Workload Size Research Articles

Migration of On-Premises Database to Cloud and Perform Explanatory Analytics on Sales Data

This paper explores the migration of an industrial company's sales database from its servers to the cloud using Microsoft Azure, emphasizing three unique aspects of the process. First, it integrates the Delta Lake format within Azure Data Lake Storage Gen2, which is critical for maintaining multiple versions of data securely and ensuring ACID compliance. Second, the paper addresses significant adoption challenges such as data security, recovery, and vendor lock-in. It provides practical advice and strategic insights to help organizations navigate the complexities of cloud migration. Third, it offers a comparative analysis of two cloud storage methods—serverless and dedicated pool storage. This analysis evaluates their performance, cost-effectiveness, and suitability for different workload sizes, providing valuable insights for selecting the optimal storage strategy. Overall, this study contributes to a deeper understanding of cloud migrations, emphasizing practical applications and strategic decision-making necessary for enhancing operational efficiency and effective data management in cloud environments.

Distributed Graph Processing in Cloud Computing: A Review of Large-Scale Graph Analytics

The rapid growth of graph data in various domains has propelled the need for efficient distributed graph processing techniques in cloud computing environments. This paper presents a comprehensive review of distributed graph processing for graph analytics of massive size in the context of cloud computing. The paper begins by highlighting the challenges associated with distributed graph processing, including load balancing, communication overhead, scalability, and partitioning strategies. It provides an overview of existing frameworks and tools specifically designed for distributed graph processing in cloud environments. Furthermore, the review encompasses various techniques and algorithms employed in distributed graph processing. The paper also reviews recent research advancements in optimizing distributed graph processing in cloud computing. To provide practical insights, the paper presents a comparative analysis of representative large-scale graph analytics applications implemented on different cloud computing platforms. Performance, scalability, and efficiency metrics are evaluated under varying workload sizes and graph characteristics. Overall, this comprehensive review paper serves as a highly prized asset for researchers and large-scale graph analytics professionals who are practitioners in the field. It provides a holistic understanding of the state-of-the-art distributed graph processing techniques in cloud computing and guides future research efforts towards more efficient and scalable graph processing in cloud environments.

On the scalability of the speedup considering the overhead of consolidating virtual machines in servers for data centers

Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation.

MAS-Cloud+: A novel multi-agent architecture with reasoning models for resource management in multiple providers

Nowadays, scientific and commercial applications are often deployed to cloud environments requiring multiple resource types. This scenario increases the necessity for efficient resource management. However, efficient resource management remains challenging due to the complex nature of modern cloud-distributed systems since resources involve different characteristics, technologies, and financial costs. Thus, optimized cloud resource management to support the heterogeneous nature of applications balancing cost, time, and waste remains a challenge. Multi-agent technologies can offer noticeable improvements for resource management, with intelligent agents deciding on Virtual Machine (VM) resources. This article proposes MAS-Cloud+, a novel agent-based architecture for predicting, provisioning, and monitoring optimized cloud computing resources. MAS-Cloud+ implements agents with three reasoning models including heuristic, formal optimization, and metaheuristic. MAS-Cloud+ instantiates VMs considering Service Level Agreement (SLA) on cloud platforms, prioritizing user needs considering time, cost, and waste of resources providing appropriate selection for evaluated workloads. To validate MAS-Cloud+, we use a DNA sequence comparison application subjected to different workload sizes and a comparative study with state-of-the-art work with Apache Spark benchmark applications executed on the AWS EC2. Our results show that to execute the sequence comparison application, the best performance was obtained by the optimization model, whereas the heuristic model presented the best cost. By providing the choice among multiple reasoning models, our results show that MAS-Cloud+ could provide a more cost-effective selection of the instances reducing ≈58% of execution average cost of WorkdCount, Sort, and PageRank BigDataBench benchmarking workloads. As for the execution time, the WorkdCount and PageRank present reduction, the latter with ≈58%. The results indicate a promising solution for efficient cloud resource management.

Additional axial decomposition parallelism for MOC solver in the neutron transport code PANDAS-MOC

Method of Characteristics (MOC) is an extensively implemented technique for solving the Boltzmann neutron transport equation. In the PANDAS-MOC neutron transport code, MOC is used to determine the 2D radial solution. However, in the Whole-code OpenMP threading hybrid model (WCP) of PANDAS-MOC, the parallel performance of the MOC solver is still restricted by frequent synchronizations and spatial domain decomposition. This paper describes two new parallel algorithms: additional axial decomposition with barriers (AADb) and with status flags (AADsf). They further break down the axial layers based on the existing MPI domain decomposition and split the executed OpenMP threads into multiple groups to increase the workload size and decrease the number of synchronizations per thread. The AADb schedule synchronizes with #pragma omp barrier, but the AADsf schedule replaces those explicit barriers with status flags. C5G7 3D core is used to evaluate their parallel performance. The observed maximum efficiencies of the AAD algorithms are around 0.87, and their efficiencies were approximately 1.15x-1.32x that of the previous No-atomic schedule when using an identical amount of computing resources. Moreover, when launching many OpenMP threads, the AADsf schedule performs better than the AADb schedule.

Performance Bug Analysis and Detection for Distributed Storage and Computing Systems

This article systematically studies 99 distributed performance bugs from five widely deployed distributed storage and computing systems (Cassandra, HBase, HDFS, Hadoop MapReduce and ZooKeeper). We present the TaxPerf database, which collectively organizes the analysis results as over 400 classification labels and over 2,500 lines of bug re-description. TaxPerf is classified into six bug categories (and 18 bug subcategories) by their root causes; resource, blocking, synchronization, optimization, configuration, and logic. TaxPerf can be used as a benchmark for performance bug studies and debug tool designs. Although it is impractical to automatically detect all categories of performance bugs in TaxPerf, we find that an important category of blocking bugs can be effectively solved by analysis tools. We analyze the cascading nature of blocking bugs and design an automatic detection tool called PCatch , which (i) performs program analysis to identify code regions whose execution time can potentially increase dramatically with the workload size; (ii) adapts the traditional happens-before model to reason about software resource contention and performance dependency relationship; and (iii) uses dynamic tracking to identify whether the slowdown propagation is contained in one job. Evaluation shows that PCatch can accurately detect blocking bugs of representative distributed storage and computing systems by observing system executions under small-scale workloads.

Acceleration of Bi-Objective Optimization of Data-Parallel Applications for Performance and Energy on Heterogeneous Hybrid Platforms

Accelerating the bi-objective optimization of applications for performance and energy is crucial to achieving energy efficiency objectives and meeting quality-of-service requirements in modern high-performance computing platforms and cloud computing infrastructures. In this work, we highlight the crucial challenges to accelerate model-based methods proposed for the bi-objective optimization of data-parallel applications for performance and energy that employ workload distribution between the executing processors as the decision variable. The methods solve unconstrained bi-objective optimization problems and take input, the processors’ performance and energy profiles in the form of discrete functions of workload size, and output Pareto-optimal solutions (workload distributions), minimizing the execution time and the total energy consumption of computations during the parallel execution of the application. One of the challenges is the fast computation of Pareto-optimal solutions. We then formulate the bi-objective optimization problem of data-parallel applications for performance and energy through workload distribution on a cluster of p identical hybrid nodes, each containing h heterogeneous processors. The state-of-the-art algorithm for solving the problem is sequential and takes exorbitant execution times to find Pareto-optimal solutions for even moderate numbers of processors. We propose two algorithms that address this shortcoming. The first algorithm is an exact sequential algorithm that is more efficient and amenable to parallelization and achieves a complexity reduction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (m × h) over the state-of-the-art sequential algorithm where m is the cardinality of the input discrete execution time and dynamic energy functions. The second algorithm is a parallel algorithm executed by q identical parallel processes that reduces the complexity of our proposed sequential algorithm by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (q) and therefore achieves a complexity reduction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (m×h×q) over the state-of-the-art sequential algorithm. Finally, we experimentally analyze the practical efficacy of our proposed algorithms for two data-parallel applications, matrix multiplication and fast Fourier transform, on a heterogeneous hybrid node containing an Intel Haswell multicore CPU, an Nvidia k40c GPU, and an Nvidia P100 GPU and simulations of clusters of such hybrid nodes. The experiments demonstrate that our proposed algorithms provide tremendous speedups over state-of-the-art solutions.

Evolutionary Computing based Web Service Composition Technique for Scheduling of Workload under Cloud Environment

Objectives: The objective of this model is to design a technique to schedule the workload for the computation of web services in the heterogeneous cloud environment. As the Web Service Composition (WSC) for the execution of scientific workload in a heterogeneous cloud computing environment is a challenging task. Modern workload requires dynamic resource provisioning technique as there exist parallelization among sub-tasks and different task demands different Quality of Service (QoS) requirement. Methods: This study presents an Evolutionary Computing based Web Service Composition (EC-WSC) technique to execute a large-scale scientific workload in a heterogeneous cloud environment. A multi-objective metric for improving energy efficiency and resource utilization is modelled. Then, an improved searching mechanism for the dragonfly evolutionary computing algorithm is modelled. Findings: Experiment outcomes show EC-WSC model attains superior performance in execution time performance analysis and energy efficiency performance analysis when compared with existing resource provisioning models of workload service composition such as Deadline and Budget-Aware Workflow Scheduling (DBAWS)(1), Evolutionary Computing Multi-objective optimization for Hybrid Clouds (EC-MOH)(2), Web Service Composition (WSC)(3), and Evolutionary Multi-Objective Optimization for clouds (EMOC)(4) in terms of heterogeneous computing, workload size, multi-objective optimization, QoS metric, and optimization strategy. Our model EC-WSC has proved to be more efficient in terms of energy efficiency by a reduction of 52.13% and also reduction in execution time by 71% when compared with the WSC(3) existing Web Service Composition model. Novelty: Existing resource provisioning predominantly focused on reducing computation cost and time; however, induces task execution latency and energy overhead. However, EC-WSC is modelled to utilize resources more efficiently and meet task QoS requirements by assuring energy minimization constraints. Keywords: Cloud Computing; Heterogeneous Computing Environment; Multiobjective optimization problem; Resource Provisioning; Workload Scheduling

Enterprise Robotic Process Automation

Robotic Process Automation (RPA) is an emerging technology widely used across multiple sectors such as human resources, healthcare, finance, accounting, manufacturing, higher education and supply chain management, etc. RPA, also known as ‘software bots’, replaces manual, rule-based, repetitive tasks humans perform. These software bots are currently in a journey, evolving to be more sophisticated, mimicking human activities and enabling humans to achieve higher-valued tasks. Hence, RPA impacts the overall operational efficiency in organisations through multiple facets by its integration with employees, existing technologies and infrastructure, and business processes. It reduces the burden on IT as it does not disturb the underlying legacy systems. It increases reliability as bots can work 24x7 effectively. It is used as a time and cost reduction technology as it reduces the size of the manual workload. The tasks performed through RPA is accurate as it is less prone to errors. It increases compliances as it follows the rules and keeps audit trails. The productivity rate of organisations increase as the execution time through RPA is faster than tasks being performed by human employees. Furthermore, RPA is introduced as a low code technology that uses drag and drop functionalities with little to no programming knowledge [1].

SparkCruise

Today cloud companies offer fully managed Spark services. This has made it easy to onboard new customers but has also increased the volume of users and their workload sizes. However, both cloud providers and users lack the tools and time to optimize these massive workloads. To solve this problem, we designed SparkCruise that can help understand and optimize workload instances by adding a workload-driven feedback loop to the Spark query optimizer. In this paper, we present our approach to collecting and representing Spark query workloads and use it to improve the overall performance on the workload, all without requiring any access to user data. These methods scale with the number of workloads and apply learned feedback in an online fashion. We explain one specific workload optimization developed for computation reuse. We also share the detailed analysis of production Spark workloads and contrast them with the corresponding analysis of TPC-DS benchmark. To the best of our knowledge, this is the first study to share the analysis of large-scale production Spark SQL workloads.

MENTAL WORKLOAD OF AIR TRAFFIC CONTROL (ATC) PERSONNEL AT ADISUTJIPTO INTERNATIONAL AIRPORT

Air traffic controllers are under excessive stress because of their job. This has been linked to aspects of ATC work such as high job demands, time or responsibility pressure, or inadequate equipment. Types of work that require more vigilance, such as air traffic controllers at airports, are closely related to mental jobs that require high concentration. Because there is a negative impact on a company if it gives mental workload too high or too low for its employees, it is necessary to measure it to find out the right mental workload for its employees. This study aims to calculate the mental workload felt by ATC personnel in the APP unit. Measurement of mental workload in this study using the NASA-TLX (National Aeronautics and Space Administration Task Load Index). This method measures 6 (six) dimensions of workload size, namely Mental Demand, Physical Demand, Temporal Demand, Performance, Effort and Frustation Level

Bi-Objective Optimization of Data-Parallel Applications on Heterogeneous HPC Platforms for Performance and Energy Through Workload Distribution

Performance and energy are the two most important objectives for optimization on modern parallel platforms. In this article, we show that moving from single-objective optimization for performance or energy to their bi-objective optimization on heterogeneous processors results in a tremendous increase in the number of optimal solutions (workload distributions) even for the simple case of linear performance and energy profiles. We then study full performance and energy profiles of two real-life data-parallel applications and find that they exhibit shapes that are non-linear and complex enough to prevent good approximation of them as analytical functions for input to exact algorithms or optimization software for determining the Pareto front. We, therefore, propose a solution method solving the bi-objective optimization problem on heterogeneous processors. The method's novel component is an efficient and exact global optimization algorithm that takes as an input performance and energy profiles as arbitrary discrete functions of workload size, which accurately and realistically take into account resource contention and NUMA inherent in modern parallel platforms, and returns the Pareto-optimal solutions (generally speaking, load imbalanced). To construct the input discrete energy functions, the method employs a methodology that accurately models the energy consumption by a hybrid data-parallel application executing on a heterogeneous HPC platform containing different computing devices using system-level power measurements provided by power meters. We experimentally analyse the proposed solution method using three data-parallel applications, matrix multiplication, 2D fast Fourier transform (2D-FFT), and gene sequencing, on two connected heterogeneous servers consisting of multicore CPUs, GPUs, and Intel Xeon Phi. We show that it determines a superior Pareto front containing the best load balanced solutions and all the load imbalanced solutions that are ignored by load balancing methods.

Improving the Performance of Deduplication-Based Storage Cache via Content-Driven Cache Management Methods

Data deduplication, as a proven technology for effective data reduction in backup and archiving storage systems, is also showing promises in increasing the logical space capacity for storage caches by removing redundant data. However, our in-depth evaluation of the existing deduplication-aware caching algorithms reveals that they only work well when the cached block size is set to 4 KB. Unfortunately, modern storage systems often set the block size to be much larger than 4 KB, and in this scenario, the overall performance of these caching schemes drops below that of the conventional replacement algorithms without any deduplication. There are several reasons for this performance degradation. The first reason is the deduplication overhead, which is the time spent on generating the data fingerprints and their use to identify duplicate data. Such overhead offsets the benefits of deduplication. The second reason is the extremely low cache space utilization caused by read and write alignment. The third reason is that existing algorithms only exploit access locality to identify block replacement. There is a lost opportunity to effectively leverage the content usage patterns such as intensity of content redundancy and sharing in deduplication-based storage caches to further improve performance. We propose CDAC, a Content-driven Deduplication-Aware Cache, to address this problem. CDAC focuses on exploiting the content redundancy in blocks and intensity of content sharing among source addresses in cache management strategies. We have implemented CDAC based on LRU and ARC algorithms, called CDAC-LRU and CDAC-ARC respectively. Our extensive experimental results show that CDAC-LRU and CDAC-ARC outperform the state-of-the-art deduplication-aware caching algorithms, D-LRU, and D-ARC, by up to 23.83X in read cache hit ratio, with an average of 3.23X, and up to 53.3 percent in IOPS, with an average of 49.8 percent, under a real-world mixed workload when the cache size ranges from 20 to 50 percent of the workload size and the block size ranges from 4KB to 32 KB.

PENENTUAN JUMLAH TENAGA KERJA OPTIMAL BAGIAN PEMANENAN BERDASARKAN ANALISIS BEBAN KERJA DI PT. EQUALINDO MAKMUR ALAM SEJAHTERA

Each division in the company has a different workload size so that a different workload calculation is needed in determining the effective and efficient time for workers in their field of work with predetermined targets. Workload analysis can provide information on the completion time of work that can be achieved by each workforce so that it can be seen how much optimal workforce is needed by the company to achieve the predetermined target.&#x0D; &#x0D; Equalindo Makmur Alam Sejahtera is engaged in the oil palm plantation industry with the main activity of the company is to develop, plant, care for and harvest oil palm fresh fruit bunches (FFB). In oil palm plantation companies, harvesting activities are one of the determinants of company productivity, therefore researchers try to identify the workload and calculate the optimal number of harvesting workers at PT. equalindo Makmur Alam Sejahtera division 1 part of harvesting.&#x0D; &#x0D; This study aims to determine the time for completing the work of the main tasks assigned, perform workload analysis to determine optimal labor requirements, and determine policies that need to be done to improve the performance of labor in harvesting so that moreeffective and efficient.&#x0D; The method used in this study was the observation of the work activities of 16 harvesting workers with the following research procedures; Calculating Effective Working Days per month, Calculating productive Working Time, Calculating Work Completion Time (WPK) and Calculating Labor Needs Optimal. The conclusion of this research is the number of WPK harvesting part is 108,772 minutes/year or 227 days/year and the optimal number of labor as a result of the calculation is 13 people.

TH-DPMS

The rapidly increasing data in recent years requires the datacenter infrastructure to store and process data with extremely high throughput and low latency. Fortunately, persistent memory (PM) and RDMA technologies bring new opportunities towards this goal. Both of them are capable of delivering more than 10 GB/s of bandwidth and sub-microsecond latency. However, our past experiences and recent studies show that it is non-trivial to build an efficient and distributed storage system with such new hardware. In this article, we design and implement TH-DPMS (&lt;underline&gt;T&lt;/underline&gt;sing&lt;underline&gt;H&lt;/underline&gt;ua &lt;underline&gt;D&lt;/underline&gt;istributed &lt;underline&gt;P&lt;/underline&gt;ersistent &lt;underline&gt;M&lt;/underline&gt;emory &lt;underline&gt;S&lt;/underline&gt;ystem) based on persistent memory and RDMA, which unifies the memory, file system, and key-value interface in a single system. TH-DPMS is designed based on a unified distributed persistent memory abstract, pDSM. pDSM acts as a generic layer to connect the PMs of different storage nodes via high-speed RDMA network and organizes them into a global shared address space. It provides the fundamental functionalities, including global address management, space management, fault tolerance, and crash consistency guarantees. Applications are enabled to access pDSM with a group of flexible and easy-to-use APIs by using either raw read/write interfaces or the transactional ones with ACID guarantees. Based on pDSM, we implement a distributed file system and a key-value store named pDFS and pDKVS, respectively. Together, they uphold TH-DPMS with high-performance, low-latency, and fault-tolerant data storage. We evaluate TH-DPMS with both micro-benchmarks and real-world memory-intensive workloads. Experimental results show that TH-DPMS is capable of delivering an aggregated bandwidth of 120 GB/s with 6 nodes. When processing memory-intensive workloads such as YCSB and Graph500, TH-DPMS improves the performance by one order of magnitude compared to existing systems and keeps consistent high efficiency when the workload size grows to multiple terabytes.

A Novel Statistical Learning-Based Methodology for Measuring the Goodness of Energy Profiles of Applications Executing on Multicore Computing Platforms

Accurate energy profiles are essential to the optimization of parallel applications for energy through workload distribution. Since there are many model-based methods available for efficient construction of energy profiles, we need an approach to measure the goodness of the profiles compared with the ground-truth profile, which is usually built by a time-consuming but reliable method. Correlation coefficient and relative error are two such popular statistical approaches, but they assume that profiles be linear or at least very smooth functions of workload size. This assumption does not hold true in the multicore era. Due to the complex shapes of energy profiles of applications on modern multicore platforms, the statistical methods can often rank inaccurate energy profiles higher than more accurate ones and employing such profiles in the energy optimization loop of an application leads to significant energy losses (up to 54% in our case). In this work, we present the first method specifically designed for goodness measurement of energy profiles. First, it analyses the underlying energy consumption trend of each energy profile and removes the profiles that exhibit a trend different from that of the ground truth. Then, it ranks the remaining energy profiles using the Euclidean distances as a metric. We demonstrate that the proposed method is more accurate than the statistical approaches and can save a significant amount of energy.

Teachers’ Workload and Class Size as Correlate of Students’ Academic Performance in Selected Secondary Schools in Singida Urban

The study sought to establish the relationship between teachers’ workload, class size and students’ academic performance from five selected secondary schools in Singida urban. The study was guided by three objectives and three research hypotheses. The study employed correlational cross-sectional design. A sample size of two hundred nighty four respondents was randomly chosen from five secondary schools. A self-administered questionnaire was used to collect information from respondents. The collected data were analysed by Statistical Package for Social Scientists (SPSS) using Karl Pearson’s Linear Correlation Coefficient and Multiple Regression Analysis to assess the correlation between teachers’ workload and class size and students’ academic performance. The study results indicated that, there was no significant relationship between teachers’ workload and students’ academic performance in surveying secondary schools in Singida Urban at the five percent level of significance. Further, the findings show that class size was positively correlated with students’ academic performance in secondary schools in Singida urban. Also, there is a significant difference between teachers’ work load and class size in relation to students of academic performance in selected secondary schools in Singida urban. Based on the study findings, it is recommended that, emphasis should be directed at improving class size if teachers and education stakeholders in Singida urban have to increase students’ academic performance. The study concluded class size is found to be the best predictor of students’ academic performance in the surveyed secondary schools in Singida Urban.

Timing Is Money: The Flexibility and Precariousness of Login Employment

In the gig economy, workers gain scheduling flexibility but lose guarantees of paid workload. Since a schedule without work has no value to workers, the size and variance of workload within an observed schedule become key dimensions of precariousness. Using detailed work data on over ten thousand agents working in a virtual call center (VCC), which pioneered a gig-based on-demand employment model fifteen years ago, we show that workers’ control over their schedule does little to enhance employment security. The only simultaneous increase in size and reduction in variance of workload comes from trading off flexibility for security by making advance commitments to a specific schedule in exchange for priority in receiving calls. The case gives the first evidence of an innovative economic work arrangement – Login Employment – that combines formal relational contracts with informal directive control and thus becomes a mirror image of the traditional employment relationship.

Online scheduling of deadline‐constrained bag‐of‐task workloads on hybrid clouds

SummaryThe advent of hybrid cloud technologies and public Infrastructures as a Service (IaaS) allows service developers to offer services to their customers with little upfront investment and to adapt services to different workload sizes. The problem of minimizing the costs of the hired public infrastructure while providing the quality of service needed by the final customer arises when using hybrid clouds. Several scheduling strategies have been proposed to solve this problem for services dealing with deadline‐constrained bag‐of‐tasks workloads. Most of these solutions do not consider the variable performance of the clouds, the provisioning delay of virtual machine instances that affects the elasticity, and the impracticality of having good processing time estimations in real systems. We propose a scheduler algorithm that overcomes previous limitations and can minimize the cost of the infrastructure while maximizing the number of deadlines met by the service. Our solution can work autonomously by using sampled observations of the processing times and considers the heterogeneity and the provisioning time of the virtual machine instances. An evaluation was conducted by simulating different scenarios and workload types. Simulation results show that our solution obtains better or similar results than previous techniques in most scenarios in terms of deadlines met.

Accelerating Persistent Scatterer Pixel Selection for InSAR Processing

Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technology used for estimating the displacement of an object on the ground or the earth's surface itself. Persistent Scatterer-InSAR (PS-InSAR) is a category of time series algorithms enabling high resolution monitoring. PS-InSAR relies on successful selection of points that appear stable across a set of satellite images taken over time. This paper presents PtSel, a new algorithm for selecting these points, a problem known as Persistent Scatterer Selection . The key advantage of PtSel over the key existing techniques is that it does not require model assumptions, yet preserves solution accuracy. Motivated by the abundance of parallelism the algorithm exposes, we have implemented it for GPUs. Our evaluation using real-world data shows that the GPU implementation not only offers superior performance but also scales linearly with GPU count and workload size. We compare the GPU implementation and a parallel CPU implementation: a consumer grade GPU offers 18x speedup over a 16-core Ivy Bridge Xeon System, while four GPUs offer 65x speedup. The GPU solution consumes 28x less energy than the CPU-only solution. Additionally, we present a comparison with the most widely used PS-interferometry software package StaMPS, in terms of point selection coverage and precision.