Application Execution Performance Research Articles

Decoder-ROI based Versatile Video Coding for Multi-Object Tracking Vision Task

The video encoding standards High Efficiency Video Coding (HEVC) and, more recently, Versatile Video Coding (VVC) have introduced significant advancements in multimedia communication applications, such as video conferencing, broadcasting, and notably, E-learning. However, recent developments in artificial intelligence (AI) and big data have given rise to an urgent need for a specialized video encoding model designed specifically for image and video analysis applications using machine vision. In this paper, we propose a novel video encoding approach that effectively combines the ROI Coding algorithm and the VVC encoding model. The proposed method identifies regions of interest within video frames through fundamental and deep features. Based on this, we propose an adaptive compression method for each frame block, ensuring both the execution performance of machine learning applications and minimal data encoding requirements. To achieve new coding scheme without adding bitrate, New feature extraction approach are utilizing only decoded information (Decoder-ROI). The results demonstrate that the Decoder-ROI achieved significant compression rate improvement when compared to standard and relevant VCM schemes. Furthermore, ROI exploitation contributes to a 3.25\% reduction in encoding time compared to the baseline VVC encoding standard.

Design and implementation of user task offloading algorithm

After the service provider temporarily selects the required edge nodes based on social and storage capabilities, application execution causes the edge nodes to cache part of the application data. Therefore, offloading part of the application computing tasks to the selected edge nodes can effectively improve application execution performance. However, in cases where the resources of user’s IoT devices are insufficient, tasks can be further offloaded to traditional edge servers or even to the cloud to maximize application execution efficiency. In this paper, the entire uninstall utility is modeled as a weighted sum of task completion time and energy consumption. Under the premise of considering users’ preferences for completion time and energy consumption, a game-based uninstallation algorithm is proposed. The algorithm performs uninstallation by optimizing the uninstallation decision. Based on user preferences, the total system overhead is relatively small. The subsequent simulation experiments show that the algorithm can reduce system overhead on the basis of satisfying user preferences and has relatively good adaptability.

A Memory-Constraint-Aware List Scheduling Algorithm for Memory-Constraint Heterogeneous Muti-Processor System

An effective scheduling algorithm is vital for the execution efficiency of applications on Heterogeneous Muti-Processor System (HMPS), especially Memory-Constraint Heterogeneous Muti-Processor System (MCHMPS). Stringent local and external memory constraints have significant impact on the execution performance of applications executed on MCHMPS, predictability is also a critical factor for task scheduling on MCHMPS. Therefore, a novel list scheduling algorithm termed Memory-constraint-aware Improved Predict Priority and Optimistic Processor Selection Scheduling (MIPPOSS), essentially a heuristic search optimization algorithm, is proposed in this paper. In MIPPOSS, a predictive approach is applied for task prioritization and processor selection, and a novel memory-constraint-aware approach is employed in the processor selection phase. MIPPOSS has polynomial complexity and produces better results for application scheduling on target architecture. Randomly generated DAGs and 3 real-world applications experiments, including Cybershake, LIGO, and Montage, show that MIPPOSS outperforms the other five competing algorithms by a large margin.

Improving the Performance of MapReduce for Small-Scale Cloud Processes Using a Dynamic Task Adjustment Mechanism

The MapReduce architecture can reliably distribute massive datasets to cloud worker nodes for processing. When each worker node processes the input data, the Map program generates intermediate data that are used by the Reduce program for integration. However, as the worker nodes process the MapReduce tasks, there are differences in the number of intermediate data created, due to variation in the operating-system environments and the input data, which results in the phenomenon of laggard nodes and affects the completion time for each small-scale cloud application task. In this paper, we propose a dynamic task adjustment mechanism for an intermediate-data processing cycle prediction algorithm, with the aim of improving the execution performance of small-scale cloud applications. Our mechanism dynamically adjusts the number of Map and Reduce program tasks based on the intermediate-data processing capabilities of each cloud worker node, in order to mitigate the problem of performance degradation caused by the limitations on the Google Cloud platform (Hadoop cluster) due to the phenomenon of laggards. The proposed dynamic task adjustment mechanism was compared with a simulated Hadoop system in a performance analysis, and an improvement of at least 5% in the processing efficiency was found for a small-scale cloud application.

A Novel Mobility-Aware Offloading Management Scheme in Sustainable Multi-Access Edge Computing

The concept of Multi-access Edge Computing (MEC) extends the provisioning of computing and storage capabilities from remote Cloud Data Centers (DC) to the proximity of end users via heterogeneous networks. By augmenting User Equipment (UE) with external computing power under the local coverage, Cloudlet-based offloading performs as a critical enabler to boost application execution performance and to prolong battery lifespan in the mobile devices. However, the mobility of UEs introduces intra-Cloudlet intermittent connections and inter-Cloudlet unbalanced load distributions in the MEC environment, which consequently leads to offloading failures and service downgrading. In this paper, we propose a novel MEC-based mobility-aware offloading model to solve the intra-Cloudlet offloading scheduling issue and inter-Cloudlet load-aware heterogeneous resource allocation issue in terms of concerning the offloading execution efficiency, task processing time constraints, and energy efficiency. A priority-based queue model is designed to formulate the intra-Cloudlet mobility-aware offloading scheduling problem, resolved by the adoption of the Particle Swarm heuristic. The energy-aware inter-Cloudlet resource selection procedure is formalized in a mobility-aware multi-site resource allocation model, which is further solved by lightweight dynamic load balancing. The results of the experiment indicate that the proposed framework can effectively improve the overall offloading service provisioning quality in the intra-Cloudlet and inter-Cloudlet offloading scenarios, compared to the current works.

UltraSynth: Insights of a CGRA Integration into a Control Engineering Environment

Coarse Grained Reconfigurable Arrays (CGRAs) or Architectures are a concept for hardware accelerators based on the idea of distributing workload over Processing Elements. These processors exploit instruction level parallelism, while being energy efficient due to their simplistic internal structure. However, the incorporation into a complete computing system raises severe challenges at the hardware and software level. This article evaluates a CGRA integrated into a control engineering environment targeting a Xilinx Zynq System on Chip (SoC) in detail. Besides the actual application execution performance, the practicability of the configuration toolchain is validated. Challenges of the real-world integration are discussed and practical insights are highlighted.

Performance Improvement of P2P using Location Awareness and Interest Clustering

ABSTRACT Because Peer-to-Peer (P2P) has the characteristics of providing scalable network services, related applications of P2P are one of the hot topics of network services today. However, too many P2P applications may lead to inefficient use of network resources or extremely poor application execution performance. At the same time, it may also occupy a large amount of bandwidth, causing an increase in the load of Internet Service Providers (ISPs). In order to avoid excessive network traffic burden, ISPs often use a flow control mechanism to prevent P2P from generating excessive traffic, which leads to the degradation of P2P software service quality. Since P4P (Proactive network Provider Participation for P2P) is one of the technologies used for P2P network optimization, it provides communication between ISPs and P2P service providers in a cooperative manner to reduce P2P traffic and ISP operating costs. Therefore, in order to reduce the operating costs of ISPs and the bandwidth required for P2P application services, a three-layer architecture combining location awareness with interest clustering and P4P framework is proposed in this study, which is called ICP4P (interest clusters based P4P ). Using the ICP4P proposed by this research will reduce Internet traffic and increase the robustness of the network. In addition, this study provides a candidate peer election mechanism to improve the service quality of P2P. When super peer fails, leaves or overloads, the backup super peer or cooperative peers will be enabled to maintain the stability of whole network.

A novel replica placement algorithm for minimising communication cost in distributed storage platform

In large-scale distributed systems, replication service has been playing a critical role to improve the availability and reliability of user data. Conventionally, the existing replication services mainly concentrate on how many replicas are needed to maintain desirable availability and reliability rather than how to place replicas on the most suitable storage nodes. As a result, the communication-related costs when accessing data are significantly increased, which in turn degrades the execution performance of user applications. In this paper, we propose a novel replica placement algorithm which is designed to minimise the communication cost when accessing or managing replicas in a large-scale storage platform. In the proposed algorithm, the replica placement problem is formulised a classical multi-knapsack problem, and two heuristic metrics are introduced to obtain the sub-optimal solution of this problem. A lot of experiments are conducted to investigate the performance of the proposed algorithm. The experimental results indicate that our replica placement algorithm outperforms many existing approaches in terms of different performance metrics. In addition, the proposed algorithm can also significantly improve the execution efficiency for data-intensive applications, which are very common in nowadays large-scale distributed systems, such as grid and cloud.

A novel replica placement algorithm for minimising communication cost in distributed storage platform

In large-scale distributed systems, replication service has been playing a critical role to improve the availability and reliability of user data. Conventionally, the existing replication services mainly concentrate on how many replicas are needed to maintain desirable availability and reliability rather than how to place replicas on the most suitable storage nodes. As a result, the communication-related costs when accessing data are significantly increased, which in turn degrades the execution performance of user applications. In this paper, we propose a novel replica placement algorithm which is designed to minimise the communication cost when accessing or managing replicas in a large-scale storage platform. In the proposed algorithm, the replica placement problem is formulised a classical multi-knapsack problem, and two heuristic metrics are introduced to obtain the sub-optimal solution of this problem. A lot of experiments are conducted to investigate the performance of the proposed algorithm. The experimental results indicate that our replica placement algorithm outperforms many existing approaches in terms of different performance metrics. In addition, the proposed algorithm can also significantly improve the execution efficiency for data-intensive applications, which are very common in nowadays large-scale distributed systems, such as grid and cloud.

Performance evaluation of job schedulers on Hadoop YARN

SummaryTo solve the limitation of Hadoop on scalability, resource sharing, and application support, the open‐source community proposes the next generation of Hadoop's compute platform called Yet Another Resource Negotiator (YARN) by separating resource management functions from the programming model. This separation enables various application types to run on YARN in parallel. To achieve fair resource sharing and high resource utilization, YARN provides the capacity scheduler and the fair scheduler. However, the performance impacts of the two schedulers are not clear when mixed applications run on a YARN cluster. Therefore, in this paper, we study four scheduling‐policy combinations (SPCs for short) derived from the two schedulers and then evaluate the four SPCs in extensive scenarios, which consider not only four application types, but also three different queue structures for organizing applications. The experimental results enable YARN managers to comprehend the influences of different SPCs and different queue structures on mixed applications. The results also help them to select a proper SPC and an appropriate queue structure to achieve better application execution performance. Copyright © 2016 John Wiley & Sons, Ltd.

An energy-efficient adaptive resource provision framework for cloud platforms

In cloud computing, resource provision service plays an important role for operating large-scale datacentres. Conventional resource provision policies or services mainly concentrate on optimising costs and application execution performance. In this paper, we present an integrated and adaptive resource provision framework, which is based on our previous work on performance monitor in cloud environments. In the proposed framework, several novel mechanisms are implemented, aiming at improving the energy-efficiency as well as the execution performance for cloud systems. Extensive experiments are conducted to evaluate the performance of the proposed framework in terms of different metrics. The experimental results show that the proposed framework can significantly improve the energy-efficiency metric, especially when a cloud system is in presence of intensive hybrid workloads.

An energy-efficient adaptive resource provision framework for cloud platforms

In cloud computing, resource provision service plays an important role for operating large-scale datacentres. Conventional resource provision policies or services mainly concentrate on optimising costs and application execution performance. In this paper, we present an integrated and adaptive resource provision framework, which is based on our previous work on performance monitor in cloud environments. In the proposed framework, several novel mechanisms are implemented, aiming at improving the energy-efficiency as well as the execution performance for cloud systems. Extensive experiments are conducted to evaluate the performance of the proposed framework in terms of different metrics. The experimental results show that the proposed framework can significantly improve the energy-efficiency metric, especially when a cloud system is in presence of intensive hybrid workloads.

User-policy-based dynamic remote attestation in cloud computing

As one of the most influential technologies, cloud computing brings users more benefits and improves the efficiency of computing in comparison to the traditional model. However, it also introduces a number of unknown risks and challenges, upon which effectiveness and efficiency of those traditional security protection solutions could have very little influence. We introduce DTEM into JVM, which gives robust trust guarantees to Java applications and makes a dynamic attestation about the integrity state of the current applications. DTEM advanced the state of the art in trusted computing and is designed to tackle the dynamic issues by verifying the runtime bytecode integrity of these applications in cloud environments. DTEM not only provides user-defined policy to determine the complexity of integrity checks and the frequency of their executions, but also enhances the availability to pledge the continuous business. What is more, it has less impact on execution performance of cloud applications.

A static region-based compiler for the Dalvik virtual machine

The Dalvik virtual machine VM is an integral component used to execute applications in Android, which is one of the leading operating systems for mobile devices. The Dalvik VM is an interpreter and is equipped with a trace-based just-in-time compiler for enhancing the execution performance of frequently executed paths, or traces. However, traces generated by the Dalvik VM can be stopped in a conditional branch or a method call/return, which means that these traces usually have a short lifetime, decreasing the effectiveness of the compiler optimizations applied to them. Furthermore, the just-in-time compiler applies only a few simple optimizations because of performance considerations. In this article we present a traces-to-region T2R framework that extends traces to regions and statically compiles these regions into native binaries so as to improve the execution of Android applications. The T2R framework involves three main stages: i the profiling stage, in which the run-time trace information of an application is extracted; ii the compilation stage, in which regions are constructed from the extracted traces and are statically compiled into a native binary; and iii the execution stage, in which the compiled binary is loaded into the code cache when the application starts to execute. Experiments performed on an Android tablet demonstrated that the T2R framework was effective in improving the execution performance of applications by 10.5-16.2% and decreasing the size of the code cache by 4.6-28.5%. Copyright © 2015 John Wiley & Sons, Ltd.

Latency-aware DVFS for efficient power state transitions on many-core architectures

Energy efficiency is quickly becoming a first-class design constraint in high-performance computing (HPC). We need more efficient power management solutions to save energy costs and carbon footprint of HPC systems. Dynamic voltage and frequency scaling (DVFS) is a commonly used power management technique for making a trade-off between power consumption and system performance according to the time-varying program behavior. However, prior work on DVFS seldom takes into account the voltage and frequency scaling latencies, which we found to be a crucial factor determining the efficiency of the power management scheme. Frequent power state transitions without latency awareness can make a real impact on the execution performance of applications. The design of multiple voltage domains in some many-core architectures has made the effect of DVFS latencies even more significant. These concerns lead us to propose a new latency-aware DVFS scheme to adjust the optimal power state more accurately. Our main idea is to analyze the latency characteristics in depth and design a novel profile-guided DVFS solution which exploits the varying execution patterns of the parallel program to avoid excessive power state transitions. We implement the solution into a power management library for use by shared-memory parallel applications. Experimental evaluation on the Intel SCC many-core platform shows significant improvement in power efficiency after using our scheme. Compared with a latency-unaware approach, we achieve 24.0 % extra energy saving, 31.3 % more reduction in the energy---delay product and 15.2 % less overhead in execution time in the average case for various benchmarks. Our algorithm is also proved to outperform a prior DVFS approach attempted to mitigate the latency effects.

Incorporating Energy-Aware Mechanism into Workflow Scheduling Policy in Heterogeneous Distributed Systems

In the past decades, plenty scheduling policies have been proposed for improving the execution performance of workflow applications. However, few of them have addressed the issue of energy conservation. In this paper, a novel scheduling metric, namely Minimal Energy Consumption Path, is introduced to reducing the energy consumption when scheduling large-scale workflows. The proposed scheduling metric can be incorporated into current schedulers so as to enhance their capability of energy conservation as well as maintain their scheduling performance. A lot of experiments are performed by using different scheduler and experimental settings to evaluate the proposed scheduling metric and the results indicate it is effective for reducing the storage-related energy consumption, especially when the target workflows are data-intensive.

DA-TC: a novel application execution model in multicluster systems

The availability of a large number of separate clusters has given rise to the field of multicluster systems in which these resources are coupled to obtain their combined benefits to solve large-scale compute-intensive applications. However, it is challenging to achieve automatic load balancing of the jobs across these participating autonomic systems. We developed a novel user space execution model named DA-TC to address the workload allocation techniques for the applications with large number of sequential jobs in multicluster systems. Through this model, we can achieve dynamic load balancing for task assignment, and slower resources become beneficial factors rather than bottlenecks for application execution. The effectiveness of this strategy is demonstrated through theoretical analysis. This model is also evaluated through extensive experimental studies and the results show that when compared with the traditional method, the proposed DA-TC model can significantly improve the performance of application execution in terms of application turnaround time and system reliability in multicluster circumstances.

Improving performance of adaptive component-based dataflow middleware

Making the best use of modern computational resources for distributed applications requires expert knowledge of low-level programming tools, or a productive high-level and high-performance programming framework. Unfortunately, even state-of-the-art high-level frameworks still require the developer to conduct a tedious manual tuning step to find the work partitioning which gives the best application execution performance. Here, we present a novel framework, with which developers can easily create high-performance dataflow applications, without the tedious tuning process. We compare the performance of our approach to that of three distributed programming frameworks which differ significantly in their programming paradigm, their support for multi-core CPUs and accelerators, and their load-balancing approach. These three frameworks are DataCutter, a component-based dataflow framework, KAAPI, a framework using asynchronous function calls, and MR-MPI, a MapReduce implementation. By highly optimizing the implementations of three applications on the four frameworks and comparing the execution time performance of the runtime engines, we show their strengths and weaknesses. We show that our approach achieves good performance for a wide range of applications, with a much-reduced development cost.

Parallel and Distributed Computing Techniques, Selection of papers from ISPDC 2008

Parallel and Distributed Computing Techniques, Selection of papers from ISPDC 2008

Performance-based parallel application toolkit for high-performance clusters

Advances in computer technology, encompassed with fast emerging of multicore processor technology, have made the many-core personal computers available and more affordable. The availability of network of workstations and cluster of many-core SMPs have made them an attractive solution for high performance computing by providing computational power equal or superior to supercomputers or mainframes at an affordable cost using commodity components. In order to search alternative ways to extract unused and idle computing power from these computing resources targeting to improve overall performance, as well as to fully utilize the underlying new hardware platforms, these are major topics in this field of research. In this research paper, the design rationale and implementation of an effective toolkit for performance measurement and analysis of parallel applications in cluster environments is introduced; not only generating parallel applications' timing graph representation, but also to provide application execution's performance data charts. The goal in developing this toolkit is to permit application developers have a better understanding of the application's behavior among selected computing nodes purposed for that particular execution. Additionally, multiple execution results of a given application under development can be combined and overlapped, permitting application developers to perform "what-if" analysis, i.e., to deeper understand the utilization of allocated computational resources. Experimentations using this toolkit have shown its effectiveness on the development and performance tuning of parallel applications, extending the use in teaching of message passing, and shared memory model parallel programming courses.