The content-aware load balancing strategy uses the services or content requested for the scheduling of a request. This paper considers a special scenario where the application server and database server are deployed over a Distributed Computing Environment (DCE). The content aware algorithms developed till now are specially designed for the clusters computing, but this paper considers all possible DCE like cluster, grid and cloud computing. There are two types of users in a DCE, namely the service providers and consumers. Service providers provide the services to its consumers' requests. Generally the services providers offer the computing nodes for processing of the users requests. This paper proposes a new content aware load balancing policy called workload and client aware policy (WCAP). This method is a hybrid approach of client aware policy and workload aware request distribution policy discussed below. A parameter unique and special property (USP) is defined to specify the property of the service provider's computing nodes and the consumer's requests for data. This parameter specifies the unique and special property of the requests as well as computing nodes. Based on this parameter, the scheduler decides the node that is best suitable for the processing of the requests. This load balancing strategy is implemented in a decentralized manner with low overhead. Preliminary investigation proves that the proposed method is a promising content aware load balancing.

With continuous advancements in Internet technology and the increased use of cryptographic techniques, the cloud has become the obvious choice for data sharing. Generally, the data are outsourced to cloud storage servers in encrypted form. Access control methods can be used on encrypted outsourced data to facilitate and regulate access. Multi-authority attribute-based encryption is a propitious technique to control who can access encrypted data in inter-domain applications such as sharing data between organizations, sharing data in healthcare, etc. The data owner may require the flexibility to share the data with known and unknown users. The known or closed-domain users may be internal employees of the organization, and unknown or open-domain users may be outside agencies, third-party users, etc. In the case of closed-domain users, the data owner becomes the key issuing authority, and in the case of open-domain users, various established attribute authorities perform the task of key issuance. Privacy preservation is also a crucial requirement in cloud-based data-sharing systems. This work proposes the SP-MAACS scheme, a secure and privacy-preserving multi-authority access control system for cloud-based healthcare data sharing. Both open and closed domain users are considered, and policy privacy is ensured by only disclosing the names of policy attributes. The values of the attributes are kept hidden. Characteristic comparison with similar existing schemes shows that our scheme simultaneously provides features such as multi-authority setting, expressive and flexible access policy structure, privacy preservation, and scalability. The performance analysis carried out by us shows that the decryption cost is reasonable enough. Furthermore, the scheme is demonstrated to be adaptively secure under the standard model.

Introduction to multi-core processors a decade ago has been a breakthrough for High Performance Computing (HPC) community. Cluster of tiny multi-core processor nodes produces competitive results as comparable to expensive SMP cluster. These clusters have attracted attention from HPC community, as it provides cost-effective solutions for parallel applications. However, to exploit each core and other computing resources of multi-core cluster, complexities of parallel programming issues have been increased. The challenge and current research area for these clusters is that all available resources should be efficiently utilized to provide optimal performance results for parallel applications. In this paper, we present a dynamic load balancing algorithm for heterogeneous multi-core cluster, based on node's assignment factor and data proximity policy, which efficiently and effectively utilize power of multi-core processors. We show that the proposed algorithm improves system utilization and provides better execution results.

Scheduling of tasks in distributed environment requires decision making approach for fair utilization and allocation of computing resources. In this paper, a scheduling model based on analytic hierarchy process (AHP) is discussed. The user tasks are compared using pair-wise comparison matrix and the machines are allocated on the basis of task priority. The paper also discusses an inconsistency identification method to determine the inconsistent element and to improve the consistency ratio of pair-wise comparison matrix. The proposed method is suitable for scheduling of tasks of different nature depending on different resources requirement like processing power, main memory, bandwidth etc.

The state-of-art web applications communicate and coordinate with number of geographically dispersed information resources providing information to huge number of users. Homogeneous server clusters are not capable of satisfying the growing demand of such applications including real time audio and video, PHP, JSP, ASP etc. Heterogeneity involves handling of low level interoperability issues e.g. mismatch of hardware, operating platforms, programming languages, database schema, topology etc. Scalable server cluster allows addition of new servers as the load increases without disrupting the services. Moreover, it also provides better reliability by gracefully transferring the load from server which is unavailable due to failure or for preventive maintenance. Heterogeneity with scalability makes the system more complex. The existing dynamic load balancing (DLB) algorithms are not directly applicable for distributed scheduling in such environments. In this paper, we propose a DLB algorithm for scalable heterogeneous server cluster using content awareness. The algorithm considers server's processing capability, queue length, utilization ratio etc. as load indices. As the cluster supports multiple services, at the primary level, we have used content awareness forwarding algorithm and at the secondary level, waited round robin algorithm has been used.