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PhoenixCloud: Provisioning Resources for Heterogeneous Workloads in Cloud Computing  [PDF]
Jianfeng Zhan,Lei Wang,Weisong Shi,Shimin Gong,Xiutao Zang
Computer Science , 2010,
Abstract: As more and more service providers choose Cloud platforms, which is provided by third party resource providers, resource providers needs to provision resources for heterogeneous workloads in different Cloud scenarios. Taking into account the dramatic differences of heterogeneous workloads, can we coordinately provision resources for heterogeneous workloads in Cloud computing? In this paper we focus on this important issue, which is investigated by few previous work. Our contributions are threefold: (1) we respectively propose a coordinated resource provisioning solution for heterogeneous workloads in two typical Cloud scenarios: first, a large organization operates a private Cloud for two heterogeneous workloads; second, a large organization or two service providers running heterogeneous workloads revert to a public Cloud; (2) we build an agile system PhoenixCloud that enables a resource provider to create coordinated runtime environments on demand for heterogeneous workloads when they are consolidated on a Cloud site; and (3) A comprehensive evaluation has been performed in experiments. For two typical heterogeneous workload traces: parallel batch jobs and Web services, our experiments show that: a) in a private Cloud scenario, when the throughput is almost same like that of a dedicated cluster system, our solution decreases the configuration size of a cluster by about 40%; b) in a public Cloud scenario, our solution decreases not only the total resource consumption, but also the peak resource consumption maximally to 31% with respect to that of EC2 +RightScale solution.
A Cloud Computing Infrastructure on Heterogeneous Computing Resources  [cached]
Baomin XU,Ning Wang,Chunyan Li
Journal of Computers , 2011, DOI: 10.4304/jcp.6.8.1789-1796
Abstract: Cloud computing is a state-of-the-art distributed computing paradigm which can support on-demand service sharing with flexibility and scalability. Cloud computing provides sharable heterogeneous computing resources using internet and data storage on a third party server. In order to use the heterogeneous computing resources in a much more efficient, scalable and flexible way, a Cloud computing infrastructure HCCloud (Heterogeneous Computing Cloud) has developed. With HCCloud, users no longer have to manually setup machine, or determine where and when to schedule their tasks. By pooling together clusters all over the network, resources are used more efficiently as the infrastructure is self-adaptive to the resources changes, and tasks distribution is fully automated with the best match between task requirements and compute capacity which deployed across a variety physical resources. In this paper we introduce the basic principles of the HCCloud design, and discuss some techniques that have made in order to allow HCCloud to be easily accessed over the Web. The main intention of HCCloud is to decrease the configuration scale of the cluster system through heterogeneous workloads, while increasing the number of requests for parallel workload by provisioning enough resources
An Effective Economic Management of Resources in Cloud Computing  [cached]
Ghalem Belalem,Samah Bouamama,Larbi Sekhri
Journal of Computers , 2011, DOI: 10.4304/jcp.6.3.404-411
Abstract: In Cloud computing, the availability and performance of services are two important aspects to be lifted, because users require a certain level of quality service in terms of timeliness of their duties in a lower cost. Several studies have overcome this problem by the proposed algorithms borrowed from economic models of real world economy to ensure that quality of service, our job is to extend and enrich the simulator CloudSim by auction algorithms inherited from GridSim simulator, but its algorithms do not support the virtualization which is an important part of Cloud Computing, why we introduced several parameters and functions adapted to the environment of cloud computing as well as users to meet their requirements.
The Application of Cloud Computing in Virtual Enterprise’s Information Resources Sharing  [PDF]
Wei Wang, Guan-Quan Liu, Qiu Zhang
Journal of Software Engineering and Applications (JSEA) , 2013, DOI: 10.4236/jsea.2013.63B011
Abstract: With the rapid development of social,science and technology ,we are always looking for the advanced and rapid manufacturing method and the management pattern.thus a new enterprise cooperation pattern-Virtual Enterprise arises at the historic moment. The cooperation is a process which advantages the temporary enterprise resources each other. Therefore, the virtual enterprise must encounter the problem that how to realize the virtual enterprises’ information resources sharing and improve the efficiency of enterprise cooperation. This paper uses the cloud computing’s advantage to solve the problem of virtual enterprise information resources sharing. Then enterprise is able to share the information of different regions,different computing environment and improve the efficiency of virtual enterprise cooperation.
Cloud Computing: An Overview  [PDF]
Libor Sarga
Journal of Systems Integration , 2012,
Abstract: As cloud computing is gaining acclaim as a cost-effective alternative to acquiring processing resources for corporations, scientific applications and individuals, various challenges are rapidly coming to the fore. While academia struggles to procure a concise definition, corporations are more interested in competitive advantages it may generate and individuals view it as a way of speeding up data access times or a convenient backup solution. Properties of the cloud architecture largely preclude usage of existing practices while achieving end-users’ and companies’ compliance requires considering multiple infrastructural as well as commercial factors, such as sustainability in case of cloud-side interruptions, identity management and off-site corporate data handling policies. The article overviews recent attempts at formal definitions of cloud computing, summarizes and critically evaluates proposed delimitations, and specifies challenges associated with its further proliferation. Based on the conclusions, future directions in the field of cloud computing are also briefly hypothesized to include deeper focus on community clouds and bolstering innovative cloud-enabled platforms and devices such as tablets, smart phones, as well as entertainment applications.
The Objective Function Value Optimization of Cloud Computing Resources Security Allocation of Artificial Firefly Algorithm  [PDF]
Xiaoxi Hu
Open Journal of Optimization (OJOp) , 2015, DOI: 10.4236/ojop.2015.42005
Abstract: Based on the current cloud computing resources security distribution model’s problem that the optimization effect is not high and the convergence is not good, this paper puts forward a cloud computing resources security distribution model based on improved artificial firefly algorithm. First of all, according to characteristics of the artificial fireflies swarm algorithm and the complex method, it incorporates the ideas of complex method into the artificial firefly algorithm, uses the complex method to guide the search of artificial fireflies in population, and then introduces local search operator in the firefly mobile mechanism, in order to improve the searching efficiency and convergence precision of algorithm. Simulation results show that, the cloud computing resources security distribution model based on improved artificial firefly algorithm proposed in this paper has good convergence effect and optimum efficiency.
Deployment and management of SDR cloud computing resources: problem definition and fundamental limits
Ismael Gomez-Miguelez, Vuk Marojevic and Antoni Gelonch
EURASIP Journal on Wireless Communications and Networking , 2013, DOI: 10.1186/1687-1499-2013-59
Abstract: Software-defined radio (SDR) describes radio transceivers implemented in software that executes on general-purpose hardware. SDR combined with cloud computing technology will reshape the wireless access infrastructure, enabling computing resource sharing and centralized digital-signal processing (DSP). SDR clouds have different constraints than general-purpose grids or clouds: real-time response to user session requests and real-time execution of the corresponding DSP chains. This article addresses the SDR cloud computing resource management problem. We show that the maximum traffic load that a single resource allocator (RA) can handle is limited. It is a function of the RA complexity and the call setup delay and user blocking probability constraints. We derive the RA capacity analytically and provide numerical examples. The analysis demonstrates the fundamental tradeoffs between short call setup delays (few processors) and low blocking probability (many processors). The simulation results demonstrate the feasibility of a distributed resource management and the necessity of adapting the processor assignment to RAs according to the given traffic load distribution. These results provide new insights and guidelines for designing data centers and distributed resource management methods for SDR clouds.
Pre-allocation Strategies of Computational Resources in Cloud Computing using Adaptive Resonance Theory-2  [PDF]
T. R. Gopalakrishnan Nair,P Jayarekha
Computer Science , 2012, DOI: 10.5121/ijccsa.2011.1203
Abstract: One of the major challenges of cloud computing is the management of request-response coupling and optimal allocation strategies of computational resources for the various types of service requests. In the normal situations the intelligence required to classify the nature and order of the request using standard methods is insufficient because the arrival of request is at a random fashion and it is meant for multiple resources with different priority order and variety. Hence, it becomes absolutely essential that we identify the trends of different request streams in every category by auto classifications and organize preallocation strategies in a predictive way. It calls for designs of intelligent modes of interaction between the client request and cloud computing resource manager. This paper discusses about the corresponding scheme using Adaptive Resonance Theory-2.
Joint Optimization of Radio Resources and Code Partitioning in Mobile Cloud Computing  [PDF]
Paolo Di Lorenzo,Sergio Barbarossa,Stefania Sardellitti
Computer Science , 2013,
Abstract: The aim of this paper is to propose a computation offloading strategy, to be used in mobile cloud computing, in order to minimize the energy expenditure at the mobile handset necessary to run an application under a latency constraint. We exploit the concept of call graph, which models a generic computer program as a set of procedures related to each other through a weighted directed graph. Our goal is to derive the partition of the call graph establishing which procedures are to be executed locally or remotely. The main novelty of our workis th at the optimal partition is obtained jointly with the selection of the transmit power and constellation size, in order to minimize the energy consumption at the mobile handset, under a latency constraint taking into account transmit time, packet drops, and execution time. We consider both a single channel and a multi-channel transmission strategy, thus proving that a globally optimal solution can be achieved in both cases with affordable complexity. The theoretical findings are corroborated by numerical results and are aimed to show under what conditions, in terms of call graph topology, communication strategy, and computation parameters, the proposed offloading strategy can provide a significant performance gain.
New Proposed Robust, Scalable and Secure Network Cloud Computing Storage Architecture  [PDF]
Fawaz S. Al-Anzi, Ayed A. Salman, Noby K. Jacob
Journal of Software Engineering and Applications (JSEA) , 2014, DOI: 10.4236/jsea.2014.75031
Abstract:

Cloud computing describes highly scalable computing resources provided as an external service via the internet. Economically, the main feature of cloud computing is that customers only use what they need, and only pay for what they actually use. Resources are available to be accessed from the cloud at any time, and from any location via the internet. There’s no need to worry about how things are being maintained behind the scenes—you simply purchase the IT service you require. This new, web-based generation of computing utilizes remote servers for data storage and management. One of the challenging issues tackled in the cloud computing is the security of data stored in the service providers’ site. In this paper, we propose a new architecture for secure data storage in such a way that users’ data are encrypted and split into various cipher blocks and distributed among different service providers site rather than solely depend on single provider for data storage. This architecture ensures better reliability, availability, scalability and security.

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