Journal of Information Engineering and Applications www.iiste.org ISSN 2224-5782 (print) ISSN 2225-0506 (online) Vol.5, No.8, 2015 56 Spatial Query Performance For GIS cloud Raffat Izzat Saheel 1* Bahaa Shabana 2* A. M. Riad 3* Hazem M. El-Bakry 4* 1. Master degree candidate, Information Systems Dept, Faculty of computer and information system, Mansoura University, Mansoura Egypt. 2. Bahaa Shabana, Doctor of computer Sciences, Misr Higher Institute for Commerce & Computer, Mansoura Egypt. 3. A. M. Riad , Dept of Information systems, Faculty of Computer and Information Sciences, Mansoura University, Mansoura, Egypt. 4. Hazem M. El-Bakry, Dept of Information systems, Faculty of Computer and Information Sciences, Mansoura University, Mansoura, Egypt. * Rafateeerura@yahoo.com * BahaaShabana@yahoo.com * amriad2014@gmail.com * helbaky5@yahoo.com Abstract Geographic Information System (GIS) is very important in our live and spatial data is required for several fields. Cloud computing is one of the most technology used in the modern data interchange. Spatial data query response time over cloud depends on the cloud data resource. This paper presents a query response time measurement for cloud GIS query. Spatial Query Performance (SQP) is a software designed and represented in Java programming language for measuring query response time. SQP's main functionality is to compare the response time for two spatial data resource servers by asking one query for both servers in the same time and calculate the response time for each server. Google and Bing map servers are used as spatial data resources for measuring the query response time for each server. Google and Bing map servers are used as spatial data resources for measuring the query response time for each. SQP determines that Google is faster than Bing over different test times. Keywords: Cloud Computing, GIS, GIS Cloud, Bing map, Google map. Montilva et al. (2010) 1. Introduction As of late, Infrastructure as a Service (IaaS) distributed computing has developed as a suitable distinct option for the obtaining and administration of physical assets. With IaaS, clients can rent storage and processing time from extensive datacenters. Renting of calculation time is proficient by permitting clients to convey virtual machines (VMs) on the datacenter's assets. Since the client has complete control over the design of the VMs utilizing on-interest arrangements, IaaS renting is equivalent to obtaining committed equipment yet without the long haul responsibility and expense. The on-interest nature of IaaS is discriminating to making such rents appealing, since it empowers clients to extend or shrink their assets as per their computational needs, by utilizing outside assets to supplement their nearby asset base. B. Claudel et al.(2009). This rising model prompts new difficulties identifying with the outline and improvement of IaaS frameworks. One of the normally happening examples in the operation of IaaS is the need to convey an extensive number of VMs on numerous hubs of a data centre at the same time, beginning from an arrangement of VM in ages already put away in an industrious manner. For example, this pattern happens when the client needs to convey a virtual cluster that executes a circulated application or an arrangement of situations to bolster a work process. We allude to this example as multi deployment. Such an expansive sending of numerous VMs without a moment's delay can take a long time. This issue is especially intense for VM pictures utilized as a part of experimental figuring where picture sizes are huge (from a couple of gigabytes up to more than 10 GB). A run of the mill sending comprises of hundreds or even a great many such pictures. Customary organization procedures telecast the pictures to the nodes before beginning the VM occasions, a procedure that can take several minutes to hours, not including the time to boot the working framework itself. This can set aside a few minutes of the IaaS establishment any longer than worthy and delete the on-interest advantages of cloud computing. Once the VM examples are running, a comparable test applies to snapshotting the organization: numerous VM pictures that were by regional standards altered should be simultaneously exchanged to stable storage with the reason for catching the VM state for later utilize (e.g., for verify guiding or off-line movement toward another group or cloud). We allude to this example as multi snapshotting. Ordinary snapshotting approaches depend on custom VM picture file formats to store just incremental contrasts in another document that relies on upon the first VM picture as the sponsorship record, figure1.