Page 1

International Journal of Engineering Science Invention ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726 Volume 2 Issue 12 ǁ December. 2013 ǁ PP.16-20

Resourceful multideployement and multisnapshotting to retroduce Traffic and burden in cloud agent-based metropolitan transport systems 1

Ms. S. J. Sapra, 2Prof. C. U. Chauhan , 3Dr. C. G. Dethe 1

Research Scholar Department of Computer science & Engineering Priyadarshini Institute of Engineering & Technology Nagpur University, Maharashtra, India 2 Assistant Professor Department of Computer science & Engineering Priyadarshini Institute of Engineering & Technology Nagpur University, Maharashtra, India 3 Principal Department of Computer Science & Engineering Priyadarshini Institute of Engineering & Technology Nagpur University, Maharashtra, India

ABSTRACT: Agent-based traffic management systems can use the independence, flexibility, and elasticity of mobile agents to deal with dynamic traffic environments. Cloud computing will help such system manage the large amounts of storage and computing resources required to use traffic strategy agents and mass transport data efficiently. This reviews the history of the improvement of traffic control and management systems within the evolving computing paradigm and shows the state of traffic control and management systems based on mobile multi agent technology. Infrastructure as a Service (IaaS) cloud computing has revolutionized the way we think of acquiring resources by introducing a simple change: allowing users to lease computational resources from the cloud provider’s datacenter for a short time by deploying virtual machines (VMs) on these resources. This new model raises new challenges in the design and development of IaaS middleware. One of those challenges is the need to arrange a large number (hundreds or even thousands) of VM instances simultaneously. Once the VM instances are organized, another challenge is to instantaneously take a snapshot of many images and transfer them to persistent storage to support controlling tasks, such as suspend-resume and migration. With datacenters increasing quickly and configurations becoming dissimilar, it is important to enable well-organized concurrent deployment and snapshotting that are at the same time hypervisor independent and ensure a maximum compatibility with different configurations. This paper addresses these challenges by proposing a virtual file system specifically optimized for virtual machine image storing. It is based on a lazy transfer structure coupled with object versioning that handles snapshotting transparently in a hypervisor-autonomous fashion, confirming high portability for different configurations. Extensive experiments on hundreds of nodes establish excellent performance results: speedup for concurrent VM deployments ranges from a factor of 2 up to 25, with a decrease in bandwidth use of as much as 90%.This paper is a tutorial review of the urban based transportation system.

GENERAL TERMS: Operating Systems: Storage Management. Design, Performance, Experimentation KEYWORDS: Agents, Intelligent transportation systems (ITS), Intelligent traffic clouds (ITC), Mobile agent systems (MAS), Multiagent systems (MS)



Intelligent transportation clouds could provide services such as judgment support, a standard growth of environment for traffic management policies, and so on. With mobile agent knowledge, an urban-traffic administration system based on Agent-Based Distributed and Adaptive Platforms for Transportation Systems (Adapts) is both feasible and operative. However, the extensive use of mobile agents will lead to the emergence of a complex, powerful association layer that requires enormous computing and power resources. To deal with this problem, we propose a prototype urban-traffic management system using intelligent traffic clouds. In recent years, Infrastructure as a Service (IaaS) cloud computing [30] has emerged as a viable alternative to the acquisition and management of physical resources. With IaaS, users can lease storage and calculation time from large datacenters. Hiring of computation time is consummate by allowing users to deploy virtual machines (VMs) on the datacenter’s incomes. Since the user has whole control over the configuration of the VMs using on-demand deployments [5, 6], IaaS leasing is equivalent to purchasing dedicated hardware but without the long-term assurance and cost. The on-demand nature of IaaS is serious to making such agreements

16 | Page

Resourceful multideployement and multisnapshotting‌ attractive, since it enables users to enlarge or contract their resources according to their computational needs, by using external resources to supplement their local resource base [2]. This emerging model leads to new challenges relating to the design and development of IaaS systems. One of the frequently occurring patterns in the operation of IaaS is the need to deploy a large number of VMs on many nodes of a datacenter at the similar time, starting from a set of VM images earlier stored in a determined fashion. For example, this pattern occurs when the user wants to deploy a virtual cluster that executes a distributed application or a set of environments to support a workflow. We refer to this pattern as multi deployment. Such a large deployment of many VMs at once can take extensive time. This problem is particularly severe for VM images used in scientific computing where image sizes are large (from a few gigabytes up to more than 10 GB). A typical organization consists of hundreds or even thousands of such images. Conventional deployment techniques broadcast the images to the nodes before starting the VM instances, a procedure that can take tens of minutes to hours, not as well as the time to boot the operating system itself. This can make the reply time of the IaaS installation much longer than acceptable and erase the on-demand benefits of cloud computing. Once the VM occurrences are running, a analogous challenge applies to snapshotting the deployment: many VM images that were locally modified need to be concurrently transferred to stable storage with the purpose of capturing the VM state for later use (e.g., for check pointing or off-line migration to another cluster or cloud). We refer to this pattern as multi snapshotting. Conventional snapshotting techniques rely on custom VM image file formats to store only incremental differences in a new file that depends on the original VM image as the support file. When taking regular snapshots for a large number of VMs, such methods generate a large number of files and interdependencies among them, which are problematic to manage and which inhibit with the ease-of-use rationale behind clouds. Furthermore, with growing datacenter trends and tendencies to federate clouds, configurations are becoming more and more heterogeneous. Tradition image formats are not uniform and can be used with precise hypervisors only, which limits the ability to easily transfer VMs among different hypervisors. Therefore, multi snapshotting must be handled in a transparent and portable fashion that hides the interdependencies of incremental differences and exposes standalone VM images, while trust maximum movability among different hypervisor arrangements. In addition to experiencing important delays and raising manageability issues, these patterns may also produce high network traffic that interferes with the execution of applications on leased resources and generates high utilization costs for the user. This paper proposes a distributed virtual file system specifically optimized for both the multi deployment and multi snapshotting arrangements. Since the arrangements are complementary, we inspect them in conjunction. Our suggestion offers a good balance between performance, storage space, and network traffic depletion, while handling snapshotting visibly and exposing standalone, raw image files (understood by most hypervisors) to the outside.



In [1] overview is given of current applications of computers to traffic control. It contains a argument of types of hardware and control strategies used in computerized systems developed for the control of urban street networks, as well as serious traffic links such as freeways and channels. Some remarks are made concerning possible future development in the use of computers for better management of traffic facilities. In [2] to build an emergency management parallel system for a chemical plant, and progress the success and competence of emergency management, a refined decomposition method for the emergency response plans (ERPs) is proposed in this paper to convert an unstructured ERP document into a structured description with cell activities. Based on the cell activities, evaluation and training of ERPs and emergency response support are convenient to be implemented. Moreover, the proposed method is a basic technique to build a rule database for an artificial emergency management system. The paper shows a preliminary probe into the emergency management parallel system. [3] In this paper, many distributed-memory parallel computers and high-speed communication networks, the particular structure of the essential communication network may be unnoticed. These systems undertake that the network creates a complete communication graph between the processors, in which passing messages is related with communication latencies. In this paper we explore the influence of communication expectancies on the design of broadcasting algorithms for fully connected message-passing systems. For this purpose, we make known to the postal model that incorporates a communication latency parameter Ν ≼ 1. This

17 | Page

Resourceful multideployement and multisnapshotting… parameter measures the inverse of the ratio between the time it takes an originator of a message to send the message and the time that passes until the recipient of the message accepts it. We present an best algorithm for broadcasting one message in systems with n processors and communication latency λ, the in succession time of which is Θ((λ log n)/log(λ + 1)). For broadcasting m ≥ 1 messages, we first inspect several generalizations of the algorithm for broadcasting one message and then analyze a family of broadcasting algorithms based on degree-d trees. In [4] Agent technology was used in traffic management systems as early as 1992, while multiagent traffic organization systems were presented later. However, all these systems focus on negotiation and collaboration between static agents for coordination and optimization. In 2004, mobile agent technology began to attract the attention of the transport field. The characteristics of mobile agents—autonomous, mobile, and adaptive—make them appropriate to handling the uncertainties and inconstant states in a self-motivated environment. The mobile agent changes through the network to reach control devices and implements appropriate strategies in either autonomous or submissive modes. In this way, traffic devices only essential to provide an operating platform for mobile traffic agents working in dynamic environments, without having to cover every traffic strategies. This method saves storage and computing capacity in physical control devices, which helps moderate their update and replacement rates. Moreover, when handled with the different requirements of dynamic traffic divisions, a multiagent system taking advantage of mobile agents will perform better than any static agent system. In [5] As cloud computing becomes progressively popular, well-organized management of VM images, such as image circulation to calculate nodes and image snapshotting for check pointing or migration, is serious. The concert of these operations directly affects the usability of the benefits offered by cloud work out systems. This paper introduced numerous techniques that integrate with cloud middleware to efficiently handle two patterns: multi deployment and multi snapshotting.



In this paper a distributed virtual file system specifically optimized for both the multi deployment and multi snapshotting arrangements. Since the arrangements are corresponding, we examine them in conjunction. Our application offers a good balance between presentation, storage space, and network traffic depletion, while conduct snapshotting transparently and exposing standalone, raw image files (understood by most hypervisors) to the outside. We introduce a series of design principles that optimize multi deployment and multi snapshotting patterns and describe how our design can be integrated with IaaS infrastructures. We show how to realize these design principles by building a virtual file system that leverages versioning-based distributed storage services. To demonstrate this point, we define an implementation on top of Blob Seer, a versioning storage service precisely designed for high throughput under concurrency. It proposes to aggregate the storage space from the compute nodes in a shared common pool that is managed in a circulated fashion, on top of which we form our virtual file system. This methodology has two key advantages. First, it has a possible for high scalability, as increasing number of compute nodes spontaneously leads to a larger VM image origin, which is not the case if the origin is presented by enthusiastic machines. Second, it releases a large amount of storage space and overhead related to VM management on dedicated storage nodes, which can improve enactment and/or quality-of-service guarantees for specialized storage services that the applications running inside the VMs require and are often offered by the cloud provider (e.g., database engines, distributed hash tables, exceptional perseverance file systems etc.) An important issue in this context is to be able to lever age the storage space provided by the local disks without interfering with the normal VM execution. For this purpose, only a part of the local disk is allocated to the common pool, while the rest is freely usable by the hypervisor and the VMs.

18 | Page

Resourceful multideployement and multisnapshotting‌

Fig 1: Related work of the cloud agent- based metropolitan system When a new VM needs to be instantiated, the essential VM image is presented to the hypervisor as a regular file accessible from the local disk. Read and write right to use to the file, however, are trapped and canned in a special fashion. A read that is hand out on a fully or partially empty region in the file that has not been accessed before (by either a previous read or write) results in fetching the missing content remotely from the VM repository, mirroring it on the local disk and relaying the read to the local copy. If the complete region is available locally, no distant read is performed. Writes, on the other hand, are continuously performed in the neighborhood. Each VM image is fragmented into small, equal-sized chunks that are evenly circulated among the local disks participating in the shared pool. After a read accesses area of the image that is not obtainable locally, the chunks that embrace this region are resolute and transferred in parallel from the remote disks that are responsible for loading them. Under concurrency, this scheme efficiently enables the distribution of the I/O workload, because right to use dissimilar parts of the image is served by different disks. Saving a full VM image for each VM is not feasible in the context of multisnapshotting. Since only lesser parts of the VMs are improved, this would mean enormous unnecessary replication of data, leading not only to an eruption of utilized storage space but also to unacceptably high snapshotting time and network bandwidth utilization. For this reason, several custom image file formats were proposed that optimize taking incremental VM image snap- shots. For example, Qemu/KVM introduced the QCOW2 format for this purpose, while other works such as proposes the Mirage Image Format (MIF). This approach enables snapshots to share unchanged content, which sinks storage space requirements.



In this paper propose that conclude that, it is Application based and used to control the heavy (Metros) traffic etc. Further, ATS will also efficiently store and update traffic strategy agents and their performance. Lastly, set up an ATS to test performance of the Urban-traffic management system based on the map showing the distribution of agents. In this used Multideployement and Multisnapshotting algorithm for future work.



We like to express our appreciation to Mr. S. Deshmukh, for his guidance and encouragement.

19 | Page

Resourceful multideployement and multisnapshotting… REFERENCES [1] [2] [3]

[4] [5] [6] [7] [8] [9] [10] [11] [12]

[13] [14] [15] [16]

D.C. Gazis, ―Traffic Control: From Hand Signals to Computers,‖ Proc. IEEE, vol. 59, no. 7, 1971, pp. 1090–1099. F.-Y. Wang, ―Parallel System Methods for Management and Control of Complex Systems,‖ Control and Decision, vol. 19, no. 5, 2004, pp. 485–489. A. Bar-Noy and S. Kipnis. Designing broadcasting algorithms in the postal model for message-passing systems. In SPAA ’92: Proceedings of the 4th Annual ACM Symposium on Parallel Algorithms and Architectures, pages 13–22, New York, 1992. ACM. Zhenjiang Li and Cheng Chen and Kai Wang ―Cloud Computing for Agent Based Urban Transportation Systems‖ Proc. IEEE.pp.2011. F.-Y. Wang, ―Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications,‖ IEEE Trans. Intelligent Transportation Systems, vol. 11, no. 3, 2010, pp. 1–9. Bogdan Nicolae, John Bresnahan, Kate Keahey, ―Going Back and Forth: Efficient Multideployment and Multisnapshotting on Clouds ― B. Chen and H. H. Cheng, ―A Review of the Applications of Agent Technology in Traffic and Transportation Systems,‖ IEEE Trans. Intelligent Transportation Systems, vol. 11, no. 2, 2010, pp. 485–497. M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia. A view of cloud computing. Commun. ACM, 53:50–58, April 2010. F.-Y. Wang, ―Toward a Revolution in Transportation Operations: AI for Complex Systems,‖ IEEE Intelligent Systems, vol. 23, no. 6, 2008, pp. 8–13. P. H. Carns, W. B. Ligon, R. B. Ross, and R. Thakur. Pvfs: A parallel file system for Linux clusters. In Proceedings of the 4th Annual Linux Showcase and Conference, pages 317–327, Atlanta, GA, 2000. USENIX Association. B. Claudel, G. Huard, and O. Richard. Taktuk, adaptive deployment of remote executions. In HPDC ’09: Proceedings of the 18th ACM International Symposium on High Performance Distributed Computing, pages 91–100, New York, 2009. ACM. G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels. Dynamo: Amazon’s highly available key-value store. In SOSP ’07: Proceedings of 21st ACM SIGOPS Symposium on Operating Systems Principles, pages 205–220, New York, 2007. ACM. M. Gagn´e. Cooking with Linux—still searching for the ultimate Linux distro? Linux J., 2007(161):9, 2007. J. G. Hansen and E. Jul. Scalable virtual machine storage using local disks. SIGOPS Oper. Syst. Rev., 44:71–79, December 2010. M. Hibler, L. Stoller, J. Lepreau, R. Ricci, and C. Barb. Fast, scalable disk imaging with Frisbee. In ATC ’03: Proceedings of the 2003 USENIX Annual Technical Conference, pages 283–296, San Antonio, TX, 2003. Y. J´egou, S. Lant´eri, J. Leduc, M. Noredine, G. Mornet, R. Namyst, P. Primet, B. Quetier, O. Richard, E.-G. Talbi, and T. Ir´ea. Grid’5000: A large scale and highly reconfigurable experimental grid testbed. International Journal of High Performance Computing Applications, 20(4):481–494, November 2006.

20 | Page