Demonstrable data retention (DDR) is a technique which certain the integrity of data in storage outsourcing. In this paper we propose an efficient DDR protocol that prevent attacker in gaining information from multiple cloud storage node. Our technique is for distributed cloud storage and support the scalability of services and data migration. This technique Cooperative store and maintain the clientâs data on multi cloud storage. To insure the security of our technique we use zero-knowledge proof system, which satisfies zero-knowledge properties, knowledge soundness and completeness. We present a Cooperative DDR (CDDR) protocol based on hash index hierarchy and homomorphic verification response. In order to optimize the performance of our technique we use a novel technique for selecting optimal parameter values to reduce the storage overhead and computation costs of client for service providers. Keyword: Demonstrable Data Retention, homomorphic, zero knowledge, storage outsourcing, multiple cloud, Cooperative, data Retention.
Grid computing is the computing paradigm that is concerned with coordinated resource sharing and problem solving in dynamic, autonomous multi-institutional virtual organizations. Data exchange and service allocation between virtual organizations are challenging problems in the field of Grid computing, due to the decentralization of Grid systems. The resource management in a Grid system ensures efficiency and usability. The required efficiency and usability of Grid systems can be achieved by building a decentralized multi-virtual Grid system. In this thesis we present a decentralized multi-virtual resource management framework in which the system is divided into virtual organizations, each controlled by a broker. An overlay network of brokers is responsible for global resource management and managing the allocation of services. We address two main issues for both local and global resource management: 1) decentralized allocation of tasks to suitable nodes to achieve both local and global load balancing; and 2) handling of both regular and broker failures. Experimental results verify that the system achieves dependable performance with various loads of services and broker failures.
In the Grid/Cloud environment, applications or services and resources belong to different organizations with different objectives. Entities in the Grid/Cloud are autonomous and self-interested; however, they are willing to share their resources and services to achieve their individual and collective goals. In such open environment, the scheduling decision is a challenge given the decentralized nature of the environment. Each entity has specific requirements and objectives that need to achieve. In this thesis, we review the Grid/Cloud computing technologies, environment characteristics and structure and indicate the challenges within the resource scheduling. We capture the Grid/Cloud scheduling model based on the complete requirement of the environment. We further create a mapping between the Grid/Cloud scheduling problem and the combinatorial allocation problem and propose an adequate economic-based optimization model based on the characteristic and the structure nature of the Grid/Cloud. By adequacy, we mean that a comprehensive view of required properties of the Grid/Cloud is captured. We utilize the captured properties and propose a bidding language that is expressive where entities have the ability to specify any set of preferences in the Grid/Cloud and simple as entities have the ability to express structured preferences directly. We propose a winner determination model and mechanism that utilizes the proposed bidding language and finds a scheduling solution. Our proposed approach integrates concepts and principles of mechanism design and classical scheduling theory. Furthermore, we argue that in such open environment privacy concerns by nature is part of the requirement in the Grid/Cloud. Hence, any scheduling decision within the Grid/Cloud computing environment is to incorporate the feasibility of privacy protection of an entity. Each entity has specific requirements in terms of scheduling and privacy preferences. We analyze the privacy problem in the Grid/Cloud computing environment and propose an economic based model and solution architecture that provides a scheduling solution given privacy concerns in the Grid/Cloud. Finally, as a demonstration of the applicability of the approach, we apply our solution by integrating with Globus toolkit (a well adopted tool to enable Grid/Cloud computing environment). We also, created simulation experimental results to capture the economic and time efficiency of the proposed solution.
Jun 1, 2012¡2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems
Bogdan Florin Cornea, Julien Bourgeois, The Tung Nguyen, Didier El Baz
Recently, a new environment for high performance peer-to-peer distributed computing was proposed. This environment, named P2PDC, addresses stable or volatile systems communicating in a decentralized manner using the self-adaptive protocol P2PSAP. P2PDC is devoted to task parallel applications like numerical simulation problems or optimization problems solved via parallel or distributed iterative algorithms. For distributed applications meant to run with P2PDC, a performance prediction tool named dPerf was proposed. dPerf combines static and dynamic analysis with trace-based simulation to provide scientist with information about the execution of their large scale numerical simulation applications. dPerf addresses real parallel and distributed numerical simulation and optimisation applications written in C, C++ or Fortran for P2PDC. This paper introduces an enhancement of the dPerf tool which provides scalable performance prediction results. Scaling is done with respect to (i) network configuration and (ii) number of peers. Scaling predictions based on network configuration is achieved through trace-based simulation, where various architectures can be studied. Scaling predictions based on the number of peers implies analyzing the communication topology and modifying trace files prior to simulation. We present experimental results obtained for the obstacle problem, a C/P2PDC implementation of the code used in mechanics and finance. Prediction for this application is computed under real conditions, with a reduced slowdown and by providing user with scalable results.
Cloud computing is an emerging evolutionary computing model that provides highly scalable services over high-speed Internet on a pay-as-usage model. However, cloud-based solutions still have not been widely deployed in some sensitive areas, such as banking and healthcare. The lack of widespread development is related to usersâ concern that their confidential data or privacy would leak out in the cloudâs outsourced environment. To address this problem, we propose a novel active data-centric framework to ultimately improve the transparency and accountability of actual usage of the usersâ data in cloud. Our data-centric framework emphasizes âactiveâ feature which packages the raw data with active properties that enforce data usage with active defending and protection capability. To achieve the active scheme, we devise the Triggerable Data File Structure (TDFS). Moreover, we employ the zero-knowledge proof scheme to verify the requestâs identification without revealing any vital information. Our experimental outcomes demonstrate the efficiency, dependability, and scalability of our framework.
Welcome to the special issue of Concurrency and Computation: Practice and Experience (CCPE) journal. This special issue compiles a number of excellent technical contributions that significantly advance the state-of-the-art in autonomic cloud computing. Cloud computing 1, 2 is an emerging utility computing model that allows users to dynamically access, select, and configure a large pool of IT resources (virtual machine templates, storage, and networking elements) and deliver them as âcomputing utilitiesâ to consumers in a pay-as-you-go manner. Several vendors have emerged in this space including IBM, VMware, Microsoft, Manjrasoft, and Yahoo. This model of computing is quite attractive, especially for small and medium sized enterprises, as it allows them to focus on consuming or offering services on top of cloud infrastructure. At high-level, cloud computing might not seem radically different from the existing paradigms: World Wide Web, grid computing, and cluster computing. However, key differentiators of cloud computing are its technical characteristics such as on-demand resource pooling or rapid elasticity, self-service, almost infinite scalability, end-to-end virtualization support, and robust support of resource usage metering and billing. Additionally, nontechnical differentiators include services that are offered under pay-as-you-go-model, guaranteed Service Level Agreement (SLA), faster time to deployments, lower upfront costs, little or no maintenance overhead, and environment friendliness. Unpredictability is a fact in a distributed computing environment, and the Cloud is no exception. Performance unpredictability 3 in the Cloud is in fact a major issue for many users and it is coined as one of the major obstacles for cloud computing. For instance, researchers (biologists, physicists, finance analysts, etc.) expect guaranteed performance for their experiments, independent of the current workload and state 4 of IT resources of the Cloud, because this is key to repeatability of results. Other examples are small and medium sized enterprises (gaming company, web application providers) that want strict assurance on SLA; for example, an end-user request for a web page or multimedia content has to be served within the agreed time-limit. Hence, it is highly important for Cloud vendors that they have the ability to offer guaranteed SLAs based on performance metrics â such as response time and throughput. Interestingly, vendors seem to base their SLAs on availability of their offering, while completely ignoring response time and throughput. Hence, it is clear that dealing with performance unpredictability is critical to exploiting the full potential of clouds. In this special issue, we have tried to compile some high quality papers that exhaustively deal with some of the aforementioned issues. Next, we briefly describe the technical contributions, which were selected for publication in this special issue. All of the selected papers underwent a rigorous peer-review process. The end-to-end QoS negotiation for SLA establishment for composite services involves compound multiparty negotiations in which the composite service provider concurrently negotiates with multiple candidates for each atomic service, selecting the one that best satisfies the atomic service QoS preferences while ensuring that the end-to-end QoS requirements are also fulfilled. It is necessary to derive the atomic utility boundaries from the global utility boundary to be able to negotiate with potential candidates. Additionally, there has to be a mechanism for updating these boundaries in subsequent negotiation rounds based upon the individual negotiation outcomes. To counter these complexities, in paper 5 titled âEstablishing Composite SLAs through Concurrent QoS Negotiation with Surplus Redistributionâ, Richter et al. propose an algorithm for the decomposition of global utility boundary into atomic service utility boundaries, and the surplus redistribution from successful negotiation outcomes among the remaining negotiations. The proposed mechanism is a practical approach to efficiently coordinate concurrent service negotiations within complex workflows, enabling the iterative and interactive adjustment of the negotiation boundaries for each atomic service in a composition based on the performance of other atomic negotiations. They demonstrate the feasibility of our approach by evaluating it with some popular negotiation strategies using the Specialised Property Search Scenario. Many scientific workflows are data intensive where large volumes of intermediate data are generated during their execution. Some valuable intermediate data need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity determined manually. As doing science in the Cloud has become popular nowadays, more intermediate data can be stored in scientific cloud workflows based on a pay-for-use model. In the paper in 6 titled âA data dependency based strategy for intermediate data storage in scientific cloud workflow systemsâ, Yuan et al. build an intermediate data dependency graph (IDG) from the data provenance in scientific workflows. With the IDG, deleted intermediate data can be regenerated, and as such they develop a novel intermediate data storage strategy that can reduce the cost of scientific cloud workflow systems by automatically storing appropriate intermediate data sets with one Cloud service provider. The strategy has significant research merits, that is, it achieves a cost-effective trade-off of computation cost and storage cost and is not strongly impacted by the forecasting inaccuracy of data setsâ usages. Meanwhile, the strategy also takes the usersâ tolerance of data accessing delay into consideration. Authors utilize Amazon's cost model and apply the strategy to general random and specific astrophysics pulsar searching scientific workflows for evaluation. The results show that our strategy can reduce the overall cost of scientific cloud workflow execution significantly. Recall that, one of the biggest premises of cloud computing is the flexibility of delivering IT resources and virtual appliances as an utility such as phone, electricity, gas, and water services. It enables users to have access to computing infrastructure, platform, and software as services over the Internet. To be competitive, however, Cloud providers need to be able to adapt to the dynamic loads from users, not only optimizing the local usage and costs but also engaging into agreements with other clouds to complement local capacity. The infrastructure in which competing clouds are able to cooperate to maximize their benefits is called a Federated Cloud. Just as clouds enable users to cope with unexpected demand loads, a Federated Cloud will enable individual clouds to cope with unforeseen variations of demand. The definition of the mechanism to ensure mutual benefits for the individual clouds composing the federation, however, is one of its main challenges. Gomes et al. in their paper 7 âPure exchange markets for resource sharing in federated cloudsâ propose and investigate the application of market-oriented mechanisms based on the General Equilibrium Theory of Microeconomics to coordinate the sharing of resources between the clouds in a Federated Cloud. Several research institutions and universities own computational capacity that is not effectively utilized, thereby providing an opportunity for such institutions to use such capacity to offer Cloud services (to both internal and external users). However, the unreliability and unpredictability of these resources mean that their use in the context of an SLA is high risk, leading to a reduction in reputation and economic penalties in case of SLA violation. To overcome these challenges, in the paper 8 titled âTowards autonomic management for Cloud services based upon volunteered resourcesâ, Caton and Rana propose a methodology that addresses the issues of unreliability and unpredictability such that Cloud software services could be hosted upon volunteered resources. To enable the harnessing of these resources, they rely on autonomic fault management techniques that allow such systems to independently adapt to the resources they use based upon their perception of individual resource reliability. Using the proposed approach they were able to scale out the backend infrastructure of the Cloud service elastically (minimum 30 s per worker), opportunistically, and autonomically. To summarize, the authors address two key questions in their paper: Can a campus volunteer infrastructure be used in Cloud provisioning? and What measures are necessary to ensure reliability at the resource level? To improve the hosting and delivery of applications through cloud-based IT resources, Champrasert et al. in the paper 9 titled âExploring self-optimization and self-stabilization properties in bio-inspired autonomic cloud computingâ, describe architecture to build self-optimizable and self-stabilizable applications. The design of the proposed architecture, SymbioticSphere, is inspired by key biological principles such as decentralization, evolution, and symbiosis. In SymbioticSphere, each cloud application consists of application services and middleware platforms. Each service and platform is designed as a biological entity, and implements biological behaviors such as energy exchange, migration, reproduction, and death. Each service/platform possesses behavior policies, as genes, each of which defines when and how to invoke a particular behavior. SymbioticSphere allows services and platforms to autonomously adapt to dynamic network conditions by optimizing their behavior policies with a multi-objective genetic algorithm. Moreover, SymbioticSphere allows services and platforms to autonomously seek stable adaptation decisions as equilibria (or symbiosis) between them with a game theoretic algorithm. This symbiosis augments evolutionary optimization to expedite the adaptation of agents and platforms. It also contributes to stable performance that contains a very limited amount of fluctuations. Simulation results demonstrate that agents and platforms successfully attain self-optimization and self-stabilization properties in their adaptation processes. We hope that the readers will find the articles of this special issue to be informative and useful.
Sonu Mariam Paulose, R. Venkatesan, K. Ramalakshmi
Conjunction of massive amount of idle computers or resources that may be loosely coupled, heterogeneous and geographically dispersed to reach a common goal leads to a virtual computing platform for sharing resources across the world. Resource management, application development and usage models in these environments has some dilemma in undertaking resources due to resource providers with multiple administrative domains having their own policies and terms. A ditch in the Grid computing environment is how to coordinate the distributed resources amongst a dynamic set of individuals and organizations where the requesters and providers are allowed to join and leave Grid environment at any time. Bidding model prevents single point of failure and server overload problems of match making model while minimizing turnaround time by using some set of deterministic and probabilistic selection heuristics where resource requesters and resource providers were given the privilege to take autonomous decisions regarding resource selection. Autonomous decision making is enabled via peer-to-peer decentralized scheduling frame work. In decentralized environment, lack of global information is a key challenge to facilitate optimum decision making which can lead to greedy selection of the best provider. Therefore some probabilistic selection is used to reduce the fairness deviation among processors while minimizing the turnaround time. Currently just various level of information about providers has been concentrated to minimize the turnaround time. Simply concentrating on various level of information may also leads to failure due to rejection factor resulted by number of failures occurred at provider. However by merging trust oriented mechanisms along with various level of information, rejection factor can also be minimized along with minimization of the turnaround time.
Stelios Sotiriadis, Nik Bessis, Ye Huang, Paul Sant ¡ 5 authors
Grid technology enables resource sharing among a massive number of dynamic and geographically distributed resources. The significance of such environments is based on the aptitude of grid members to look across multiple grids for resource discovery and allocation. Parallel to grid, agents are autonomous problem solvers capable of self-directed actions in flexible environments. As grid systems require self-sufficiency, agents may be the means by which to achieve a robust autonomy infrastructure. In this direction we propose a resource discovery method of interoperable grid agents which travel within Virtual Organizations (VOs) and by capturing resource information regarding their action domain; they update the internal data of each grid member. Moreover we propose that resource discovery is a systematic and continually updating process that occurs within a VO and allows information exchange to happen. This exchange takes place between various community members at a pre-defined interval, aiming to distribute internal knowledge about the domain.
Divisibility of e-cash helps expend digital coin exactly.Most divisible e-cash schemes are based on binary tree,but few e-cash schemes offer fairness and divisibility at the same time.Based on binary tree,blind signature and zero-knowledge proof,a new fair indivisible electronic coins scheme was proposed.
Computational grids enable the sharing, aggregation, and selection of (geographically distributed) computational resources and can be used for solving large scale and data intensive computing applications. Computational grids are an appealing target application for market-based resource allocation especially given the attention in recent years to âvirtual organizations â and policy requirements. In this paper, we present a framework for truthful, decentralized, dynamic auctions in computational grids. Rather than a fullyspecified auction, we propose an open, extensible framework that is sufficient to promote simple, truthful bidding by endusers while supporting distributed and autonomous control by resource owners. Our auction framework incorporates resource prediction in enabling an expressive language for end-users, and highlights the role of infrastructure in enforcing rules that balance the goal of simplicity for end users with autonomy for resource owners. The technical analysis leverages simplifying assumptions of âuniform failureâ and âthreshold-reliabilityâ beliefs.
FlexiInternational develops and maintains FlexiFinancials, a suite of object-oriented, client/server software products for accounting. These interacting applications support general ledger, accounts payable and receivable, purchasing, fixed assets, order processing, inventory maintenance, and workflow functions. This paper describes a distributed component, transaction-based architecture that comprises the foundation for future development.
The focus of this research was the individual or small organization. These organizations include small businesses, community groups, K-12 schools or community colleges, local government, and the individual user, as well as many others. In this work, all of these organizations as well as the individual user were collectively referred to as users. The common element shared by each of these users was that they each have legitimate purposes for access to Internet services or each provides a service or services that could be enhanced if distributed via the connectivity provided by the Internet. However, the costs of establishing a conventional Internet server and the associated connectivity are prohibitive to such small-scale organizations. The objectives of this research were to: Establish a definition of a low-cost decentralized distributed computing environment for Intel-based personal computers that will provide users the capability to access the full spectrum of Internet services while enabling them with the ability to retain control of their computing environment. Develop a replication process to replicate and distribute the defined environment in a modular form so as to facilitate installation on a target system. Conduct testing and evaluation of the architecture and replication process to validate its ease of configuration and installation, and compliance with the requirements to provide users the capability to access the full spectrum of Internet services while retaining complete control of their computing environment. This was accomplished in three phases: (a) Phase I - Define an objective architecture, (b) Phase II - Develop a technique for replicating and distributing the architecture, and (c) Phase III - Test and validate the architecture and the replication and distribution processes. Definition of the objective architecture was accomplished through development of a prototype system that successfully demonstrated all of the characteristics required by the objectives of this research. Following the definition of the architecture on the prototype system, development of a technique for replicating and distributing the architecture was undertaken. This was accomplished by developing a group of programs that configured a system to the needs of a target user, captured that configured system on a removable medium, and restored that configured system on the target hardware. Finally the architecture, as well as its replication and distribution processes were evaluated for validity using statistical analysis of data collected from test subjects acting as users. All of these tasks were accomplished within the Linux Operating System environment using only software tools developed by the researcher or tools that are a native component of Linux. The first objective of this research was satisfied by the researcher's selection of Linux and its suite of associated applications as the operating system that would host the solution system. The second objective of this research was accomplished by the researcher's development of a suite of software tools that replicated the configured environment, moved the replication to an appropriate media and restored the environment on a target system. Inviting a group of Linux users to use the tools and provide feedback via a survey satisfied the third objective of this research. It was concluded that the three objectives of this research and therefore the overall goal of this research were accomplished. In each measured evaluation of the architecture, procedures and programs developed by the researcher, the resulting data were plotted in the advanced area or the area tending toward the advanced level of maturity as defined by the Boloix and Robillard (1995) evaluation scale. In a like manner the resulting data were plotted in the exceptionally compliant range or higher on the normal distribution curve survey scale. The trend of results was consistently at the advanced level of maturity on the Boloix and Robillard (1995) evaluation scale or in the exceptionally compliant range of the normal distribution curve survey scale. The researcher found that the results of testing the defined architecture and replication process revealed users are able to quickly implement a fully configured Linux system with all the capabilities defined in the architecture. This resulting Linux system provided a low cost, decentralized, distributed computing environment for Intel-based personal computers that enabled users to access the full spectrum of Internet services while maintaining control of their computing environment. By accomplishing this objective the researcher's Linux system can provide fiscally constrained individuals or small organizations full access to Internet services without the high costs of establishing a conventional Internet server and associated connectivity, prohibitive to a small-scale organization.
The extraordinarily rapid advance of microelectronics is causing a rapid drop in both the cost and economy of scale of computing equipment. In less than a decade, the powerful economic incentive for centralizing the production of computing cycles has reversed. Simultaneously, the productivity of computing professionals has not kept pace with inflation, and it appears that staff productivity gains will be difficult to achieve in a decentralized environment.Distribution of computing equipment is making support for the individual computer user more difficult in the following ways:-- There is a need for support of multiple types of small computer hardware.-- There is a need to maintain multiple operating systems and versions of applications software.-- There is little knowledge and experience in providing documentation, user training, and consulting support in a highly distributed multi-architecture computing environment.This paper outlines Cornell's response to the challenge of decentralized computing. The paper details the rationale, planning, organization, financing, and staffing for the new Decentralized Academic Computer Support group in Cornell Computing Services. In addition, the responsibilities of that group and its interaction with the other groups in Computing Services is described. Finally, attention is given to the relationship between Computing Services and the Cornell community regarding small computers.The accelerating trend of decentralizing the production of computing cycles presents user support groups with both a challenge and an opportunity. Failure to respond will result in more expensive and lower quality computing for the university community as well as declining utilization of user support services.