Security Enhancement in Cloud Environment using Secure Secret Key Sharing

Securing the data in distributed cloud system is considered one of the major concern for the cloud customers who faces security risks. The data leakage or data tampering are widely used by attackers to extract the private information of other users who shares the confidential data through virtualization. This paper presents Secure Secret Sharing (SSS) technique which is being recognized as one of the leading method to secure the sensitive data. It shares encrypted data over cloud and generated secret key is split into different parts distributed to qualified participants (Qn) only which is analyzed by malicious checkers. It verifies the clients based on their previous performances, whether these users proved to be authorized participant or not. The key computation is evaluated by the Key handler (KH) called trusted party which manages authorized control list, encryption/decryption and reconstruction of key shares. The Lagrange’s interpolation method is used to reconstruct the secret from shares. The experimental results shows that the proposed secure data sharing algorithm not only provides excellent security and performance, but also achieves better key management and data confidentiality than previous countermeasures. It improves the security by using secure VM placement and evaluated based on time consumption and probability computation to prove the efficacy of our algorithm. Experiments are performed on cloudsim based on following parameters i.e. time computation of key generation; response time and encryption/decryption. The experimental results demonstrate that this method can effectively reduce the risks and improves the security and time consumption up to 27.81% and 43.61% over existing algorithms

Dynamic Resource Allocation Method for Load Balance Scheduling over Cloud Data Center Networks

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of clients applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89 and 58.49 in the network

A smart resource management mechanism with trust access control for cloud computing environment

The core of the computer business now offers subscription-based on-demand services with the help of cloud computing. We may now share resources among multiple users by using virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. It provides infinite computing capabilities through its massive cloud datacenters, in contrast to early distributed computing models, and has been incredibly popular in recent years because to its continually growing infrastructure, user base, and hosted data volume. This article suggests a conceptual framework for a workload management paradigm in cloud settings that is both safe and performance-efficient. A resource management unit is used in this paradigm for energy and performing virtual machine allocation with efficiency, assuring the safe execution of users' applications, and protecting against data breaches brought on by unauthorised virtual machine access real-time. A secure virtual machine management unit controls the resource management unit and is created to produce data on unlawful access or intercommunication. Additionally, a workload analyzer unit works simultaneously to estimate resource consumption data to help the resource management unit be more effective during virtual machine allocation. The suggested model functions differently to effectively serve the same objective, including data encryption and decryption prior to transfer, usage of trust access mechanism to prevent unauthorised access to virtual machines, which creates extra computational cost overhead

TRENDCASTING FMCG SECTOR IN INDIA

ABSTRAC

A Review on Secure Cloud Resource Management

Security is always a prime concern of every cloud user. Many pioneer surveys have been done concerning security during data communication among owners, users, and stakeholders. However, there is a lack of literature surveys that address the concern of security during data processing or the workload execution within cloud data centers. In this context, we present a concise review of the existing methods that have addressed the security of workload or user data during load scheduling and execution within cloud environments. These methods have applied load-balancing strategies that helped to minimize the security attacks via co-residency or covert channels. The pandect comparative summary of these methods follows the corresponding review. The paper is concluded with limitations and future direction concerning secure load management.</p

EoG based Biopotential Instrumentation Amplifier

To examine the biomedical signals from the human body is of a challenging task. Signal acquisition of the biosignal needs proper design methodology with the estimation of consequences further. In this paper we discuss about the signal acquisition of EoG using the surface electrodes and to amplify the weak biopotential signal an amplifier is proposed with the additive noise reduction filter. The biopotential instrumentation amplifier is designed using standard operational amplifier that posses the high voltage swing. However the major artifacts present in the EoG signal acquisition is because of power line interference can be eliminated by notch filter