TECHNOLOGY: JAVA
DOMAIN: Cloud Computing
| S. No. | IEEE TITLE | ABSTRACT | IEEE YEAR |
| 1 | TEES: An Efficient Search Scheme over Encrypted Data on Mobile Cloud | Cloud storage provides a convenient, massive, and scalable storage at low cost, but data privacy is a major concern that prevents users from storing files on the cloud trustingly. One way of enhancing privacy from data owner point of view is to encrypt the files before outsourcing them onto the cloud and decrypt the files after downloading them. However, data encryption is a heavy overhead for the mobile devices, and data retrieval process incurs a complicated communication between the data user and cloud. Normally with limited bandwidth capacity and limited battery life, these issues introduce heavy overhead to computing and communication as well as a higher power consumption for mobile device users, which makes the encrypted search over mobile cloud very challenging. In this paper, we propose traffic and energy saving encrypted search (TEES), bandwidth and energy efficient encrypted search architecture over mobile cloud. The proposed architecture offloads the computation from mobile devices to the cloud, and we further optimize the communication between the mobile clients and the cloud. It is demonstrated that the data privacy does not degrade when the performance enhancement methods are applied. Our experiments show that TEES reduces the computation time by 23 to 46 percent and save the energy consumption by 35 to 55 percent per file retrieval; meanwhile the network traffics during the file retrievals are also significantly reduced. | 2017 |
| 2 | Joint Pricing and Capacity Planning in the IaaS Cloud Market | In the cloud context, pricing and capacity planning are two important factors to the profit of the infrastructure-as-a-service (IaaS) providers. This paper investigates the problem of joint pricing and capacity planning in the IaaS provider market with a set of software-as-a-service (SaaS) providers, where each SaaS provider leases the virtual machines (VMs) from the IaaS providers to provide cloud-based application services to its end-users. We study two market models, one with a monopoly IaaS provider market, the other with multiple-IaaS-provider market. For the monopoly IaaS provider market, we first study the SaaS providers’ optimal decisions in terms of the amount of end-user requests to admit and the number of VMs to lease, given the resource price charged by the IaaS provider. Based on the best responses of the SaaS providers, we then derive the optimal solution to the problem of joint pricing and capacity planning to maximize the IaaS provider’s profit. Next, for the market with multiple IaaS providers, we formulate the pricing and capacity planning competition among the IaaS providers as a three-stage Stackelberg game. We explore the existence and uniqueness of Nash equilibrium, and derive the conditions under which there exists a unique Nash equilibrium. Finally, we develop an iterative algorithm to achieve the Nash equilibrium. | 2017 |
| 3 | Mathematical Programming Approach for Revenue Maximization in Cloud Federations
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This paper assesses the benefits of cloud federation for cloud providers. Outsourcing and insourcing are explored as means to maximize the revenues of the providers involved in the federation. An exact method using a linear integer program is proposed to optimize the partitioning of the incoming workload across the federation members. A pricing model is suggested to enable providers to set their offers dynamically and achieve highest revenues. The conditions leading to highest gains are identified and the benefits of cloud federation are quantified. | 2017 |
| 4 | kBF: Towards Approximate and Bloom Filter based Key-Value Storage for Cloud Computing Systems | As one of the most popular cloud services, data storage has attracted great attention in recent research efforts. Key-value (k-v) stores have emerged as a popular option for storing and querying billions of key-value pairs. So far, existing methods have been deterministic. Providing such accuracy, however, comes at the cost of memory and CPU time. In contrast, we present an approximate k-v storage for cloud-based systems that is more compact than existing methods. The tradeoff is that it may, theoretically, return errors. Its design is based on the probabilistic data structure called “bloom filter”, where we extend the classical bloom filter to support key-value operations. We call the resulting design as the kBF (key-value bloom filter). We further develop a distributed version of the kBF (d-kBF) for the unique requirements of cloud computing platforms, where multiple servers cooperate to handle a large volume of queries in a load-balancing manner. Finally, we apply the kBF to a practical problem of implementing a state machine to demonstrate how the kBF can be used as a building block for more complicated software infrastructures. | 2017 |
| 5 | Cloud-Based Utility Service Framework for Trust Negotiations Using Federated Identity Management | Utility based cloud services can efficiently provide various supportive services to different service providers. Trust negotiations with federated identity management are vital for preserving privacy in open systems such as distributed collaborative systems. However, due to the large amounts of server based communications involved in trust negotiations scalability issues prove to be less cumbersome when offloaded on to the cloud as a utility service. In this view, we propose trust based federated identity management as a cloud based utility service. The main component of this model is the trust establishment between the cloud service provider and the identity providers. We propose novel trust metrics based on the potential vulnerability to be attacked, the available security enforcements and a novel cost metric based on policy dependencies to rank the cooperativeness of identity providers. Practical use of these trust metrics is demonstrated by analyses using simulated data sets, attack history data: published by MIT Lincoln laboratory, real-life attacks and vulnerabilities extracted from Common Vulnerabilities and Exposures (CVE) repository and fuzzy rule based evaluations. The results of the evaluations imply the significance of the proposed trust model to support cloud based utility services to ensure reliable trust negotiations using federated identity management. | 2017 |
| 6 | On the Latency and Energy Efficiency of Distributed Storage Systems | The increase in data storage and power consumption at data-centers has made it imperative to design energy efficient distributed storage systems (DSS). The energy efficiency of DSS is strongly influenced not only by the volume of data, frequency of data access and redundancy in data storage, but also by the heterogeneity exhibited by the DSS in these dimensions. To this end, we propose and analyze the energy efficiency of a heterogeneous distributed storage system in which n storage servers (disks) store the data of R distinct classes. Data of class i is encoded using a (n, ki) erasure code and the (random) data retrieval requests can also vary across classes. We show that the energy efficiency of such systems is closely related to the average latency and hence motivates us to study the energy efficiency via the lens of average latency. Through this connection, we show that erasure coding serves the dual purpose of reducing latency and increasing energy efficiency. We present a queuing theoretic analysis of the proposed model and establish upper and lower bounds on the average latency for each data class under various scheduling policies. Through extensive simulations, we present qualitative insights which reveal the impact of coding rate, number of servers, service distribution and number of redundant requests on the average latency and energy efficiency of the DSS. | 2017 |
| 7 | Orchestrating Bulk Data Transfers across Geo-Distributed Datacenters | As it has become the norm for cloud providers to host multiple datacenters around the globe, significant demands exist for inter-datacenter data transfers in large volumes, e.g., migration of big data. A challenge arises on how to schedule the bulk data transfers at different urgency levels, in order to fully utilize the available inter-datacenter bandwidth. The Software Defined Networking (SDN) paradigm has emerged recently which decouples the control plane from the data paths, enabling potential global optimization of data routing in a network. This paper aims to design a dynamic, highly efficient bulk data transfer service in a geo-distributed datacenter system, and engineer its design and solution algorithms closely within SDN architecture. We model data transfer demands as delay tolerant migration requests with different finishing deadlines. Thanks to the flexibility provided by SDN, we enable dynamic, optimal routing of distinct chunks within each bulk data transfer (instead of treating each transfer as an infinite flow), which can be temporarily stored at intermediate datacenters to mitigate bandwidth contention with more urgent transfers. An optimal chunk routing optimization model is formulated to solve for the best chunk transfer schedules over time. To derive the optimal schedules in an online fashion, three algorithms are discussed, namely a bandwidth-reserving algorithm, a dynamically-adjusting algorithm, and a future-demand-friendly algorithm, targeting at different levels of optimality and scalability. Webuild an SDN systembased on the Beacon platform and OpenFlow APIs, and carefully engineer our bulk data transfer algorithms in the system. Extensive real-world experiments are carried out to compare the three algorithms as well as those from the existing literature, in terms of routing optimality, computational delay and overhead | 2017 |