Multiprocessor Platforms Research Articles

Authorized Update in Multi-user Homomorphic Encrypted Cloud Database

Cloud Computing is a promising solution in distributed internet computing for IT and scientific research. However, data storage and computing in the cloud domain raise added questions in terms of security. Data encrypted with traditional encryption schemes may confirm confidentiality, but computation in cloud domain becomes infeasible. This paper focuses on designing an encrypted database considering homomorphic encryption (HE) as an underlying scheme so that query execution is carried out on encrypted version of the database without any need of intermediate decryption. Existing encrypted databases are either based on partial HE or deterministic HE to achieve practical performance; hence, they are either limited in terms of types of query execution or prone to known attacks. To mitigate these issues, we explore the practical challenges of a fully homomorphic encryption (FHE)-based database design. FHE theoretically promises to perform arbitrary operations on encrypted data. However, realizing any algorithm in homomorphic domain requires a circuit-based representation of that specific algorithm, which is a non-trivial task. In this work, we explore the practical challenges of FHE database design, mostly in the case of multi-user organizational scenarios, and propose a scheme for secure modification or conditional update of the encrypted database. Moreover, when an organization outsources a database to the cloud, a single FHE key is used to encrypt all columns of the database. However, all users(or employees) of the same organization should not have equal read and write access permission to the whole database. In this work, we propose an architecture to apply Attribute-Based Access Control (ABAC) on FHE databases with minimum overhead in terms of performance and storage. We propose required changes in registration, login, and user revocation phases for our scheme to perform conditional SQL query processing on FHE encrypted database (EDB). Our proposed framework is capable of performing end-to-end encrypted conditional UPDATE with suitable access control within 17 minutes on a multi-core processor platform for 769 rows and 9 columns of database size with 16-bit size of data. To the best of our knowledge, our proposed technique is the first one in literature to support arbitrary secure encrypted SQL query execution with suitable access control.

Enhancement of Resource Scheduling on Gui Based Operating System

In computer architecture, multithreading is ability of a central processing unit (CPU) or a single core within a multi-core processor to execute multiple processes or threads concurrently, appropriately supported by operating system. This approach differs from multiprocessing, as with multithreading processes & threads have to share resources of a single or multiple cores: computing units, CPU caches, & translation lookaside buffer (TLB). Multiprocessing systems include multiple complete processing units, multithreading aims to increase utilization of a single core by using thread-level as well as instruction-level parallelism. Objective of research is increase efficiency of scheduling dependent task using enhanced multithreading. gang scheduling of parallel implicit-deadline periodic task systems upon identical multiprocessor platforms is considered. In this scheduling problem, parallel tasks use several processors simultaneously. first algorithm is based on linear programming & is first one to be proved optimal for considered gang scheduling problem. Furthermore, it runs in polynomial time for a fixed number m of processors & an efficient implementation is fully detailed. Second algorithm is an approximation algorithm based on a fixed-priority rule that is competitive under resource augmentation analysis in order to compute an optimal schedule pattern. Precisely, its speedup factor is bounded by (2?1/m). Both algorithms are also evaluated through intensive numerical experiments. In our research we have enhanced capability of Gang Scheduling by integration of multi core processor & Cache & make simulation of performance in MATLAB.

Runtime Adaptive IoMT Node on Multi-Core Processor Platform

The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. Thanks to innovative technologies, latest-generation communication networks, and state-of-the-art portable devices, IoTM opens up new scenarios for data collection and continuous patient monitoring. Two very important aspects should be considered to make the most of this paradigm. For the first aspect, moving the processing task from the cloud to the edge leads to several advantages, such as responsiveness, portability, scalability, and reliability of the sensor node. For the second aspect, in order to increase the accuracy of the system, state-of-the-art cognitive algorithms based on artificial intelligence and deep learning must be integrated. Sensory nodes often need to be battery powered and need to remain active for a long time without a different power source. Therefore, one of the challenges to be addressed during the design and development of IoMT devices concerns energy optimization. Our work proposes an implementation of cognitive data analysis based on deep learning techniques on resource-constrained computing platform. To handle power efficiency, we introduced a component called Adaptive runtime Manager (ADAM). This component takes care of reconfiguring the hardware and software of the device dynamically during the execution, in order to better adapt it to the workload and the required operating mode. To test the high computational load on a multi-core system, the Orlando prototype board by STMicroelectronics, cognitive analysis of Electrocardiogram (ECG) traces have been adopted, considering single-channel and six-channel simultaneous cases. Experimental results show that by managing the sensory node configuration at runtime, energy savings of at least 15% can be achieved.

Semi-partitioned scheduling of mixed-criticality system on multiprocessor platforms

Semi-partitioned scheduling of mixed-criticality system on multiprocessor platforms

Developing a robust and flexible smart tool to predict a full range Critical Heat Flux (CHF) in different LWRs by using deep learning Artificial Neural Networks (ANN) via parallel multi-processing

In this paper, a practical smart tool has been developed to predict CHF in LWRs. The tool is based on the strengthen cascade feed forward ANNs in addition of some of the deep learning features. The most complete experimental CHF data available for LWRs are chosen as the training and test data sets. Input layer can get different thermodynamic qualities, mass flow rates, and pressures to predict the CHF. Then it gains a full range CHF simulator for LWRs fortunately covering both anticipated and unknown transients. Although training such a huge ANN structure and the huge training data set is time consuming, but after the training it acts fast especially via a multi-processing platform. Results are quite satisfactory due to the very good network performance and regressions. Briefly, this is a fast, flexible, and robust tool to predict the CHF deep inside the physics of the most broadening CHF data set ever published for the LWRs.

A Multithreading Based Enhanced Process Scheduling Technique for Heterogeneous Distributed Environment

Multithreading is ability of a central processing unit (CPU) or a single core within a multi-core processor to execute multiple processes or threads concurrently, appropriately supported by operating system. This approach differs from multiprocessing, as with multithreading processes & threads have to share resources of a single or multiple cores: computing units, CPU caches, & translation lookaside buffer (TLB). Multiprocessing systems include multiple complete processing units, multithreading aims to increase utilization of a single core by using thread-level as well as instruction-level parallelism. Objective of research is increase efficiency of scheduling dependent task using enhanced multithreading. gang scheduling of parallel implicit-deadline periodic task systems upon identical multiprocessor platforms is considered. In this scheduling problem, parallel tasks use several processors simultaneously. first algorithm is based on linear programming & is first one to be proved optimal for considered gang scheduling problem. Furthermore, it runs in polynomial time for a fixed number m of processors & an efficient implementation is fully detailed. Second algorithm is an approximation algorithm based on a fixed-priority rule that is competitive under resource augmentation analysis in order to compute an optimal schedule pattern. Precisely, its speedup factor is bounded by (2?1/m). Both algorithms are also evaluated through intensive numerical experiments. In our research we have enhanced capability of Gang Scheduling by integration of multi core processor & Cache & make simulation of performance in MATLAB.

Combining admission tests for heuristic partitioning of real-time tasks on ARM big.LITTLE multi-processor architectures

This paper tackles the problem of admitting real-time tasks on non-symmetric multiprocessor platforms, where partitioned EDF-FF is used. We extend the already obtained results for symmetric multiprocessor platforms and provide an improved equation based on the number of tasks in the taskset that considers the first few heaviest tasks in the taskset. This equation has been enveloped in three effective and efficient admission tests that are suitable for being used online and in dynamic environments to partition and admit tasks on NUMP platforms and, specifically, on ARM big.LITTLE architectures. This results in an improvement of the state of the art in terms of the considered architectures and looks promising in terms of admitted tasksets as shown by an extensive number of tests performed on tasksets generated randomly with the widely adopted randfixedsum algorithm.

Federated Scheduling of Sporadic DAGs on Unrelated Multiprocessors

This paper presents a federated scheduling algorithm for implicit-deadline sporadic DAGs that execute on an unrelated heterogeneous multiprocessor platform. We consider a global work-conserving scheduler to execute a single DAG exclusively on a subset of the unrelated processors. Formal schedulability analysis to find the makespan of a DAG on its dedicated subset of the processors is proposed. The problem of determining each subset of dedicated unrelated processors for each DAG such that the DAG meets its deadline (i.e., designing the federated scheduling algorithm) is tackled by proposing a novel processors-to-task assignment heuristic using a new concept called processor value . Empirical evaluation is presented to show the effectiveness of our approach.

Excluding Parallel Execution to Improve Global Fixed Priority Response Time Analysis

Response Time Analysis (RTA) is an effective method for testing the schedulability of real-time tasks on multiprocessor platforms. Existing RTAs for global fixed priority scheduling calculate the upper bound of the worst case response time of each task. Given a target task, existing RTAs first calculate the workload upper bound of each higher priority task (than the target task), and then calculate the interference on the target task by each higher priority task according to the obtained workload upper bounds. The workload of a task consists of three parts: carry-in, body and carry-out. The interference from all these three parts may be overestimated in existing RTAs. However, although the overestimation of the interference from body is the major factor that causes the low accuracy of existing RTAs, all existing work only focuses on how to reduce the overestimation of the interference from carry-in, and there is no method to reduce the overestimation of the interference from body or carry-out. In this work, we propose a method to calculate the lower bound of the accumulative time in which the target task and higher priority tasks are executed in parallel. By excluding the parallel execution time from the interference, we derive a new RTA test that can reduce the overestimation of the interference from all three parts of the workload. Extensive experiments are conducted to verify the superior performance of the proposed RTA test.

Healthcare and Fitness Data Management Using the IoT-Based Blockchain Platform.

Because of the availability of more than an actor and a wireless component among e-health applications, providing more security and safety is expected. Moreover, ensuring data confidentiality within different services becomes a key requirement. In this paper, we propose to collect data from health and fitness smart devices deployed in connection with the proposed IoT blockchain platform. The use of these devices helps us in extracting an amount of highly valuable heath data that are filtered, analyzed, and stored in electronic health records (EHRs). Different actors of the platform, coaches, patients, and doctors, collaborate to provide an on-time diagnosis and treatment for various diseases in an easy and cost-effective way. Our main purpose is to provide a distributed, secure, and authorized access to these sensitive data using the Ethereum blockchain technology. We have designed an integrated low-powered IoT blockchain platform for a healthcare application to store and review EHRs. This architecture, based on the blockchain Ethereum, includes a web and mobile application allowing the patient as well as the medical and paramedical staff to have a secure access to health information. The Ethereum node is implemented on an embedded platform, which should provide an efficient, flexible, and secure system despite the limited resources and low power consumption of the multiprocessor platform.

Boosted System Performance based on Execution Time Using LiMca Scheduling Algorithm

The scheduling algorithms have been examined by the process of task execution in a system to achieve maximum utilization of multiprocessors. Consequently, the research attempted to propose a new real-time scheduling algorithm to support a multiprocessor platform. The proposed scheduling algorithm, List Mcnaughton’s amalgamation (LiMca) scheduling algorithm has been developed for an optimum solution with the features of List and Mcnaughton’s scheduling algorithms to overcome the individual drawbacks (pre-emption and Precedence constraints). In LiMca, Workload has been distributed to the processors in a system by sorting the tasks in decreasing order with their precedence constraints including due dates, pre-emption, and context switching. In this arena, the LiMca scheduling algorithm has been developed on a traditional avionic mission system and also simulated on a real-time application. The extensive simulations were carried out on the time optimization of resources scheduling (TORSCHE) simulation toolbox. LiMca scheduling algorithm performed superior as compared to recently reported scheduling algorithms like list, hu’s, McNaughton’s, Brucker’s, and Hodgson’s in terms of computational performance.

Cluster formation techniques for hierarchical real time tasks allocation on multiprocessor system

SummaryReal‐time systems are nowadays extensively used in most time‐critical embedded applications. The existing multiprocessor real‐time scheduling algorithms are based on partitioned and global scheduling approaches. The partitioned‐based algorithms suffer poor utilization bound, load balancing, not compatible for an open system environment, and the global scheduling approach faces problems like high scheduling, migration overhead. Cluster scheduling represents a hybrid scheduling approach which consists of a set of processors as clusters and tasks scheduled to each processor of clusters using a global scheduling approach. The different cluster formation heuristics are investigated for homogenous clusters. The main objective of this article is to propose task utilization‐based cluster formation and harmonic period aware task allocation for a hierarchical real‐time system on a multiprocessor platform. Tasks with high utilization, cluster size tune to a smaller value to achieve high utilization. Tasks with low utilization, the size of cluster tune to larger to reduce migration and scheduling overheads. Experimentation performed on multiprocessor real‐time simulator with a different set of tasks, multiprocessor system, different tasks utilization, and cluster size. Simulation results show that cluster base approach increases the number of tasks scheduled on a multiprocessor system, system utilization, and reduces response time, migration, and preemption overheads.

Narrowing the speedup factor gap of partitioned EDF

Schedulability is a fundamental problem in analyzing real-time systems, but it often has to be approximated because of the intrinsic computational hardness. Partitioned earliest deadline first (EDF) is one of the most popular polynomial-time and practical scheduler on multiprocessor platforms, and it was shown to have a speedup factor of at most 2.6322−1/m. This paper further improves the factor to 2.5556−1/m for both the constrained-deadline case and the arbitrary-deadline case, and it is very close to the known (non-tight) lower bound of 2.5−1/m. The key ideas are that we develop a novel method to discretize and regularize sporadic task sets that are schedulable on uniprocessors, and we find that the ratio (ρ) of the approximate demand bound value to the machine capacity is upper-bounded by 1.5556 for the arbitrary-deadline case, which plays an important role in estimating the speed factor of partitioned EDF.

Implementing synchronous reactive components upon multiprocessor platforms

Model-based design methodologies based on the synchrony assumption are widely used in many safety-critical application domains. The synchrony assumption asserts that actions (such as the execution of code) occur instantaneously; however, physical platforms obviously do not possess this property. This paper considers a scheduling problem that arises when one seeks to implement programs that are written under the synchrony assumption upon actual multiprocessor platforms, and proposes algorithms for solving this problem exactly and approximately.

High-precision Registration Algorithm and Parallel Design Method for High-Resolution Optical Remote Sensing Images

In optical remote sensing image reconstruction, image registration is an important issue to address in order to ensure satisfactory reconstruction performance. In this study, a multi-frame image registration algorithm for high-resolution images and its parallel design method are proposed. The algorithm realizes an improved feature point detection method based on an adaptive gradient bilateral tensor filter and carries out weighted Gaussian surface sub-pixel interpolation to obtain more accurate corner positions, which better guarantees the registration accuracy. On this basis, multi-scale expansion is carried out to generate descriptors for image registration. In addition, the operation-level parallel analysis and design are carried out on a GPU platform based on compute unified device architecture (CUDA), and the memory model of the GPU is utilized reasonably. The task-level parallel analysis and design are carried out based on the GPU stream model. Moreover, based on the open multi-processing (OpenMP) platform, a multi-core CPU carries out parallel design at the operation level and task level, which realizes post-processing operations such as optical remote sensing images loading, accurate matching, and coordinate mapping, thereby effectively improving registration speed. Compared with feature point algorithms and deep learning algorithm, our algorithm and its parallel design significantly improve the registration accuracy and speed of high-resolution optical remote sensing images.

Mixed-Criticality Scheduling Upon Permitted Failure Probability and Dynamic Priority

Many safety-critical real-time systems are considered certified when they meet failure probability requirements with respect to the maximum permitted incidences of failure per hour. In this article, the mixed-criticality task model with multiple worst case execution time (WCET) estimations is extended to incorporate such system-level certification restrictions. A new parameter is added to each task, characterizing the distribution of WCET estimations—the likelihood of all jobs of a task finishing their executions within the less pessimistic WCET estimates. Efficient algorithms are derived for scheduling mixed-criticality systems represented using this model for both uniprocessor and multiprocessor platforms for independent tasks. Furthermore, a 0/1 covariance matrix is introduced to represent the failure dependency between tasks. An efficient algorithm is proposed to schedule such failure-dependent tasks. Experimental analyses show our new model and algorithm outperform current state-of-the-art mixed-criticality scheduling algorithms.

Partitioning-Based Scheduling of OpenMP Task Systems With Tied Tasks

OpenMP is a popular programming framework in both general and high-performance computing and has recently drawn much interest in embedded and real-time computing. Although the execution semantics of OpenMP are similar to the DAG task model, the constraints posed by the OpenMP specification make them significantly more challenging to analyze. A tied task is an important feature in OpenMP that must execute on the same thread throughout its entire life cycle. A previous work [1] succeeded in analyzing the real-time scheduling of tied tasks by modifying the Task Scheduling Constraints (TSCs) in OpenMP specification. In this article, we also study the real-time scheduling of OpenMP task systems with tied tasks but without changing the original TSCs. In particular, we propose a partitioning-based algorithm, P-EDF-omp, by which the tied constraint can be automatically guaranteed as long as an OpenMP task system can be successfully partitioned to a multiprocessor platform. Furthermore, we conduct comprehensive experiments with both synthetic workloads and established OpenMP benchmarks to show that our approach consistently outperforms the work in [1] —even without modifying the TSCs.

Algorithms for implementing elastic tasks on multiprocessor platforms: a comparative evaluation

The elastic task model enables the adaptation of systems of recurrent real-time tasks under uncertain or potentially overloaded conditions. A range of permissible periods is specified for each task in this model; during run-time a period is selected for each task from the specified range of permissible periods to ensure schedulability in a manner that maximizes the quality of provided service. This model was originally defined for sequential tasks executing upon a preemptive uniprocessor platform; here we consider the implementation of sequential tasks upon multiprocessor platforms. We define algorithms for scheduling sequential elastic tasks under the global and partitioned paradigms of multiprocessor scheduling for both dynamic and static-priority tasks, and we provide an extensive simulation-based comparison of the different approaches.

A Smart Semipartitioned Real‐Time Scheduling Strategy for Mixed‐Criticality Systems in 6G‐Based Edge Computing

With the rapid growth of 6G communication and smart sensor technology, the Internet of Things (IoT) has attracted much attention now. In the 6G‐based IoT applications on the multiprocessor platform, the partitioned scheduling has been widely applied. However, these partitioned scheduling approaches could cause system resource waste and uneven workload among processors. In this paper, a smart semipartitioned scheduling strategy (SSPS) was proposed for mixed‐criticality systems (MCS) in 6G‐based edge computing. Besides tasks’ acceptance rate and weighted schedulability, QoS is considered in SSPS to improve the service quality of the system. The SSPS allocates tasks into each processor, and some tasks can migrate to other processors as soon as possible. By comparing with the several existing algorithms, the experimental results show that the SSPS achieves the best in the schedulability and QoS of the system.

FT-PIP: Flush Task Incorporated Priority-Inheritance Protocol to Reduce Information Leakage on Multiprocessor Real-Time Systems

In a traditional real-time system, the major requirement is to finish every real-time task within its predefined time. However, as a modern real-time system is connected to external networks and its subsystems are developed by different vendors, it becomes an important requirement to address the problem of information leakage on resources shared by different real-time tasks. This triggers studies that incorporate security mechanisms into the real-time scheduling. While it is one of the most well-known approaches to add the flush task (FT) mechanism and analyze its effect on timing guarantees, the existing FT approaches have been limited to a real-time system that employs at most one resource shared by real-time tasks executed on a uniprocessor platform. In this paper, we propose a flush task incorporated priority-inheritance protocol (FT-PIP) for global fixed-priority multiprocessor scheduling and develop its schedulability analysis, which is the first attempt that not only (a) supports timing guarantees but also (b) satisfies security constraints for (c) a multiprocessor platform with multiple shared resources. Via simulations, we demonstrate that FT-PIP and its schedulability analysis are effective in achieving both (a) and (b) for (c).