Execution Of Applications Research Articles

An Adaptive Application Framework with Customizable Quality Metrics

Many embedded environments require applications to produce outcomes under different, potentially changing, resource constraints. Relaxing application semantics through approximations enables trading off resource usage for outcome quality. Although quality is a highly subjective notion, previous work assumes given, fixed low-level quality metrics that often lack a strong correlation to a user’s higher-level quality experience. Users may also change their minds with respect to their quality expectations depending on the resource budgets they are willing to dedicate to an execution. This motivates the need for an adaptive application framework where users provide execution budgets and a customized quality notion. This article presents a novel adaptive program graph representation that enables user-level, customizable quality based on basic quality aspects defined by application developers. Developers also define application configuration spaces, with possible customization to eliminate undesirable configurations. At runtime, the graph enables the dynamic selection of the configuration with maximal customized quality within the user-provided resource budget. An adaptive application framework based on our novel graph representation has been implemented on Android and Linux platforms and evaluated on eight benchmark programs, four with fully customizable quality. Using custom quality instead of the default quality, users may improve their subjective quality experience value by up to 3.59×, with 1.76× on average under different resource constraints. Developers are able to exploit their application structure knowledge to define configuration spaces that are on average 68.7% smaller as compared to existing, structure-oblivious approaches. The overhead of dynamic reconfiguration averages less than 1.84% of the overall application execution time.

Towards Accurate Run-Time Hardware-Assisted Stealthy Malware Detection: A Lightweight, yet Effective Time Series CNN-Based Approach

According to recent security analysis reports, malicious software (a.k.a. malware) is rising at an alarming rate in numbers, complexity, and harmful purposes to compromise the security of modern computer systems. Recently, malware detection based on low-level hardware features (e.g., Hardware Performance Counters (HPCs) information) has emerged as an effective alternative solution to address the complexity and performance overheads of traditional software-based detection methods. Hardware-assisted Malware Detection (HMD) techniques depend on standard Machine Learning (ML) classifiers to detect signatures of malicious applications by monitoring built-in HPC registers during execution at run-time. Prior HMD methods though effective have limited their study on detecting malicious applications that are spawned as a separate thread during application execution, hence detecting stealthy malware patterns at run-time remains a critical challenge. Stealthy malware refers to harmful cyber attacks in which malicious code is hidden within benign applications and remains undetected by traditional malware detection approaches. In this paper, we first present a comprehensive review of recent advances in hardware-assisted malware detection studies that have used standard ML techniques to detect the malware signatures. Next, to address the challenge of stealthy malware detection at the processor’s hardware level, we propose StealthMiner, a novel specialized time series machine learning-based approach to accurately detect stealthy malware trace at run-time using branch instructions, the most prominent HPC feature. StealthMiner is based on a lightweight time series Fully Convolutional Neural Network (FCN) model that automatically identifies potentially contaminated samples in HPC-based time series data and utilizes them to accurately recognize the trace of stealthy malware. Our analysis demonstrates that using state-of-the-art ML-based malware detection methods is not effective in detecting stealthy malware samples since the captured HPC data not only represents malware but also carries benign applications’ microarchitectural data. The experimental results demonstrate that with the aid of our novel intelligent approach, stealthy malware can be detected at run-time with 94% detection performance on average with only one HPC feature, outperforming the detection performance of state-of-the-art HMD and general time series classification methods by up to 42% and 36%, respectively.

On revisiting energy and performance in microservices applications: A cloud elasticity-driven approach

Monolithic applications are a subject that includes several knowledge areas. Sometimes it can be a challenge to optimize CPU or IO requirements because it is not trivial to recognize the problem itself and improve it. There are many approaches to resolve this situation, where a trending one is the microservices. As a variant of the service-oriented architecture, microservices is a technique that arranges an application as a collection of loosely coupled services. This decomposition enables better software management in cloud-based environments since we can replicate each part individually using cloud elasticity to avoid execution bottlenecks. Also, since elasticity mitigates resource overprovisioning, it favors better energy consumption: the cloud owner can redistribute finite available resources among different tenants, and users can pay less to use the infrastructure. However, elasticity tuning is not trivial and depends on several factors, such as user experience, application architecture, and parameter modeling. Today, we observe a lack of initiatives in the literature that address both performance and energy perspectives to support the execution of microservices applications in the cloud. Concerning this context, this article introduces Elergy as a lightweight proactive elasticity model that provides resource reorganization for a cloud-based microservices application. Its differential approach appears in improving energy consumption by periodically handling the most appropriate amount of resources to execute an application while maintaining or yet improving the performance of CPU-bound applications. Elergy performs these functions proactively, in such a way of preventing future problems related to either resource under- or overprovisioning. The results showed energy consumption reduction and a competitive cost (application time x consumed resources) when comparing Elergy with a non-elastic scenario. Elergy obtained savings from 1.93% to 27.92% for energy consumption.

Managing Time-Sensitive IoT Applications via Dynamic Application Task Distribution and Adaptation

The recent proliferation of the Internet of Things has led to the pervasion of networked IoT devices such as sensors, video cameras, mobile phones, and industrial machines. This has fueled the growth of Time-Sensitive IoT (TS-IoT) applications that must complete the tasks of (1) collecting sensor observations they need from appropriate IoT devices and (2) analyzing the data within application-specific time-bounds. If this is not achieved, the value of these applications and the results they produce depreciates. At present, TS-IoT applications are executed in a distributed IoT environment that consists of heterogeneous computing and networking resources. Due to the heterogeneous and volatile nature (e.g., unpredictable data rates and sudden disconnections) of the IoT environment, it has become a major challenge to ensure the time-bounds of TS-IoT applications. Many existing task management techniques (i.e., techniques that are used to manage the execution of IoT applications in distributed computing resources) that have been proposed to support TS-IoT applications to meet their time-bounds do not provide a sophisticated and complete solution to manage the TS-IoT applications in a manner in which their time-bounds are guaranteed. This paper proposes TIDA, a comprehensive platform for managing TS-IoT applications that includes a task management technique, called DTDA, which incorporates novel task sizing, distribution, and dynamic adaptation techniques. DTDA’s task sizing technique measures the computing resources required to complete each task of the TS-IoT application at hand in each available IoT device, edge computer (e.g., network gateways), and cloud virtual machine. DTDA’s task distribution technique distributes and executes the tasks of each TS-IoT application in a manner that their time-bound requirements are met. Finally, DTDA includes a task adaptation technique that dynamically adapts the distribution of tasks (i.e., redistributes TS-IoT application tasks) when it detects a potential application time-bound violation. The paper describes a proof-of-concept implementation of TIDA that uses Microsoft’s Orleans Actor Framework. Finally, the paper demonstrates that the DTDA task management technique of TIDA meets the time-bound requirements of TS-IoT applications by presenting an experimental evaluation involving real time-sensitive IoT applications from the smart city domain.

EA-DFPSO: An intelligent energy-efficient scheduling algorithm for mobile edge networks

Cloud data centers have become overwhelmed with data-intensive applications due to the limited computational capabilities of mobile terminals. Mobile edge computing is emerging as a potential paradigm to host application execution at the edge of networks to reduce transmission delays. Compute nodes are usually distributed in edge environments, enabling crucially efficient task scheduling among those nodes to achieve reduced processing time. Moreover, it is imperative to conserve edge server energy, enhancing their lifetimes. To this end, this paper proposes a novel task scheduling algorithm named Energy-aware Double-fitness Particle Swarm Optimization (EA-DFPSO) that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution time. The proposed EA-DFPSO algorithm applies a dual fitness function to search for an optimal tasks-scheduling scheme for saving edge server energy while maintaining service quality for tasks. Extensive experimentation demonstrates that our proposed EA-DFPSO algorithm outperforms the existing traditional scheduling algorithms to achieve reduced task completion time and conserve energy in an edge computing environment.

Reliability of computer structures of integrated modular avionics for hardware configurations

The problem of designing advanced computing systems in the class of structures of integrated modular avionics is considered. The unified topology of the internal network of the computer on the basis of Space Wire exchange channels and variants of its execution for various onboard applications is offered. Equivalent reliability schemes of each of the specific structures are introduced and the probabilities of trouble-free operation of each structure are analyzed. Families of graphic dependencies are given. The analysis of the existing principles and algorithms for testing multiprocessor multimodal onboard digital computer systems is given; the new testing algorithm for the multiprocessor systems which follows the software design standards for products of integrated modular avionics is offered. The structure of the unified automated workplace for checking the functional modules of integrated modular avionics is considered. Specific requirements inherent in the workplaces for testing integrated avionics are identified: an increased level of control of the hardware component of products; the ability to simulate the failure state of individual components of avionics to check the mode of reconfiguration of the computer system; modular construction of software with the division of verification tests into components performed at the level of each CPM and the computer as a whole in single-task and multitasking modes; openness of architecture of a workplace, which provides an ability to change the level of control complexity of a product and control of one class of complexity; intra-project unification of both hardware and software of the workstation of the inspection.

Smart resource allocation of concurrent execution of parallel applications

AbstractThread‐level parallelism (TLP) has been widely exploited to optimize computational resource usage in high‐performance systems. However, as many applications do not scale as the number of threads increase, resources will be wasted when the application executes with the maximum possible number of threads (i.e., the default execution) rather than fewer threads (thread throttling) that may use the resources more efficiently. Hence, instead of executing only one application with as many threads as possible, one can run more applications simultaneously by applying thread throttling to each one. The primary outcome of this strategy is a significant reduction in the total execution time and energy consumption when the system needs to execute a list of applications. Given that, we propose a smart resource allocation (SRA) for concurrent parallel application execution. It automatically finds the ideal degree of TLP for each application and guides the simultaneous parallel applications execution. When running 25 well‐known benchmarks on three multicore systems and comparing SRA to state‐of‐the‐art strategies (e.g., Batch, Equal policy, and Scalability), SRA improves the EDP by 87.4% over the Batch strategy; 75.5% over the Equal policy; and 38.8% over the scalability strategy.

A Survey on Security Mechanisms for NoC-based Many-Core SoCs

The adoption of many-cores systems introduces the concern for data protection as a critical design requirement due to the resource sharing and the simultaneous executions of several applications on the platform. A secure application that processes sensitive data may have its security harmed by a malicious process. The literature contains several proposals to protect many-cores against attacks, focusing on the protection of the application execution or the access to shared memories. However, there is a gap to be fulfilled: a solution covering the entire application lifetime, including its admission, execution, and peripheral's access. This survey discusses three security-related issues: the secure admission of applications, the prevention of resource sharing during their execution, and the safe access to external devices. This survey concludes with an evaluation of the studied methods, pointing out directions and research opportunities.

Juridical Review Of The Executorial Strength Of Liability Certificates In Overcoming Non-Loading Loans (Study At Bank Danamon Cab. Sukaramai Assistant)

The increase in development activities has resulted in an increasing need for the availability of funds, most of which are obtained through credit activities. So it can be said that credit is one of the most important sources of development financing. Credit in banking activities is the most important business activity, because the largest income from bank business comes from income from credit business activities in the form of interest and fees. The problem in this research is how is the procedure for binding the mortgage certificate as collateral for the credit agreement at PT. Bank Danamon Sub-Branch Sukaramai Medan. What are the obstacles to Mortgage in Fulfilling the Rights of the Parties in the Mortgage Execution Process at PT. Bank Danamon Sukaramai Medan Sub-Branch and How the Executional Strength of Mortgage Certificate at PT. Bank Danamon Sub-Branch Sukaramai Medan?. The method used in this research is empirical juridical, namely an approach from the point of view of the rules and implementation of regulations that apply in society, which is carried out by researching secondary data first, then continued by conducting research on primary data in the field. The procedures that must be fulfilled by the customer from the time the credit application is submitted until the payment of a loan granted by the Bank is: Credit Application, Credit Investigation and Analysis, Types of Credit. The importance of binding debt guarantees is so that we can anticipate exactly whether the guarantee will be able to be executed or easily executed. This is because the different procedures for binding guarantees have a direct correlation with how they are executed. Obstacles in the Execution of Mortgage as collateral for credit for legal protection for the interests of Creditors. There are several factors that become obstacles that often occur, namely the resistance by the Mortgage holder himself to the execution of the first Mortgage holder's application. This issue is not regulated in UUHT but is in the Material of Civil Procedure Law. The Mortgage Law has given great executorial power to the Mortgage Certificate, namely by the inclusion of an Irah-Irah which reads "For Justice Based on the One Godhead", so that the position of the Mortgage Certificate is the same as the Court's Decision which has obtained permanent legal force

A context-oriented framework for computation offloading in vehicular edge computing using WAVE and 5G networks

Despite technological advances, vehicles are still unable to meet the demands of some applications for massive computational resources in a feasible time. One way to deal with this situation is to integrate the computation offloading technique into a vehicular edge computing system. This integration allows application tasks to be executed on neighboring vehicles or edge servers coupled to base stations. However, the dynamic nature of vehicular networks, allied to overloaded servers, can lead to failures and reduce the effectiveness of the offloading technique. Therefore, we propose a context-oriented framework for computation offloading to reduce the application execution time and maintain high reliability in vehicular edge computing. The framework modules perform computational resources discovery, contextual data gathering, computation tasks distribution, and failure recovery. Its main part is a task assignment algorithm that seeks the best possible server to execute each application task, using contextual information and WAVE and 5G networks. The results of extensive experiments in different vehicular environments show that our framework reduces up to 70.3% of total execution time compared to totally local execution and up to 42.9% compared to other literature approaches. Concerning reliability, our framework achieves to offload up to 89.4% of all tasks and needs to recover only 0.8% of them. Thus, our solution outperforms the totally local execution of the application and other existing computation offloading solutions.

Load balancing in virtualized environments using virtual machine migration: A comprehensive survey

Virtualization is a core and requisite technology in Cloud Computing that provisions scalable virtual resources for execution of varied applications. It enables the cloud datacenter resources to be multiplexed within numerous virtual computing environments recognized as virtual machines. These virtual machines consolidates varied applications with diversified resource requirements. It prompts to increase in load imbalance level leading to reduced performance and SLA violations. In order to achieve load balancing across virtual machines varied approaches are presented in literature and virtual machine migration based load balancing is a popular move in this direction. In this work, recent literature on different migration based load balancing schemes are reviewed. The objective of the work is highlight the features, advantages and shortcomings of the considered literature. Alongside that, the effort is conferred to provide an analytical view over different perspectives which will motivate the research in this area.

Data-flow driven optimal tasks distribution for global heterogeneous systems

As a result of advances in technology and highly demanding users expectations, more and more applications require intensive computing resources and, most importantly, high consumption of data distributed throughout the environment. For this reason, there has been an increasing number of research efforts to cooperatively use geographically distributed resources, working in parallel and sharing resources and data. In fact, an application can be structured into a set of tasks organized through interdependent relationships, some of which can be effectively executed in parallel, notably speeding up the execution time. In this work a model is proposed aimed at offloading tasks execution in heterogeneous environments, considering different nodes computing capacity connected through distinct network bandwidths, and located at different distances. In the envisioned model, the focus is on the overhead produced when accessing remote data sources as well as the data transfer cost generated between tasks at run-time. The novelty of this approach is that the mechanism proposed for tasks allocation is data-flow aware, considering the geographical location of both, computing nodes and data sources, ending up in an optimal solution to a highly complex problem. Two optimization strategies are proposed, the Optimal Matching Model and the Staged Optimization Model, as two different approaches to obtain a solution to the task scheduling problem. In the optimal model approach a global solution for all application’s tasks is considered, finding an optimal solution. Differently, the staged model approach is designed to obtain a local optimal solution by stages. In both cases, a mixed integer linear programming model has been designed intended to minimizing the application execution time. In the studies carried out to evaluate this proposal, the staged model provides the optimal solution in 76% of the simulated scenarios, while it also dramatically reduces the solving time with respect to optimal. Both models have pros and cons and, in fact, can be used together to complement each other. The optimal model finds the global optimal solution at high running time cost, which makes this model unpractical on some scenarios. The staged model instead, is faster enough to be used on those scenarios; however, the given solution might not be optimal in some cases.

Cricket: A virtualization layer for distributed execution of CUDA applications with checkpoint/restart support

AbstractIn high‐performance computing and cloud computing the introduction of heterogeneous computing resources, such as GPU accelerator have led to a dramatic increase in performance and efficiency. While the benefits of virtualization features in these environments are well researched, GPUs do not offer virtualization support that enables fine‐grained control, increased flexibility, and fault tolerance. In this article, we present Cricket: A transparent and low‐overhead solution to GPU virtualization that enables future research into other virtualization techniques, due to its open‐source nature. Cricket supports remote execution and checkpoint/restart of CUDA applications. Both features enable the distribution of GPU tasks dynamically and flexibly across computing nodes and the multitenant usage of GPU resources, thereby improving flexibility and utilization for high‐performance and cloud computing.

FogFrame: a framework for IoT application execution in the fog.

Recently, a multitude of conceptual architectures and theoretical foundations for fog computing have been proposed. Despite this, there is still a lack of concrete frameworks to setup real-world fog landscapes. In this work, we design and implement the fog computing framework FogFrame—a system able to manage and monitor edge and cloud resources in fog landscapes and to execute Internet of Things (IoT) applications. FogFrame provides communication and interaction as well as application management within a fog landscape, namely, decentralized service placement, deployment and execution. For service placement, we formalize a system model, define an objective function and constraints, and solve the problem implementing a greedy algorithm and a genetic algorithm. The framework is evaluated with regard to Quality of Service parameters of IoT applications and the utilization of fog resources using a real-world operational testbed. The evaluation shows that the service placement is adapted according to the demand and the available resources in the fog landscape. The greedy placement leads to the maximum utilization of edge devices keeping at the edge as many services as possible, while the placement based on the genetic algorithm keeps devices from overloads by balancing between the cloud and edge. When comparing edge and cloud deployment, the service deployment time at the edge takes 14% of the deployment time in the cloud. If fog resources are utilized at maximum capacity, and a new application request arrives with the need of certain sensor equipment, service deployment becomes impossible, and the application needs to be delegated to other fog resources. The genetic algorithm allows to better accommodate new applications and keep the utilization of edge devices at about 50% CPU. During the experiments, the framework successfully reacts to runtime events: (i) services are recovered when devices disappear from the fog landscape; (ii) cloud resources and highly utilized devices are released by migrating services to new devices; (iii) and in case of overloads, services are migrated in order to release resources.

New approach to global data access in computational infrastructures

Data access and management on a large, global scale is currently at the center of scientific interest. This follows from the need for data access from multi- and hybrid-cloud applications. In most cases existing solutions provide sufficient functionality to scale computing resources but scaling resources in terms of efficient data access e.g. for data-intensive applications is still not comprehensively resolved.In this paper, we present a new approach to global data access that supports the execution of data-intensive applications on globally distributed heterogeneous resources provided and managed by independent providers. We identify the functionality, representation, and processing of contextual information in the form of metadata, and the organization of data, resulting in four models describing the details of the approach. An experimental evaluation of this approach is discussed. We show overheads for single- and multi-site environments, system scalability, and usage of context awareness to achieve desired behavior proving insignificant overhead introduced by the system. The results confirm usability of the approach to supporting computations in heterogeneous environments with unified access to data distributed worldwide achieved by using broad contextual information.

Joint computing and communication resource allocation for satellite communication networks with edge computing

Benefit from the enhanced onboard processing capacities and high-speed satellite-terrestrial links, satellite edge computing has been regarded as a promising technique to facilitate the execution of the computation-intensive applications for satellite communication networks (SCNs). By deploying edge computing servers in satellite and gateway stations, SCNs can achieve significant performance gains of the computing capacities at the expense of extending the dimensions and complexity of resource management. Therefore, in this paper, we investigate the joint computing and communication resource management problem for SCNs to minimize the execution latency of the computation-intensive applications, while two different satellite edge computing scenarios and local execution are considered. Furthermore, the joint computing and communication resource allocation problem for the computation-intensive services is formulated as a mixed-integer programming problem. A game-theoretic and many-to-one matching theory-based scheme (JCCRA-GM) is proposed to achieve an approximate optimal solution. Numerical results show that the proposed method with low complexity can achieve almost the same weight-sum latency as the Brute-force method.

Pengembangan Sistem Informasi Pendaftaran Praktek Kerja Lapangan (PKL) dengan Konsep Hierarchical Model View Controller (HMVC)

Registration for Praktek Kerja Lapangan (PKL) in the Administration Business department has not been maximized. Some of the problems that occur include student registration which is still semi-manual so that it prolongs the registration process, one company only have three students for each Study Program so that students have to fight, this makes it difficult for the admin, manual PKL registration makes students have to queue in the administration room. The development of an information system with the HMVC pattern is able to make easier for programmers. Programmer can divide the system into more specific modules so that the execution of applications is more flexible. This research purpose to develop a registration information system for Praktek Kerja Lapangan (PKL) by applying the HMVC (Hierarchical Model View Controller) concept. The test results of the three experts show that all features of the PKL registration information system are running and functioning properly. And then, stress testing with Jmeter also showed satisfactory results, using 42 users / samples the average response time was less than 5 seconds and there were no errors.

User input enrichment via sensing devices

Nowadays a user may have many electronic devices. However, these devices suffer from different resource and usage constraints. On one hand, mobile devices, such as smartphones, have short battery life, limited computing power, and small-sized display. On the other hand, more powerful devices such as desktops or smart TVs are bulky and lack motion-related input data. In this paper, we aim to integrate these two types of devices into one computing platform when both devices are accessible to a user, in which their individual advantages are combined for users’ convenience. To this end, we develop a new user-centric paradigm on computing systems, called Application Execution with Sensing Input (AESIP), which can transparently inject sensing data to a powerful yet stationary device using an auxiliary mobile device. Not requiring any modification on the mobile device’s OS and applications, AESIP supports all mobile-specific input data sources, such as touchscreen, gyroscope, and accelerometer. As one design goal of AESIP is to maximize the user’s Quality of Experience (QoE), we tackle several usability challenges and enable richer functionality. We implement a prototype of AESIP on a Nexus 5, a Raspberry Pi and a desktop machine. Our performance evaluation shows that AESIP induces little latency and negligible bandwidth usage, and it significantly increases the battery life of a mobile device. We further conduct a user study to evaluate the usability of AESIP.

SWSA: A Hybrid Scientific Workflow Scheduling Algorithm Based on Metaheuristic Approach in Cloud Computing Environment

The cloud computing environment with a set of distributed computing resources is a suitable platform for the execution of large-scale applications. One of these applications is scientific workflow applications in which a large set of interrelated tasks are executed for a certain purpose. Scientific workflow scheduling is one of the main challenges in this area, which aims at the optimal assignment of tasks to computational resources. Given the heterogeneity of cloud computing resources, the scientific workflow scheduling is an NP-Complete problem that can be solved by heuristic methods. In this paper, an improved evolutionary algorithm called Scientific Workflow Scheduling Algorithm (SWSA) for scheduling scientific workflows in the cloud will be provided by ranking tasks and improving the initial population of tasks. The objective of this algorithm is to create a balance and an improvement in the parameters of the execution cost and workflow execution completion time. In this proposed approach, a heuristic algorithm is used to rank and generate the initial population, which increases the convergence rate. The experimental results show that SWSA is more efficient in terms of cost and execution time compared with other approaches.

Helastic: On combining threshold-based and Serverless elasticity approaches for optimizing the execution of bioinformatics applications

Recent advances in Next Generation Gene Sequencing (NGS) technologies brought an abundance of bioinformatics and phylogenetics data. The available datasets create new opportunities for studies about the genetic relationships among organisms, which previously relied mainly on manual observations. The state-of-the-art shows that the software employed in this area is based on technologically outdated solutions with ample space for adopting modern computing techniques such as cloud resource elasticity and dynamic load balancing. This article aims to fill this gap with the proposal of Helastic, a model to explore cloud elasticity on jModelTest. The latter is a widely used software for performing statistical selection of nucleotide replacement models in phylogenetic analyzes. Helastic’s contributions appear in a dual elasticity layer that combines the traditional threshold-based, reactive approach with Serverless (also referred to in the literature as Function-as-a-Service, or FaaS). Design decisions include interoperability as a requirement, enabling existing jModelTest applications to benefit from Helastic without significant code changes. We evaluate our proposal through a prototype, which was tested on both elastic and non-elastic scenarios. Data regarding execution time and resource usage are presented in this article. Results demonstrate our solution’s feasibility and the benefits of working with a dual-elasticity approach rather than a single resource rearrangement technique.