The Experts below are selected from a list of 79308 Experts worldwide ranked by ideXlab platform

Gagangeet Singh Aujla - One of the best experts on this subject based on the ideXlab platform.

  • optimal decision making for big data processing at edge Cloud Environment an sdn perspective
    IEEE Transactions on Industrial Informatics, 2018
    Co-Authors: Gagangeet Singh Aujla, Albert Y Zomaya, Neeraj Kumar, Rajiv Ranjan
    Abstract:

    With the evolution of Internet and extensive usage of smart devices for computing and storage, Cloud computing has become popular. It provides seamless services such as e-commerce, e-health, e-banking, etc., to the end users. These services are hosted on massive geodistributed data centers (DCs), which may be managed by different service providers. For faster response time, such a data explosion creates the need to expand DCs. So, to ease the load on DCs, some of the applications may be executed on the edge devices near to the proximity of the end users. However, such a multiedge-Cloud Environment involves huge data migrations across the underlying network infrastructure, which may generate long migration delay and cost. Hence, in this paper, an efficient workload slicing scheme is proposed for handling data-intensive applications in multiedge-Cloud Environment using software-defined networks (SDN). To handle the inter-DC migrations efficiently, an SDN-based control scheme is presented, which provides energy-aware network traffic flow scheduling. Finally, a multileader multifollower Stackelberg game is proposed to provide cost-effective inter-DC migrations. The efficacy of the proposed scheme is evaluated on Google workload traces using various parameters. The results obtained show the effectiveness of the proposed scheme.

  • secsva secure storage verification and auditing of big data in the Cloud Environment
    IEEE Communications Magazine, 2018
    Co-Authors: Gagangeet Singh Aujla, Rajat Chaudhary, Neeraj Kumar, Ashok Kumar Das, Joel J P C Rodrigues
    Abstract:

    With the widespread popularity of Internet-enabled devices, there is an exponential increase in the information sharing among different geographically located smart devices. These smart devices may be heterogeneous in nature and may use different communication protocols for information sharing among themselves. Moreover, the data shared may also change with respect to various Vs (volume, velocity, variety, and value) to categorize it as big data. However, as these devices communicate with each other using an open channel, the Internet, there is a higher chance of information leakage during communication. Most of the existing solutions reported in the literature ignore these facts. Keeping focus on these points, in this article, we propose secure storage, verification, and auditing (SecSVA) of big data in Cloud Environment. SecSVA includes the following modules: an attribute-based secure data deduplication framework for data storage on the Cloud, Kerberos-based identity verification and authentication, and Merkle hash-tree-based trusted third-party auditing on Cloud. From the analysis, it is clear that SecSVA can provide secure third party auditing with integrity preservation across multiple domains in the Cloud Environment.

Kamalraj Subramaniam - One of the best experts on this subject based on the ideXlab platform.

  • Secure and efficient data forwarding in untrusted Cloud Environment
    Cluster Computing, 2019
    Co-Authors: Balamanigandan Ramachandran, Kamalraj Subramaniam
    Abstract:

    Nowadays, Cloud storage services increased the popular for data storage in the Cloud and retrieve from any location without any time limitations. In recent days one of the most important demands required in Cloud is secured data transmission in un-trusted Cloud applications. Due to user’s data security, the encrypted data is stored in Cloud server to protect from unauthorized users. Existing methods offer either data transformation efficiency or security. They fail to maintain end to end security during massive transformations. However, existing methods are not capable of solving the key complexity and avoiding key secrecy disclosure. The main objective of this study is to design and develop a secured efficient data forwarding algorithm for increasing the security level. It is developed specially for untrusted Cloud Environment. In order to provide a better solution, an efficient framework is proposed for forwarding and retrieving the content in the untrusted Cloud Environment. Proposed system implements dual privacy for reliable data transmission in an untrusted Cloud Environment. It develops efficient secret key exposure to minimize the key complexity during data transmission. SEDFA is used for one to many communications as a public key used for encryption and decryption. This scheme provides reliable data transmission between the data owner and end user in untrusted Cloud Environment. Proposed mechanisms minimized the data encryption time, decryption time and improved the communication cost. Based on experimental results, SEDFA reduces the communication cost 5%, encryption time (ET), 2 s, decryption time (DT) 0.5 s.

Prasanta K Jana - One of the best experts on this subject based on the ideXlab platform.

  • sla based task scheduling algorithms for heterogeneous multi Cloud Environment
    The Journal of Supercomputing, 2017
    Co-Authors: Sanjaya K Panda, Prasanta K Jana
    Abstract:

    Service-level agreement (SLA) is a major issue in Cloud computing because it defines important parameters such as quality of service, uptime, downtime, period of service, pricing, and security. However, the service may vary from one Cloud service provider (CSP) to another. The collaboration of the CSPs in the heterogeneous multi-Cloud Environment is very challenging, and it is not well covered in the recent literatures. In this paper, we present two SLA-based task scheduling algorithms, namely SLA-MCT and SLA-Min-Min for heterogeneous multi-Cloud Environment. The former algorithm is a single-phase scheduling, whereas the latter one is a two-phase scheduling. The proposed algorithms support three levels of SLA determined by the customers. Furthermore, the algorithms incorporate the SLA gain cost for the successful completion of the service and SLA violation cost for the unsuccessful end of the service. We simulate the proposed algorithms using benchmark and synthetic datasets. The experimental results of the proposed SLA-MCT are compared with three single-phase task scheduling algorithms, namely CLS, Execution-MCT, and Profit-MCT, and the results of the proposed SLA-Min-Min are compared with two-phase scheduling algorithms, namely Execution-Min-Min and Profit-Min-Min in terms of four performance metrics, namely makespan, average Cloud utilization, gain, and penalty cost of the services. The results clearly show that the proposed algorithms properly balance between makespan and gain cost of the services in comparison with other algorithms.

  • efficient task scheduling algorithms for heterogeneous multi Cloud Environment
    The Journal of Supercomputing, 2015
    Co-Authors: Sanjaya K Panda, Prasanta K Jana
    Abstract:

    Cloud Computing has grown exponentially in the business and research community over the last few years. It is now an emerging field and becomes more popular due to recent advances in virtualization technology. In Cloud Computing, various applications are submitted to the datacenters to obtain some services on pay-per-use basis. However, due to limited resources, some workloads are transferred to other data centers to handle peak client demands. Therefore, scheduling workloads in heterogeneous multi-Cloud Environment is a hot topic and very challenging due to heterogeneity of the Cloud resources with varying capacities and functionalities. In this paper, we present three task scheduling algorithms, called MCC, MEMAX and CMMN for heterogeneous multi-Cloud Environment, which aim to minimize the makespan and maximize the average Cloud utilization. The proposed MCC algorithm is a single-phase scheduling whereas rests are two-phase scheduling. We perform rigorous experiments on the proposed algorithms using various benchmark as well as synthetic datasets. Their performances are evaluated in terms of makespan and average Cloud utilization and experimental results are compared with that of existing single-phase and two-phase scheduling algorithms to demonstrate the efficacy of the proposed algorithms.

  • a multi objective task scheduling algorithm for heterogeneous multi Cloud Environment
    International Conference on Electronic Design, 2015
    Co-Authors: Sanjaya K Panda, Prasanta K Jana
    Abstract:

    Cloud Computing has become a popular computing paradigm which has gained enormous attention in delivering on-demand services. Task scheduling in Cloud computing is an important issue that has been well researched and many algorithms have been developed for the same. However, the goal of most of these algorithms is to minimize the overall completion time (i.e., makespan) without looking into minimization of the overall cost of the service (referred as budget). Moreover, many of them are applicable to single-Cloud Environment. In this paper, we propose a multi-objective task scheduling algorithm for heterogeneous multi-Cloud Environment which takes care both these issues. We perform rigorous experiments on some synthetic and benchmark data sets. The experimental results show that the proposed algorithm balances both the makespan and total cost in contrast to two existing task scheduling algorithms in terms of various performance metrics including makespan, total cost and average Cloud utilization.

  • a smoothing based task scheduling algorithm for heterogeneous multi Cloud Environment
    Grid Computing, 2014
    Co-Authors: Sanjaya K Panda, Subhrajit Nag, Prasanta K Jana
    Abstract:

    Task scheduling for heterogeneous multi-Cloud Environment is a well-known NP-complete problem. Due to exponential increase of client applications (i.e., workloads), Cloud providers need to adopt an efficient task scheduling algorithm to handle workloads. Furthermore, the Cloud provider may require to collaborate with other Cloud providers to avoid fluctuation of demands. This workload sharing problem is referred as heterogeneous multi-Cloud task scheduling problem. In this paper, we propose a task scheduling algorithm for heterogeneous multi-Cloud Environment. The algorithm is based on smoothing concept to organize the tasks. We perform rigorous experiments on synthetic and benchmark datasets and compare their results with two well-known multi-Cloud algorithms namely, CMMS and CMAXMS. The comparison results show the superiority of the proposed algorithm in terms of two evaluation metrics, makespan and average Cloud utilization.

Joel J P C Rodrigues - One of the best experts on this subject based on the ideXlab platform.

  • secsva secure storage verification and auditing of big data in the Cloud Environment
    IEEE Communications Magazine, 2018
    Co-Authors: Gagangeet Singh Aujla, Rajat Chaudhary, Neeraj Kumar, Ashok Kumar Das, Joel J P C Rodrigues
    Abstract:

    With the widespread popularity of Internet-enabled devices, there is an exponential increase in the information sharing among different geographically located smart devices. These smart devices may be heterogeneous in nature and may use different communication protocols for information sharing among themselves. Moreover, the data shared may also change with respect to various Vs (volume, velocity, variety, and value) to categorize it as big data. However, as these devices communicate with each other using an open channel, the Internet, there is a higher chance of information leakage during communication. Most of the existing solutions reported in the literature ignore these facts. Keeping focus on these points, in this article, we propose secure storage, verification, and auditing (SecSVA) of big data in Cloud Environment. SecSVA includes the following modules: an attribute-based secure data deduplication framework for data storage on the Cloud, Kerberos-based identity verification and authentication, and Merkle hash-tree-based trusted third-party auditing on Cloud. From the analysis, it is clear that SecSVA can provide secure third party auditing with integrity preservation across multiple domains in the Cloud Environment.

Venugopal K R - One of the best experts on this subject based on the ideXlab platform.

  • dscesm data security for Cloud Environment with scheduled key managers
    Social Science Research Network, 2020
    Co-Authors: Jeevitha B K, D Sindhura, J Thriveni, Venugopal K R
    Abstract:

    Distributed Cloud Environment (DCE) has the ability to store the data and share it with others. One of the main issues arises during this is, how safe the data in the Cloud while storing and sharing. Therefore, the communication media should be safe from any intruders residing between the two entities. What if the key generator compromises with intruders and shares the keys used for both communication and data? Therefore, the proposed system makes use of the Station-to-Station (STS) protocol to make the channel safer. The concept of encrypting the secret key confuses the intruders. Duplicate File Detector (DFD) checks for any existence of the same file before uploading. The scheduler assigns the work of generating keys to the key manager who has less task to complete or free of any task. By these techniques, the proposed system makes time-efficient, cost-efficient, and resource efficient compared to the existing system. The performance is analysed in terms of time, cost and resources.