Parallel Requests Research Articles

Impact of Design Decisions on Performance of Embarrassingly Parallel .NET Database Application

The implementation of parallel applications is always a challenge. It embraces many distinctive design decisions that are to be taken. The paper presents issues of parallel processing with use of .NET applications and popular Database Management Systems (DBMSes). In the paper, four design dilemmas are addressed: how efficient is the auto-parallelism implemented in the .NET TPL library, how do popular DBMSes differ in serving parallel requests, what is the optimal size of data chunks in the data parallelism scheme, and how the TPL auto-parallelism behaves in the public clouds. They are analyzed in the context of the typical and practical business case originated from IT solutions which are dedicated for the energy market participants. The paper presents the results of experiments conducted in a controlled, on-premises and cloud environments. The experiments allowed to compare the performance of the TPL auto-parallelism with a wide range of manually set numbers of worker threads. They also helped to evaluate four DBMSes: Oracle, MySQL, PostgreSQL, and MSSQL in the scenario of serving parallel queries. Finally, they showed the impact of data chunk sizes on the overall performance.

Microservices and serverless functions—lifecycle, performance, and resource utilisation of edge based real-time IoT analytics

Edge Computing harnesses resources close to the data sources to reduce end-to-end latency and allow real-time process automation for verticals such as Smart City, Healthcare and Industry 4.0. Edge resources are limited when compared to traditional Cloud data centres; hence the choice of proper resource management strategies in this context becomes paramount. Microservice and Function as a Service architectures support modular and agile patterns, compared to a monolithic design, through lightweight containerisation, continuous integration/deployment and scaling. The advantages brought about by these technologies may initially seem obvious, but we argue that their usage at the Edge deserves a more in-depth evaluation. By analysing both the software development and deployment lifecycle, along with performance and resource utilisation, this paper explores microservices and two alternative types of serverless functions to build edge real-time IoT analytics. In the experiments comparing these technologies, microservices generally exhibit slightly better end-to-end processing latency and resource utilisation than serverless functions. One of the serverless functions and the microservices excel at handling larger data streams with auto-scaling. Whilst serverless functions natively offer this feature, the choice of container orchestration framework may determine its availability for microservices. The other serverless function, while supporting a simpler lifecycle, is more suitable for low-invocation scenarios and faces challenges with parallel requests and inherent overhead, making it less suitable for real-time processing in demanding IoT settings.

LAE-GAN: a novel cloud-based Low-light Attention Enhancement Generative Adversarial Network for unpaired text images

With the widespread adoption of mobile multimedia devices, the deployment of compute-intensive inference tasks on edge and resource-constrained devices, particularly in the context of low-light text detection, remains a formidable challenge. Existing deep learning approaches have shown limited effectiveness in restoring images for extremely dark scenes. To address these limitations, this paper presents a novel cloud-based Low-light Attention Enhancement Generative Adversarial Network for unpaired text images (LAE-GAN) for the non-paired text image enhancement task in extremely low-light conditions. In the first stage, compressed low-light images are transmitted from edge devices to a cloud server for image enhancement. The LAE-GAN, an end-to-end network comprising a Zero-DCE and AGM-net generator, is designed with a global and local discriminator structure. The initial illumination restoration of extremely low-light images is accomplished using the Zero-DCE network. To enhance text details, we propose an Enhanced Text Attention Mechanism (ETAM) that transforms text information into a comprehensive text attention mechanism across the entire network. The Sobel operator is employed to extract text edge information, while attention is focused on text region details through constraints imposed on the attention map and edge map. Additionally, an AGM-Net module is integrated to reduce noise and fine-tune illumination. In the second stage, the cloud server makes decisions based on user requirements and processes requests in parallel, scaling with the quantity of requests. In the third stage, the enhanced results are transmitted back to edge devices for text detection. Experimental results on widely used LOL and SID low-light datasets demonstrate significant improvements in both quantitative and qualitative analysis, surpassing state-of-the-art enhancement methods in terms of image restoration and text detection.

A Method for Time Characteristics Calculating in the Service Platforms of Infocommunication Transactional Services with Parallel Requests Processing

A Method for Time Characteristics Calculating in the Service Platforms of Infocommunication Transactional Services with Parallel Requests Processing

Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing

In multi-access edge computing (MEC) networks, parallelized service function chains (P-SFCs) can provide low-delay network services for mobile users by deploying virtualized network functions (VNFs) to process user requests in parallel. These VNFs are unreliable due to software faults and server failures. A practical way to address this is to deploy idle backup VNFs (BVNFs) near these active VNFs and activate them when active VNFs fail. However, deploying BVNFs preempts server resources and decreases the number of accepted user requests. Thus, this paper proposes a reliability enhancement approach that uses BVNFs satisfying the delay requirement as active VNFs to form P-SFCs, which contributes to the delay reduction and reliability enhancement. Since the resource capacities of edge servers can only deploy a certain number of P-SFCs and BVNFs, establishing how to deploy the minimum number of P-SFCs and BVNFs to satisfy the delay and reliability requirements of mobile users and maximize the number of accepted user requests is a challenging problem. In this paper, we first model the dynamics of delay and reliability caused by VNF parallelization and BVNFs deployment, then formulate the joint deployment problem of P-SFCs and BVNFs. Next, we design an approximation algorithm to deploy critical VNFs and BVNFs on a target edge server and schedule the data traffic of user requests processed by P-SFCs. Experimental results based on real-world datasets show that our proposed algorithm outperforms two benchmark algorithms in terms of throughput, delay, reliability, and resource utilization.

ProScale: Proactive Autoscaling for Microservice With Time-Varying Workload at the Edge

Deploying microservice instances on the edge device close to end users can provide on-site processing thus reducing request response time. Each microservice has multiple instances that can process requests in parallel. To achieve high processing efficiency, the number of these instances is scaled according to the workload, which is also known as autoscaling. Previous studies of microservice autoscaling in the edge computing environment lack in-depth consideration of time-varying workload, they assume that the workload of each microservice always depends on that of its upstream. However, through an analysis of Alibaba's microservice trace with hundreds of millions of records, we find that the assumption is impractical thus hurting autoscaling effectiveness. To solve this problem, we propose ProScale, a prediction-driven proactive autoscaling framework for microservices at the edge. ProScale proactively forecasts the workload for each individual microservice per timeslot. Then it utilizes an efficient online algorithm to leverage the predicting results to determine the instance number for each microservice jointly with making placement decisions. For each microservice instance deployed on the edge device, ProScale handles burst requests using a designed offloading strategy. In addition, ProScale can also balance the load for multiple instances of each microservice. Extensive trace-driven experiments show that ProScale has great scalability. It can reduce average response time by 96.7% and resource usage by 96.5% compared with existing strategies and designed baselines.

Queueing System with Two Phases of Service and Service Rate Degradation

In the paper, a queueing system with an unlimited number of servers and two phases of service with degradation in the service rate is studied. The problem of service rate degradation emerges in cloud nodes, where there is contention for hardware resources including computational resources such as CPU cores. In a node, we have a limited number of CPU cores that should execute potentially an unlimited number of processes (requests) in parallel. In our model, the term “server” means a process allocated in the node for execution. So, the number of “servers” is unlimited but their individual performances decrease because CPUs should switch between them during the execution. We consider processes executed in the node with two phases of life cycle that reflects periods with different activity of a process; e.g., in the first phase, the process may require intensive usage of CPU cores but low usage in the second one. Our model distinguishes the phases using different service parameters for them as well as different influence on the service rate degradation in the node. In the paper, two analytical methods are proposed: exact solving of the system of the local balance equation and the asymptotic analysis of the global balance equations. Formulas for the stationary probability distribution of the number of customers in the phases are obtained for both cases. Several numerical examples are provided that illustrate some properties and applicability of the obtained results.

A Parallel Fuzzy Load Balancing Algorithm for Distributed Nodes Over a Cloud System

Cloud Computing is an IT organization concept that places the Internet at the heart of business activity, allowing it to use hardware resources. In recent years, Load Balancing has been an active area of research, and has played a very important role in the case of the Cloud environment. There is a wide range of improvements in this arena. In this paper, we propose a new Load Balancing algorithm, based on the weights of the nearest servers in the cloud platform. We use the fuzzy logic to represent the weight of the different nodes. Moreover, we implement separate requests in parallel, and use a token to dispatch tasks efficiently. The several scenarios in this paper are considered for experimentation and compare the result of existing Round Robin, Throttled Load Balancing, Equal Spread Load and proposed algorithm .The experiment results show that this approach improves the Load Balancing process effectively in terms of overall response time, data center processing time, total virtual machine cost, and total data transfer cost.

Copper Oxide-Based Photocatalysts and Photocathodes: Fundamentals and Recent Advances.

This work aims at reviewing the most impactful results obtained on the development of Cu-based photocathodes. The need of a sustainable exploitation of renewable energy sources and the parallel request of reducing pollutant emissions in airborne streams and in waters call for new technologies based on the use of efficient, abundant, low-toxicity and low-cost materials. Photoelectrochemical devices that adopts abundant element-based photoelectrodes might respond to these requests being an enabling technology for the direct use of sunlight to the production of energy fuels form water electrolysis (H2) and CO2 reduction (to alcohols, light hydrocarbons), as well as for the degradation of pollutants. This review analyses the physical chemical properties of Cu2O (and CuO) and the possible strategies to tune them (doping, lattice strain). Combining Cu with other elements in multinary oxides or in composite photoelectrodes is also discussed in detail. Finally, a short overview on the possible applications of these materials is presented.

RETRACTED ARTICLE: A3C: Access Appropriate Analogous Computing for Cloud-Assisted Edge Users

Edge computing architectures are designed for providing in-network computational and resource allocation support for the connected users. Such architectures provide ease of computations and latency-less resource allocations for improving the efficiency of service disseminations. The problem of multi-access to the resources increases the latency in distributing the computing features for concurrent users. In this article, access to appropriate analogous computing (A3C) is proposed for addressing this issue. The proposed computing model performs the parallel request and migration processing for providing multiple accesses to the available edge resources. The multiple processing instances are identified based on the service dissemination and lag to the migrating resource factors. The instances are differentiated, and supportive migration access is analyzed for the two factors using transfer learning. This learning helps to improve the recommendations for distributing the resources for additive access. The proposed computing model improves the ratio of service dissemination under controlled latency and service lag. The stagnancy in distributing the service computations using the edge devices helps provide reliable access to edge users. The experimental analysis shows that the proposed A3C improves service dissemination by 11.52%, computation distribution by 8.6%, and access rate by 10.03%, whereas it reduces the service lag and latency by 22.2% and 11.07%, respectively, for the different edge devices.

A GPU-Accelerated In-Memory Metadata Management Scheme for Large-Scale Parallel File Systems

Driven by the increasing requirements of high-performance computing applications, supercomputers are prone to containing more and more computing nodes. Applications running on such a large-scale computing system are likely to spawn millions of parallel processes, which usually generate a burst of I/O requests, introducing a great challenge into the metadata management of underlying parallel file systems. The traditional method used to overcome such a challenge is adopting multiple metadata servers in the scale-out manner, which will inevitably confront with serious network and consistence problems. This work instead pursues to enhance the metadata performance in the scale-up manner. Specifically, we propose to improve the performance of each individual metadata server by employing GPU to handle metadata requests in parallel. Our proposal designs a novel metadata server architecture, which employs CPU to interact with file system clients, while offloading the computing tasks about metadata into GPU. To take full advantages of the parallelism existing in GPU, we redesign the in-memory data structure for the name space of file systems. The new data structure can perfectly fit to the memory architecture of GPU, and thus helps to exploit the large number of parallel threads within GPU to serve the bursty metadata requests concurrently. We implement a prototype based on BeeGFS and conduct extensive experiments to evaluate our proposal, and the experimental results demonstrate that our GPU-based solution outperforms the CPU-based scheme by more than 50% under typical metadata operations. The superiority is strengthened further on high concurrent scenarios, e.g., the high-performance computing systems supporting millions of parallel threads.

Many At Once: Capturing Intentions to Create And Use Many Views At Once In Large Display Environments

AbstractThis paper describes results from an observational, exploratory study of visual data exploration in a large, multi‐view, flexible canvas environment. Participants were provided with a set of data exploration sub‐tasks associated with a local crime dataset and were instructed to pose questions to a remote mediator who would respond by generating and organizing visualizations on the large display. We observed that participants frequently posed requests to cast a net around one or several subsets of the data or a set of data attributes. They accomplished this directly and by utilizing existing views in unique ways, including by requesting to copy and pivot a group of views collectively and posing a set of parallel requests on target views expressed in one command. These observed actions depart from multi‐view flexible canvas environments that typically provide interfaces in support of generating one view at a time or actions that operate on one view at a time. We describe how participants used these ‘cast‐a‐net’ requests for tasks that spanned more than one view and describe design considerations for multi‐view environments that would support the observed multi‐view generation actions.

DQ-DASH

The significant popularity of HTTP adaptive video streaming (HAS), such as Dynamic Adaptive Streaming over HTTP (DASH), over the Internet has led to a stark increase in user expectations in terms of video quality and delivery robustness. This situation creates new challenges for content providers who must satisfy the Quality-of-Experience (QoE) requirements and demands of their customers over a best-effort network infrastructure. Unlike traditional single server DASH, we developed a D istributed Q ueuing theory bitrate adaptation algorithm for DASH (DQ-DASH) that leverages the availability of multiple servers by downloading segments in parallel. DQ-DASH uses a M x /D/1/K queuing theory based bitrate selection in conjunction with the request scheduler to download subsequent segments of the same quality through parallel requests to reduce quality fluctuations. DQ-DASH facilitates the aggregation of bandwidth from different servers and increases fault-tolerance and robustness through path diversity. The resulting resilience prevents clients from suffering QoE degradations when some of the servers become congested. DQ-DASH also helps to fully utilize the aggregate bandwidth from the servers and download the imminently required segment from the server with the highest throughput. We have also analyzed the effect of buffer capacity and segment duration for multi-source video streaming.

The k-Server Problem with Parallel Requests and the Compound Work Function Algorithm

In this paper the compound work function algorithm for solving the generalized k-server problem is proposed. This problem is an online k-server problem with parallel requests where several servers can also be located on one point. In 1995 Koutsoupias and Papadimitriouhave proved that the well-known work function algorithm is competitive for the (usual) k-server problem. A proof, where a potential-like function argument is included, was given by Borodinand El-Yaniv in 1998. Unfortunately, certain techniques of these proofs cannot be applied to show that a natural generalization of the work function algorithm is competitive for the problem with parallel requests. Values of work functions, which are used by the compound work function algorithm are derived from a surrogate problem, where at most one server must be moved in servicing the request in each step. We can show that the compound work function algorithm is competitive with the same bound of the ratio as in the case of the usual problem.

Reference Exascale Architecture (Extended Version)

While political commitments for building exascale systems have been made, turning these systems into platforms for a wide range of exascale applications faces several technical, organisational and skills-related challenges. The key technical challenges are related to the availability of data. While the first exascale machines are likely to be built within a single site, the input data is in many cases impossible to store within a single site. Alongside handling of extreme-large amount of data, the exascale system has to process data from different sources, support accelerated computing, handle high volume of requests per day, minimize the size of data flows, and be extensible in terms of continuously increasing data as well as an increase in parallel requests being sent. These technical challenges are addressed by the general reference exascale architecture. It is divided into three main blocks: virtualization layer, distributed virtual file system, and manager of computing resources. Its main property is modularity which is achieved by containerization at two levels: 1) application containers - containerization of scientific workflows, 2) micro-infrastructure - containerization of extreme-large data service-oriented infrastructure. The paper also presents an instantiation of the reference architecture - the architecture of the PROCESS project (PROviding Computing solutions for ExaScale ChallengeS) and discusses its relation to the reference exascale architecture. The PROCESS architecture has been used as an exascale platform within various exascale pilot applications. This paper also presents performance modelling of exascale platform with its validation.

The German Public Prosecutor as (no) judicial authority within the meaning of the European Arrest Warrant: A case note on the CJEU’s judgment in OG (C-508/18) and PI (C 82/19 PPU)

The judgment analysed concerns a request for a preliminary ruling by the Irish Supreme Court that casts doubts upon the judicial nature of the German Public Prosecutor’s Office as the issuing authority of a European Arrest Warrant. On the same day, another judgment was passed on a parallel request for a preliminary ruling by the same court concerning the Lithuanian Prosecutor General. The Court disqualifies, the German Prosecution Office in this regard but accepts the judicial character of the Lithuanian one. The gist of the issue is the (material) independence of the two Prosecution services which the Court denies regarding the former since it can be subjected to external directives by the executive branch (Minister of Justice). The Judgment applies mutatis mutandis also to other Prosecution services being subject to external directives and to other instruments of mutual recognition which need a “judicial authority” to be issued (e.g. the European Investigation Order).

TailCutter: Wisely Cutting Tail Latency in Cloud CDNs Under Cost Constraints

Cloud computing platforms enable applications to offer low-latency services to users by deploying data storage in multiple geo-distributed data centers. In this paper, through benchmark measurements on Amazon AWS and Microsoft Azure together with an analysis of a large-scale dataset collected from a major cloud CDN provider, we identify the high tail latency problem in cloud CDNs, which can substantially undermine the efficacy of cloud CDNs. One crucial idea to reduce the tail latency is to send requests in parallel to multiple clouds in cloud CDNs. However, since application providers often have a budget for using cloud services, deciding how many chunks to download from each cloud and when to download chunks in a cost-efficient manner still remain as open problems in our concerned scenario. To address the problem, we present TailCutter, a workload scheduling framework that aims at optimizing the tail latency while meeting cost constraints given by application providers. Specifically, we formulate the tail latency minimization (TLM) problem in cloud CDNs and design the receding horizon control based maximum tail minimization algorithm (RHC-based MTMA) to efficiently solve the TLM problem in practice. We implement TailCutter across multiple data centers of Amazon AWS and Microsoft Azure. Extensive evaluations using a large-scale real-world data trace (collected from a major ISP) illustrate that TailCutter can reduce up to 58.9% of the 100th-percentile user-perceived latency, as compared with alternative solutions under the cost constraint.

An Efficient GPU Cache Architecture for Applications with Irregular Memory Access Patterns

GPUs provide high-bandwidth/low-latency on-chip shared memory and L1 cache to efficiently service a large number of concurrent memory requests. Specifically, concurrent memory requests accessing contiguous memory space are coalesced into warp-wide accesses. To support such large accesses to L1 cache with low latency, the size of L1 cache line is no smaller than that of warp-wide accesses. However, such L1 cache architecture cannot always be efficiently utilized when applications generate many memory requests with irregular access patterns especially due to branch and memory divergences that make requests uncoalesced and small. Furthermore, unlike L1 cache, the shared memory of GPUs is not often used in many applications, which essentially depends on programmers. In this article, we propose Elastic-Cache, which can efficiently support both fine- and coarse-grained L1 cache line management for applications with both regular and irregular memory access patterns to improve the L1 cache efficiency. Specifically, it can store 32- or 64-byte words in non-contiguous memory space to a single 128-byte cache line. Furthermore, it neither requires an extra memory structure nor reduces the capacity of L1 cache for tag storage, since it stores auxiliary tags for fine-grained L1 cache line managements in the shared memory space that is not fully used in many applications. To improve the bandwidth utilization of L1 cache with Elastic-Cache for fine-grained accesses, we further propose Elastic-Plus to issue 32-byte memory requests in parallel, which can reduce the processing latency of memory instructions and improve the throughput of GPUs. Our experiment result shows that Elastic-Cache improves the geometric-mean performance of applications with irregular memory access patterns by 104% without degrading the performance of applications with regular memory access patterns. Elastic-Plus outperforms Elastic-Cache and improves the performance of applications with irregular memory access patterns by 131%.

Cache replication for information-centric networks through programmable networks

Software-Defined Networking (SDN) is a new approach that decouples control from the data transfer function and can be directly programmable by the programming language. At the same time, Information-Centric Networks (ICNs) influence the search for information through network caching and multipart communication. Moreover, these projects are developed to solve traffic problems and have content transfer through a scalable network structure with simple management. The SDN covers ICNs, besides decoupling the control to allow flexibility of network settings to reduce overhead of segments. Based on this information, an architecture was designed to provide reliable content that can be replicated in the network. The ICN architecture proposed stores information through a logical volume to the next access, enabling connection with remote controllers for reliable storage in cloud environments. The cache efficiency achieved almost 50% reduction of time response, in addition, there was improvement around 10% when the number of parallel requests was increased.

VBMq: pursuit baremetal performance by embracing block I/O parallelism in virtualization

Barely acceptable block I/O performance prevents virtualization from being widely used in the High-Performance Computing field. Although the virtio paravirtual framework brings great I/O performance improvement, there is a sharp performance degradation when accessing high-performance NAND-flash-based devices in the virtual machine due to their data parallel design. The primary cause of this fact is the deficiency of block I/O parallelism in hypervisor, such as KVM and Xen. In this paper, we propose a novel design of block I/O layer for virtualization, named VBMq. VBMq is based on virtio paravirtual I/O model, aiming to solve the block I/O parallelism issue in virtualization. It uses multiple dedicated I/O threads to handle I/O requests in parallel. In the meanwhile, we use polling mechanism to alleviate overheads caused by the frequent context switches of the VM’s notification to and from its hypervisor. Each dedicated I/O thread is assigned to a non-overlapping core to improve performance by avoiding unnecessary scheduling. In addition, we configure CPU affinity to optimize I/O completion for each request. The CPU affinity setting is very helpful to reduce CPU cache miss rate and increase CPU efficiency. The prototype system is based on Linux 4.1 kernel and QEMU 2.3.1. Our measurements show that the proposed method scales graciously in the multi-core environment, and provides performance which is 39.6x better than the baseline at most, and approaches bare-metal performance.