Minimum Dependencies Energy-Efficient Scheduling in Data Centers

The scheduling of parallel real-time tasks on multiprocessor systems is more complicated than the one of independent sequential tasks, specially for the Directed Acyclic Graph (DAG) model. The complexity is due to the structure of DAG tasks and the precedence constraints between their subtasks. The trivial DAG scheduling approach is to directly apply common real-time scheduling algorithms on DAGs despite their lack of compatibility with the parallel model. Another scheduling approach, which is called the stretching method, aims at transforming each parallel DAG task in the set into a collection of independent sequential threads that are easier to be scheduled. In this paper, we are interested in analyzing global preemptive scheduling of DAGs using both approaches by showing that they are not comparable when associated with Deadline Monotonic (DM) and Earliest Deadline First (EDF) scheduling algorithms. Then we use extensive simulations to evaluate their schedulability performance. To this end, we use our simulation tool YARTISS to generate random DAG tasks with many parameter variations so as to guarantee reliable experimental results.

An algorithm for network and data-aware placement of multi-tier applications in cloud data centers

With the fast development of information technology (IT) industry, the demand and market volume for off-line power supplies keep increasing, especially those for telecommunication, computer servers and data centers. As the capital expenditure was measured by the square footage occupied rather than power consumption, the development of high power density converter system is of greater interesting. The rising energy prices have resulted in the cost of power and cooling exceeding the purchase cost in less than two years. Therefore, highly efficient power conversion is required for the power converter system. Generally, the power supply unit (PSU) for power distribution system (PDS) in data center and telecom are the standard two-stage approach which normally consists of power factor correction (PFC) circuit and isolated DC-DC converter. The two-stage power conversion has demonstrated excellent performance and high reliability, since the design can be optimized for each stage. However, limitations to prevent the existing two-stage PSU to fulfill future requirements for the PDS in data center and telecom applications are revealed, and a very promising and fundamentally different approach with the single-stage isolated converter is proposed in this dissertation. The development of single-stage converters with the option of placing the energy storage outside of the PSU creates new degrees of freedom regarding e.g. simplified rectifier racks in telecom and data center. This provides tangible benefits in the form of space saving, better airflow for power unit in rectifier racks and improved lifespan. The three-phase isolated buck matrix-type rectifier, capable of achieving high power density and high efficiency, is identified as an excellent candidate for the medium power level (5 kW~10 kW) single-stage power supply design. Nevertheless, the matrix-type rectifiers are known for their relatively complex modulation and commutation techniques, and lack of ride-through capability such as the stringent case of one phase loss operation. This dissertation work provides comprehensive study on the commutation method and modulation scheme design for the isolated buck matrix-type rectifier. It aims to analyze the operation principle of the rectifier and propose viable modulation and commutation schemes for this rectifier under both three-phase and single-phase operation. The method is verified by the hardware experiments of the PSUs with high efficiency (> 98%) and high power density (> 70 W/in3 ) for 54 V and 380 VDC applications. The prototypes demonstrated in the experiments show the effectiveness of the proposed modulation and commutation schemes for industry.

Computational grids have recently attracted considerable attention as cost‐effective platforms for parallel processing. Generally, a parallel program carried out on such a platform can be characterized as a directed acyclic graph (DAG). To take full advantage of available resources in grids and achieve high computational performance, an effective DAG scheduling algorithm is indispensable. This study presents a novel constructive algorithm based on the Fast Ant System (FANT). The proposed algorithm, namely, the Fast Ant System for Task Matching and Scheduling (FANT‐TMS) algorithm, determines the scheduling order of all tasks in a parallel program and, then, allocates these tasks to appropriate processing elements in a computational grid, thereby minimizing total completion time of the parallel program. Performance of the FANTTMS algorithm is demonstrated by comparison with a genetic algorithm (GA)‐based scheduling technique in terms of overall schedule length of randomly generated DAGs. Experimental results demonstrate that the proposed algorithm outperforms the conventional GA‐based approach. Additionally, a novel local search strategy is devised that further enhances solution quality obtained with the FANT‐TMS algorithm.

Directed acyclic graph (DAG) scheduling is a well-known problem, because a DAG can be used to describe a wide range of complex applications, including scientific applications and parallel computing jobs. Most DAG scheduling algorithms were proposed to minimize the job makespan (i.e., execution time) on a multiprocessor computer or cluster. However, as the cost-driven public cloud services have become an attractive and popular platform for providing computing resources, cost minimization emerges as a new critical issue. Therefore, the objective of this work is to propose and solve the cost optimization problem for scheduling DAGs on an IaaS cloud platform where task scheduling must cope with resource provisioning to achieve the optimal solution. In this paper, we proposed both optimal and heuristic scheduling algorithms, and we evaluated them across a variety of DAGs using the price model from EC2. Comparing to other cost-oblivious DAG schedules that aim to minimize makespan or resource usage, the results show that our cost-aware heuristic algorithm can reduce cost by 20---50 % and achieve a cost within x1.16 of the optimal one.

With ever more complex functionalities being implemented in emerging real-time applications, multiprocessor systems are demanded for high performance, and directed acyclic graphs (DAGs) are used to model functional dependencies. In this work, we study a single periodic non-preemptive DAG running on a homogeneous multiprocessor platform, which is a common setup in many domains, such as automotive, robotics, and industrial automation. Aiming to reduce the makespan of the DAG and provide a tight yet safe bound, our contributions involve the exploitation of node-level parallelism and inter-node dependency, which are the two key factors of a DAG topology. First, we introduce a concurrent provider and consumer (CPC) model that precisely captures the above two factors, and can be recursively applied when parsing a DAG. Building upon CPC, we propose a novel scheduling method focused on reducing the makespan that orders the nodes in the following sequence: (i) the critical path, (ii) early predecessor paths of the critical path, and (iii) longer paths. Secondly, new response time analysis is presented, which provides a generic bound for any execution order of the non-critical nodes and a specific (tighter) bound for a fixed such order. Comprehensive evaluation demonstrates that our scheduling approach and analysis outperforms the state-of-the-art methods.

A DAG scheduling scheme on heterogeneous computing systems using double molecular structure-based chemical reaction optimization

Traditional real-time task scheduling strategies focus on solely on the CPU, and do not take in account memory access and cache effects. In this paper we propose real-time parallel scheduling (PRTTS) strategy on multicore platforms. Each task is represented as a directed acyclic graph (DAG). Tasks priorities are assigned according to task periods (rate monotonic, RM). In PRTTS scheduling strategy priorities of tasks which access memory are promoted over priorities of tasks not accessing memory. Tasks which read/write data in cache dynamically have their priority increased above all tasks. The results of simulation experiment show that proposed scheduling strategy offers better performance in terms of core utilization and schedulability rate of tasks.

In data center, applications which are typically multi-server, high-parallel, long-running like web-related applications are really common. They always compute over large-scale dataset and consume plenty of energy. Up until now, most researches focuses on trading a loss of performance for energy saving. However, managing the power is more important compared with reducing it. In this paper, we add energy consumption to the list of managed resources and help managers to control power profile of applications in data center. Tenants put forward the power budget and corresponding response time target of their own applications before they rent servers. We designed strategies to make every application in data center running under a global power cap and their own power budget. We first propose a Global Feedback Power Allocation Policy to periodically allocate the global power cap among the applications. We also devise a Local Efficient Power Policy to determine application-level power cap and allocate it among servers running the application. Extra budget during each period can be used in the rest of the tenancy to increase the application-level power cap to minimize the response time. We use Shell, WWW, DNS and Mail workloads to evaluate our policy.

Global scheduling of real time tasks with precedence constraints has received significant attention recently. Specifically, on multiprocessor systems, the directed acyclic graph (DAG) model well-represents parallelizable workloads with precedence constraints. Hence, many studies have recently been published analyzing DAG structures using decomposition and window-based analysis methods. We identified a set of schedulable DAG taskets that are hard-to-analyze using state-of-the-art window-based schedulability tests. Additionally, we observe that the window-based test solely depends on one structural feature of the DAG taskset, which raises concerns about its pessimism in many settings. In this paper, to address these concerns, for hard real-time sporadic implicit-deadline DAG tasksets, we perform LAG-based schedulability analysis, which offers a more holistic view of the taskset than the window-based analysis. We present a companion utilization-based schedulability test to the state-of-the-art, which considers additional structural features of DAGs. Our results show that by considering such features, our LAG-based test empirically dominates the state-of-the-art test on over 80% of the evaluated DAG tasksets. Moreover, combining our LAG-based test in conjunction with the window-based tests can achieve high schedulability in many cases.

Large data centers consume large amounts of electricity. Estimating the energy consumption in a data center can be of great importance to data centers administrators in order to know the energy-consuming tasks and take actions for reducing the total energy consumption. Smart workflow mechanisms can be built to reduce the energy consumption of data centers significantly. In this paper, we are investigating the factors that affect the energy consumption of scientific applications in data centers. We also use eight machine learning methods to estimate the energy consumption of multi-threaded scientific applications. Extensive computational results on a computer with 20 cores show that the CPU usage is the most important parameter in the power consumed by an application. However, better results can be obtained when the CPU utilization is combined with other parameters. We generate various regression models that predict the energy consumption of an application with an average accuracy of 99%. Simpler models with one and two parameters can achieve comparable accuracy with more complex models. We also compare various machine learning methods for their ability to obtain accurate predictions using as few parameters as possible.

This paper proposes a directed acyclic graph (DAG) scheduling algorithm for cluster-based many-core architecture. Most of DAG scheduling methods that consider multiple processors and communication delays use a heuristic approach because it is difficult to shorten a schedule length (i.e.,makespan). Unfortunately, existing heuristic algorithms do not consider tasks that require a large number of computational resources. Such tasks typically need to be offloaded to a large-scale computational resource such as a many-core system or graphics processing unit (GPU). Therefore, we propose a DAG scheduling algorithm that uses a cluster-based many-core architecture to offload such computation; here, we use Kalray MPPA-256 as the many-core architecture. Our algorithm divides many-core computational resources for such tasks. Comparing our results with existing algorithms, we succeeded in shortening makespan with many types of DAGs while also considering Amdahl's law. We also compared the deadline miss ratio and execution time of our algorithm to existing algorithms, and show that the proposed algorithm is superior to the existing algorithms for any evaluation metrics.

With the emerging diverse applications in data centers, the demands on quality of service in data centers also become diverse, such as high throughput of elephant flows and low latency of deadline-sensitive flows. However, traditional TCPs are ill-suited to such situations and always result in the inefficiency (e.g. missing the flow deadline, inevitable throughput collapse) of data transfers. This further degrades the user-perceived quality of service (QoS) in data centers. To reduce the flow completion time of mice and deadline-sensitive flows along with promoting the throughput of elephant flows, an efficient and deadline-aware priority-driven congestion control (PCC) protocol, which grants mice and deadline-sensitive flows the highest priority, is proposed in this paper. Specifically, PCC computes the priority of different flows according to the size of transmitted data, the remaining data volume, and the flows' deadline. Then PCC adjusts the congestion window according to the flow priority and the degree of network congestion. Furthermore, switches in data centers control the input/output of packets based on the flow priority and the queue length. Different from existing TCPs, to speed up the data transfers of mice and deadline-sensitive flows, PCC provides an effective method to compute and encode the flow priority explicitly. According to the flow priority, switches can manage packets efficiently and ensure the data transfers of high priority flows through a weighted priority scheduling with minor modification. The experimental results prove that PCC can improve the data transfer performance of mice and deadline-sensitive flows while guaranting the throughput of elephant flows.

Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation.

Cloud computing has been a cost-efficient paradigm for deploying various applications in datacenters in recent years. Therefore, efficient provisioning for virtual data center (VDC) requests from different service providers (SPs) over physical data centers plays a vital role in improving the quality of service (QoS) and reducing the operational cost of SPs. Therefore, a significant attention has been paid for the VDC provisioning problem. However, few approaches have been proposed for the problem of reliable VDC embedding across multiple data centers, as most of them only consider the problem of VDC mapping within a single data center. In this paper, we study the problem of QoS-aware VDC provisioning across multiple data centers, such that the total bandwidth consumption in the inter-data center backbone network is minimized while satisfying the reliability requirement of each VDC request. We formulate this problem as a mathematical optimization problem by using integer linear programming (ILP) and propose an efficient heuristic algorithm called reliable VDC embedding (RVDCE) algorithm to solve this NP-hard problem. The simulation results show that the proposed algorithm performs better in terms of blocking ratio, CPU resource consumption, and bandwidth consumption of backbone network than the existing solution. In addition, this paper has also incorporated integrated security to minimize security vulnerabilities seen in other similar approaches. Apart from demonstrating how to resolve security challenges in our VDC proposal, cost calculations have been implemented to demonstrate the robustness, resiliency, validity, and effectiveness of the VDC provisioning solution for cloud computing.