ieee projects 2012 2013 - cloud computing

Elysium Technologies Private Limited Approved by ISO 9001:2008 and AICTE for SKP Training Singapore | Madurai | Trichy | Coimbatore | Cochin | Kollam | Chennai http://www.elysiumtechnologies.com, [email protected]

IEEE Final Year Projects 2012 |Student Projects | Cloud Computing Projects

IEEE FINAL YEAR PROJECTS 2012 – 2013

Cloud Computing

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0452 – 4390702, 4392702, +9199447933980

Email: [email protected], [email protected]

Website: www.elysiumtechnologies.com

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15, III Floor, SI Towers, Melapudur main road, Trichy – 620 001.

0431 – 4002234, +919790464324.

Email: [email protected], [email protected].


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+919677751577

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0474 – 2723622, +919446505482.

Email: [email protected].


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4th

Floor, Anjali Complex, near south over bridge, Valanjambalam,

Cochin – 682 016, Kerala.

0484 – 6006002, +917736004002.

Email: [email protected], Website: www.elysiumtechnologies.com

mailto:[email protected]









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CLOUD COMPUTING 2012 – 2013

A In wireless sensor networks, adversaries can make use of traffic information to locate the monitored objects, e.g., to

hunt endangered animals or kill soldiers. In this paper, we first define a hotspot phenomenon that causes an obvious

inconsistency in the network traffic pattern due to a large volume of packets originating from a small area. Second, we

develop a realistic adversary model, assuming that the adver-sary can monitor the network traffic in multiple areas,

rather than the entire network or only one area. Using this model, we introduce a novel attack called Hotspot-Locating

where the adversary uses traffic analysis techniques to locate hotspots. Finally, we propose a cloud-based scheme for

efficiently pro-tecting source nodes' location privacy against Hotspot-Locating attack by creating a cloud with an

irregular shape of fake traffic, to counteract the inconsistency in the traffic pat-tern and camouflage the source node in

the nodes forming the cloud. To reduce the energy cost, clouds are active only during data transmission and the

intersection of clouds creates a larger merged cloud, to reduce the number of fake packets and also boost privacy

preservation. Simulation and analyti-cal results demonstrate that our scheme can provide stronger privacy protection

than routing-based schemes and requires much less energy than global-adversary-based schemes.

Scientific workflow has recently become an enabling technology to automate and speed up the scientific discovery

process. Although several scientific workflow management systems (SWFMSs) have been developed, a formal scientific

workflow composition model in which workflow constructs are fully compositional one with another is still missing. In

this paper, we propose a dataflow-based scientific workflow composition framework consisting of (1) a dataflow-based

scientific workflow model that separates the declaration of the workflow interface from the definition of its functional

body; (2) a set of workflow constructs, including Map, Reduce, Tree, Loop, Conditional, and Curry, which are fully

compositional one with another; (3) a dataflow-based exception handling approach to support hierarchical exception

propagation and user-defined exception handling. Our workflow composition framework is unique in that workflows are

the only operands for composition; in this way, our approach elegantly solves the two-world problem in existing

composition frameworks, in which composition needs to deal with both the world of tasks and the world of workflows.

The proposed framework is implemented and several case studies are conducted to validate our techniques.

A Dataflow-Based Scientific Workflow Composition Framework

A Cloud-Based Scheme for Protecting Source-Location Privacy against Hotspot-

Locating Attack in Wireless Sensor Networks



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A cloud storage system, consisting of a collection of storage servers, provides long-term storage services over the

Internet. Storing data in a third party's cloud system causes serious concern over data confidentiality. General

encryption schemes protect data confidentiality, but also limit the functionality of the storage system because a few

operations are supported over encrypted data. Constructing a secure storage system that supports multiple functions

is challenging when the storage system is distributed and has no central authority. We propose a threshold proxy re-

encryption scheme and integrate it with a decentralized erasure code such that a secure distributed storage system is

formulated. The distributed storage system not only supports secure and robust data storage and retrieval, but also

lets a user forward his data in the storage servers to another user without retrieving the data back. The main technical

contribution is that the proxy re-encryption scheme supports encoding operations over encrypted messages as well as

forwarding operations over encoded and encrypted messages. Our method fully integrates encrypting, encoding, and

forwarding. We analyze and suggest suitable parameters for the number of copies of a message dispatched to storage

servers and the number of storage servers queried by a key server. These parameters allow more flexible adjustment

between the number of storage servers and robustness.

Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in

order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands

of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its

quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the

physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and

predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely

difficult. We address this problem by trying to allocate a placement that not only satisfies the predicted communication

demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the

Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NP-Hard, hence, we introduce several

heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement

computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good

results (compared to the optimal solution) for medium size data centers.

A Secure Erasure Code-Based Cloud Storage System with Secure Data Forwarding

A Stable Network-Aware VM Placement for Cloud Systems

A Time-Series Pattern Based Noise Generation Strategy for Privacy Protection in Cloud

Computing



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Cloud computing promises an open environment where customers can deploy IT services in a pay-as-you-go fashion

while saving huge capital investment in their own IT infrastructure. Due to the openness, various malicious service

providers may exist. Such service providers may record service information in a service process from a customer and

then collectively deduce the customer's private information. Therefore, from the perspective of cloud computing

security, there is a need to take special actions to protect privacy at client sides. Noise obfuscation is an effective

approach in this regard by utilising noise data. For instance, it generates and injects noise service requests into real

customer service requests so that service providers would not be able to distinguish which requests are real ones if

their occurrence probabilities are about the same. However, existing typical noise generation strategies mainly focus

on the entire service usage period to achieve about the same final occurrence probabilities of service requests. In fact,

such probabilities can fluctuate in a time interval such as three months and may significantly differ than other time

intervals. In this case, service providers may still be able to deduce the customers' privacy from a specific time interval

although unlikely from the overall period. That is to say, the existing typical noise generation strategies could fail to

protect customers' privacy for local time intervals. To address this problem, we develop a novel time-series pattern

based noise generation strategy. Firstly, we analyse previous probability fluctuations and propose a group of time-

series patterns for predicting future fluctuated probabilities. Then, based on these patterns, we present our strategy by

forecasting future occurrence probabilities of real service requests and generating noise requests to reach about the

same final probabilities in the next time interval. The simulation evaluation demonstrates that our strateg- can cope

with these fluctuations to significantly improve the effectiveness of customers' privacy protection.

Cloud computing paradigm allows subscription-based access to computing and storages services over the Internet.

Since with advances of Cloud technology, operations such as discovery, scaling, and monitoring are accomplished

automatically, negotiation between Cloud service requesters and providers can be a bottleneck if it is carried out by

humans. Therefore, our objective is to offer a state-of-the-art solution to automate the negotiation process in Cloud

environments. In previous works in the SLA negotiation area, requesters trust whatever QoS criteria values providers

offer in the process of negotiation. However, the proposed negotiation strategy for requesters in this work is capable of

assessing reliability of offers received from Cloud providers. In addition, our proposed negotiation strategy for Cloud

providers considers utilization of resources when it generates new offers during negotiation and concedes more on the

price of less utilized resources. The experimental results show that our strategy helps Cloud providers to increase their

profits when they are participating in parallel negotiation with multiple requesters.

This paper is aimed to create implementation crawler engine or search engine using cloud computing infrastructure.

This approach use virtual machines on a cloud computing infrastructure to run service engine crawlers and also for

An Autonomous Reliability-Aware Negotiation Strategy for Cloud Computing

Environments

Building crawler engine on cloud computing infrastructure



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application servers. Based on our initial experiments, this research has successfully built crawler engine that runs on

Virtual Machine (VM) of cloud computing infrastructure. The use of Virtual Machine (VM) on this architecture will help to

ease setup or installation, maintenance or VM terminating that has been running with some particular service crawler

engine as needed. With this infrastructure, the increasing or decreasing in capacity and capability of multiple engine

crawlers could set easily and more efficiently.

In Utility computing business model, the owners of the computing resources negotiate with their potential clients to sell

computing power. The terms of the Quality of Service (QoS) and the economic conditions are established in a Service-

Level Agreement (SLA). There are many scenarios in which the agreed QoS cannot be provided because of errors in the

service provisioning or failures in the system. Since providers have usually different types of clients, according to their

relationship with the provider or by the fee that they pay, it is important to minimize the impact of the SLA violations in

preferential clients. This paper proposes a set of policies to provide better QoS to preferential clients in such

situations. The criterion to classify clients is established according to the relationship between client and provider

(external user, internal or another privileged relationship) and the QoS that the client purchases (cheap contracts or

extra QoS by paying an extra fee). Most of the policies use key features of virtualization: Selective Violation of the

SLAs, Dynamic Scaling of the Allocated Resources, and Runtime Migration of Tasks. The validity of the policies is

demonstrated through exhaustive experiments.

In this paper, we describe COCA -- Computation Offload to Clouds using AOP (aspect-oriented programming). COCA is

a programming framework that allows smart phones application developers to offload part of the computation to

servers in the cloud easily. COCA works at the source level. By harnessing the power of AOP, COCA inserts

appropriate offloading code into the source code of the target application based on the result of static and dynamic

profiling. As a proof of concept, we integrate COCA into the Android development environment and fully automate the

new build process, making application programming and software maintenance easier. With COCA, mobile applications

can now automatically offload part of the computation to the cloud, achieving better performance and longer battery

life. Smart phones such as iPhone and Android phones can now easily leverage the immense computing power of the

cloud to achieve tasks that were considered difficult before, such as having a more complicated artificial-intelligence

engine.

The Cloud represents a computing paradigm where shared configurable resources are provided as a service over the

Client Classification Policies for SLA Enforcement in Shared Cloud Datacenters

COCA: Computation Offload to Clouds Using AOP

Efficient Resource Mapping Framework over Networked Clouds via Iterated Local Search

based Request Partitioning



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Internet. Adding intra or inter cloud communication resources to the resource mix leads to a networked cloud

computing environment. Following the Cloud Infrastructure as a Service paradigm and in order to create a flexible

management framework, it is of paramount importance to address efficiently the resource mapping problem within this

context. To deal with the inherent complexity and scalability issue of the resource mapping problem across different

administrative domains, in this article a hierarchical framework is described. First, a novel request partitioning

approach based on Iterated Local Search is introduced that facilitates the cost-efficient and on-line splitting of user

requests among eligible Cloud service Providers (CPs) within a networked cloud environment. Following and

capitalizing on the outcome of the request partitioning phase, the embedding phase - where the actual mapping of

requested virtual to physical resources is performed – can be realized through the use of a distributed intra-

cloud resource mapping approach that allows for efficient and balanced allocation of cloud resources. Finally, a

thorough evaluation of the proposed overall framework on a simulated networked cloud environment is provided and

critically compared against an exact request partitioning solution as well as another common intra-domain virtual

resource embedding solution.

In wireless sensor networks, adversaries can make use of traffic information to locate the monitored objects, e.g., to

hunt endangered animals or kill soldiers. In this paper, we first define a hotspot phenomenon that causes an obvious

inconsistency in the network traffic pattern due to a large volume of packets originating from a small area. Second, we

develop a realistic adversary model, assuming that the adver-sary can monitor the network traffic in multiple areas,

rather than the entire network or only one area. Using this model, we introduce a novel attack called Hotspot-Locating

where the adversary uses traffic analysis techniques to locate hotspots. Finally, we propose a cloud-based scheme for

efficiently pro-tecting source nodes' location privacy against Hotspot-Locating attack by creating a cloud with an

irregular shape of fake traffic, to counteract the inconsistency in the traffic pat-tern and camouflage the source node in

the nodes forming the cloud. To reduce the energy cost, clouds are active only during data transmission and the

intersection of clouds creates a larger merged cloud, to reduce the number of fake packets and also boost privacy

preservation. Simulation and analyti-cal results demonstrate that our scheme can provide stronger privacy protection

than routing-based schemes and requires much less energy than global-adversary-based schemes.

The remote monitoring system is growing very rapidly due to the growth of supporting technologies as well. Problem

that may occur in remote monitoring such as the number of objects to be monitored and how fast, how much data to be

transmitted to the data center to be processed properly. This study proposes using a cloud computing infrastructure as

processing center in the remote sensing data. This study focuses on the situation for sensing on the environment

condition and disaster early detection. Where those two things, it has become an important issue, especially in big cities

big cities that have many residents. This study proposes to build the conceptual and also prototype model in a

Environmental and disaster sensing using cloud computing infrastructure

Energy-Aware Autonomic Resource Allocation in Multitier Virtualized Environments



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comprehensive manner from the remote terminal unit until development method for data retrieval. We also propose

using FTR-HTTP method to guarantee the delivery from remote client to server.

Cloud computing enables IT systems to be scalable and elastic. One significant advantage of it is users no longer need

to determine their exact computing resource requirements upfront. Instead, they request computing resources as

required, on-demand. This paper is written to introduce a framework specific for large public sector entities on how to

migrate to cloud computing. This paper can then be also be a reference for the Organizations to overcome its limitations

and to convince their stakeholders to further implement various types of Cloud Computing service models.

Cloud Computing allows the use of information technology based on the on-demand utility. This technology can provide

benefits to small and medium enterprises with limited capital, human resources, and access to marketing network. A

survey conducted on SMEs in the district of Coblong Bandung to dig up the IT needs and analyze their readiness to

adopt cloud computing technologies. The survey results stated that SMEs' respondents are more suitable to implement

Software as a Service with public cloud deployment method. SMEs are ready to implement this technology, but require

appropriate training and role models that can be used as an example because their technology adoption characteristics

that are late majority.

The social networks have revolutionized the online communication and data sharing. The researchers are now focusing

on mining and analysis of large amount of social network data for a variety of purposes. However, because of the huge

amount of continuously changing data, the data analysis in a daunting task. OLAP analysis is a famous data analysis

method which can be used to analyze social data. This work extends our previous work in which we developed

interactive 3D visual data cubes for high volume/dimension OLAP data analysis. The implementation of this scheme on

traditional computing resources is much time consuming and resource intensive. The advances in cloud computing

motivated us to use the cost effective cloud computing for the task of 3D visualization of social networks data.

Therefore, in this paper, we propose the usage of cloud computing platforms as a possible solution for analyzing large

amount of social network data. .

Framework on large public sector implementation of cloud computing

Identification of SME readiness to implement cloud computing

Interactive 3D visualization of soical network data using cloud computing



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In hosting environments such as IaaS clouds, desirable application performance is usually guaranteed through the use

of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated for

unencumbered use for proper operation. Arbitrary colocation of applications with different SLAs on a single host may

result in inefficient utilization of the host's resources. In this paper, we propose that periodic resource allocation and

consumption models -- often used to characterize real-time workloads -- be used for a more granular expression of

SLAs. Our proposed SLA model has the salient feature that it exposes flexibilities that enable the infrastructure provider

to safely transform SLAs from one form to another for the purpose of achieving more efficient colocation. Towards that

goal, we present MORPHOSYS: a framework for a service that allows the manipulation of SLAs to enable efficient

colocation of arbitrary workloads in a dynamic setting. We present results from extensive trace-driven simulations of

colocated Video-on-Demand servers in a cloud setting. These results show that potentially-significant reduction in

wasted resources (by as much as 60%) are possible using MORPHOSYS.

Large-scale polynomial product computations often used in aerospace applications such as satellite image processing

and sensor networks data processing always pose considerable challenge when processed on networked computing

systems. With non-zero communication and computation time delays of the links and processors on a networked

infrastructure, the computation becomes all the more challenging. In this research, we attempt to investigate the use of a

divisible load paradigm to design efficient strategies to minimize the overall processing time for performing large-scale

polynomial product computations in compute cloud environments. We consider a compute cloud system with the

resource allocator distributing the entire load to a set of virtual CPU instances (VCI) and the VCIs propagating back the

processed results to resource allocator for postprocessing. We consider heterogeneous networks in our analysis and

we derive fundamental recursive equations and a closed-form solution for the load fractions to be assigned to each VCI.

Our analysis also attempts to eliminate any redundant VCI-link pairs by carefully considering the overheads associated

with load distribution and processing. Finally, we quantify the performance of the strategies via rigorous simulation

studies.

As cloud computing becomes more and more popular, understanding the economics of cloud computing becomes

critically important. To maximize the profit, a service provider should understand both service charges and business

Optimal Multiserver Configuration for Profit Maximization in Cloud Computing

MORPHOSYS: Efficient Colocation of QoS-Constrained Workloads in the Cloud

On Handling Large-Scale Polynomial Multiplications in Compute Cloud Environments

using Divisible Load Paradigm



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costs, and how they are determined by the characteristics of the applications and the configuration of a multiserver

system. The problem of optimal multiserver configuration for profit maximization in a cloud computing environment is

studied. Our pricing model takes such factors into considerations as the amount of a service, the workload of an

application environment, the configuration of a multiserver system, the service level agreement, the satisfaction of a

consumer, the quality of a service, the penalty of a low quality service, the cost of renting, the cost of energy

consumption, and a service provider's margin and profit. Our approach is to treat a multiserver system as an M/M/m

queueing model, such that our optimization problem can be formulated and solved analytically. Two server speed and

power consumption models are considered, namely, the idle-speed model and the constant-speed model. The

probability density function of the waiting time of a newly arrived service request is derived. The expected service

charge to a service request is calculated. The expected net business gain in one unit of time is obtained. Numerical

calculations of the optimal server size and the optimal server speed are demonstrated.

In cloud computing, cloud providers can offer cloud consumers two provisioning plans for computing resources,

namely reservation and on-demand plans. In general, cost of utilizing computing resources provisioned by reservation

plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in advance. With

the reservation plan, the consumer can reduce the total resource provisioning cost. However, the best advance

reservation of resources is difficult to be achieved due to uncertainty of consumer's future demand and providers'

resource prices. To address this problem, an optimal cloud resource provisioning (OCRP) algorithm is proposed by

formulating a stochastic programming model. The OCRP algorithm can provision computing resources for being used in

multiple provisioning stages as well as a long-term plan, e.g., four stages in a quarter plan and twelve stages in a yearly

plan. The demand and price uncertainty is considered in OCRP. In this paper, different approaches to obtain the solution

of the OCRP algorithm are considered including deterministic equivalent formulation, sample-average approximation,

and Benders decomposition. Numerical studies are extensively performed in which the results clearly show that with the

OCRP algorithm, cloud consumer can successfully minimize total cost of resource provisioning in cloud computing

environments.

We propose a policy-based framework for the automated establishment of SLAs for cloud computing services. The

proposed framework supports multiple interaction models for SLA establishment giving consumers and providers the

flexibility to choose one that is most appropriate in a given context, while simultaneously supporting multiple

concurrent SLA interactions using different interaction models. We describe the underlying policies, focussing on the

key features and contributions of the framework. We also validate our framework through a real-world use-case scenario

using the Amazon EC2 service.

Optimization of Resource Provisioning Cost in Cloud Computing

Policy-Based Automation of SLA Establishment for Cloud Computing Services



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Applications stored in the cloud enable users to access and perform tasks in real time, reducing costs in the acquisition

of computer resources. Although there are benefits, this paradigm also brings security and privacy risks to users, such

as theft of information or identity. This paper proposes a mechanism able to provide privacy protection for users to use

applications that address issues of identity, confidentiality and user preferences.

Real-time rendering for massive terrain data is a challenging work. Previous GPUs are not suitable for rendering

massive mesh data. Recently, with tessellation shaders and geometry shader added on a GPU, it is possible to tessellate

triangles or quad patches to improve geometrical features of mesh objects. In this paper, we propose a massive terrain

rendering technique in real-time using GPUs. We made displacement and normal map from massive terrain data on the

GPU. As a result, we could tessellate a coarse base mesh with a high resolution texture as displacement map and

normal map for shading from massive terrain data.

As more public cloud computing platforms are emerging in the market, a great challenge for these Infrastructure as a

Server (IaaS) providers is how to measure the cost and charge the Software as a Service (SaaS) clients for the cloud

computing services. This problem is compounded as virtualization technology is deployed in many cloud platforms to

consolidate servers and improve their utilization. This paper studies three different but related models for apportioning

costs in a private or public cloud environment supported by virtualized data centers. With given workload placement

scenarios and randomly selected workloads, these models estimate the cost for each workload. Through simulations

and thorough comparisons of the results, we finally champion the RO-BURST model tailored for the service providers'

need, that is characterized by robustness and burstiness. What is more, we import Cost Volatility Factors to ensure that

our model is able to adjust itself to the market and multiform demands in power and hardware components, such as

disks and CPU, showing its compatibility and extensibility. We also come up with a pricing strategy with respect to

servers the workload employs, which generates an applicable and less placement-sensitive fee for the clients

In Cloud Computing, Service Level Agreement (SLA) is a contract that defines a level and a type of QoS between a cloud

provider and a client. Since applications in a Cloud share resources, we propose two tree-based distributed mutual

exclusion algorithms that support the SLA concept. The first one is a modified version of the priority-based Kanrar-

Privacy Mechanism for Applications in Cloud Computing

Real-time rendering for massive terrain data using GPUs

RO-BURST: A Robust Virtualization Cost Model for Workload Consolidation over Clouds

Service Level Agreement for Distributed Mutual Exclusion in Cloud Computing



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Chaki algorithm [1] while the second one is a novel algorithm, based on Raymond algorithm [2], where a deadline is

associated with every request. In both cases, our aim is to improve Critical Section execution rate and to reduce the

number of SLA violations, which, for the first algorithm represents the number of priority inversions (i.e. a higher priority

request is satisfied after a lower one) and for the second one, the number of requests whose deadline is not respected.

Performance evaluation results show that our solutions significantly reduce SLA violations avoiding message overhead.

Cloud computing systems (or hosting datacenters) have attracted a lot of attention in recent years. Utility computing,

reliable data storage, and infrastructure-independent computing are example applications of such systems. Electrical

energy cost of a cloud computing system is a strong function of the consolidation and migration techniques used to

assign incoming clients to existing servers. Moreover, each client typically has a service level agreement (SLA), which

specifies constraints on performance and/or quality of service that it receives from the system. These constraints result

in a basic trade-off between the total energy cost and client satisfaction in the system. In this paper, a resource

allocation problem is considered that aims to minimize the total energy cost of cloud computing system while meeting

the specified client-level SLAs in a probabilistic sense. The cloud computing system pays penalty for the percentage of

a client's requests that do not meet a specified upper bound on their service time. An efficient heuristic algorithm based

on convex optimization and dynamic programming is presented to solve the aforesaid resource allocation problem.

Simulation results demonstrate the effectiveness of the proposed algorithm compared to previous work.

With the advent of cloud computing and the need to satisfy growing customers resource demands, cloud providers now

operate increasing amounts of large data centers. In order to ease the creation of private clouds, several open-source

Infrastructure-as-a-Service (IaaS) cloud management frameworks (e.g. Open Nebula, Nimbus, Eucalyptus, Open Stack)

have been proposed. However, all these systems are either highly centralized or have limited fault tolerance support.

Consequently, they all share common drawbacks: scalability is limited by a single master node and Single Point of

Failure (SPOF). In this paper, we present the design, implementation and evaluation of a novel scalable and autonomic

(i.e. self-organizing and healing) virtual machine (VM) management framework called Snooze. For scalability the system

utilizes a self-organizing hierarchical architecture and performs distributed VM management. Moreover, fault tolerance is

provided at all levels of the hierarchy, thus allowing the system to self-heal in case of failures. Our evaluation conducted

on 144 physical machines of the Grid'5000 experimental test bed shows that the fault tolerance features of the

framework do not impact application performance. Moreover, negligible cost is involved in performing distributed VM

management and the system remains highly scalable with increasing amounts of resources.

SLA-based Optimization of Power and Migration Cost in Cloud Computing

Snooze: A Scalable and Autonomic Virtual Machine Management Framework for Private

Clouds



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Cloud computing is an extension of Service Oriented Architecture (SOA). For cloud elastic nature, it will often need to

dynamically reconfiguring and reorganising the services interaction as some unpredictable events, such as crashes or

network problems, will typically cause service unavailability. The complexity and dynamism of current global network

system require an architecture that is capable of autonomously changing its structure and functionality to meet the

changes with little human intervention. In this paper, an autonomic SOA framework is proposed to extend the

intelligence and capability in the cloud. The use of case-based reasoning and the architectural consideration of

autonomic computing paradigm are presented.

With the widespread adoption of cloud computing, the ability to record and account for the usage of cloud resources in

a credible and verifiable way has become critical for cloud service providers and users alike. The success of such a

billing system depends on several factors: the billing transactions must have integrity and nonrepudiation capabilities;

the billing transactions must have a minimal computation cost; and the SLA monitoring should be provided in a trusted

manner. Existing billing systems are limited in terms of security capabilities or computational overhead. In this paper,

we propose a secure and nonobstructive billing system called THEMIS as a remedy for these limitations. The system

uses a novel concept of a cloud notary authority for the supervision of billing. It generates mutually verifiable binding

information that can be used to resolve future disputes between a user and a cloud service provider in a computationally

efficient way. Furthermore, to provide a forgery-resistive SLA monitoring mechanism, we devised a SLA monitoring

module enhanced with a trusted platform module (TPM), called S-Mon. This work has been undertaken on a real cloud

computing service called iCubeCloud.

Cloud storage enables users to remotely store their data and enjoy the on-demand high quality cloud applications

without the burden of local hardware and software management. Though the benefits are clear, such a service is also

relinquishing users' physical possession of their outsourced data, which inevitably poses new security risks toward the

correctness of the data in cloud. In order to address this new problem and further achieve a secure and dependable

cloud storage service, we propose in this paper a flexible distributed storage integrity auditing mechanism, utilizing the

homomorphic token and distributed erasure-coded data. The proposed design allows users to audit the cloud storage

with very lightweight communication and computation cost. The auditing result not only ensures strong cloud storage

correctness guarantee, but also simultaneously achieves fast data error localization, i.e., the identification of

misbehaving server. Considering the cloud data are dynamic in nature, the proposed design further supports secure and

THEMIS: A Mutually Verifiable Billing System for the Cloud Computing Environment

Toward Secure and Dependable Storage Services in Cloud Computing

Taking up autonomous SOA framework into cloud computing



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efficient dynamic operations on outsourced data, including block modification, deletion, and append. Analysis shows

the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and

even server colluding attacks.

Recent development in Internet-scale data applications and services, combined with the proliferation of cloud

computing, has created a new computing model for data intensive computing best characterized by the MapReduce

paradigm. The MapReduce computing paradigm, pioneered by Google in its Internet search application, is an

architectural and programming model for efficiently processing massive amount of raw unstructured data. With the

availability of the open source Hadoop tools, applications built based on the MapReduce computing model are rapidly

growing. In this work, we focus on a unique security concern on the MapReduce architecture. Given the potential

security risks from lazy or malicious servers involved in a MapReduce task, we design efficient and innovative

mechanisms for detecting cheating services under the MapReduce environment based on watermark injection and

random sampling methods. The new detection schemes are expected to significantly reduce the cost of verification

overhead. Finally, extensive analytical and experimental evaluation confirms the effectiveness of our schemes in

MapReduce result verification.

We present a fast moving object detection application by extending the functionality of open source tools that are

available freely on the Internet. This application can be placed on a cloud infrastructure and performs fast processing so

that the costs needed to use the cloud resources can be minimized.

Towards Trusted Services: Result Verification Schemes for MapReduce

Video analysis tools for cloud-based motion detection

ieee projects 2012 2013 - cloud computing

Technology