Personal health record system (PHR system) stores health-related information of an individual. PHR system allows the data owner to manage and share his/her data with selected individuals. The originality or tamper resistance feature is crucial for PHR system because of the irreversible consequence of incorrect information. Blockchain technology becomes a potential solution due to its immutability and irreversibility properties. Unfortunately, some technical impediments such as limited storage, privacy concern, consent irrevocability, inefficient performance, and energy consumption exist. This work aims to handle these blockchain drawbacks and propose a blockchain-based PHR model. The proposed model is built using the blockchain technology to support a tamper resistance feature. Proxy reencryption and other cryptographic techniques are employed to preserve privacy. Features of the proposed model include fine-grained and flexible access control, revocability of consent, auditability, and tamper resistance. A detailed security analysis shows that the proposed model is provably secure for privacy and tamper resistance. The performance analysis shows that the proposed model achieves a better overall performance compared with the existing approach in the literature. Thus the proposed model is more suitable for the PHR system usage.

To balance multiple scheduling performance requirements on parallel computer systems, traditional job schedulers use many parameters that can be configured to define job or queue priorities. Offering many parameters seems flexible, but in reality tuning the values for the parameters is highly challenging. To simplify the task of resource management, we propose goal-oriented policies, which allow system administrators to specify high-level performance objectives, rather than tuning low-level scheduling parameters. We study the design of goal-oriented policies, including (1) appropriate multi-objective models for specifying trade-offs between objectives, (2) efficient search algorithms for searching the best schedule at each scheduling decision point, and (3) appropriate performance measures to be optimized in the objectives with respect to two common performance requirements: preventing starvation and favoring shorter jobs. We compare goal-oriented policies with widely used backfill policies. Policies are evaluated by simulation using ten monthly workloads that ran on a Linux cluster (IA-64) from NCSA. Our results show that by automatically optimizing performance according to the given objectives through search, goal-oriented policies can simultaneously outperform FCFS-backfill and LXF-backfill, which are designed in favor of the maximum wait and average slowdown, respectively.

This paper is concerned with the design of goal-oriented scheduling policies that deal with multiple goals on production parallel systems. Several objective models are compared, including two new models that allow an explicit specification of tradeoff among different goals and a hierarchical model studied in our previous paper. We focus on two performance goals commonly placed on production systems: starvation prevention and favoring shorter jobs. In this paper, we study how to best formulate high-level performance goals, in an attempt to provide guideline for defining the objectives

To balance performance goals and allow administrators to declaratively specify high-level performance goals, we apply complete search algorithms to design on-line job scheduling policies for workloads that run on parallel computer systems. We formulate a hierarchical two-level objective that contains two goals commonly placed on parallel computer systems: (1) minimizing the total excessive wait; (2) minimizing the average slowdown. Ten monthly workloads that ran on a Linux cluster (IA-64) from NCSA are used in our simulation of policies. A wide range of measures are used for performance evaluation, including the average slowdown, average wait, maximum wait, and new measures based on excessive wait. For the workloads studied, our results show that the best search-based scheduling policy (i.e., DDS/lxf/dynB) reported here simultaneously beats both FCFS-backfill and LXF-backfill, each roughly providing a lower bound on maximum wait and the average slowdown, respectively, among backfill policies