Page 1

Tutorial Paper Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013

Agent based Aggregation of Cloud Services- A Research Agenda Dr. Nandini Sidnal1 and Sreedevi R. Nagarmunoli1 1

KLEDRMSSCET, Belgaum, Karnataka, India

Abstract--Cloud computing has come to the forefront as it overcomes some of the issues in computing such as storage space and processing power. It enables ubiquitous accessing and processing of information without the need of excessive computing facilities. In this work, we plan to brief some of the issues in aggregating the cloud services, discovering futuristic cloud service requests, develop a repository of the same and propose an agent based Quality of Service (QoS) provisioning system for cloud clients. Index Terms—Aggregation, Futuristic cloud services, Repository.


deployment of services across multiple cloud providers, Service Level Agreement (SLA) negotiation and management between cloud providers, additional privacy, security and trust management layers atop providers and support of context-aware applications. Works in [1-5, 15-27, 11-14] depict that not much attention is paid in monitoring and developing repository of cloud services, customized aggregation of services and distribution of services. The field of software agent technology is a rapidly developing area of research which encompasses a diverse range of topics and interests [31-37]. Cognitive agents that mimic human thought process and represent the logical transition of research on human information processing to practical application are deployed to develop an autonomous aggregating system. Section 2 presents the literature survey and section 3 describes the objectives of the research to be carried out. Section 4 describes the proposed methodology and section 5 provides the possible outcome and conclusion.

With the rapid development of processing and storage technologies and the success of the Internet, computing resources have become cheaper, more powerful and more ubiquitously available than ever before. This technological trend has enabled the realization of a new computing model called cloud computing, in which computing resources such II. LITERATURE SURVEY as CPU, storage etc. are provided as general utilities that can be leased and released by users through the Internet in an The survey is based on the research works carried out in on-demand fashion. the universities, by the academicians and works in research National Institute of Standards and Technology (NIST) laboratories such as HP, IBM etc. [13] discusses the concept defines cloud computing as a model for enabling convenient, of “cloud” computing, it tries to address some of the issues on-demand network access to a shared pool of configurable related to research topics, and the “cloud” implementation computing resources that can be rapidly provisioned and available today. [14] investigates the challenges of released with minimal management effort or service provider developing a Campus Cloud based on aggregating resources interaction [29,30]. It enables ubiquitous accessing and in multiple universities. [10] presents a policy-centered QoS processing of information without the need of excessive meta-model which can be used by service providers and computing facilities. Compared to other distributed consumers alike to express capabilities, requirements, computing paradigms such as Grid computing and High constraints, and general management characteristics relevant Performance Computing (HPC), cloud computing provides for SLA establishment in service aggregations. HP Lab is broader interoperability over the world-wide web networks focusing on delivering the secure application and computing [6, 9]. end-state of “Everything-as-a-Service”. Some of the issues in cloud computing environment can IBM researchers’ adopted cloud computing for faster be classified as Platform Management, Cloud-enabled turnaround times in provisioning of resources for specific Applications, challenges in Cloud Management, Cloud research projects [14]. Google and IBM are jointly working Enablement, Cloud Interoperability, elastic scalability, trust, on data centers in cloud. The Cloud Computing and security, privacy, data handling, programming models, Distributed Systems (CLOUDS) Laboratory is actively resource control, systems development and systems engaged in the design and development of next-generation management and Aggregation of Cloud Services. computing systems and applications that aggregate or lease Aggregation of Cloud Services - The research challenges services of distributed resources depending on their in the aggregation of resources from diverse cloud providers availability, capability, performance, cost, and users’ QoS adding additional layers of service management, novel requirements [9]. architectural models for aggregation of cloud providers, Some of the ongoing research projects in cloud computing brokering algorithms for high availability, performance, area by different universities are discussed in the following proximity, legal domains, price, or energy efficiency, sharing paragraphs. The Researchers at Boston University are of resources between cloud providers, networking in the 116 © 2013 ACEEE DOI: 03.LSCS.2013.1. 560

Tutorial Paper Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013 exploring the merits of “Collocation Games” (CGs) as a novel, earlier work done on Web and Grid computing. economically-sound framework upon which emerging cloud architectures could be implemented [38]. Research is going III. OBJECTIVES on at Duke University to explore and test Trustworthy Virtual The objectives of the proposed research work that will be Cloud Computing [38]. Florida International University (FIU) carried out are discussed in this section. The following issues researchers are leveraging cloud computing to analyze aerial in cloud computing are studied and some of the issues in images and objects to help support disaster mitigation and aggregating the cloud services would be resolved. Cloud environmental protection [38]. The researchers at Indiana aggregator is a platform or service that combines multiple University are working on Large-Scale Distributed Scientific clouds with similar characteristics (geographic area, cost, Experiments on Shared Substrate, exploring the use of cloud technology, size, etc.) into a single point of access, format, techniques to overcome current medical computing obstacles and structure. Value is derived from cost savings and greater [38]. The team at MIT is working in collaboration with Yale efficiency found from the ability to easily leverage multiple Universit and the University of Wisconsin at Madison on a services providers. comparative study of approaches to cluster-based, large-scale As a cost-effective and time-efficient way to develop new data analysis and cloud for education [38]. applications and services, service aggregation in cloud The aim of the project work at University of St. Andrew is computing empowers all service providers and consumers to investigate how underused computing resources within and creates tremendous opportunities in various industry an enterprise may be harvested and harnessed to improve sectors. However, it also poses various challenges in securing return on IT investment [15]. [16] aims at specifying, the information on cloud. measuring and understanding high level cloud properties. Some of the issues that need to be resolved in aggregating The aim of the project [17] is to develop and evaluate [7-8, 10] the cloud services are availability of services that techniques to allow desired high-level properties to be may be hired in real time without conflicts, novel architectural specified, mapped into appropriate low-level actions, and the models for aggregation of cloud providers, brokering results to be measured and reported in terms of the highalgorithms for high availability, performance, proximity, legal level properties. The goal of the research work defined in [18] domains, price, or energy efficiency, sharing of resources is to make experiments better by using the Cloud in a number between cloud providers, networking in the deployment of of ways. The project [19] would investigate a range of services across multiple cloud providers, additional privacy, problems in the established area of computational abstract security and trust management layers atop providers, support algebra in order to see whether, or how, they can be effectively of context-aware applications and automatic management of parallelized using this framework. service elasticity . The aim of the project [20] is to investigate the practical Objectives of the research are to design an agent focused issues which affect data migration in the cloud and to propose on aggregation of services for cloud clients. The issues mechanisms to specify policies on data migration and to use considered in our research are: these as a basis for a data management system. The work  To design a cognitive agent based novel architecture/ defined in [21] aims to investigate how a migration of scheme for discovering futuristic cloud services (that applications may result in changes to the way that work is may be in demand) and develop a repository of the same actually done. [22] investigates the use of cloud computing by networking multiple cloud providers. for mobile network data archiving: there are varieties of topics  To design a scheme to autonomously and intelligently in distributed systems including network measurement, monitor, negotiate and aggregate the resources from the privacy, sanitization, data protection and computation cloud repository based on the QoS (time, price, caching. availability) defined in the cloud client’s requests. The The research work in [23] discusses security issues. The scheme shall explore the use of virtualization in system topics of research within the topic of Cloud Verification, and resources in order to minimize energy usage whilst Validation and Testing (VV&T) from formal verification still meeting the service requirements and operational through to empirical research and metric validation of multi constraints of a cloud. part or parallel analysis are discussed in [24]. The aim of the  To design a scheme to dynamically and automatically project in [25] is to apply constraint programming techniques schedule and deliver the services to the requested clients to solve issues in cloud environment efficiently. ensuring high availability of services and to develop The work in [26] explores the use of virtualization in billing and pricing model for measuring cloud services system and network resources in order to minimize energy utility. usage whilst still meeting the service requirements and operational constraints of a cloud. [27] discusses the IV. PROPOSED METHODOLOGY consequence of dynamically provisioned resource allocation under denial of service attacks, in order to reduce the wasting The research work aims at aggregating and providing of resources. [28] proposes to revise the analytical model to customized set of services to the requesting clients in an accommodate Cloud Computing and carry out experiments efficient manner. and measurements, to compare the responsiveness with 117 © 2013 ACEEE DOI: 03.LSCS.2013.1.560

Tutorial Paper Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013 A. Discovery of cloud services The cloud providers are networked by segmenting or clustering them based on type of services provided, geographical locations etc. Cognitive agents crawl blindly through the cloud to discover the cloud services fulfilling the futuristic requests and build the repository of services. Further request prediction may be done using log record, click stream record and user information or Markov model to anticipate futuristic requests for discovering the cloud services. Discovery process may be carried out in parallel using the concept of agent cloning. Repository shall be updated at regular frequency to eliminate the stale information using aging techniques. Multidimensional data structure shall be deployed to store the cloud services in the repository. Efficient indexing algorithms and meta-services (service cache) shall be adopted to retrieve the service information from the repository to improve the performance of the repository access. The repository shall store the services offered, vendor details, pricing, current status, QoS etc. B. Aggregation of requested services Based on the service requests from cloud clients cognitive agents monitor the status of the services, negotiate with the vendors, and aggregate them based on the specified QoS. Unsupervised learning mechanism may help the agents to negotiate intelligently for better prices to aggregate and distribute the cloud services. English auctions may be used to maximize the profits for vending the services. Multiple options of aggregated services are to be given to the clients in order to increase their satisfaction level. C. Distribution of services After the services are aggregated they are to be distributed in a customized way. Scheduling has to be done in an optimal way so as to maximize the availability and utility of services. Billing and pricing algorithms are to be developed for the delivered services. All the above objectives will be simulated under various scenarios to assess the performance and effectiveness of the proposed scheme. The simulation shall be carried out on IBM Blade Center HS22 using compatible programming language. V. POSSIBLE OUTCOME AND CONCLUSIONS Services on the cloud are plenty but the clients are not able to get the required services. There is no common repository of availability of cloud services. The proposed work will develop a framework to overcome the above mentioned issues. Further the usage of cognitive agents offers several benefits in aggregating cloud services such as autonomy in discovering the cloud services, developing and updating the repository, embedding intelligence, flexibility in negotiation, adaptability to network environments, customization of QoS requirements etc. The research work may be enhanced in future by employing some agent based solutions to other issues such as cloud management, enablement, and interoperability and to develop some applications. Further we have planned to publish our research 118 © 2013 ACEEE DOI: 03.LSCS.2013.1.560

findings in referred journals and present in national/ international conferences. REFERENCES [1] Ignacio M. Llorente Key Research Challenges in Cloud computing challenges_in_cloud_computing.pdf [2] B. Rochwerger, J. Caceres, R.S. Montero, D. Breitgand, E. Elmroth, A. Galis, E. Levy,I.M. Llorente, K. Nagin, Y. Wolfsthal, “The RESERVOIR Model and Architecture for Open Federated Cloud Computing”, IBM Systems Journal, Vol. 53, No. 4. (2009) [3] B. Sotomayor, R. S. Montero, I. M. Llorente and I. Foster, “Virtual Infrastructure Management in Private and Hybrid Clouds”, IEEE Internet Computing, September/October 2009 (vol. 13 no. 5) [4] Rafael Moreno-Vozmediano, Ruben S. Montero, Ignacio M. Llorente, “Multi-Cloud Deployment of Computing Clusters for Loosely- Coupled MTC Applications”, IEEE Transactions 0n Parallel and Distributed Systems, in press [5] Mark Vanderwiele “The IBM Research Cloud Computing Initiative”, Keynote talk at ICVCI 2008, RTP, NC, USA, 15–16 May 2008. [6] WIKIPEDIA, “Cloud Computing”, wiki/Cloud computing, May 2008. [7] Hany H Ammar, Alaa Hamouda, Mustafa Gamal, Walid Abdelm oez and Ah med Moussa “Cam pusClou d: Aggregating Universities Computing Resources in Ad-Hoc Clouds” p273-ammar.pdf. [8] Bernstein, David; Ludvigson, Erik; Sankar, Krishna; Diamond, Steve; Morrow, Monique, “ Blueprint for the Intercloud – Protocols and Formats for Cloud Computing Interoperability”, IEEE Computer Society, 24-5-2009. [9] Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, James Broberg, Ivona Brandic, “Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility”, Journal of Future Generation Computer Systems, December 2008. openNebula http:// [10] Marty Humphrey, and Glenn Wasson, The University of Virginia Campus Grid: Integrating Grid Technologies with the Campus Information Infrastructure, Lecture Notes in Computer Science, Volume 3470/2005, pp 50-58. [11] CamGrid. [12] OxGrid 278 [13] Mladen A. Vouk Cloud Computing – Issues, Research and Implementations Journal of Computing and Information Technology – CIT 16, 2008, 4, 235–246 [14] W. M. BULKELEY, “IBM, Google, Universities Combine ‘Cloud’ Foces”, Wall Street Journal, October 8, 2007, available on [15] A. Dearle & Dr G. Kirby, Harvesting Unused Resources available from [16] A. Dearle & Dr G. Kirby, Ad-Hoc Clouds available from [17] G. Kirby & Prof. A. Dearle, Specifying, Measuring and Understanding High-Level Cloud Properties, available from [18] I. Gent, An Experimental Laboratory in the Cloud, available from

Tutorial Paper Proc. of Int. Conf. on Advances in Communication, Network, and Computing 2013 [19] S. Linton , Computational Group Theory with Map-Reduce , available from [20] I Sommerville, Data migration in the cloud, available from [21] I Sommerville, Socio-technical issues in cloud computing available from [22] T. Henderson, Mobile data archiving in the cloud, available from [23] I Duncan, Cloud Security, available from [24] I. Duncan, Cloud VV&T and Metrics, available from http:/ / [25] I. Miguel, A Dearle & G Kirby, Constraint- Based Cloud Managemen t, available from ht tp://w [26] Saleem, Bhatti, The Green Cloud, available from http:// [27] Colin Allison and Alan Miller, Denial of Service Issues in Cloud Com put ing available from h ttp://w ww. [28] Mohan Baruwal Chhetri, Bao Quoc Vo and Ryszard Kowalczyk Policy-based Management of QoS in Service Aggregations 201 0 1 0th IEE E/ACM Internation al Conference on Cluster, Cloud and Grid Computing [29] National institute of standards and technology definition of cloud computing available from researchers blog with URL as def-v15.pdf

© 2013 ACEEE DOI: 03.LSCS.2013.1. 560

[30] Evelyn Brown, Final Version of NIST Cloud Computing Definition Pu blished, 2 011, available from h ttp:// [31] G. Weiss. “Multiagent Systems: A Modern Approach to Distributed Articial Intelligence.” USA:MIT Press, 1999, pp. 619. [32] “UMBC Agents Web”,, [May 2010]. [33] S. Franlin and A. Graser, “Is it an agent or just a program”, Proc. Int ern ational Workshop on Agent Theories, Architectures and Languages (ATAL-96), 1996, pp. 2135. [34] N. R. Jennings. “Developing Agent based Systems.” IEEE Transactions on Proc. Software Enggineering, Vol. 144, pp. 424- 430, 1997. [35] J. Bradshaw , “Software Agents”, USA: AAAI Press, http:/ /ww w.taibah CITpapers/pdf/p2 73ammar.pdf. [36] A. S. Rao and Michel G. “Modeling Agents within a BDIArchitecture.” In Proc.International conference on Principles 0f Knowledge Representation and Reasoning,1991, pp. 473484. [37] P. Cohen and H. J. Levesque. “Intention Is Choice With Commitment.” Journal of Artificial Intelligence, Vol. 42, pp. 213-216, 1999. [38] Cloud computing Research directories/research- clouds/cloud-computing-research.php