAM QUESTIONS & ANSWERS
IBM 000-281 EXAM QUESTIONS & ANSWERS Number: 000-281 Passing Score: 800 Time Limit: 120 min File Version: 58.8 http://www.gratisexam.com/ IBM 000-281 EXAM QUESTIONS & ANSWERS Exam Name: Foundations of
More informationCLOUD COMPUTING. DAV University, Jalandhar, Punjab, India. DAV University, Jalandhar, Punjab, India
CLOUD COMPUTING 1 Er. Simar Preet Singh, 2 Er. Anshu Joshi 1 Assistant Professor, Computer Science & Engineering, DAV University, Jalandhar, Punjab, India 2 Research Scholar, Computer Science & Engineering,
More informationFuture of Cloud Computing. Irena Bojanova, Ph.D. UMUC, NIST
Future of Cloud Computing Irena Bojanova, Ph.D. UMUC, NIST No Longer On The Horizon Essential Characteristics On-demand Self-Service Broad Network Access Resource Pooling Rapid Elasticity Measured Service
More informationPerformance Management for Cloud-based Applications STC 2012
Performance Management for Cloud-based Applications STC 2012 1 Agenda Context Problem Statement Cloud Architecture Key Performance Challenges in Cloud Challenges & Recommendations 2 Context Cloud Computing
More informationGrid Computing Vs. Cloud Computing
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 6 (2013), pp. 577-582 International Research Publications House http://www. irphouse.com /ijict.htm Grid
More informationResource Provisioning in Clouds via Non-Functional Requirements
Resource Provisioning in Clouds via Non-Functional Requirements By Diana Carolina Barreto Arias Under the supervision of Professor Rajkumar Buyya and Dr. Rodrigo N. Calheiros A minor project thesis submitted
More informationINCREASING SERVER UTILIZATION AND ACHIEVING GREEN COMPUTING IN CLOUD
INCREASING SERVER UTILIZATION AND ACHIEVING GREEN COMPUTING IN CLOUD M.Rajeswari 1, M.Savuri Raja 2, M.Suganthy 3 1 Master of Technology, Department of Computer Science & Engineering, Dr. S.J.S Paul Memorial
More informationUsing Data Mining for Mobile Communication Clustering and Characterization
Using Data Mining for Mobile Communication Clustering and Characterization A. Bascacov *, C. Cernazanu ** and M. Marcu ** * Lasting Software, Timisoara, Romania ** Politehnica University of Timisoara/Computer
More informationEmbracing Microsoft Vista for Enhanced Network Security
Embracing Microsoft Vista for Enhanced Network Security Effective Implementation of Server & Domain Isolation Requires Complete Network Visibility throughout the OS Migration Process For questions on this
More informationThe 5 New Realities of Server Monitoring
Uptime Infrastructure Monitor Whitepaper The 5 New Realities of Server Monitoring How to Maximize Virtual Performance, Availability & Capacity Cost Effectively. Server monitoring has never been more critical.
More informationSLA BASED SERVICE BROKERING IN INTERCLOUD ENVIRONMENTS
SLA BASED SERVICE BROKERING IN INTERCLOUD ENVIRONMENTS Foued Jrad, Jie Tao and Achim Streit Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Karlsruhe, Germany {foued.jrad, jie.tao, achim.streit}@kit.edu
More informationDatasheet FUJITSU Cloud Monitoring Service
Datasheet FUJITSU Cloud Monitoring Service FUJITSU Cloud Monitoring Service powered by CA Technologies offers a single, unified interface for tracking all the vital, dynamic resources your business relies
More informationMiddleware and Web Services Lecture 11: Cloud Computing Concepts
Middleware and Web Services Lecture 11: Cloud Computing Concepts doc. Ing. Tomáš Vitvar, Ph.D. tomas@vitvar.com @TomasVitvar http://vitvar.com Czech Technical University in Prague Faculty of Information
More informationInfrastructure as a Service (IaaS)
Infrastructure as a Service (IaaS) (ENCS 691K Chapter 4) Roch Glitho, PhD Associate Professor and Canada Research Chair My URL - http://users.encs.concordia.ca/~glitho/ References 1. R. Moreno et al.,
More informationHow In-Memory Data Grids Can Analyze Fast-Changing Data in Real Time
SCALEOUT SOFTWARE How In-Memory Data Grids Can Analyze Fast-Changing Data in Real Time by Dr. William Bain and Dr. Mikhail Sobolev, ScaleOut Software, Inc. 2012 ScaleOut Software, Inc. 12/27/2012 T wenty-first
More informationIBM Platform Computing Cloud Service Ready to use Platform LSF & Symphony clusters in the SoftLayer cloud
IBM Platform Computing Cloud Service Ready to use Platform LSF & Symphony clusters in the SoftLayer cloud February 25, 2014 1 Agenda v Mapping clients needs to cloud technologies v Addressing your pain
More informationCLOUD CLOUT WITH OPEN APIS WHAT YOU SHOULD ASK OF YOUR CLOUD PROVIDER
CLOUD CLOUT WITH OPEN APIS WHAT YOU SHOULD ASK OF YOUR CLOUD PROVIDER STRATEGIC WHITE PAPER As cloud services become increasingly popular, more questions arise about the capabilities of cloud solutions.
More informationBPM in Cloud Architectures: Business Process Management with SLAs and Events
BPM in Cloud Architectures: Business Process Management with SLAs and Events Vinod Muthusamy and Hans-Arno Jacobsen University of Toronto 1 Introduction Applications are becoming increasingly distributed
More informationPayment minimization and Error-tolerant Resource Allocation for Cloud System Using equally spread current execution load
Payment minimization and Error-tolerant Resource Allocation for Cloud System Using equally spread current execution load Pooja.B. Jewargi Prof. Jyoti.Patil Department of computer science and engineering,
More informationIT Security Risk Management Model for Cloud Computing: A Need for a New Escalation Approach.
IT Security Risk Management Model for Cloud Computing: A Need for a New Escalation Approach. Gunnar Wahlgren 1, Stewart Kowalski 2 Stockholm University 1: (wahlgren@dsv.su.se), 2: (stewart@dsv.su.se) ABSTRACT
More informationInternational Journal of Advance Research in Computer Science and Management Studies
Volume 3, Issue 6, June 2015 ISSN: 2321 7782 (Online) International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online
More informationSistemi Operativi e Reti. Cloud Computing
1 Sistemi Operativi e Reti Cloud Computing Facoltà di Scienze Matematiche Fisiche e Naturali Corso di Laurea Magistrale in Informatica Osvaldo Gervasi ogervasi@computer.org 2 Introduction Technologies
More informationA Mock RFI for a SD-WAN
A Mock RFI for a SD-WAN Ashton, Metzler & Associates Background and Intended Use After a long period with little if any fundamental innovation, the WAN is now the focus of considerable innovation. The
More informationEfficient Scheduling Of On-line Services in Cloud Computing Based on Task Migration
Efficient Scheduling Of On-line Services in Cloud Computing Based on Task Migration 1 Harish H G, 2 Dr. R Girisha 1 PG Student, 2 Professor, Department of CSE, PESCE Mandya (An Autonomous Institution under
More informationNetwork Virtualization for Large-Scale Data Centers
Network Virtualization for Large-Scale Data Centers Tatsuhiro Ando Osamu Shimokuni Katsuhito Asano The growing use of cloud technology by large enterprises to support their business continuity planning
More informationService Oriented Architecture
Service Oriented Architecture Charlie Abela Department of Artificial Intelligence charlie.abela@um.edu.mt Last Lecture Web Ontology Language Problems? CSA 3210 Service Oriented Architecture 2 Lecture Outline
More information[Sudhagar*, 5(5): May, 2016] ISSN: 2277-9655 Impact Factor: 3.785
IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY AVOID DATA MINING BASED ATTACKS IN RAIN-CLOUD D.Sudhagar * * Assistant Professor, Department of Information Technology, Jerusalem
More informationCloud Computing. Adam Barker
Cloud Computing Adam Barker 1 Overview Introduction to Cloud computing Enabling technologies Different types of cloud: IaaS, PaaS and SaaS Cloud terminology Interacting with a cloud: management consoles
More informationPlanning the Migration of Enterprise Applications to the Cloud
Planning the Migration of Enterprise Applications to the Cloud A Guide to Your Migration Options: Private and Public Clouds, Application Evaluation Criteria, and Application Migration Best Practices Introduction
More informationCluster, Grid, Cloud Concepts
Cluster, Grid, Cloud Concepts Kalaiselvan.K Contents Section 1: Cluster Section 2: Grid Section 3: Cloud Cluster An Overview Need for a Cluster Cluster categorizations A computer cluster is a group of
More informationA Study on Analysis and Implementation of a Cloud Computing Framework for Multimedia Convergence Services
A Study on Analysis and Implementation of a Cloud Computing Framework for Multimedia Convergence Services Ronnie D. Caytiles and Byungjoo Park * Department of Multimedia Engineering, Hannam University
More informationSLA Business Management Based on Key Performance Indicators
, July 4-6, 2012, London, U.K. SLA Business Management Based on Key Performance Indicators S. Al Aloussi Abstract-It is increasingly important that Service Level Agreements (SLAs) are taken into account
More informationBuilding a SaaS Application. ReddyRaja Annareddy CTO and Founder
Building a SaaS Application ReddyRaja Annareddy CTO and Founder Introduction As cloud becomes more and more prevalent, many ISV s and enterprise are looking forward to move their services and offerings
More informationApplication Performance Management: New Challenges Demand a New Approach
Application Performance Management: New Challenges Demand a New Approach Introduction Historically IT management has focused on individual technology domains; e.g., LAN, WAN, servers, firewalls, operating
More informationA Middleware Strategy to Survive Compute Peak Loads in Cloud
A Middleware Strategy to Survive Compute Peak Loads in Cloud Sasko Ristov Ss. Cyril and Methodius University Faculty of Information Sciences and Computer Engineering Skopje, Macedonia Email: sashko.ristov@finki.ukim.mk
More informationHighly Available Mobile Services Infrastructure Using Oracle Berkeley DB
Highly Available Mobile Services Infrastructure Using Oracle Berkeley DB Executive Summary Oracle Berkeley DB is used in a wide variety of carrier-grade mobile infrastructure systems. Berkeley DB provides
More informationCA Automation Suite for Data Centers
PRODUCT SHEET CA Automation Suite for Data Centers agility made possible Technology has outpaced the ability to manage it manually in every large enterprise and many smaller ones. Failure to build and
More informationWeb Server Software Architectures
Web Server Software Architectures Author: Daniel A. Menascé Presenter: Noshaba Bakht Web Site performance and scalability 1.workload characteristics. 2.security mechanisms. 3. Web cluster architectures.
More informationCompany & Solution Profile
Company & Solution Profile About Us NMSWorks Software Limited is an information technology company specializing in developing Carrier grade Integrated Network Management Solutions for the emerging convergent
More informationCloud Infrastructure Pattern
1 st LACCEI International Symposium on Software Architecture and Patterns (LACCEI-ISAP-MiniPLoP 2012), July 23-27, 2012, Panama City, Panama. Cloud Infrastructure Pattern Keiko Hashizume Florida Atlantic
More informationSecurity Issues in Cloud Computing
Security Issues in Cloud Computing Dr. A. Askarunisa Professor and Head Vickram College of Engineering, Madurai, Tamilnadu, India N.Ganesh Sr.Lecturer Vickram College of Engineering, Madurai, Tamilnadu,
More informationAmazon Web Services vs. Horizon
Amazon Web Services vs. February 2016 Comparing Cloud Services Every Cloud Service Provider (CSP) and every Cloud Service is different. Some CSPs focus on being price competitive and providing self-service,
More informationArchitectural Implications of Cloud Computing
Architectural Implications of Cloud Computing Grace Lewis Research, Technology and Systems Solutions (RTSS) Program Lewis is a senior member of the technical staff at the SEI in the Research, Technology,
More informationDeploying a distributed data storage system on the UK National Grid Service using federated SRB
Deploying a distributed data storage system on the UK National Grid Service using federated SRB Manandhar A.S., Kleese K., Berrisford P., Brown G.D. CCLRC e-science Center Abstract As Grid enabled applications
More informationEnergetic Resource Allocation Framework Using Virtualization in Cloud
Energetic Resource Allocation Framework Using Virtualization in Ms.K.Guna *1, Ms.P.Saranya M.E *2 1 (II M.E(CSE)) Student Department of Computer Science and Engineering, 2 Assistant Professor Department
More information