High-throughput computing projects require the solution of large numbers of problems. In many cases, these problems can be solved on desktop PCs, or can be broken down into independent "PC-solvable" sub-problems. In such cases, the projects are high-performance computing projects, but only because of the sheer number of the needed calculations. We briefly describe our efforts to increase the throughput of one such project. We then explain how to easily set up a distributed computing facility composed of standard networked PCs running Windows 95, 98, 2000, or NT. The facility requires no special software or hardware, involves little or no re-coding of application software, and operates almost invisibly to the owners of the PCs. Depending on the number and quality of PCs recruited, performance can rival that of supercomputers.
Scheduling of service requests in Cloud computing has traditionally focused on the reduction of pre-service wait, generally termed as waiting time. Under certain conditions such as peak load, however, it is not always possible to give reasonable response times to all users. This work explores the fact that different users may have their own levels of tolerance or patience with response delays. We introduce scheduling strategies that produce better assignment plans by prioritising requests from users who expect to receive the results earlier and by postponing servicing jobs from those who are m…
In this paper, we study the market-oriented online bi-objective service scheduling problem for pleasingly parallel jobs with variable resources in cloud environments, from the perspective of SaaS (Software-as-as-Service) providers who provide job-execution services. The main process of scheduling SaaS services in clouds is: a SaaS provider purchases cloud instances from IaaS providers to schedule end users' jobs and charges users accordingly. This problem has several particular features, such as the job-oriented end users, the pleasingly parallel jobs with soft deadline constraints, the online…
Deep learning (DL) models have achieved great success in many application domains. As such, many industrial companies such as Google and Facebook have acknowledged the importance of multi-tenant DL services. Although the multi-tenant service has been studied in conventional workloads, it is not been deeply studied on deep learning service, especially on general-purpose hardware. In this work, we systematically analyze the opportunities and challenges of providing multi-tenant deep learning services on the general-purpose CPU architecture from the aspects of scheduling granularity and code gene…
Mobile edge computing (MEC) emerges as a promising solution for servicing delay-sensitive tasks at the edge network. A body of recent literature started to focus on cost-efficient service placement and request scheduling. This work investigates the joint optimization of service placement and request scheduling in a dense MEC network, and develops an efficient online algorithm that achieves close-to-optimal performance. Our online algorithm consists of two basic modules: (1) a regularization with look-ahead approach from competitive online convex optimization, for decomposing the offline relaxe…
Companies are rushing to deliver their services and solutions through the cloud. The scheduling process is very critical in reducing delays. Scheduling also has a role in accessing resources without excessive waiting time. All this in context of modern advances in infrastructure and the emergence of Blockchain-as-a-service. What if integration is done between a hybrid scheduling algorithm and blockchain technology via the cloud. This integration aims to enhance and provide the service uninterruptedly. This method is distinguished, compared to other scheduling algorithms such as shortest-job-fi…
Loop scheduling techniques aim to achieve load-balanced executions of scientific applications. Dynamic loop self-scheduling (DLS) libraries for distributed-memory systems are typically MPI-based and employ a centralized chunk calculation approach (CCA) to assign variably-sized chunks of loop iterations. We present a distributed chunk calculation approach (DCA) that supports various types of DLS techniques. Using both CCA and DCA, twelve DLS techniques are implemented and evaluated in different CPU slowdown scenarios. The results show that the DLS techniques implemented using DCA outperform the…
Cloud computing environments often have to deal with random-arrival computational workloads that vary in resource requirements and demand high Quality of Service (QoS) obligations. It is typical that a Service-Level-Agreement (SLA) is employed to govern the QoS obligations of the cloud computing service provider to the client. A typical challenge service-providers face every day is maintaining a balance between the limited resources available for computing and the high QoS requirements of varying random demands. Any imbalance in managing these conflicting objectives may result in either dissat…
Fast, reliable, and efficient data transmission across wide-area networks is a predominant bottleneck for data-intensive cloud applications. This paper introduces OneDataShare, which is designed to eliminate the issues plaguing effective cloud-based data transfers of varying file sizes and across incompatible transfer end-points. The vision of OneDataShare is to achieve high-speed data communication, interoperability between multiple transfer protocols, and accurate estimation of delivery time for advance planning, thereby maximizing user-profit through improved and faster data analysis for bu…
Cloud Computing is a paradigm of both parallel processing and distributed computing. It offers computing facilities as a utility service in pay as par use manner. Virtualization, self service provisioning, elasticity and pay per use are the key features of Cloud Computing. It provides different types of resources over the Internet to perform user submitted tasks. In cloud environment, huge number of tasks are executed simultaneously, an effective Task Scheduling is required to gain better performance of the cloud system. Various Cloud Based Task Scheduling algorithms are available that schedul…
The essence of distributed computing systems is how to schedule incoming requests and how to allocate all computing nodes to minimize both time and computation costs. In this paper, we propose a cost-aware optimal scheduling and allocation strategy for distributed computing systems while minimizing the cost function including response time and service cost. First, based on the proposed cost function, we derive the optimal request scheduling policy and the optimal resource allocation policy synchronously. Second, considering the effects of incoming requests on the scheduling policy, the additiv…
League Championship Algorithm (LCA) is a sports-inspired population based algorithmic framework for global optimization over a continuous search space first proposed by Ali Husseinzadeh Kashan in the year 2009. A common characteristic between all population based optimization algorithms similar to the LCA is that, they attemt to move a population of achievable solutions to potential areas of the search space during optimization. In this paper, we proposed a job scheduling algorithm based on the L CA optimization technique for the infrastructure as a service (IaaS) cloud. Three other establishe…
Computational Grids, emerging as an infrastructure for next generation computing, enable the sharing, selection, and aggregation of geographically distributed resources for solving large-scale problems in science, engineering, and commerce. As the resources in the Grid are heterogeneous and geographically distributed with varying availability and a variety of usage and cost policies for diverse users at different times and, priorities as well as goals that vary with time. The management of resources and application scheduling in such a large and distributed environment is a complex task. This …
Dynamic Service Scheduling and Resource Management in Energy-Harvesting Multi-access Edge Computing
University of Leeds（利兹大学）；Southern University of Science and Technology (SUSTech)（南方科技大学）；University of Thessaly（色萨利大学）；Research Institute of Trustworthy Autonomous Systems (RITAS), SUSTech（南方科技大学可信自主系统研究院）
Edge computing is an emerging technology which places computing at the edge of the network to provide an ultra-low latency. Computation offloading, a paradigm that migrates computing from mobile devices to remote servers, can now use the power of edge computing by offloading computation to cloudlets in edge-clouds. However, the task scheduling of computation offloading in edge-clouds faces a two-fold challenge. First, as cloudlets are geographically distributed, it is difficult for each cloudlet to perform load balancing without centralized control. Second, as tasks of computation offloading h…
Modern latency-critical online services often rely on composing results from a large number of server components. Hence the tail latency (e.g. the 99th percentile of response time), rather than the average, of these components determines the overall service performance. When hosted on a cloud environment, the components of a service typically co-locate with short batch jobs to increase machine utilizations, and share and contend resources such as caches and I/O bandwidths with them. The highly dynamic nature of batch jobs in terms of their workload types and input sizes causes continuously cha…
Cloud computing has attracted both end-users and Cloud Service Providers (CSPs) in recent years. Improving resource utilization rate (RUtR), such as CPU and memory usages on servers, while maintaining Quality-of-Service (QoS) is one key challenge faced by CSPs with warehouse-scale data centers. Prior works proposed various algorithms to reduce energy cost or to improve RUtR, which either lack the fine-grained task scheduling capabilities, or fail to take a comprehensive system model into consideration. This article presents H2O-Cloud, a Hierarchical and Hybrid Online task scheduling framework …
The existing distributed TDMA-scheduling techniques can be classified as either static or dynamic. The primary purpose of static TDMA-scheduling algorithms is to improve the channel utilization by generating a schedule of shorter length. But, they usually take a longer time to schedule, and hence, are not suitable for WSNs, in which the network topology changes dynamically. On the other hand, dynamic TDMA-scheduling algorithms generate a schedule quickly, but they are not efficient in terms of generated schedule length. In this paper, we propose a new approach to TDMA scheduling for WSNs, that…
Training Graph Neural Networks (GNN) on large graphs is resource-intensive and time-consuming, mainly due to the large graph data that cannot be fit into the memory of a single machine, but have to be fetched from distributed graph storage and processed on the go. Unlike distributed deep neural network (DNN) training, the bottleneck in distributed GNN training lies largely in large graph data transmission for constructing mini-batches of training samples. Existing solutions often advocate data-computation colocation, and do not work well with limited resources where the colocation is infeasibl…
The aim of this paper is to provide a description of deep-learning-based scheduling approach for academic-purpose high-performance computing systems. The share of academic-purpose distributed computing systems (DCS) reaches 17.4 percents amongst TOP500 supercomputer sites (15.6 percents in performance scale) that makes them a valuable object of research. The core of this approach is to predict the future workflow of the system depending on the previously submitted tasks using deep learning algorithm. Information on predicted tasks is used by the resource management system (RMS) to perform effi…
Clusters, grids, and peer-to-peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing. The management of resources and scheduling of applications in such large-scale distributed systems is a complex undertaking. In order to prove the effectiveness of resource brokers and associated scheduling algorithms, their performance needs to be evaluated under different scenarios such as varying number of resources and users with different requirements. In a grid environment, it is hard and even impossible to perform scheduler performance evaluation in …
Scheduling the maintenance based on the condition, respectively the degradation level of the system leads to improved system's reliability while minimizing the maintenance cost. Since the degradation level changes dynamically during the system's operation, we face a dynamic maintenance scheduling problem. In this paper, we address the dynamic maintenance scheduling of manufacturing systems based on their degradation level. The manufacturing system consists of several units with a defined capacity and an individual dynamic degradation model, seeking to optimize their reward. The units sell thei…
Latency-critical services have been widely deployed in cloud environments. For cost-efficiency, multiple services are usually co-located on a server. Thus, run-time resource scheduling becomes the pivot for QoS control in these complicated co-location cases. However, the scheduling exploration space enlarges rapidly with the increasing server resources, making the schedulers hardly provide ideal solutions quickly. More importantly, we observe that there are "resource cliffs" in the scheduling exploration space. They affect the exploration efficiency and always lead to severe QoS fluctuations. …
Distributed Stream Processing frameworks are being commonly used with the evolution of Internet of Things(IoT). These frameworks are designed to adapt to the dynamic input message rate by scaling in/out.Apache Storm, originally developed by Twitter is a widely used stream processing engine while others includes Flink, Spark streaming. For running the streaming applications successfully there is need to know the optimal resource requirement, as over-estimation of resources adds extra cost.So we need some strategy to come up with the optimal resource requirement for a given streaming application…
Function as a Service (FaaS) permits cloud customers to deploy to cloud individual functions, in contrast to complete virtual machines or Linux containers. All major cloud providers offer FaaS products (Amazon Lambda, Google Cloud Functions, Azure Serverless); there are also popular open-source implementations (Apache OpenWhisk) with commercial offerings (Adobe I/O Runtime, IBM Cloud Functions). A new feature of FaaS is function composition: a function may (sequentially) call another function, which, in turn, may call yet another function - forming a chain of invocations. From the perspective …
The workflow is a general notion representing the automated processes along with the flow of data. The automation ensures the processes being executed in the order. Therefore, this feature attracts users from various background to build the workflow. However, the computational requirements are enormous and investing for a dedicated infrastructure for these workflows is not always feasible. To cater to the broader needs, multi-tenant platforms for executing workflows were began to be built. In this paper, we identify the problems and challenges in the multiple workflows scheduling that adhere t…
Cloud computing distributes computing tasks across numerous distributed resources for large-scale calculation. The task scheduling problem is a long-standing problem in cloud-computing services with the purpose of determining the quality, availability, reliability, and ability of the cloud computing. This paper is an extension and a correction to our previous conference paper entitled Multi Objective Scheduling in Cloud Computing Using MOSSO published in 2018 IEEE Congress on Evolutionary Computation. More new algorithms, testing, and comparisons have been implemented to solve the bi-objective…
Community networks (CNs) have seen an increase in the last fifteen years. Their members contact nodes which operate Internet proxies, web servers, user file storage and video streaming services, to name a few. Detecting communities of nodes with properties (such as co-location) and assessing node eligibility for service placement is thus a key-factor in optimizing the experience of users. We present a novel solution for the problem of service placement as a two-phase approach, based on: 1) community finding using a scalable graph label propagation technique and 2) a decentralized election proc…
Scheduling is essentially a decision-making process that enables resource sharing among a number of activities by determining their execution order on the set of available resources. The emergence of distributed systems brought new challenges on scheduling in computer systems, including clusters, grids, and more recently clouds. On the other hand, the plethora of research makes it hard for both newcomers researchers to understand the relationship among different scheduling problems and strategies proposed in the literature, which hampers the identification of new and relevant research avenues.…
TS-EoH: An Edge Server Task Scheduling Algorithm Based on Evolution of Heuristic
School of Computer Science Central China Normal University Wuhan, China yatong\；School of Computer Science Central China Normal University Wuhan, China
This paper considers the problem of service placement and task scheduling on a three-tiered edge-to-cloud platform when user requests must be met by a certain deadline. Time-sensitive applications (e.g., augmented reality, gaming, real-time video analysis) have tight constraints that must be met. With multiple possible computation centers, the "where" and "when" of solving these requests becomes paramount when meeting their deadlines. We formulate the problem of meeting users' deadlines while minimizing the total cost of the edge-to-cloud service provider as an Integer Linear Programming (ILP)…
In Software-Defined Networking (SDN)-enabled cloud data centers, live migration is a key approach used for the reallocation of Virtual Machines (VMs) in cloud services and Virtual Network Functions (VNFs) in Service Function Chaining (SFC). Using live migration methods, cloud providers can address their dynamic resource management and fault tolerance objectives without interrupting the service of users. However, in cloud data centers, performing multiple live migrations in arbitrary order can lead to service degradation. Therefore, efficient migration planning is essential to reduce the impact…
Queuing systems with redundant requests have drawn great attention because of their promise to reduce the job completion time and variability. Despite a large body of work on the topic, we are still far from fully understanding the benefits of redundancy in practice. We here take one step towards practical systems by studying queuing systems with bi-modal job service time distribution. Such distributions have been observed in practice, as can be seen in, e.g., Google cluster traces. We develop an analogy to a classical urns and balls problem, and use it to study the queuing time performance of…
Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey
Artificial Intelligence Research Institute, Shenzhen MSU-BIT University（深圳北理莫斯科大学人工智能研究院）；Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing, Shenzhen MSU-BIT University（深圳北理莫斯科大学粤港澳情感智能与普适计算联合实验室）；School of Information Science and Engineering, Lanzhou University（兰州大学信息科学与工程学院）；Department of Electrical and Computer Engineering, The University of British Columbia（不列颠哥伦比亚大学电气与计算机工程系）
Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks
BNU-HKBU United International College（北师港浸会联合国际学院）；Hong Kong Baptist University（香港浸会大学）；Institute of Artificial Intelligence and Future Networks, Beijing Normal University（北京师范大学人工智能与未来网络研究院）
Cloud Computing is the latest blooming technology in the era of Computer Science and Information Technology domain. There is an enormous pool of data centres, which are termed as Clouds where the services and associated data are being deployed and users need a constant Internet connection to access them. One of the highlights in Cloud is the delivering of applications or services in an on-demand environment. One of the most promising areas in Cloud scheduling is Scheduling of workflows which is intended to match the request of the user to the appropriate resources. There are several algorithms…
A customary solution to reduce the energy consumption of wireless communication devices is to periodically put the radio into low-power sleep mode. A relevant problem is to schedule the wake-up of nodes in such a way as to ensure proper coordination among devices, respecting delay constraints while still saving energy. In this paper, we introduce a simple algebraic characterization of the problem of periodic wake-up scheduling under both energy consumption and delay constraints. We demonstrate that the general problem of wake-up times coordination is equivalent to integer factorization and dis…
DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service
UM-SJTU Joint Institute, Shanghai Jiao Tong University（上海交通大学密西根学院）；Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University（上海交通大学合作媒体创新中心）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）
The overall performance of a distributed system is highly dependent on the communication efficiency of the system. Although network resources (links, bandwidth) are becoming increasingly more available, the communication performance of data transfers involving large volumes of data does not necessarily improve at the same rate. This is due to the inefficient usage of the available network resources. A solution to this problem consists of data transfer scheduling techniques, which manage and allocate the network resources in an efficient manner. In this paper we present several online and offli…
We study the performance of non-adaptive scheduling policies in computing systems with multiple servers. Compute jobs are mostly regular, with modest service requirements. However, there are sporadic data intensive jobs, whose expected service time is much higher than that of the regular jobs. Forthis model, we are interested in the effect of scheduling policieson the average time a job spends in the system. To this end, we introduce two performance indicators in a simplified, only-arrival system. We believe that these performance indicators are good predictors of the relative performance of t…
To amortize cost, cloud vendors providing DNN acceleration as a service to end-users employ consolidation and virtualization to share the underlying resources among multiple DNN service requests. This paper makes a case for a "preemptible" neural processing unit (NPU) and a "predictive" multi-task scheduler to meet the latency demands of high-priority inference while maintaining high throughput. We evaluate both the mechanisms that enable NPUs to be preemptible and the policies that utilize them to meet scheduling objectives. We show that preemptive NPU multi-tasking can achieve an average 7.8…
