Execution Time Of Applications Research Articles

Dynamic microservice placement in multi-tier Fog networks

Fog computing extends Cloud-like infrastructure to the Edge, advancing Industrial Internet-of-Things (IIoT) applications in which parts of business-oriented enterprise systems, decoupled via fine-grained microservices, can be deployed in Fog networks in proximity to sensor devices. It boosts the responsiveness to IIoT events significantly where timely decision-making and actions are required, particularly for problematic trends and anomalies. However, microservice placement is a non-trivial problem in IIoT settings, given the dynamic occurrence of different IIoT events and variable resource needs of microservices against resource-constrained, heterogeneous, and distributed Fog devices. The cost of matching and deploying microservices in real-time, highly dynamic, and contentious settings can easily exceed the benefit of just-in-time Edge deployments. Current studies largely focus on managing the microservice placement from Cloud servers and offloading to the Cloud when Fog devices are resource-deficient. Yet, there is a lack of research focusing on efficient Fog resource use and managing the placement at the edge to improve the responsiveness. The complexity increases when multiple inter-dependent microservices with similar priorities are required to be placed on the same Fog devices. To mitigate this, we develop a tiered framework for dynamic microservice placement, in which costly resource decision-making for microservices is dedicated to Master Fog devices and Citizen Fog devices are exclusively assigned to executing microservice requests. Firstly, we implement a priority-based algorithm in each Master Fog device to identify the high-priority Edge-required microservices. Secondly, we sort the Fog devices according to their resource availability and place the microservices based on their priority and dependencies. Thirdly, we implement the strategies for microservice placement, scaling and request escalation in each Master Fog, managing a small number of Citizen Fog devices. These exempt the Citizen Fog devices from common resource and communication management responsibilities and enable them to allocate more resources to Edge-required microservices with a higher number of instances. Finally, we implement and evaluate our framework in a simulated Fog environment. The results outperform state-of-the-art frameworks in terms of efficient resource utilisation, resulting in reduced Cloud dependency by one-third compared to other approaches and average application execution time by 65–70%.

Migration-aware slot-based memory request scheduler to guarantee QoS in DRAM-PCM hybrid memories

Emerging hybrid memory technologies composed of non-volatile memories (NVM) and DRAMs exhibit significant access speeds and capacity improvement. High application performance is feasible by dynamic migration (or relocation) of pages (data) between the memory types. While NVM is used for its density during memory allocation, moving write-intensive pages from NVM to DRAM helps to improve the execution and response time of applications. Existing techniques propose solutions to dynamically identify the pages that need to be moved immediately or in regular intervals. Such an immediate or interval-based rigid migration regime may hamper the service of the regular memory requests, which in turn affects the memory service rate.To alleviate the impact on service rate and improve the quality-of-service (QoS) of the device, this paper proposes a scheduling method to identify the instant at which to migrate the eligible page. Along with regular memory requests, these eligible migration candidate pages are given reserved time slots by taking into account the current memory request rate. Our proposed methods aim to optimize the migration overheads by avoiding unnecessary migrations and at the same time guaranteeing future accesses to the migrated pages. This results in improved execution time and memory response time for the applications.

Presenting a meta-heuristic solution for optimal resource allocation in fog computing

Given that cloud computing is a relatively new field of study, there is an urgent need for comprehensive approaches to resource provisioning and the allocation of Internet of Things (IoT) services across cloud infrastructure. Other challenging aspects of cloud computing include IoT resource virtualization and disseminating IoT services among available cloud resources. To meet deadlines, optimize application execution times, efficiently use cloud resources, and identify the optimal service location, service placement plays a crucial role in installing services on existing virtual resources within a cloud-based environment. To achieve load balance in the fog computing infrastructure and ensure optimal resource allocation, this work proposes a meta-heuristic approach based on the cat swarm optimization method. For more clarity in the difference between the work presented in this research and other similar works, we named the proposed technique MH-CSO. The algorithm incorporates a resource check parameter to determine the accessibility and suitability of resources in different situations. This conclusion was drawn after evaluating the proposed solution in the ifogsim environment and comparing it with particle swarm and ant colony optimization techniques. The findings demonstrate that the proposed solution successfully optimizes key parameters, including runtime and energy usage.

Comparison of Software Development Solution Implementations in Lightning Flow Builder and Apex Programming Language in Salesforce Technology

The recent rapid development of the Salesforce platform has induced an expansion of no-code solutions to make automation available for less technical audience. This study examines similarities and differences between Apex programming and one of the no-code solutions on the Salesforce platform, Flow Builder. The research compares the implementation of the same applications using Apex language and Flow Builder in terms of operating time to fulfil requirements (first part) and execution time of both applications (second part). The applications were classified into two categories regarding the complexity of the solution, namely simple and intermediate, so that the research could thoroughly verify the impact of the amount of code and the simplification of programming concepts Flow Builder allows for. In the first part of the research, the majority of the developers who took part in the research managed to implement the simple application much quicker using Apex than Flow Builder. The intermediate application needed much more Apex code and made use of concepts like bulkification of records, which are automated in Flow Builder, so it was much easier to develop the second application in Flow Builder than Apex. In the second part of the research, execution times of the applications using Apex and Flow Builder were compared, and the findings show the impact of Flow Builder “behind the scenes” automation, which was the root cause of its more complex solutions slowdown.

Opposition‐based improved memetic algorithm for placement of concurrent Internet of Things applications in fog computing

AbstractThe transpiration of a new computing standard, Fog computing exploits the computing resources immediacy to the Internet of Things (IoT) devices and hence together with the cloud servers bestow the required resources promptly. Even though the fog server provides timely services due to the fewer resources it cannot tackle the resource‐consuming applications developed by more IoT devices. Hence the placement of applications in the fog computing paradigm with more cloud and fog servers is a major challenging problem. To overcome this problem and to minimize the execution time and energy consumption of the IoT applications in a computing platform consisting of numerous IoT devices, and several fog and cloud servers, a weighted cost model is proposed. The number of parallel jobs for concurrent execution is increased due to the heterogeneity of IoT applications, and hence a pre‐scheduling technique is presented to accomplish this. In addition to that, a novel application placement method using the Opposition‐based Improved Memetic Algorithm (OBIMA) is adapted to formulate the placement of parallel IoT operations. The experimental results show that the proposed model outperforms well compared to other state‐of‐the‐art methods.

A Novel Multi-Task Performance Prediction Model for Spark

Performance prediction of Spark plays a vital role in cluster resource management and system efficiency improvement. The performance of Spark is affected by several variables, such as the size of the input data, the computational power of the system, and the complexity of the algorithm. At the same time, less research has focused on multi-task performance prediction models for Spark. To address these challenges, we propose a multi-task Spark performance prediction model. The model integrates a multi-head attention mechanism and a convolutional neural network. It implements the prediction of execution times for single or multiple Spark applications. Firstly, the data are dimensionally reduced by a dimensionality reduction algorithm and fed into the model. Secondly, the model integrates a multi-head attention mechanism and a convolutional neural network. It captures complex relationships between data features and uses these features for Spark performance prediction. Finally, we use residual connections to prevent overfitting. To validate the performance of the model, we conducted experiments on four Spark benchmark applications. Compared to the benchmark prediction model, our model obtains better performance metrics. In addition, our model predicts multiple Spark benchmark applications simultaneously and maintains deviations within permissible limits. It provides a novel way for the assessment and optimization of Spark.

RACE Resource Aware Cost-Efficient Scheduler for Cloud Fog Environment

Abstract: Fog computing is one of the new computing structures which takes the Cloud to the verge of the network. The structure is formulated for applications that need low latency. Fog Computing has been projected to improve the disadvantages of Cloud Computing. The system is confronted with the variability of dynamic resources that are heterogeneous and distributed. Hence, efficient scheduling and resource allocation are necessary to maximize the use of these resources and the satisfaction of users. In this paper, a resource-aware scheduler RACE (Resource Aware Cost-Efficient Scheduler) is proposed to distribute the incoming application modules to Fog devices that maximize resource utilization at the Fog layer, reduces the monetary cost of using Cloud resources with minimum execution time of applications and minimum bandwidth usage. This RACE comprises of two algorithms. The Module Scheduler in RACE categorizes the incoming application modules according to their computation and bandwidth requirements which are then placed by Compare Module. Comprehensive experimental results obtained from the simulation by using ifogsim simulator show that our approach performs better in most of the cases as compared to the Traditional Cloud placement and the baseline algorithm.

A learning automata based approach for module placement in fog computing environment

Today, fog computing is an emerging technology to support resource-constrained Internet of Things (IoT) applications. The scalability, geographic distribution, and heterogeneity of edge computing nodes, as well as the diversity of users' expectations, have made optimal module placement, considering the maximum use of fog resources, a challenging optimization problem. This paper proposes a method based on distributed learning automata to reduce the search space by using the maximum capacity of fogs in a heterogeneous fog network. In this method, fog topology is mapped to a distributed learning automata. With the cooperation of the automaton in this DLA, the problem of module placement has been solved to reduce energy consumption and delay of applications. To evaluate the amount of energy consumption and the execution time of IoT applications, two single-objective cost functions for energy and delay and another single-objective function with simultaneous consideration of energy and delay have been used. The results indicate that the average efficiency of the proposed method is 15.99%, 18.21%, and 15.53%, respectively, compared to other methods.

A Bee Colony-Based Algorithm for Task Offloading in Vehicular Edge Computing

Complex vehicular applications, such as automatic driving and augmented reality are delay sensitive and require massive computational resources. Despite being more connected and smarter, vehicles still cannot appropriately meet the demands of these applications. By allowing neighboring vehicles and edge servers coupled to base stations to share their available computing resources, vehicular edge computing systems help to handle these applications. Then, vehicles can use the task offloading technique by sending application tasks to be executed remotely and receiving the processing results later. Although this technique aims to reduce application execution time, performing it in vehicular scenarios is challenging. In such scenarios, network nodes vary their computing and energy loads and move quickly, causing frequent disconnections and failures. Thus, we propose an algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bee colony-based Task offloading in Vehicular edge computing</i> (BTV) to reliably reduce the execution time of applications in vehicular edge computing systems. The BTV algorithm provides task scheduling solutions to different servers in a feasible time, using several contextual parameters and wireless access in vehicular environments and fifth-generation networks. Experimental results show that our solution can reduce the average execution time of applications by up to 74.4% and with up to 0.0% of failures, outperforming other existing solutions.

The Virtual Rotor Kit Project: A virtual rotor test rig for balancing experiments

The “Virtual Rotor Kit (VRK) Project” is a lightweight and standalone application developed in response to the challenges posed by the COVID-19 outbreak in the field of rotating machinery dynamics. This alternative emulates a rotor test rig, enabling virtual balancing tests on a single-plane rotor and offering students a comprehensive learning experience that simulates on-site balancing. The Virtual Rotor Kit utilizes a three-dimensional model based on a well-known rotor test rig widely used in educational and research settings. To simulate the dynamics of the rotor test rig, the application employs PyChrono, a dedicated Python library that wraps Chrono, a validated open-source multi-physics simulation engine. A detailed usability assessment involving 41 senior mechanical engineering students yielded promising results, indicating the Virtual Rotor Kit’s potential to enhance comprehension of vibration phenomena in unbalanced rotating machines. Despite revealing certain issues related to the graphical user interface and the application's execution time, three defined achievement indicators demonstrated positive outcomes for a virtual balancing laboratory experience using the Virtual Rotor Kit application. By focusing on the dynamic behavior of a rotor test rig instead of simulating a specific laboratory experience, the application offers instructors greater flexibility in developing learning experiences. Unlike other virtual testbeds, the Virtual Rotor Kit allows students to conduct both steady-state and transient experiments, offering a comprehensive understanding of rotating machinery dynamics. Additionally, the application is distributed under the BSD-3 license and can serve as a complement to on-site laboratory experiences. The results presented in this study inspire further research and development to enhance its efficacy.

QoS-SLA-aware adaptive genetic algorithm for multi-request offloading in integrated edge-cloud computing in Internet of vehicles

The Internet of Vehicles over vehicular ad hoc network is an emerging technology enabling the development of smart applications focused on improving traffic safety, traffic efficiency, and the overall driving experience. These applications have stringent requirements detailed in the Service Level Agreement. Since vehicles have limited computational and storage capabilities, applications' requests are offloaded onto an integrated edge-cloud computing system. Existing offloading solutions focus on optimizing the application's Quality of Service (QoS) in terms of execution time while respecting a single SLA constraint. They do not consider the impact of overlapped multi-request processing nor the vehicle's varying speed. This paper proposes a novel Artificial Intelligence QoS-SLA-aware adaptive genetic algorithm (QoS-SLA-AGA) to optimize the application's execution time for multi-request offloading in a heterogeneous edge-cloud computing system, which considers the impact of processing multi-requests overlapping and dynamic vehicle speed. The proposed genetic algorithm integrates an adaptive penalty function to assimilate the SLA constraints regarding latency, processing time, deadline, CPU, and memory requirements. Numerical experiments and analysis compare our QoS-SLA-AGA to baseline genetic-based, meta-heuristic Particle Swarm Optimization (PSO), random offloading, All Edge Computing (AEC), and All Cloud Computing (ACC) approaches. Results show QoS-SLA-AGA executes the requests 1.04, 1.23, 1.05, and 9.41 times faster on average compared to the PSO, random offloading, ACC, and AEC approaches respectively. Moreover, the proposed algorithm violates 49.58%, 60.36%, 16.26%, and 80.42% fewer SLAs compared to PSO, random, ACC, and AEC respectively. In contrast, the baseline genetic-based approach increases the requests' performance by 1.14 times, with 24.03% more SLA violations.

Dynamic GPU Scheduling With Multi-Resource Awareness and Live Migration Support

In clouds and data centers, GPU servers with multiple GPUs are widely deployed. Current state-of-the-art GPU scheduling policies are “static” in assigning applications to different GPUs. These policies usually ignore the dynamics of the GPU utilization and are often inaccurate in estimating resource demand before assigning/running applications, so there is a large opportunity to further balance the loads and improve GPU utilization. Based on CUDA (Compute Unified Device Architecture), we develop a runtime system called DCUDA which supports <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“dynamic”</i> scheduling of running applications between multiple GPUs. In particular, DCUDA takes into consideration multidimensional resources, including computing cores, memory usage, and energy consumption. It first provides a real-time and lightweight method to accurately monitor the resource demand of applications and GPU utilization. Furthermore, it provides a universal migration facility to migrate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“running applications”</i> between GPUs with negligible overhead. More importantly, DCUDA transparently supports all CUDA applications without changing their source code. Experiments with our prototype system show that DCUDA can reduce 78.3% of overloaded time of GPUs on average. As a result, for different workloads consisting of a wide range of applications we studied, DCUDA can reduce the average execution time of general applications by up to 42.1%, and even up to 67% for memory-intensive tasks. Besides, DCUDA also reduces 13.3% of energy in light-load scenarios.

Efficient Sender-Based Message Logging Tolerating Simultaneous Failures with Always No Rollback Property

Most of the existing sender-based message logging protocols cannot commonly handle simultaneous failures because, if both the sender and the receiver(s) of each message fail together, the receiver(s) cannot obtain the recovery information of the message. This unfortunate situation may happen due to their asymmetric logging behavior. This paper presents a novel sender-based message logging protocol for broadcast network based distributed systems to overcome the critical constraint of the previous ones with the following three features. First, when more than one process crashes at the same time, the protocol enables the system to ensure the always no rollback property by symmetrically replicating the recovery information at each process or group member connected on a network. Second, it can make the first feature persist even if the general form of communication for the system is a combination of point-to-point and group ones. Third, the communication overhead resulting from the replication can be highly lessened by making full use of the capability of the standard broadcast network in both communication modes. Experimental outcomes verify that, no matter which communication patterns are applied, it can reduce about 4.23∼9.96% of the total application execution time against the latest enabling the traditional ones to cope with simultaneous failures.

HEP-Frame: an efficient tool for big data applications at the LHC

HEP-Frame is a new C++ package designed to efficiently perform analyses of datasets from a very large number of events, like those available at the Large Hadron Collider (LHC) at CERN, Geneva. It mainly targets high-performance servers and mini-clusters, and it was designed for natural science researchers with a user-friendly interface to access structured databases. HEP-Frame automatically evaluates the underlying computing resources and builds an adequate code skeleton when creating a data analysis application. At run-time, HEP-Frame analyses a sequence of datasets exploring the available parallelism in the code and hardware resources: it concurrently reads inputs from a user-defined data structure and processes them, following the user-specific sequence of requirements to select relevant data; it manages the efficient execution of that sequence; and it outputs results in user-defined objects (e.g., ROOT structures), stored together with the used input dataset. This paper shows how a domain expert software development can benefit from HEP-Frame, and how it significantly improved the performance of analyses of large datasets produced in proton-proton collisions at the LHC. Two case studies are discussed: the associated production of top quarks together with a Higgs boson (tbar{t}H ) at the LHC, and a double- and single-top quark productions at the high-luminosity phase of the LHC (HL-LHC). Results show that the HEP-Frame awareness of the analysis code behaviour and structure, and the underlying hardware system, provides powerful and transparent parallelization mechanisms that largely improve the execution time of data analysis applications.

A scalable simulator for cloud, fog and edge computing platforms with mobility support

In recent years, the devices that make up the Internet of Things (IoT) paradigm have been increasing in number and complexity as different layers of network computing, such as Cloud, Edge and Fog Computing, have emerged. Thanks to these latest paradigms and their different types of communications, it has been possible to reduce and distribute the computational load on the network. However, to build these infrastructures and reduce the cost, it is necessary to use a simulation platform to model these environments and analyze their behavior (power consumption, CPU usage, bandwidth, etc.) beforehand. An essential aspect of simulators is scalability, the ability to add new components and simulate large infrastructures without compromising the performance of the simulator. Many existing simulators, as will be discussed in this article, cannot scale adequately as new elements are added to the system. Furthermore, in these environments, it is useful to simulate mobile devices and to know the location of these devices for the development and analysis of new algorithms. To solve these problems, this article presents and describes the ENIGMA simulator. ENIGMA is a scalable simulator of Edge, Fog and Cloud computing infrastructures, which allows to efficiently simulate a large number of devices and elements and to analyze different characteristics (CPU usage, power consumption, network bandwidth, application execution time, etc.). Its objective is to analyze new infrastructures and algorithms. ENIGMA also includes support for including mobile devices and an API to integrate their visualization in graphical maps. The article presents an evaluation to compare the scalability of ENIGMA with other state-of-the-art simulators and different use cases that illustrate its operation.

An intelligent scheduling framework for DNN task acceleration in heterogeneous edge networks

With the upgrade of hardware architecture and device capacities, many accelerator-based hardware platforms have been widely deployed in Mobile Edge Computing (MEC) environments. The execution time of many computation-intensive applications (e.g., face recognition and pedestrian detection) can be significantly reduced when deployed on these heterogeneous devices. Moreover, thanks to the popularity of Deep Learning (DL), most terminal applications are integrated with Deep Neural Networks (DNN) and can be divided into interdependent tasks. The structure of these applications can be represented as the Directed Acyclic Graph (DAG). Therefore, it is critical to seek the optimal scheduling order and execution placement of tasks according to the acceleration effects of edge servers and the task dependency. However, conventional scheduling strategies focus on the short-term performance, potentially leading to service quality degradation in the long term. Besides, many studies use Deep Reinforcement Learning (DRL) algorithms to seek a long-term optimal scheduling strategy but ignore the device acceleration and the task dependency. Furthermore, training a well-performed DRL agent is time-consuming, and the large scale of trial-and-error will take up tremendous computation and storage resources. In this paper, we model the scheduling process as a Markov Decision Process (MDP) and design an adaptive scheduling framework for task acceleration. Fully considering the data dependencies, resource conditions, and network conditions, the proposed scheduling algorithm called Meta-AC uses policy gradient combined with meta-learning to minimize the average task delay and the ratio of time-out tasks. As a hierarchical DRL algorithm, Meta-AC uses meta data to learn directed exploration strategies in the high-level agent, improving the learning efficiency from experience samples. Extensive simulations demonstrate the superiority of the proposed method over the counterpart methods.

Cloud Environment Task Scheduling Optimization of Modified Genetic Algorithm

From the availability of resources to the accomplishment of tasks, cloud computing is a development of supercomputing. One of the most trustworthy paradigms in computing technology is built on internet-based parallel and distributed computing models. Optimization algorithms can be used to distribute user workloads to provided logical resources termed 'Virtual Machines' in the cloud computing system, which is a major aspect of resource management (VM). A fundamental challenge in cloud computing is the dynamic heterogeneity of resources and workloads, which necessitates efficient task scheduling and distribution. It is possible that task scheduling in distributed environments will improve our understanding of workflow scheduling, independent task scheduling that takes into account security and execution time for applications, trust between various system entities, and improved system utilisation and energy efficiency, among other things. The goal of this research is to contribute to these advancements in these areas: An independent task scheduling system based on genetics is presented to obtain the best outcomes in terms of time and resource consumption while allocating tasks to resources in accordance with the task's security needs. Various meta-heuristic algorithms, such as Genetic Algorithm, are currently being used to solve task scheduling difficulties.

A Multi-Objective Based Scheduling Framework for Effective Resource Utilization in Cloud Computing

Cloud computing is a promising platform for running massive workflow applications based on a pay-per-use model. In cloud computing, the reduction of energy consumption and providing security to workflow scheduling are the key research areas. The primary focus of the existing algorithms, viz., particle swarm optimization (PSO), crow Search optimization (CSO) and Other non-metaheuristic algorithms like Round Robbin(RR), SJF, Min-Min, Min-Max etc., is based on the execution time and cost of the workflow applications as a budget constraint. However, these algorithms failed to adequately determine energy consumption, resource utilization, and security in workflow scheduling. To address this issue, a multi-objective scheduling framework is proposed. In this paper, the framework performs dynamic workflow scheduling using universal unique identification- Blake (UUID-Blake), Manhattan Distance-Partition around algorithm (MD-PAM), Linear Scaling-Crow Search Optimization (LS-CSO), Anova-Recurrent Neural Network. The implementation of this framework was achieved in three phases (Phase 1, Phase 2, and Phase 3). Phase 1 is about user registration and authentication using UUID-Blake, which enhances security by allowing legitimate users into the cloud environment. Phase 2 deals with clustering and resource monitoring using MD-PAM and A-RNN, to reduce makespan the similar tasks are clustered using task length and maximize the resource utilization by predicting the resource availability. Phase 3 deals with the scheduling of dynamic workflows using LS-CSO by selecting suitable virtual machines. We have considered the heterogeneous computing scheduling problem (HCSP) and grid workload archive (GWA)-T-12 Bitbrains datasets for comparing our proposed framework with existing works. Based on the result analysis, the proposed LS-SCO outperformed when compared with the algorithms CSO,PSO and RR has achieved better performance.

Energy-Aware Non-Preemptive Task Scheduling With Deadline Constraint in DVFS-Enabled Heterogeneous Clusters

Energy conservation of large data centers for high performance computing workloads, such as deep learning with Big Data, is of critical significance, where cutting down a few percent of electricity translates into million-dollar savings. This work studies energy conservation on emerging CPU-GPU hybrid clusters through dynamic voltage and frequency scaling (DVFS). We aim at minimizing the total energy consumption of processing a batch of offline tasks or a sequence of real-time tasks under deadline constraints. We derive a fast and accurate analytical model to compute the appropriate voltage/frequency setting for each task, and assign multiple tasks to the cluster with heuristic scheduling algorithms. In particular, our model stresses the nonlinear relationship between task execution time and processor speed for GPU-accelerated applications, for more accurately capturing real-world GPU energy consumption. In performance evaluation driven by real-world power measurement traces, our scheduling algorithm shows comparable energy savings to the theoretical upper bound. With a GPU scaling interval where analytically at most 36% of energy can be saved, we record 33-35% of energy savings. Our results are applicable to energy management on modern heterogeneous clusters.

Transparent serverless execution of Python multiprocessing applications

Access transparency means that both local and remote resources are accessed using identical operations. With transparency, unmodified single-machine applications could run over disaggregated compute, storage, and memory resources. Hiding the complexity of distributed systems through transparency would have great benefits, like scaling-out local-parallel scientific applications over flexible disaggregated resources in the Cloud.This paper presents a performance evaluation where we assess the feasibility of access transparency over state-of-the-art Cloud disaggregated resources for Python multiprocessing applications. We have interfaced the multiprocessing module with an implementation that transparently runs processes on serverless functions and uses an in-memory data store for shared state.To evaluate transparency, we run in the Cloud four unmodified applications: Uber Research’s Evolution Strategies, Baselines-AI’s Proximal Policy Optimization, Pandaral.lel’s dataframe, and Scikit Learn’s Hyperparameter tuning. We compare execution time and scalability of the same application running over disaggregated resources using our library, with the single-machine Python multiprocessing libraries in a large VM. For equal resources, applications efficiently using message-passing abstractions achieve comparable results despite the significant overheads of remote communication. Other shared-memory intensive applications do not perform due to high remote memory latency.The results show that Python’s multiprocessing library design is an enabler towards transparency: legacy applications using efficient disaggregated abstractions can transparently scale beyond VM limited resources for increased parallelism without changing the underlying code or architecture.