M Tech Dissertations

Permanent URI for this collectionhttp://drsr.daiict.ac.in/handle/123456789/3

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    ItemOpen Access
    Empirical Study Of Smartphones As An Edge Device
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Shah, Vyom Hiteshkumar; Sasidhar, Kalyan
    Increased automation and intelligence in computer systems have revealed Cloudbasedcomputing constraints such as unpredictable latency in safety-critical andperformance-sensitive applications. Features of smartphones attract researchersmore towards using smartphones as edge computing devices because of the presenceof the sensors inbuilt and the computing powers of CPU cores. So a smartphoneis a combination of IoT and Edge computing devices.To overcome the usage of high-end computing devices at the edge layer, thisarticle proposes the idea of using a smartphone as an edge device for processingdata. Sensors or IoT devices generally send data to the edge device ratherthan directly sending it to the cloud for processing. So mainly, this article emphasizessolving the research question of whether smartphones can be used as edgedevices. So in this, a distributed smartphone system following master-slave architectureis proposed, which helps to distribute the computation power amongslaves. Word count, average of temperature data and indoor localization. Comparedto desktop PCs computation, master-slave utilizes CPU 75% more than juston single-device computation and on an average 50% faster than on computingon a single device. This motivates us to design an architecture that can utilizethe data from the cloud and perform the computation using the CPU cores of thesmartphone.
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    Privacy preserving identity verification with binding blinding technique in cloud assisted healthcare applications
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2018) Patwa, Dolly R.; Das, Manik Lal
    In recent times, broad deployment of computers and mobile devices such as mobile phones, PDA's rigged with cost efficient sensors, have shown great potential in recognizing positive direction to healthcare services. It serves patients even in the remote locations. Remote patient monitoring enables serving patients outside of clinical conventional setting at hospitals. It may increase easy caring and monitoring of health frequently and decrease healthcare delivery costs. Healthcare applications for chronic diseases have significantly improved user's quality of life. It allows user to monitor his health frequently and decreases cost since he get personalized care as conventional doctor and hospital settings. mHealth is abbreviation of mobile health. It involves provision of healthcare services via smart phones and attached wearable wireless devices. Several healthcare systems proposed and it discusses privacy of user and security of user's data. Healthcare system involves healthcare companies, user and cloud mainly as participating entities in the system. Privacy should be maintained in a sense that nobody other than the user should be able to know his current health condition and get appropriate personalized care. Since companies lack of storage and computational resources, security of user's medical data is at risk. Several healthcare application systems have been proposed over the past decade yet user's privacy and security is a potential threat to such a system. Therefore, analysis of such systems is required. In this thesis work, we analyzed a healthcare application system and found outsider attack and proposed mitigation to prevent this attack. Our proposed mitigation borrows the idea of binding-blinding technique. We made security analysis of our proposed scheme as well as its performance analysis.
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    ItemOpen Access
    SQL-GQL inter-query translation for Google App engine datastore
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2012) Kotecha, Shyam; Bhise, Minal
    On demand services, usage based pricing, and scalability features of cloud computing has attracted many customers to move their applications into cloud. But different cloud service providers are using different standards & frameworks to host applications & data. Customers have to follow these standards and frameworks. When customer wants to migrate application and/or data to another cloud service provider, application code and database structure must be modified according to the standard of new cloud service provider. This modification is very costly and as a consequence, changing cloud service provider becomes difficult. This situation is called vendor lock-in in cloud. Focusing on database, complete database migration requires migration of data, database schema, and query. This thesis work concentrates on migration of query. Automation in migration process is achieved by translation algorithms. This thesis work introduces inter-query translation algorithms. These algorithms translate SQL (Structured Query Language) query and GQL (Google Query Language) query into each other. The implementation of these algorithms is demonstrated for MySQL Sakila database
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    Approach to build multi-tenant SaaS application with monitoring and SLA
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2012) Aghera, Piyush; Chaudhary, Sanjay
    SaaS (Software as a Service) is a modern approach to deliver large scalable enterprise software as a service on Internet. Cloud computing platform provides the scalability, availability and utility computing for services on internet. There are many technical challenges involved in SaaS development. One of them is multi-tenancy, which allows single instance of software to serve multiple organizations by accommodating their unique requirements through conguration at the same time. SaaS architecture requires both conguration and some level of customization to achieve higher maturity model. In this thesis, we propose a metadata based SaaS application architecture which is independent of underlying cloud infrastructure. We aim to propose independent SaaS platform concepts, to avoid vendor locking as observed in case of many commercial service providers. SaaS application development should be independent of underlying infrastructure so that application can be migrated from one cloud to another cloud without changing the code. It is possible only if all the players follow the identical as well dened standard SaaS architecture. Our proposed architecture includes monitoring, tenant management, tenant administration, tenant conguration and large data management services. Existing architecture has used simple XML le to store and retrieve tenant specic conguration. File operations are the bottle neck while accessing data for large organization at the same time. In this approach, we have used Memcached concept and it is supported by almost all databases to boost the performance. In addition to this, we have considered application pooling on a web server to manage priority among tenants. Application pooling works as a static load balancer for incoming large request. To realize proposed architecture, we have developed and demonstrated seleced functionalities of University Management System and it is capable to support multi-tenancy.
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    Service level agreement parameter matching in cloud computing
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Chauhan, Tejas; Chaudhary, Sanjay; Bise, Minal
    Cloud is a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or software services). It provides an on-demand, pay-asyo-ugo computing resources and had become an alternative to traditional IT infrastructure. As more and more consumers delegate their task to cloud providers, Service Level Agreement (SLA) between consumer and provider becomes an important aspect. Due to the dynamic nature of cloud the matching of service level agreement need to be dynamic and continuous monitoring of Quality of Service (QoS) is necessary to enforce SLAs. This complex nature of cloud warrants a sophisticated means of managing SLAs. SLA contains many parameters like cloud’s types of services, resources (physical memory, main memory, processor speed, ethernet speed etc.) and properties (availability, response time, server reboot time etc.). At present, actual Cloud SLAs are typically plain-text documents, and sometimes an informative document published online. Consumer needs to manually match application requirements with each and every cloud provider to identify compatible cloud provider. This work addresses the issue of matching SLA parameters to find best suitable cloud provider. Proposed algorithm identifies the compatible cloud provider by matching parameters of application requirements and cloud SLAs. It gives suggestion to a consumer in terms of number of matched parameters.
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    ItemOpen Access
    Policy based resource allocation on infrastructure as a service cloud
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Vora, Dhairya; Chaudhary, Sanjay; Bise, Minal
    Cloud computing refers to the provision of computational resources on demand. Resource allocation is an important aspect in cloud computing. Cloud user asks for resources in terms of a lease. Lease stores the information about required resources and the time at which these resources are required. Cloud provider accepts the lease if it can provide guarantee for assigning resources at asked time to the cloud user. Better scheduling algorithm can accept more number of leases and hence give better resource utilization. Cloud provides four types of leases: immediate, advance reservation, best effort and deadline sensitive. Immediate allocation policy accepts the lease if resources are available, else it rejects the lease. Advance reservation policy accepts the lease if resources are available at the asked time, else it rejects the lease. Best effort allocation policy accepts the lease as soon as the resources are available. Deadline sensitive leases have parameters like required resources, startTime, endTime and duration. Scheduler can accept such lease by providing required resources for the asked duration of time between given startTime and endTime. Haizea is a resource lease manager which handles the scheduling of the lease. Proposed algorithm extends the current scheduling algorithm of Haizea for deadline sensitive type of leases. Aim of the thesis is to improve resource utilization by extending the current scheduling algorithms of Haizea. Proposed scheduling algorithm accepts more number of leases by dividing a deadline sensitive lease into multiple slots and by back filling already.
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    Migration of database from one cloud to other clouds
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Bhatt, Shreyansh; Chaudhary, Sanjay
    On demand services and scalability features of cloud computing has attracted many customers to move their applications into cloud. Cloud service providers are following di erent standards to host applications and data. Data must be stored according the schema of a particular cloud service provider. A need can arise to migrate cloud application and/or data to another cloud service provider. In that case, the relevant code, and structure of database must be modi ed based on newly identi ed cloud service provider. Which is a costly deal and as a consequence, chang ing cloud service provider becomes di cult. This issue is regarded as vendor lock-in in terms of cloud computing. Current study will help to identify issues of migrating database between two clouds and development of novel techniques, which would facilitate this migration. For this, RDF /RDFS (Resource Description Framework/Resource Description Framework Schema) is used as an intermediate model. Automation in migration process is achieved by transformation algorithms. Bigtable, Google App Engine datastore, is taken as a cloud datastore and algorithms are developed and implemented to convert RDF/RDFS data into data that can be stored in Bigtable and vice versa. Results are shown for the same. Subsequently, the same algorithm is generalized to store RDF/RDFS data into any cloud datastore.
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    Resource allocation on infrastructure as a service cloud using policies
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2010) Nathani, Amit; Divakaran, Srikrishnan
    Conventionally, a cloud refers to an Infrastructure as a service cloud. Infrastructure as a service cloud providers manage a large set of computing resources. These resources can be provided to cloud users on demand in the form of virtual machines. Cloud consumers do not need to manage resources and be worried about the performance issues because they are handled by cloud providers. Resource allocation in the context of infrastructure as a service cloud means allocating virtual resources namely computing capacity, storage etc. to competing requests based on pre-defined resource allocation policies. In real world most of the Infrastructure as a service clouds rely on simple resource allocation policies like immediate and best effort. Immediate means the resources are allocated if they are available or the request is rejected and best effort means the requested resources are allocated if they are available or the request is placed in first come first serve queue. Sometimes it is not possible for a cloud provider to satisfy all the requests which come to them immediately because of lack of resources. In this case cloud providers can benefit from more complex resource allocation policies. Haizea is a resource lease manager that tries to address above issues. It uses resource leases as resource allocation abstraction and implements these leases as virtual machines. Currently, it supports four kinds of resource allocation policies: immediate, best-effort, advance reservation and deadline sensitive. The aim of thesis is to extend the current scheduling algorithm of Haizea to support deadline leases in an efficient manner. A dynamic planning based scheduling algorithm is proposed which will admit new leases and prepare the schedule whenever a new lease can be accommodated. The proposed algorithm is implemented in Haizea. Experiments are performed to demonstrate the effectiveness of it. The results show that it maximizes resource utilization and acceptance of leases compared to the existing algorithm of Haizea.
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    Negotiation for resource allocation on infrastructure as a service cloud
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2010) Akhani, Janki; Divakaran, Srikrishnan
    The Cloud is a computing platform that provides dynamic resource pools, virtualization, and high availability. Cloud computing infrastructures can allow enterprises to achieve more efficient use of their IT hardware and software investments. Infrastructure As A Service (IAAS) cloud providers manage a large set of computing resources. These resources can be provided to cloud consumers on demand in the form of virtual machines. Cloud consumers do not need to manage resources and be worried about the performance issues because they are handled by cloud providers. Open Nebula is an open source cloud toolkit which can be used to setup an IAAS cloud. It has three components: Open Nebula Core, Virtual Machine Scheduler and Cloud Drivers. Haizea is an open-source resource lease manager, and can act as a virtual machine scheduler for Open Nebula or used on its own as a simulator to evaluate the performance of different scheduling strategies. Haizea supports four kinds of resource allocation policies: immediate, best-effort, advance reservation and deadline sensitive. To reserve resources in advance using Haizea, consumer submits parameters like amount of resources, start time and duration of a reservation as a request. If one or more parameters can not be satisfied, then Haizea will reject the request. This method is very rigid method because it does not allow negotiation of any parameter. Consumer can resubmit new requests by modifying previously submitted request parameters. Consumer will not be aware of the current resource allocation on provider side so, the chances of new requests getting rejected are more. Thus, it will increase communication overhead between cloud provider and consumer as well as it will decrease resource utilization on provider’s side. It will also degrade the performance of a provider in managing many incoming requests due to previously rejected ones. To overcome the above problems, negotiation can be provided. Negotiation process consists of three components which are negotiation protocol, negotiation objectives and agents’ decision making algorithm. The proposed algorithm to generate set of counter offers is a part of decision making model at provider side. It provides set of counter offers to consumer when his advance reservation request gets rejected. It provides set of counter offers considering parameters’ flexibilities to maximize the chances of their acceptance. The proposed algorithm for User selection policy is a part of decision making model at consumer side. Consumer can get best suitable offer from set of counter offers using the algorithm of user selection policy. Ranking algorithm is a partof algorithm for user selection policy. Using this ranking algorithm, consumers will get suitable offers sorted according to their needs. It will reduce consumers’ efforts to go through all the provided counter offers and choose best suitable one. These algorithms are implemented in Haizea. Experiments are performed to demonstrate the effectiveness of algorithms. The results show that the proposed algorithm to generate counter offers maximizes resource utilization and acceptance of requests compared to rigid and exact methods.
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    Issues of computational resource allocation and load balancing in cloud computing using virtualization
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2009) Bhadani, Abhay Kumar; Chaudhary, Sanjay
    With the advent of world wide web, the life of every person has changed drastically. No one can imagine a computer without being connected to the Internet. This dependency is likely to grow in coming years with the adoption of technologies like virtualization and cloud computing. Cloud Computing and Virtualizations one of the foremost technology which has attracted many researchers recently, which is directly going to benefit the end-users and data center service providers. It has many underlying benefits, one of them is directly related to the costs of deploying new servers. Few others are related to harnessing the power of the existing infrastructure and resources. When we look things from server side resource usage, things become quite challenging. Just to meet peak loads, high capacity servers are deployed, which remains underutilized most of the times on an average. With the help of virtualization technique, we can run multiple instances of different operating systems simultaneously. There are many virtualization tools available, which can be used to achieve maximum resource utilization. Making efficient use of computing resource (especially computational time) has always been a critical and challenging task. Several scheduling algorithms have been in place, proposed and implemented on one or other Operating Systems from time to time. But, since we have limited processors and things work in concurrent fashion, overload situation can occur hampering the overall objective, performance and throughput of the system. Things become even more challenging when it comes to distributed systems and load balancing. One of the key commercial player in the virtualization is VMWare, whereas Xen is an open-source free software. There are also few tools like Virtual Box, Linux VServer, OpenVZ, Virtuozzo and few others. Among these the most popular virtual machine monitor is Xen. The likely choice of experimenting with Xen is due to Open Source and wide acceptance by Linux community, and their continuous effort to improve, also it meets the need of industry standards at large. Consider a hypothetical scenario, where multiple instances of operating systems are running and is being used by the clients over the web in the form of cloud service. The user will use the system as if the whole server and/or system is dedicated to him. The user is completely unaware about the actual physical location of the server on which he is running his applications and disk storage space. Since, multiple OS are running on a single physical server, and multiple servers are running in the data centre. All connected via high speed network links. At some instance of time, one server may become overloaded, while other server may remain underutilized. This again poses challenge to distribute the load and make things work perfect in this situation. This situation can be handled using load balancing mechanism over the virtual machines. This thesis work tries to find a mechanism to balance the load based on computational time parameter of the virtual machines.