Distributed, Concurrent, And Independent Access To Encrypted Cloud Databases

DISTRIBUTED, CONCURRENT, AND INDEPENDENT ACCESS TO ENCRYPTED CLOUD DATABASES

ABSTRACT:

Power efficiency is one of the main issues that will drive the design of data centers, especially of those devoted to provide Cloud computing services. In virtualized data centers, consolidation of Virtual Machines (VMs) on the minimum number of physical servers has been recognized as a very efficient approach, as this allows unloaded servers to be switched off or used to accommodate more load, which is clearly a cheaper alternative to buy more resources. The consolidation problem must be solved on multiple dimensions, since in modern data centers CPU is not the only critical resource: depending on the characteristics of the workload other resources, for example, RAM and bandwidth, can become the bottleneck. The problem is so complex that centralized and deterministic solutions are practically useless in large data centers with hundreds or thousands of servers. This paper presents ecoCloud, a selforganizing and adaptive approach for the consolidation of VMs on two resources, namely CPU and RAM. Decisions on the assignment and migration of VMs are driven by probabilistic processes and are based exclusively on local information, which makes the approach very simple to implement. Both a fluid-like mathematical model and experiments on a real data center show that the approach rapidly consolidates the workload, and CPU-bound and RAM-bound VMs are balanced, so that both resources are exploited efficiently.

EXISTING SYSTEM:

In the past few years important results have been achieved in terms of energy consumption reduction, especially by improving the efficiency of cooling and power supplying facilities in data centers. The Power Usage Effectiveness (PUE) index, defined as the ratio of the overall power entering the data center and the power devoted to computing facilities, had typical values between 2 and 3 only a few years ago, while now big Cloud companies have reached values lower than 1.1. However, much space remains for the optimization of the computing facilities themselves. It has been estimated that most of the time servers operate at 10-50 percent of their full capacity [2], [3]. This low utilization is also caused by the intrinsic variability of VMs’ workload: the data center is planned to sustain the peaks of load, while for long periods of time (for example, during nights and weekends), the load is much lower [4], [5]. Since an active but idle server consumes between 50 and 70 percent of the power consumed when it is fully utilized [6], a large amount of energy is used even at low utilization.

DISADVANTAGES OF EXISTING SYSTEM:

·       It is power consuming.

·       Large amount of energy is used even at low utilization.

PROBLEM STATEMENT:

The ever increasing demand for computing resources has led companies and resource providers to build large warehouse-sized data centers, which require a significant amount of power to be operated and hence consume a lot of energy.

SCOPE:

The optimal assignment of VM’s to reduce the power consumption.

PROPOSED SYSTEM:

We presented ecoCloud, an approach for consolidating VMs on a single computing resource, i.e., the CPU. Here, the approach is extended to the multidimension problem, and is presented for the specific case in which VMs are consolidated with respect to two resources: CPU and RAM. With ecoCloud, VMs are consolidated using two types of probabilistic procedures, for the assignment and the migration of VMs. Both procedures aim at increasing the utilization of servers and consolidating the workload dynamically, with the twofold objective of saving electrical costs and respecting the Service Level Agreements stipulated with users. All this is done by demanding the key decisions to single servers, while the data center manager is only requested to properly combine such local decisions. The approach is partly inspired by the ant algorithms used first by Deneubourg et al. [9], and subsequently by a wide research community, to model the behavior of ant colonies and solve many complex distributed problems. The characteristics inherited by such algorithms make ecoCloud novel and different from other solutions. Among such characteristics: 1) the use of the swarm intelligence paradigm, which allows a complex problem to be solved by combining simple operations performed by many autonomous actors (the single servers in our case); 2) the use of probabilistic procedures, inspired by those that model the operations of real ants; and 3) the self-organizing behavior of system, which ensures that the assignment of VMs to servers dynamically adapts to the varying workload.

ADVANTAGES OF PROPOSED SYSTEM:

·       Efficient CPU usage.

·       It reduces power consumption.

·       Efficient resource utilization.

SYSTEM ARCHITECTURE:

SYSTEM CONFIGURATION:-

HARDWARE REQUIREMENTS:-

ü Processor                  -        Pentium –IV

ü Speed                        -        1.1 Ghz

ü RAM                         -        512 MB(min)

ü Hard Disk                 -        40 GB

ü Key Board                -        Standard Windows Keyboard

ü Mouse                       -        Two or Three Button Mouse

ü Monitor                     -        LCD/LED

SOFTWARE REQUIREMENTS:

•         Operating system :         Windows XP

•         Coding Language :         Java

•         Data Base             :         MySQL

•         Tool                     :         Net Beans IDE

REFERENCE:

Carlo Mastroianni, Michela Meo and Giuseppe Papuzzo “Probabilistic Consolidation of Virtual Machines in Self-Organizing Cloud Data Centers” IEEE TRANSACTIONS ON CLOUD COMPUTING, VOL. 1, NO. 2, JULY-DECEMBER 2013.

Generating Summary Risk Scores For Mobile Applications

GENERATING SUMMARY RISK SCORES FOR MOBILE APPLICATIONS

ABSTRACT:

One of Android’s main defense mechanisms against malicious apps is a risk communication mechanism which, before a user installs an app, warns the user about the permissions the app requires, trusting that the user will make the right decision. This approach has been shown to be ineffective as it presents the risk information of each app in a “stand-alone” fashion and in a way that requires too much technical knowledge and time to distill useful information. We discuss the desired properties of risk signals and relative risk scores for Android apps in order to generate another metric that users can utilize when choosing apps. We present a wide range of techniques to generate both risk signals and risk scores that are based on heuristics as well as principled machine learning techniques. Experimental results conducted using real-world data sets show that these methods can effectively identify malware as very risky, are simple to understand, and easy to use.

EXISTING SYSTEM:

One of Android’s main defense mechanisms against malicious apps is a risk communication mechanism which warns the user about the permissions an app requires before the app is installed by the user, trusting that the user will make the right decision. The specific approach used in Android has been shown to be ineffective at informing users about potential risks. The majority of Android apps request multiple permissions. When a user sees what appears to be the same warning message for almost every app, warnings quickly lose any effectiveness as the users are conditioned to ignore such warnings.

DISADVANTAGES OF EXISTING SYSTEM:

·       It allows malicious application.

·       It reports the risk in stand alone manner.

·       Warnings quickly lose any effectiveness as the users are conditioned to ignore such warnings.

PROBLEM STATEMENT:

The main reason for the failure of the current Android warning approach is that it presents the risk information of each app in a “stand-alone” fashion.

SCOPE:

The idea of risk score functions to improve risk communication for Android apps, and identify three desiderata for an effective risk scoring function.

PROPOSED SYSTEM:

We thus propose the concept of risk scoring functions. Such a function assigns to each app a numerical score, which indicates how risky the app is. This approach presents “comparative” risk information, i.e., each app’s risk is presented in a way so that it can be easily compared to other apps. Given a risk scoring function, one can construct a risk signal by choosing threshold above which the signal is raised. However, we believe that it is better to use a risk scoring function for risk communication in the following way. Given this function, one can compute a risk ranking for each app, identifying the percentile of the app in terms of its risk score. This percentile number has a well defined and easy-to-understand meaning. Users can appreciate the difference between an app ranked in the top 1 percent group versus one in the bottom 50 percent. This ranking can be presented in a more user-friendly fashion, e.g., translated into categorical values such as high risk, medium risk, low risk, and very low risk. An important feature of the mobile app ecosystem is that users often have choices and alternatives when choosing a mobile app. If the user knows that one app is significantly more risky than another with similar functionality, then that may cause the user to choose the less risky one. Such an approach complements well other approaches that try to identify malicious apps. After malicious apps are removed, the remaining ones can be ranked according to their risks.

ADVANTAGES OF PROPOSED SYSTEM:

·       Framework that includes both the rarity-based risk signals and probabilistic models, and explore other ways to instantiate the framework.

·       Idea of risk score functions to improve risk communication for Android apps.

SYSTEM CONFIGURATION:-

HARDWARE REQUIREMENTS:-

ü Processor                  -        Pentium –IV

ü Speed                        -        1.1 Ghz

ü RAM                         -        512 MB(min)

ü Hard Disk                 -        40 GB

ü Key Board                -        Standard Windows Keyboard

ü Mouse                       -        Two or Three Button Mouse

ü Monitor                     -        LCD/LED

SOFTWARE REQUIREMENTS:

•         Operating system :         Android

•         Coding Language :         Android

•         Data Base             :         SQLite

•         Tool                     :         Eclipse

REFERENCE:

Christopher S. Gates, Ninghui Li, Hao Peng, Bhaskar Sarma, Yuan Qi,

Rahul Potharaju, Cristina Nita-Rotaru and Ian Molloy “Generating Summary Risk Scores for Mobile Applications” IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, VOL. 11, NO. 3, MAY-JUNE 2014.