Aftab Ahmed Chandio

Bio: Aftab Ahmed Chandio is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Cloud computing & Job scheduler. The author has an hindex of 6, co-authored 10 publications receiving 103 citations. Previous affiliations of Aftab Ahmed Chandio include University of Sindh.

A comparative study on resource allocation and energy efficient job scheduling strategies in large-scale parallel computing systems

Abstract: In the large-scale parallel computing environment, resource allocation and energy efficient techniques are required to deliver the quality of services (QoS) and to reduce the operational cost of the system. Because the cost of the energy consumption in the environment is a dominant part of the owner's and user's budget. However, when considering energy efficiency, resource allocation strategies become more difficult, and QoS (i.e., queue time and response time) may violate. This paper therefore is a comparative study on job scheduling in large-scale parallel systems to: (a) minimize the queue time, response time, and energy consumption and (b) maximize the overall system utilization. We compare thirteen job scheduling policies to analyze their behavior. A set of job scheduling policies includes (a) priority-based, (b) first fit, (c) backfilling, and (d) window-based policies. All of the policies are extensively simulated and compared. For the simulation, a real data center workload comprised of 22385 jobs is used. Based on results of their performance, we incorporate energy efficiency in three policies i.e., (1) best result producer, (2) average result producer, and (3) worst result producer. We analyze the (a) queue time, (b) response time, (c) slowdown ratio, and (d) energy consumption to evaluate the policies. Moreover, we present a comprehensive workload characterization for optimizing system's performance and for scheduler design. Major workload characteristics including (a) Narrow, (b) Wide, (c) Short, and (d) Long jobs are characterized for detailed analysis of the schedulers' performance. This study highlights the strengths and weakness of various job scheduling polices and helps to choose an appropriate job scheduling policy in a given scenario.

Big-Data Processing Techniques and Their Challenges in Transport Domain

Abstract: This paper describes the fundamentals of cloud computing and current big-data key technologies. We categorize big-data processing as batch-based, stream-based, graph-based, DAG-based, interactive-based, or visual-based according to the processing technique. We highlight the strengths and weaknesses of various big-data cloud processing techniques in order to help the big-data community select the appropriate processing technique. We also provide big data research challenges and future directions in aspect to transportation management systems.

A Comparative Study of Job Scheduling Strategies in Large-Scale Parallel Computational Systems

Abstract: With the advent of High Performance Computing (HPC) in the large-scale parallel computational environment, job scheduling and resource allocation techniques are required to deliver the Quality of Service (QoS) and resource management. Therefore, job scheduling on a large-scale parallel system has been studied to: (a) minimize the queue time and response time, and (b) maximize the overall system utilization. We compare and analyze thirteen job scheduling policies to analyze their behavior. The set of job scheduling policies include: (a) priority-based policies, (b) first fit, (c) backfilling techniques, and (d) window-based policies. All of the policies are extensively simulated and compared. A real data center workload comprised of 22385 jobs is used for simulation. We analyze the: (a) queue time, (b) response time, and (c) slowdown ratio to evaluate the policies. Moreover, we present a comprehensive workload characterization that can be used as a tool for optimizing system's performance and for scheduler design. We investigate four categories of the workload characteristics including: (a) Narrow, (b) Wide, (c) Short, and (d) Long for detailed analysis of the schedulers' performance. This study highlights the strengths and weakness of various job scheduling polices and helps to choose an appropriate job scheduling policy in a given scenario.

An Approach for Map-Matching Strategy of GPS-Trajectories Based on the Locality of Road Networks

Abstract: A map-matching process plays a pivotal role in ascertaining the quality of many location based services LBS. Map-matching process is to determine the accurate path of a vehicle onto road network in a form of digital map. Most of the current map-matching strategies are based on the shortest path queries SPQs providing best performance in terms of accuracy. Unfortunately, the execution of the SPQs is the most expensive part of the map-matching process in terms of computational cost, which may be unaffordable for real-time processing. This paper introduces LB-MM i.e., Locality Based Map-Matching, a novel approach for map-matching strategy that is based on locality of road network. LB-MM approach addresses a key challenge of SPQs in map-matching strategies by adaptively tuning the interior parameters of the map-matching process. The interior parameters, i.e., a number of candidate points CP and error circle radius ECR are fine-tuned based on different classes of locality of road network for each GPS sampling point. We characterize the locality of road network in different classes which result by splitting road network into small grids. In that way, a set of interior parameters is chosen based on locality that drastically reduces a number of SPQs and the overall computation time of map-matching process. The evaluation of proposed strategy against the SPQ-based ST-MM i.e., Spatio-Tempo Map-Matching strategy found in the literature is performed through simulation results based on both synthetic and real-world datasets. In LB-MM strategy, the total number of SPQs is counted as less than 27i?ź% against those of ST-MM.

Laser only feature based multi robot SLAM

Abstract: This paper presents multi-robot simultaneous localization and mapping (SLAM) framework for a team of robots with unknown initial poses. The proposed solution is using feature based Rao-Blackwellised particle filter (RBPF) SLAM for each robot working in an unknown environment equipped only with 2D range sensor and communication module. To represent the environment in compact form, line and corner features (or point features) are used. By sharing and comparing distinct feature based maps of each robot, a global map with known poses is formed without any physical meeting among the robots. This approach can easily applicable to the distributed or centralized robotic systems with ease of data handling and reduced computational cost.

Assessment of the Suitability of Fog Computing in the Context of Internet of Things

Abstract: This work performs a rigorous, comparative analysis of the fog computing paradigm and the conventional cloud computing paradigm in the context of the Internet of Things (IoT), by mathematically formulating the parameters and characteristics of fog computing—one of the first attempts of its kind. With the rapid increase in the number of Internet-connected devices, the increased demand of real-time, low-latency services is proving to be challenging for the traditional cloud computing framework. Also, our irreplaceable dependency on cloud computing demands the cloud data centers (DCs) always to be up and running which exhausts huge amount of power and yield tons of carbon dioxide ( $\text{CO}_2$ ) gas. In this work, we assess the applicability of the newly proposed fog computing paradigm to serve the demands of the latency-sensitive applications in the context of IoT. We model the fog computing paradigm by mathematically characterizing the fog computing network in terms of power consumption, service latency, $\text{CO}_2$ emission, and cost, and evaluating its performance for an environment with high number of Internet-connected devices demanding real-time service. A case study is performed with traffic generated from the $100$ highest populated cities being served by eight geographically distributed DCs. Results show that as the number of applications demanding real-time service increases, the fog computing paradigm outperforms traditional cloud computing. For an environment with $50$ percent applications requesting for instantaneous, real-time services, the overall service latency for fog computing is noted to decrease by $50.09$ percent. However, it is mentionworthy that for an environment with less percentage of applications demanding for low-latency services, fog computing is observed to be an overhead compared to the traditional cloud computing. Therefore, the work shows that in the context of IoT, with high number of latency-sensitive applications fog computing outperforms cloud computing.

Sustainable Cloud Data Centers: A survey of enabling techniques and technologies

Abstract: Cloud computing services have gained tremendous popularity and widespread adoption due to their flexible and on-demand nature. Cloud computing services are hosted in Cloud Data Centers (CDC) that deploy thousands of computation, storage, and communication devices leading to high energy utilization and carbon emissions. Renewable energy resources replace fossil fuels based grid energy to effectively reduce carbon emissions of CDCs. Moreover, waste heat generated from electronic components can be utilized in absorption based cooling systems to offset cooling costs of data centers. However, data centers need to be located at ideal geographical locations to reap benefits of renewable energy and waste heat recovery options. Modular Data Centers (MDC) can enable energy as a location paradigm due to their shippable nature. Moreover, workload can be transferred between intelligently placed geographically dispersed data centers to utilize renewable energy available elsewhere with virtual machine migration techniques. However, adoption of aforementioned sustainability techniques and technologies opens new challenges, such as, intermittency of power supply from renewable resources and higher capital costs. In this paper, we examine sustainable CDCs from various aspects to survey the enabling techniques and technologies. We present case studies from both academia and industry that demonstrate favorable results for sustainability measures in CDCs. Moreover, we discuss state-of-the-art research in sustainable CDCs. Furthermore, we debate the integration challenges and open research issues to sustainable CDCs.

A survey on scholarly data

Abstract: Survey of big scholarly data with respect to the different phases of the big data lifecycle.Identifies the different big data tools and technologies that can be used for development of scholarly applications.Investigates research challenges and limitations specific to big scholarly data and its applications.Provides research directions and paves way towards the development of a generic and comprehensive big scholarly data platform. Recently, there has been a shifting focus of organizations and governments towards digitization of academic and technical documents, adding a new facet to the concept of digital libraries. The volume, variety and velocity of this generated data, satisfies the big data definition, as a result of which, this scholarly reserve is popularly referred to as big scholarly data. In order to facilitate data analytics for big scholarly data, architectures and services for the same need to be developed. The evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing demand for scholarly applications like collaborator discovery, expert finding and research recommendation systems, in addition to several others. This research paper investigates the current trends and identifies the existing challenges in development of a big scholarly data platform, with specific focus on directions for future research and maps them to the different phases of the big data lifecycle.

Scalable I/O-Aware Job Scheduling for Burst Buffer Enabled HPC Clusters

Abstract: The economics of flash vs. disk storage is driving HPC centers to incorporate faster solid-state burst buffers into the storage hierarchy in exchange for smaller parallel file system (PFS) bandwidth. In systems with an underprovisioned PFS, avoiding I/O contention at the PFS level will become crucial to achieving high computational efficiency. In this paper, we propose novel batch job scheduling techniques that reduce such contention by integrating I/O awareness into scheduling policies such as EASY backfilling. We model the available bandwidth of links between each level of the storage hierarchy (i.e., burst buffers, I/O network, and PFS), and our I/O-aware schedulers use this model to avoid contention at any level in the hierarchy. We integrate our approach into Flux, a next-generation resource and job management framework, and evaluate the effectiveness and computational costs of our I/O-aware scheduling. Our results show that by reducing I/O contention for underprovisioned PFSes, our solution reduces job performance variability by up to 33% and decreases I/O-related utilization losses by up to 21%, which ultimately increases the amount of science performed by scientific workloads.