In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

Resource scheduling in cloud is a challenging job and the scheduling of appropriate resources to cloud workloads depends on the QoS requirements of cloud applications. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounters problems of allocation of resources, which cannot be addressed with existing resource allocation policies. Researchers still face troubles to select the efficient and appropriate resource scheduling algorithm for a specific workload from the existing literature of resource scheduling algorithms. This research depicts a broad methodical literature analysis of resource management in the area of cloud in general and cloud resource scheduling in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 110 research papers out of large collection of 1206 research papers published in 19 foremost workshops, symposiums and conferences and 11 prominent journals. The current status of resource scheduling in cloud computing is distributed into various categories. Methodical analysis of resource scheduling in cloud computing is presented, resource scheduling algorithms and management, its types and benefits with tools, resource scheduling aspects and resource distribution policies are described. The literature concerning to thirteen types of resource scheduling algorithms has also been stated. Further, eight types of resource distribution policies are described. Methodical analysis of this research work will help researchers to find the important characteristics of resource scheduling algorithms and also will help to select most suitable algorithm for scheduling a specific workload. Future research directions have also been suggested in this research work.

A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges

Cloud resource provisioning is a challenging job that may be compromised due to unavailability of the expected resources. Quality of Service (QoS) requirements of workloads derives the provisioning of appropriate resources to cloud workloads. Discovery of best workload---resource pair based on application requirements of cloud users is an optimization problem. Acceptable QoS cannot be provided to the cloud users until provisioning of resources is offered as a crucial ability. QoS parameters-based resource provisioning technique is therefore required for efficient provisioning of resources. This research depicts a broad methodical literature analysis of cloud resource provisioning in general and cloud resource identification in specific. The existing research is categorized generally into various groups in the area of cloud resource provisioning. In this paper, a methodical analysis of resource provisioning in cloud computing is presented, in which resource management, resource provisioning, resource provisioning evolution, different types of resource provisioning mechanisms and their comparisons, benefits and open issues are described. This research work also highlights the previous research, current status and future directions of resource provisioning and management in cloud computing.

Cloud resource provisioning: survey, status and future research directions

Cloud computing is an emerging technology in distributed computing which facilitates pay per model as per user demand and requirement. Cloud consist of a collection of virtual machine which includes both computational and storage facility. The primary aim of cloud computing is to provide efficient access to remote and geographically distributed resources. Cloud is developing day by day and faces many challenges, one of them is scheduling. Scheduling refers to a set of policies to control the order of work to be performed by a computer system. A good scheduler adapts its scheduling strategy according to the changing environment and the type of task. In this research paper we presented a Generalized Priority algorithm for efficient execution of task and comparison with FCFS and Round Robin Scheduling. Algorithm should be tested in cloud Sim toolkit and result shows that it gives better performance compared to other traditional scheduling algorithm.

/pdf/efficient-optimal-algorithm-of-task-scheduling-in-cloud-4ezsmwp09t.pdf

Efficient Optimal Algorithm of Task Scheduling in Cloud Computing Environment

Fog computing, similar to edge computing, has been proposed as a model to introduce a virtualized layer between the end users and the back-end cloud data centers. Fog computing has attracted much attention due to the recent rapid deployment of smart devices and Internet-of-Things (IoT) systems, which often requires real-time, stringent-delay services. The fog layer placed between client and cloud layers aims to reduce the delay in terms of transmission and processing times, as well as the overall cost. To support the increasing number of IoT, smart devices, and to improve performance and reduce cost, this paper proposes a task scheduling algorithm in the fog layer based on priority levels. The proposed architecture, queueing and priority models, priority assignment module, and the priority-based task scheduling algorithms are carefully described. Performance evaluation shows that, comparing with existing task scheduling algorithms, the proposed algorithm reduces the overall response time and notably decreases the total cost. We believe that this work is significant to the emerging fog computing technology, and the priority-based algorithm is useful to a wide range of application domains.

/pdf/prioritized-task-scheduling-in-fog-computing-4q6m8yty57.pdf

Prioritized task scheduling in fog computing

Today Cloud computing is on demand as it offers dynamic flexible resource allocation, for reliable and guaranteed services in pay-as-you-use manner, to Cloud service users So there must be a provision that all resources are made available to requesting users in efficient manner to satisfy their needs This resource provision is done by considering the Service Level Agreements (SLA) and with the help of parallel processing Recent work considers various strategies with single SLA parameter Hence by considering multiple SLA parameter and resource allocation by preemption mechanism for high priority task execution can improve the resource utilization in Cloud In this paper we propose an algorithm which considered Preemptable task execution and multiple SLA parameters such as memory, network bandwidth, and required CPU time An obtained experimental results show that in a situation where resource contention is fierce our algorithm provides better utilization of resources

Priority Based Dynamic Resource Allocation In Cloud Computing

Cloud computing which is widely emerging technology in the recent years is adopted by most of the IT companies and other organizations. Storing large amount of data by the user in the LAN may cost the user heavily. We use different electronic storage device like file servers, Storage Area Networks (SAN) and Network Attached Storage (NAS) which provide high performance and many useful features to the user but the electronic storage has many drawbacks like it cost more, its life time is less and needs backup. Cloud storage avoid these problem by storing data in a virtual pool hosted by TPA. The TPA is responsible for maintaining the user data. But cloud storage has many draw back in which integrity and security of data stored in cloud storage is issues among it. So we create a scheme by which we provide a secure saving of our confidential data in cloud storage in an efficient manner which requires low computational power and time and disallowing hacker from penetrating into our privacy data storage. And we provide a simple and easy integrity checking mechanism when compared to other already present one by which we can verify whether our data is not corrupted and deleted or modified ours is an efficient. Our integrity checking mechanism is simple that it does not take more computational power. The mechanism even prevents the TPA who maintains our data in cloud storage from editing our file.

Providing security and integrity for data stored in cloud storage

The pattern of Palmprint has high dissimilarity because of its numerous determination features like ridges, principle lines, datum points, and wrinkles and so on In this paper, a blend of Haar Wavelet, DCT and Fast ICA have been utilized to recognize the palmprint pattern using fast ICA The images of Palm were initially decomposed by the Haar Wavelet and frequency band, independent of different image resolutions chosen for dimensionality decrease Next on LL, band DCT was use Lastly, Fast ICA use to discover the independent components of the DCT features To find matching image, Euclidean Distance (ED) has been used The procedure is validated on Polytechnic- University (Poly U) storage as well as the results show the efficiency and accuracy of the proposed method

Palmprint based Pattern Recognition Using Fast ICA

The advent of technology has also increased the traffic hazards and road accidents take place frequently which causes huge loss of life and property because of the poor emergency facilities. Our project will provide an optimum solution to this drawback. A Tilt Sensor can be used in a car alarm application so that dangerous driving can be detected. It can be used as a crash or rollover detector of the vehicle during and after a crash. With signals from a tile sensor, a severe accident can be recognized. According to this project when a vehicle meets with an accident immediately Impact sensor will detect the signal or if a car rolls over, and Tilt sensor will detect the signal and sends it to a microcontroller. The microcontroller sends the alert message through the GSM MODEM including the location to the police control room or consultant people. So the police can immediately trace the location through the GPS MODEM, after receiving the information. Then after confirming the location, necessary action will be taken. If the person meets with a small accident or if there is no serious threat to anyone`s life, then the alert message can be terminated by the driver by a panic switch.

Smart Vehicle Accident Detection System

An image retrieval primarily based on texture features by the usage of the contribution based clustering method to form the cluster is an efficient way to extract the perfect image from database. Color features, shape features and texture feature approach is used for feature extraction. Clustering is an unmonitored category method which place the identical object in a cluster and dissimilar objects are place in unique clusters. Contribution based clustering algorithm is partition based clustering algorithm which offers the better results than that of k-mean clustering algorithm. This contribution based clustering algorithm process on ‘contribution of data points’. In image retrieval, use of clustering method gives the better results. To match query image with the database image, feature extraction, similarity measures, indexing and retrieval etc. steps are applied.

/pdf/a-review-on-contribution-based-clustering-algorithm-with-2zz5ddrmqv.pdf

Chandrashekhar S. Pawar

Papers

Priority Based Dynamic Resource Allocation In Cloud Computing

Providing security and integrity for data stored in cloud storage

Palmprint based Pattern Recognition Using Fast ICA

Smart Vehicle Accident Detection System

A Review on Contribution Based Clustering Algorithm with Distinct Features