Soft computing based intelligent grid architecture

SummaryThe cloud computing paradigm is an important service in the Internet for sharing and providing resources in a cost‐efficient way. Modeling of a cloud system is not an easy task because of the complexity and large scale of such systems. Cloud reliability could be improved by modeling the various aspects of cloud systems, including scheduling, service time, wait time, and hardware and software failures. The aim of this study is to survey research studies done on the modeling of cloud computing using the queuing system in order to identify where more emphasis should be placed in both current and future research directions. This paper follows the goal by investigating the articles published between 2008 and January 2017 in journals and conferences. A systematic mapping study combined with a systematic literature review was performed to find the related literature, and 71 articles were selected as primary studies that were classified in relation to the focus, research type, and contribution type. We classified the modeling techniques of cloud computing using the queuing theory in seven categories based on their focus area: (1) performance, (2) quality of service, (3) workflow scheduling, (4) energy savings, (5) resource management, (6) priority‐based servicing, and (7) reliability. A majority of the primary articles focus on performance (37%), 15% of them focus on resource management, 14% of them focus on quality of service, 13% of them focus on workflow scheduling, 13% of them focus on energy savings, 4% of them focus on priority‐based servicing for requests, and 4% of them focus on reliability. This work summarizes and classifies the research efforts conducted on applying queue theory for modeling of cloud computing (AQTMCC), providing a good starting point for further research in this area.

Volunteer computing systems exploiting large amounts of geographically dispersed resources on the Internet for solving complex scientific problems. However, scheduling scientific workflows in a fully decentralised way and low overhead is a challenging task in these environments. To counter this challenge, this paper presents a fully decentralised proximity-aware workflow-scheduling policy for these environments. The proposed scheduling consists of three phases. In the first phase, each workflow application is partitioned into sub-workflows in order to minimise data dependencies among them. The second phase of the workflow-scheduling algorithm finds some resources to execute each sub-workflow. These resources are selected based on Quality of Service (QoS) constraints of the workflow, load balancing and proximity of resources. Each workflow can have QoS constraints in terms of minimum CPU speed and minimum RAM or hard disk requirements. In the third phase, sub-workflows will be executed on each resource based on local scheduling algorithm to minimise the partial makespan. The proposed scheduling policy focuses on the reduction of communication overhead to improve the performance of I/O-intensive and data-intensive workflows. Simulation results show that the proposed workflow-scheduling policy improves the average response time of scientific workflows up to 53.6% under a moderate load.

In Cloud broker system, workflow application requests from different users are managed through workflow scheduling and resource provisioning. In workflow scheduling phase, most existing algorithms allocate each task on certain VM in serial. In general, single task does not fully utilize allocated resource such as CPU, memory, and so on. When multiple tasks are processed with same resource in parallel, the resource utilization is improved that leads to saving the cost. In order to solve this problem, the Parallel Task Merging scheme in the same VM is proposed, which saves the cost of execution while satisfying SLA deadline. After workflow scheduling, VM resource provisioning is required. Auto-scaling VM resources approach is proposed, which adjusts the number of VMs while the number of requests varies. In this paper, we do experiment the parallel task merging and auto-scaling approaches on different environments to observe on which conditions these two approaches are working well or not.

An extended Intelligent Water Drops algorithm for workflow scheduling in cloud computing environment

Collaboration task is a complex process because it involves diversified individuals and organizations. They have different per spectives and resources while sharing a particular goal. In order to support collaboration task, capturing general workflow and states of resources is important in the initial stage of system development. Multi-Context Map (MCM) is useful for describing concurrency and contextual switches of general workflow, while clarifying both information and material flows within the task. This paper is an extended version of a paper entitled, "Collaboration Task Analysis by Identifying Multi-Context and Collaborative Linkage," which appeared in Con current Engineering in 1999. We propose Collaborative Linkage Map (CLM), which is complementary to MCM, and configuration process of CLM to integrate it with MCM. The purpose of CLM is to specify state transitions of resources, which are transferred among the collaborators in the general workflow. Those states of CLM are defined as a combination of Personnel-State Transition Diagram (P-STD), Material-STD (M-STD), and Collabora tion-STD (C-STD). P-STD shows state transitions of personnel and M-STD represents those of materials. C-STD describes the relation ship between P-STD and M-STD. The construction process of CLM is incremental. First, one P-STD and one M-STD are selected from a list of state transition diagrams (STDs). Next, Collaboration-State (C-S) is defined as a common state, which can be shared between P-STD and M-STD, is selected. Next, P-STD and M-STD is connected by C-S to build C-STD. Therefore, C-STD is obtained as a collection of the C-Ss. Finally, two P-STDs are selected, and one state, which can be shared between collaborators, is selected to be defined as Communication-Inventory (C-I).

Teaching resources in higher education are an important part of the talent cultivation system of higher education institutions. Dynamic management and optimal allocation of teaching resources are the focus of the construction of each school, which reflects the teaching, research, and management level of the school to a certain extent. In this paper, after combining the current situation and management problems of teaching resources in six schools in City A and putting forward constructive opinions, we further use data envelopment analysis to study the efficiency of teaching resource allocation in School H in City A and put forward the method of resource optimization.The input redundancy rate of School X1 in School H during the three years of 2021-2023 is 30.25%, 44.15%, and 55.26%, respectively. The data shows that the school has an excess of teacher inputs and there is a shortage of resources for teachers. For the current dynamic management and resource allocation problems of teaching resources in A city school, this paper argues that the management of school teaching resources is a long-term and complex organic whole, and needs to be reformed in terms of structure, resource integration and management, and to construct a high-quality and efficient management model. In terms of resource optimization and allocation, schools can optimize the talent resource allocation mechanism and other aspects to promote the full allocation of teaching resources in higher education.

Workflow systems have become a major vehiclefor easy and efficient development of scientific applications. This type of systems can benefit from the resource provisioningtechnology offered by the cloud computing. In fact, the latteroffers on-demand virtualized resources to its users. These virtualresources can be added and released dynamically. Also, usersare charged on a pay-per-use basis. How to make appropriatedecisions when allocating resources to the tasks and dispatchingthe computing tasks to resource pool has become the main issuein cloud computing. The amount of allocated resources affects theexecution time of the applications and the cost incurred by the user. In fact, resource under-provisioning will necessarily affectthe performance. In contrast, over-provisioning can result in idleinstances and cause additional costs. Then, efficient scheduling algorithms are required for selection of best suitable resources for task execution. This paper focuses on some of the important workflow scheduling strategies. It brings out an exhaustive survey of such strategies in cloud computing and includes a detailed classification of them. Then, it presents a comparative analysis of the studied approaches. Finally, it stands out a critical challenge for further research.

The rapid rise of microservice architecture poses severe challenges to workflow scheduling, resource allocation, and goal optimization. However, faults and failures usually happen during workflow running. To ensure the workflow's successful execution during scheduling microservices, this article proposes a dynamic workflow scheduling algorithm by integrating cognition cost and reliability QoS for using reinforcement learning (WS-CCR). First, we explore the 'restart policy' of containers in the Kubernetes architecture, which lays the foundation for our work that adopts the redundancy strategy to ensure workflow operation. Then we consider the cognitive cost based on the fact that users have a cognitive process for different microservices in selecting microservices. Additionally, another optimization goal is the reliability of workflows. On this basis, we design a reasonable reward function in reinforcement learning to generate dynamic strategies. Furthermore, following some generated strategies, our engine will schedule candidate microservices for tasks to execute step by step. A series of experiments on Alibaba and business areas workflows have proven the superior performance of our algorithm. Our WS-CCR can generate better Pareto solution sets than other baselines in terms of improving the reliability of running workflows. Finally, we give a case study to prove the practicability of our method.

Grids and peer-to-peer (P2P) networks have emerged as popular platforms for the next generation parallel and distributed computing. In these environments, resources are geographically distributed, managed and owned by various organizations with different policies, and interconnected by wide-area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, and dynamic resource conditions. In these dynamic distributed computing environments, it is hard and challenging to carry out resource management design studies in a repeatable and controlled manner as resources and users are autonomous and distributed across multiple organizations with their own policies. Therefore, simulations have emerged as the most feasible technique for analyzing policies for resource allocation. This paper presents emerging trends in distributed computing and their promises for revolutionizing the computing field, and identifies distinct characteristics and challenges in building them. We motivate opportunities for modeling and simulation communities and present our discrete-event grid simulation toolkit, called GridSim, used by researchers world-wide for investigating the design of utility-oriented computing systems such as Data Centers and Grids. We present various case studies on the use of GridSim in modeling and simulation of Business Grids, parallel applications scheduling, workflow scheduling, and service pricing and revenue management.

Summary form only given. Grids and peer-to-peer (P2P) networks have emerged as popular platforms for the next generation parallel and distributed computing. In these environments, resources are geographically distributed, managed and owned by various organizations with different policies, and interconnected by wide-area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, and dynamic resource conditions. In these dynamic distributed computing environments, it is hard and challenging to carry out resource management design studies in a repeatable and controlled manner as resources and users are autonomous and distributed across multiple organizations with their own policies. Therefore, simulations have emerged as the most feasible technique for analyzing policies for resource allocation. This paper presents emerging trends in distributed computing and their promises for revolutionizing the computing field, and identifies distinct characteristics and challenges in building them. We motivate opportunities for modeling and simulation communities and present our discrete-event grid simulation toolkit, called GridSim, used by researchers world-wide for investigating the design of utility-oriented computing systems such as data centers and grids. We present various case studies on the use of GridSim in modeling and simulation of business grids, parallel applications scheduling, workflow scheduling, and service pricing and revenue management.

Workflow scheduling for microservice has become an important and challenging research topic. To design a high-performance and reliable scheduling model, we propose a dependable workflow scheduling algorithm for microservice quality of service (QoS) based on deep-Q-Network, namely the DWSM. Firstly, we utilize the redundancy strategy based on the restart strategy of the Kubernetes container to optimize the dependability of workflow. Then this paper quantifies the dependability indicator and designs the reward function based on three QoS attributes including execution time, resources consumption and dependability to generate the scheduling strategy through DQN. Finally, we utilize a real-world data set to evaluate our algorithm and compare it with several state-of-art baselines including heterogeneous earliest-finish-time (HEFT), greedy algorithm and nondominated sorting genetic algorithm (NSGA-III). Experimental results show that our DWSM algorithm achieves higher performance in dependability and saves more CPU resources. In addition to simulation, we have implemented it on a workflow engine and deployed real workflow cases to test its effectiveness in practice.

With the improvement of people's health awareness, the state increases the construction of public sport facilities, which complicates the allocation and management of resources. The existing spatial spectrum estimation method cannot eliminate the relevant interference data, resulting in duplicate data in the results of resource allocation and management, reducing the accuracy of resource allocation and management. In order to optimize the allocation and management of public sport facilities resources, this study proposes a spatial spectrum estimation method, which is used to deeply tap the potential information in public sport facilities resources and optimize the allocation and management. First, analyze the resources of public sport facilities and put forward the feature vector of allocation and management optimization. Then, use the spatial spectrum estimation method to learn the test samples, get the optimal threshold and weight, and build the resource allocation and management model of public sport facilities. Finally, the accuracy of spatial spectrum estimation method is 98% and the variation range is (0, 10), which is better than the accuracy of the original algorithm is 80% and the variation range is (0, 20). Moreover, change of the spatial spectrum estimation method is smoother, and the correlation between various analyses is better. In unit time, the amount of configuration and management data of spatial spectrum estimation method is higher than that of existing algorithms, which indirectly indicate that the configuration and management time of the spatial spectrum estimation method is short. At the same time, unstructured data account for a large proportion of the data tested this time. Therefore, the accuracy and variation range of the spatial spectrum estimation method is good, which is suitable for the construction of public sport facilities and realizes the optimization of resource allocation and management.

In this paper, we propose a system architecture for H.264/AVC video embedding transcoder (VET). In addition, the proposed platform-based design can seamlessly combine the MW-VET and decoder such that it can be dynamically configured to perform video decoding and transcoding alternatively or simultaneously. Furthermore, we perform the pruned design space exploration on the design of inter/intea prediction and the on-chip data bus width. Our proposed architecture provides a better tradeoff among execution cycles, hardware cost, resource utilization, and video quality because of the reconfigurable processing modules and the hybrid pipelining. As compared to the cascaded pixel domain transcoder that has the highest complexity, our hardware efficient VET can significantly reduce the hardware cost while maintaining similar rate-distortion performance. Finally, the proposed architecture is verified at system level using transaction level modeling (TLM) technique. From the simulation results, the proposed architecture with the best tradeoff configuration can achieve a transcoding rate up to 358 frames per second for SD video source while clocking at 162MHz.

Service-oriented architecture (SOA) provides the flexibility of dynamically composing business processes in enterprise computing. However, they must be enhanced to support real-time activities in future SOA applications such as media streaming, control applications, cyber-physical, and intelligent vehicle systems. In this paper, we present the RT-Llama SOA middleware to support predictability in real-time processes. Given a user-specified service process and deadline, the RT-Llama resource management system will reserve resources in advance for each service in the process to ensure the process can meet its end-to-end deadline. RT-Llama provides a global resource manager (GRM) to compose and reserve services in a business process. The GRM is facilitated by an efficient data structure that keeps track of the utilization of local resources. The data structure, called dTBTree, is updated by each local resource manager (LRM) periodically and cached in the Host Utilization Repository in GRM. We have implemented RT-Llama's resource management components so that they can efficiently adapt to dynamic real-time environments.

<span>Recent years have witnessed a great interest in scientific applications with large data and processing-intensive, so cloud computing is used which provides the resources needed to implement and run these applications. One of the challenges in the management of scientific workflow applications is scheduling them to solve many combinatorial optimization problems, including reducing execution time, cost, resource utilization, and energy <span>consumption. Due to the fact that the iterated local search algorithm (ILS) has been successfully applied to solve many combinatorial optimization problems, this paper investigates the performance of ILS in solving the scientific workflow scheduling problem which is a highly constrained problem. The main components that are different from one problem to others are the ILS parameters, local search, and perturbation, which must be</span> <span>carefully designed. The performance of the standard ILS has been examined and compared with the latest technology. The experimental results show that the proposed algorithm (ILS) obtained good results compared to the best-known results in the literature. This is due to the ILS being an adaptable metaheuristic, which can be simply adapted to different search situations and instances.</span></span>