1. INTRODUCTION. S. Urmela M.Tech Scholar Department of Computer Science Pondicherry University Puducherry, India

Transcription

1 International Journal of Computer Application and Engineering Technology Volume 3-Issue 3, July Pp A SLA violation reduction technique in Cloud by Resource Rescheduling Algorithm (RRA) K. Vaitheki Assistant Professor Department of Computer Science Pondicherry University Puducherry, India vaidehi.balaji@gmail.com S. Urmela M.Tech Scholar Department of Computer Science Pondicherry University Puducherry, India urmelaindra@gmail.com ABSTRACT: The services provided by the cloud computing that has achieved a protruding growth in business and Information technology over the last few years is based on pay-as-you go scheme. It distributes the on demand resources through the Software, Platform or infrastructure as a service with an ability to encounter the demands of the customers. On providing the basic services, the Service Level Agreement, which is a negotiation between the cloud consumer and the provider becomes the major aspect of cloud computing. The violation of the SLA is a major aspect of cloud computing and the violation leads to the reduction of customer satisfaction level and further affects the cloud provider leading to penalty. With an aim to improve the customer satisfaction level and an effective utilization of resources over a specified deadline and sharing of idle resources which may impact on avoidance of SLA violation. An analysis is performed on the usage and the necessity of Service level agreements and their related issues and the Resource Rescheduling Algorithm (RRA) is proposed.the algorithm aims to improve the performance of the cloud services and hence provide an exceptional Quality of service. It aims to improve the satisfaction of the users and is beneficial to the cloud providers by eluding the penalty. Keywords: Cloud computing; Service Level Agreement, RRA, Quality of Service, Cloud Consumers, and Cloud Providers. 1. INTRODUCTION Cloud computing is the new trend in computing, where readily available computing resources are exposed as Pay as you go service. The service provided at different levels (SAAS, PAAS, IAAS) may also vary depending on the deployment modes (Private, Public, Community and Hybrid) of cloud computing. In SAAS any software can be utilized for the purpose of the user through a web interface. In PAAS the user can execute the application over the platform rendered as a service by the providers and also free platforms that act as a test bed for the users [14]. The IAAS requires the service to run, deploy and create a virtualization system that in turn provides a hardware abstraction through Hypervisor layer that virtualizes the operating system. The several virtual machines that share the resources provided by the OS, which run on the same physical server or one instance of the server may also be moved to the other server [19]. Apart from the SPI services that are basic in the cloud most of the services are offered as a third party service that could be integrated based on the requirement. Security as a service, Database as a service, Storage as a service and many more services that could be extended under XAAS (Anything as a service). The cloud computing utilizes the technology of Grid Computing, utility Computing and the transactional computing and based on their architectures. The Grid computing [19] pulls the dataset from the data provider into the message queue and is processed by the worker node/process node. The two servers may run independent of each other. The transaction computing holds the data from the database and process through the web interface. The presentation and logic are hidden by the load balancer, an active component of the transaction. 217

2 The best of grid and transaction computing holds the cloud by a better virtualization system. The degraded performance of the hardware provided by cloud is even better compared to the optimal performance of the user s server. 2. SERVICE LEVEL AGREEMENTS SLA serves as the footing for the expected level of service between the consumer and the provider. The QoS attributes that enforce the Service Level Agreement need to be closely monitored to avoid the violations of SLA. The deviation in the attributes could be informed immediately through a better management scheme and thereby improve the level of fault tolerance and the completion of the job with the allocated resources. The user is to be influenced on the working parameters which aim to provide the resources as mentioned in the SLA. It is the contract of the provider to satisfy their consumers for proving their value of the service offered. The XML-based languages SLAng [16] and WSOL [17], related to the web services technologies and standards define the QoS constraints in the web services domain and a language for the expression for QOS levels. 3. RELATED WORK SLA enables the execution process to be automated and hence discharges consumers from the key burden. Pankesh Patel et al.[14] work contains descriptions on various measurement metrics with regard to SLA based on QOS. It looks into condition evaluation of Service level objectives and provided a corrective action incase of these violation. Linlin Wu et al [21] has proposed Cloud computing admission control and scheduling algorithms for SaaS providers for effective utilization of the Cloud resources inorder to maximize profit by minimizing cost and improving customer satisfaction. It provides solution to handle a new request without affecting the previously accepted request, mapping of various user requests with different QoS parameters to VMs and What available resource need to be assigned to the request. The XML based SLAng and WSOL languages define the QOS constraints in terms of web services. They do not hold for the guarantee of the lifecycle agreement. R. Buyya et al.[22] propose Service Level Agreements (SLAs) which is a formal contract between providers and consumers, as the driving force for the QoS guarantees, the provision and management of resources. A. Kerteszet. al [18] has proposed an SLA based architecture for the provisioning of on-demand virtualized services and it works to combine SLA-based resource negotiations with virtualized resources.cloud computing admission control[21] and scheduling algorithms for SaaS providers to effectively utilise public Service Level Agreement (SLA) and thereby the Cloud resources maximize profit by minimizing cost and improving customer satisfaction level. The general objective of SaaS providers is to minimize cost and maximize customer satisfaction level.csl depends on to what degree SLA is satisfied.admission control has been used as a general mechanism to avoid overloading of resources and SLA satisfaction [5]. Admission control is required for accepting a newer request without interrupting the accepted requests, map numerous user requests with diverse QoS parameters to VMs, assignment of the resource based on the request, or VM initiation for the newer request and so on. By considering the above criteria s innovative costeffective admission control and scheduling algorithms maximize the SaaS provider s profit An SLA-oriented resource provisioning model for Cloud Computing[22] architecture is realized using the Aneka platform that enables QoS-driven resource provisioning for scientific computations, and provides mechanisms for the definition of deadline constraints. SLA management framework [23] that will enable service users to s elect the best available service 218

3 provider on the basis of its reputation and then monitor the run time performance of the service provider to determine whether or not it will fulfill its promise defined in the SLA. SLAs can be fragmented into various smaller criteria called Service Level Objectives. SLO establish a threshold against which a service is expected to be delivered and a measure of service provider s performance. SLA management is important to ensure that the run-time service properties meet the criteria that are established in the agreement. SLA management should be a two-step process; the first being the establishment of SLA with the service provider who is capable of providing the required service, and the second being the monitoring or predicting of the performance of the service on delivery to ensure that the quality of service is according to the defined parameters as stated in the SLA monitoring [20]. Vincent C. Emeakarohaet.al presents the Detecting SLA Violation infrastructure (DeSVi) architecture, sensing SLA violations through sophisticated resource monitoring [27] and reports about the violation of SLA and stating the performance of resources at shorter intervals causes performance degrade of the system. Online monitoring is a continuous monitoring of service is done based on monitoring intervals (seconds, minutes, hours or days). Second monitoring approach discussed is reactive monitoring, whereas the third monitoring approach is offline monitoring. third monitoring approach is offline monitoring.the scheduling algorithms proposed by Marc Eduard Frincu [25]search for an optimal allocation of components on nodes consider the methodof scaling to be known a priori and focus on in order toensure a homogeneous spread of component types.in terms of penalties for service violations, two types are defined: reputation-based penalty and monetary-based. One of the key features of cloud computing is the capability of acquiring and releasing resources on-demand. The objective of a service provider in this case is to allocate and de-allocate resources from the cloud to satisfy its service level objectives (SLOs), while minimizing its operational cost. [24].The proposedpjsc algorithm in cloud computingdo consider the priority of jobs not upon thefinish time.priority vectors are calculated in a cloud environment for all resources and job with the higher priority value as recorded by the matrix is allocated with the resource. PJSC algorithm concentrates on allocation of resource but not about the performance of the resources further [26]. A Frameworks, Tools and Projects on cloud environment Cloud computing is an emerging trend and will be one of the major requirement of the industry. The growth of data has become incredibly more and hence a major concern is about maintenance of this data. The research has become wider related to data storage, data handling, resourceallocation, resourceutilization, integrity and security in cloud computing. The various projects, frameworks and tools discussed below that have been developed and further can be upgraded based on the dynamic nature of the cloud. B. Metrics and adaptation Mechanisms Metrics [9] define how service parameters can be measured and the adaptation mechanism helps to measure the quality and prioritize Cloud services. The Web Service level agreement contains the descriptions about the service provider, service consumer and third parties. i. Resource metrics These metrics do not require any further processing and are retrieved directly from the provider resources ii. Composite metrics This represents a combination of several resource metrics based on specific algorithm. For example transactions per hour is a combination of the transaction count and uptime 219

4 TABLE 1. CLOUD PROJECT AND DESCRIPTION Project Resovoir[1] Bonfire [2] 4caast[3] Cloud-TM[4] Description A project that facilitates the cloud providers to build their own virtualized Cloud environment. They do not remark about the negotiation and the management of the resources The project objects to provide a platform for the federation of cloud providers and hence form a unified environment It provides a platform for deployment and management of cloud services It affords a PAAS middleware for the distributed cloud applications and a self tuning mechanism for the improvement of the data operations under QOS constraints. The Service Level Agreement system is built on TABLE 2. CLOUD FRAMEWORK AND DESCRIPTION Framework Cloudcompaas [5] WSLA[6] SLA@SOI[7] Optimis [8] LoM2HiS [8] Description A PAAS cloud platform that performs the evaluation the QOS rules based on monitoring information retrieved from the cloud resources Web Services Level Agreement is a specification developed by IBM for the definition and monitoring of SLAs within the domain of web services and for the SLA-driven management of the lifecycle of web services A SLA aware management of infrastructure services A framework to provide scheduling operations by choosing the best provider to host services and to shift private to public cloud by means of cloud federation, cloud bursting, live migration and auto scaling. A novel framework that manages the mapping of Low-level resource metrics like up and down time to High-level SLAs like system Availability and detects future SLA violation TABLE 3. TOOLS AND DESCRIPTION Tools Eucalyptus [10] Abiquop, 3 Tera s Applogic [11] [12] Elastra s Cloud Server[15] Capacity Management tool[15] QueingTheory[15] KVI[15] S3 Fox Claudia[13] Description Eucalyptus, an open source software that utilizes the compute resources do allow researchersto experiment with their own security, scalability, scheduling, and interface implementations It supports thousands of machines by hypervisor independence It provides a platform based on Xen, without the need for a SAN due to its integrated distributed storage solution It aids in the administration of the rules and provisioning It guarantees an adequate performance without overpaying It lists the poor response time and the eratic performance due to overutilization of resources It provides a technique where the computer metrics are related to the forecastable work A tool that enables to move the static content of organiser your computer to the cloud A toolkit to provide dynamic provision and scalability of services in IAAS clouds Indicators Timeliness Throughput Periodicity Temporal Experiential SLA Response error rate Intelligent response TABLE 4. QUALITY AND TIME INDICATORS Characterization The degree of service responsiveness The latency of transaction and workload efficiency The frequency of demand and supply activity The frequency to real time action and their outcome Quality of user interface design and experience The frequency of defective response The level of automated response 220

5 Fig 1. SLA Negotiation between Cloud Consumer and Provider Fig 2. Resource monitoring scheme through RRA Fig. 2 gives detailed resource monitoring scheme through Resource Rescheduling Algorithm. 221

6 iii. Measurement Services The services that specify the runtime parameters of cloud provider s resources [9]. The service parameters like response time, throughput keep on changing due to inconsistency in service request from the cloud consumer. In cloud however the usage and cost parameters are dynamic due to the pay-as-you-go nature and the elasticity of the cloud. iv. Condition Evaluation Service This service is responsible of getting the results from measurement services and evaluating the Service Level Objectives. The dynamic nature of the cloud holds the condition evaluation to be performed more frequently and there is little attention on the complexity of conditions. A dynamic scheduler that depends on a metric like the transaction rate is added. v. Management Service This service is accountable for counteractive actions that enclose the violation of Service Level Objectives. C. Quality and Time indicators The quality and the time indicators[23] is one of the factor that holds for the return on investment on cloud computing 4. DISCUSSION The dynamic nature of the cloud enforces a continuous resource monitoring and the management of the services which in turn holds a good quality of service and out of SLA violation. The above framework, tools and projects works on SLA management and resource allocations, utilisation of resources, administration of rules and provisioning, experiment with their own security, scalability, scheduling, and interface implementations. There is no algorithm that performs an efficient resource monitoring of individual VMs (only it is performed on physical machines) to enable an efficient sharing and utilisation of resources. The algorithm to manage the resources and rescheduling could further improve the customer satisfaction level and reduce/ avoid SLA violation which in turn impact on reduction of penalty and serves the cloud provider. No scheduling algorithm considers the important parameters such as reliability, availability and improving scalability [24]. The cloud monitoring service that is provided that is only adopted with the physical machine has to be extended with the individual virtual machines that have been created by virtualization. The test job may be submitted to study the performance of CPU usage and memory utilization and hence determine the energy efficient performance to declare a deadline for the completion of jobs. The efficient utilization of resources may be related to the energy efficient performance and pertaining to the application the jobs could be completed within the stipulated deadline and further the resources alloted to the virtual machine are shifted to the idle mode. The job status report is input to the resource rescheduler algorithm which is invoked by the physical machine identifies the application that requires additional resource and it allocates the idle resources to the VMs on demand of resources for job completion. 222

7 The Resource Rescheduling algorithm further report job status and on completion of all jobs within / not within deadline. The performance analyzer may compare the job completions deadline as provided in the SLA agreement between the consumer and the provider.if satisfied the SLA is met or it leads to the violation of SLA. So the scheme of powerful resource monitoring of individual VM s to intimate the availability of the resource on completion of the job can be effectively utilized by the VM s on demand of resource and job may be completed based on the deadline. 5. CONCLUSION AND FUTURE ENHANCEMENTS SLA serves as the footing for the expected level of service and is contract of the provider to satisfy their consumers for proving their value of the service offered.the enforcement of Service Level Agreement needs to be closely monitored forthe violations of SLA hence for a better quality of service. The deviation in the attributes could be informed immediately through a better management scheme and thereby improve the level of fault tolerance and the completion of the job with the allocated resources. The user is to be influenced on the working parameters which aim to provide the resources as mentioned in the SLA. The concept of detecting future SLA violation threats is designed by defining more restrictive thresholds known as threat thresholds that are stricter than the normal SLA objective violation thresholds. An advanced consumer with the knowledge of the interior mechanisms of a cloud provider is very exceptional in runthrough. Other issues of trust also need to be considered during SLA enforcement. For example consumers may not completely trust the certain measurements provided solely by a service provider and regularly employ third party mediators. These mediators are responsible for measuring the critical service parameters and reporting violations of the agreement from either party. SLAs will be of a much greater issue when all the cloud providers come into a wider federation of services. There is need for the Resource Monitoring scheme with a novel algorithm like (RRA)that we are working which aids an effective cloud Resource monitoring and circumvent the percentage of SLA violations to improve their performance eventually.the work environment is open stack and in future the implementation of the algorithm may involve the performance evaluation based on the quality and the time indicators which may report a better quality of service from the point of view of SLA violation which is one of the major aspects on cloud that leads to penalty and discontent of cloud consumer/provider. 6. REFERENCES [1]. The Reservoir Consortium, Resources and services, virtualization without barriers, URLhttp:// [2]. The BonFIRE Consortium, Building service test beds on FIRE, URL [3]. The 4CaaSt Consortium, Building the PaaS cloud of the future, URL. [4]. The Cloud-TM Consortium, Cloud-TM: a novel programming paradigm for cloud computing, URLhttp:// [5]. Andrés GarcíaGarcía, Ignacio BlanquerEspert, Vicente Hernández García, SLA-driven dynamic cloud resource management, Future Generation Computer Systems Advances in Computer Supported Collaboration: Systems and Technologies Volume 31, February 2014, Pages [6]. Keller, H. Ludwig, The WSLA framework: specifying and monitoring service level agreements for web services, Journal of Network and Systems Management 11 (2003) [7]. The SLA@SOI Consortium, LA@SOI empowering the service economy with SLA-aware infrastructures, URLhttp://sla-at-soi.eu/. [8]. The OPTIMIS Consortium, OPTIMIS: optimized infrastructure services, URLhttp:// [9]. Vincent C. Emeakaroha, IvonaBrandic, Michael Maurer, SchahramDustdar, Low Level Metrics to High Level SLAs - LoM2HiS Framework: Bridging the Gap Between Monitored Metrics and SLA Parameters in Cloud Environments, The 2010 High Performance Computing and Simulation Conference (HPCS 2010) in conjunction with The 6th International Wireless Communications and Mobile Computing Conference (IWCMC 2010), Caen, France, pages 48-54, 223

8 [10]. Daniel Nurmi, Rich Wolski, Chris Grzegorczyk, GrazianoObertelli, Sunil Soman, Lamia Youseff and DmitriiZagorodnov, Eucalyptus: an open-source cloud computing infrastructure, Journal of Physics: Conference Series 180 (2009) doi: / / 180/ 1/ , SciDAC 2009 [11]. [12]. [13]. Telefónica I + D, Claudia Platform, URLhttp://claudia.morfeoproject.org/. [14]. Pankesh Patel, AjithRanabahu, AmitSheth, Service Level Agreement in cloud Computing, Kno.e.sis Publications. [15]. Cloud Application Architectures: Building Applications and Infrastructure in the Cloud. [16]. D.D. Lamanna, J. Skene, W. Emmerich, SLAng: a language for defining servicelevel agreements, 2003, pp [17]. V. Tosic, K. Patel, B. Pagurek, WSOL web service offerings language Revised Papers from the International Workshop on Web Services, E- Department of Information Systems and Computation Business, and the Semantic Web, CAiSE 02/ WES 02, Springer-Verlag, London, (DSIC), UniversitatPolitècnica de València (UPV), since UK, 2002, pp [18]. A. Kertesz, G. Kecskemeti, I. Brandic, An SLA-based resource virtualization Medical Image processing approach for on-demand service provision, in: Proceedings of the 3rd International Workshop on Virtualization Technologies in Distributed Computing, VTDC 09, ACM, NewYork, NY, USA, 2009, pp dx.doi.org/ / URLhttp:// doi.acm.org/ / [19]. [20]. M. Comuzzi, C. Kotsokalis, G. Spanoudkis, R. Yahyapour, Establishing and Monitoring SLAs in Complex Service Based Systems. IEEE International Conference on Web Services [21]. Linlin Wu, Saurabh Kumar Garg, RajkumarBuyya, SLA-basedadmission control for a Software-as-a-Service providerin Cloud computingenvironments, Journal of Computer and System Sciences, [22]. R. Buyya, S.K. Garg, R.N. Calheiros, SLA-oriented resource provisioning for cloud computing: challenges, architecture, and solutions, in: Proceedingsof the 2011 International Conference on Cloud and Service Computing, CSC 11, IEEE Computer Society, Washington, DC, USA, 2011, pp [23]. AdilHammadi,Omar KhadeerHussain, TharamDillon, FarookhKhadeerHussain, A framework for SLA management in cloud computingfor informed decision making Cluster Computing,The Journal of Networks Software Tools and Applications Springer Science+Business Media New York /s [24]. SanjeevDhiman, Harwant Singh, A Methodology on Scheduling in Cloud Computing and its Techniques, International Journal of Data & Network Security, Vol 3, No.1, June [25]. Marc Eduard Frîncu, Scheduling highly available applications on cloud environments, Future Generation Computer Systems, Volume 32, March 2014, Pages [26]. ShamsollahGhanbaria,, Mohamed Othman, A Priority based Job Scheduling Algorithm in Cloud Computing, International Conference on Advances Science and Contemporary Engineering 2012 (ICASCE 2012), Pg [27]. Vincent C. Emeakarohaa, Marco A.S. Nettob, Rodrigo N. Calheirosc, IvonaBrandica, RajkumarBuyyac, César A.F. De Rose, Towards autonomic detection of SLA violations in Cloud infrastructures Future Generation Computer Systems Volume 28, Issue 7, July 2012, Pages