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
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
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
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
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. 2011. [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. 311 327, May 2004. [3] T. L. Saaty, Multicriteria Decision Making: The Analytic Hierarchy Process: Planning, Priority Setting Resource Allocation. Pittsburgh, PA, USA: RWS Publications, 1990. [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. 17 30, Jan. 2014. [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. 356 361. [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. 247 252. [7] S. M. White, Modeling a system of systems to analyze requirements, in Proc. 3rd Annu. IEEE Int. SysCon, Mar. 2009, pp. 83 89. [8] Defense Acquisition Guidebook (DAG), Jan. 2012. [Online]. Available: https://dag.dau.mil/pages/default.aspx [9] Systems Engineering Guide for Systems of Systems, Version 1.0, ser. OUSD (A & T) SSE, Aug. 2008. [10] P. Hershey and D. Runyon, SOA monitoring for enterprise computing systems, in Proc. 11th Int. IEEE EDOC Conf., Oct. 2007, pp. 443 450. 45
[11] P. J. Denning and J. P. Buzen, The operational analysis of queueing network models, ACM Comput. Surv., vol. 10, no. 3, pp. 225 261, Sep. 1978. [12] DoD instruction 5200.40: DoD Information Technology Security Certification and Accreditation Process, Dec. 1997. [Online]. Available: http://csrc.nist.gov/groups/sma/fasp/documents/c&a/dlabsp/i520040p.pdf [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. 2010 Int. Conf. HPCS, 2010, pp. 48 54. [14] DoD instruction 5200.80: Security of DoD Installations and Resources, 2005. [Online]. Available: http://www.dtic.mil/whs/directives/corres/pdf/ 520008p.pdf [15] IETF, A one-way delay metric for IPPM, Fremont, CA, USA, RFC2679.[Online]. Available: http://www.ietf.org/rfc/rfc2679.txt [16] J.-Y. Girard, Linear logic, Theoretical Comput. Sci., vol. 50, no. 1, pp. 1 101, 1987. [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. 85 90. [18] Network Infrastructure Technology Overview. Version 8, Release 5, Apr. 27, 2012, (ArchiveEntry=U_Network_V8R5_Overview.pdf). [Online]. Available: http://goo.gl/ja8ajk [19] Enclave Security Technical Implementation Guide, Jan. 3, 2011, (ArchiveEntry=U_Enclave_V4R3_STIG.pdf). [Online]. Available: http://goo.gl/xhy4l2 [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. 85 100, Jun. 1999. 46