Mohammed I. Alghamdi

Energy-Efficient Scheduling for Parallel Applications Running on Heterogeneous Clusters

High performance clusters have been widely used to provide amazing computing capability for both commercial and scientific applications. However, huge power consumption has prevented the further application of large-scale clusters. Designing energy-efficient scheduling algorithms for parallel applications running on clusters, especially on the high performance heterogeneous clusters, is highly desirable. In this regard, we propose a novel scheduling strategy called energy efficient task duplication schedule (EETDS for short), which can significantly conserve power by judiciously shrinking communication energy cost when allocating parallel tasks to heterogeneous computing nodes. We present the preliminary simulation results for Gaussian and FFT parallel task models to prove the efficiency of our algorithm.

PARM: a power-aware message scheduling algorithm for real-time wireless networks

Real-time applications in wireless networks are emerging in multimedia product and design. However, conventional real-time message scheduling algorithms generally do not take energy efficiency into account when making scheduling decisions. In this paper, I address the issue of scheduling real-time messages in wireless networks subject to timing and power constraints. A novel message scheduling scheme, or PARM (Power-aware Real-time Message), is developed to generate optimal schedules that minimize both power consumption and the probability of missing deadlines for real-time messages. With a power-aware scheduling policy in place, the proposed PARM scheme is very energy efficient. In addition, I extended a power consumption model to calculate power consumption rates in accordance to message transmission rates. Experimental results show that PARM significantly improves the performance in terms of missed rate, energy efficiency, and overall performance over four baseline message scheduling schemes.

Improving Security of Real-Time Wireless Networks Through Packet Scheduling [Transactions Letters]

Modern real-time wireless networks require high security level to assure confidentiality of information stored in packages delivered through wireless links. However, most existing algorithms for scheduling independent packets in real-time wireless networks ignore various security requirements of the packets. Therefore, in this paper we remedy this problem by proposing a novel dynamic security-aware packet-scheduling algorithm, which is capable of achieving high quality of security for realtime packets while making the best effort to guarantee realtime requirements (e.g., deadlines) of those packets. We conduct extensive simulation experiments to evaluate the performance of our algorithm. Experimental results show that compared with two baseline algorithms, the proposed algorithm can substantially improve both quality of security and real-time packet guarantee ratio under a wide range of workload characteristics.

AWARDS: an adaptive write strategy for secure local disk systems

Since security is of critical importance for modern storage systems, it is imperative to protect stored data from being tampered or disclosed. Although an increasing number of secure storage systems have been developed, there is no way to dynamically choose security services to meet disk requests flexible security requirements. Furthermore, existing security techniques for disk systems are not suitable to guarantee desired response times of disk requests. We remedy this situation by proposing an adaptive strategy (referred to as AWARDS) that can judiciously select the most appropriate security service for each write request while endeavoring to guarantee the desired response times of all disk requests. Experimental results show that AWARDS significantly improves security and overall performance over an existing scheme by up to 325.0% and 358.9% (with averages of 199.3% and 213.4%)

Scheduling of Periodic Packets in Energy-Aware Wireless Networks

Existing packets scheduling algorithms designed for energy-efficient wireless networks ignore important features of periodic packets, thereby being inadequate for periodic packets with energy constraints. To remedy this problem, we present in this paper an approach to scheduling periodic packets in wireless networks subject to both timing and energy constraints. We propose a necessary and sufficient feasibility check for a set of periodic packets to be transmitted over a wireless link. Next, we develop an algorithm to schedule periodic packets (or ESPP for short) over a wireless link. The ESPP algorithm aims at minimizing energy dissipation of periodic packets without missing deadlines of periodic packets. We show through simulation studies that ESPP can significantly reduce energy consumption of wireless networks by an average of 46.4% while guaranteeing timing constraints of periodic packets.

Thermal Modeling and Analysis of Cloud Data Storage Systems

An explosive increment of data and a variety of data analysis make it indispensable to lower power and cooling costs of cloud datacenters. To address this issue, we investigate the thermal impact of I/O access patterns on data storage systems. Firstly, we conduct some preliminary experiments to study the thermal behavior of a data storage node. The experimental results show that disks have ignorable thermal impacts as processors to outlet temperatures of storage nodes. We raise an approach to model the outlet temperature of a storage node. The thermal models generated by our approach gains a precision error less than 6%. Next, we investigate the thermal impact of data placement strategies on storage systems. We compare the cooling cost of storage systems governed by different data placement schemes. Our study shows that evenly distributing the data leads to highest outlet temperature for the sake of shortest execution time and energy efficiency. According to the energy consumption of various data placement schemes, we propose a thermal-ware energy-efficient data placement strategy. We further show that this work can be extended to analyze the cooling cost of data centers with massive storage capacity. Big data, which is composed of a collection of huge and complex data sets, has been positioned as must have commodity and resource in industry, government, and academia. Processing big data requires a large-scale storage system, which increases both power and cooling costs. In this study, we investigate the thermal behavior of real storage systems and their I/O access patterns, which offer a guideline of building energy-efficient cloud storage systems. The cooling consumption of data centers can be considerably reduced by using an efficient thermal management for storage systems. However, disk is not considered in traditional thermal models for data centers. In this paper, we investigate the thermal impact of hard disks and propose a thermal modeling approach for storage systems. In addition, we estimate the outlet temperature of a storage server by applying the proposed

PARM: A Power-Aware Message Scheduling Algorithm for Real-Time Wireless Networks

Real-time applications in wireless networks are emerging in multimedia product and design. However, conventional real-time message scheduling algorithms generally do not take energy efficiency into account when making scheduling decisions. In this paper, I address the issue of scheduling real-time messages in wireless networks subject to timing and power constraints. A novel message scheduling scheme, or PARM (Power-aware Real-time Message), is developed to generate optimal schedules that minimize both power consumption and the probability of missing deadlines for real-time messages. With a power-aware scheduling policy in place, the proposed PARM scheme is very energy efficient. In addition, I extended a power consumption model to calculate power consumption rates in accordance to message transmission rates. Experimental results show that PARM significantly improves the performance in terms of missed rate, energy efficiency, and overall performance over four baseline message scheduling schemes.

Thermal-Aware Job Scheduling of MapReduce Applications on High Performance Clusters

In this study, we develop a thermal-aware job scheduling strategy called tDispatch tailored for MapReduce applications running on Hadoop clusters. The scheduling idea of tDispatch is motivated by a profiling study of CPU-intensive and I/O-intensive jobs from the perspective of thermal efficiency. More specifically, we investigate the thermal behaviors of these two types of jobs running on a Hadoop cluster by stress testing data nodes through extensive experiments. We show that CPU-intensive and I/O-intensive jobs exhibit various thermal and performance impacts on multicore processors and hard drives of Hadoop cluster nodes. After we quantify the thermal behaviors of Hadoop jobs on the master and data nodes of a cluster, we propose our scheduler to alternatively dispatch CPU-intensive and I/O-intensive jobs. We apply our strategy to several MapReduce applications with different resource consumption profiles. Our experimental results show that tDispatch is conducive of creating opportunities to cool down multicore processors and disks in Hadoop clusters deployed in modern data centers. Our findings can be applied in other thermal-efficient job schedulers that are aware of thermal behaviors of CPU-intensive and I/O-intensive applications submitted to Hadoop clusters.

Periodic packets in energy-aware wireless networks

Existing packets scheduling algorithms designed for energy-efficient wireless networks ignore important features of periodic packets, thereby being inadequate for periodic packets with energy constraints. To remedy this problem, I present in this paper an approach to scheduling periodic packets in wireless networks subject to both timing and energy constraints. I propose a necessary and sufficient feasibility check for a set of periodic packets to be transmitted over a wireless link. Next, I develop an algorithm to schedule periodic packets (or ESPP for short) over a wireless link. The ESPP algorithm aims at minimizing energy dissipation of periodic packets without missing deadlines of periodic packets. I show through simulation studies that ESPP can significantly reduce energy consumption of wireless networks by an average of 46.4% while guaranteeing timing constraints of periodic packets.

Integrating Fault Recovery and Quality of Security in Real-Time Systems

In the past five years, mandatory security requirements and fault tolerance have become critical criteria for most real-time systems. Although many conventional fault-tolerant or security approaches were investigated and applied to real-time systems, most existing schemes only addressed either security demands ignoring the fault-tolerant requirements or vice versa. To bridge this technology gap in real-time systems, in this paper we propose a way of integrating fault recovery and confidentiality services. The novel integration of security and fault recovery makes it possible to implement next-generation real-time systems with high reliability and quality of security. Experimental results from real-world applications show that our approach can significantly improve security over the conventional approaches by up to 661.56% while providing an efficient means of fault tolerance.