Energy Efficient Scheduling of MapReduce over Big Data

The majority of large-scale data intensive applications carried out by information centers are based on MapReduce or its open-source implementation, Hadoop. Such applications are carried out on rich clusters requiring ample amounts of energy, helping the energy costs an appreciable fraction of the data centers overall costs. Therefore, reducing the energy consumption when carrying out each MapReduce task is a critical worry for data centers. In this paper, we advise a framework for mending the energy ef?ciency of MapReduce applications, while satisfying the (SLA) Service Level Agreement. We ?rst prototype the problem of energy-aware scheduling of a single MapReduce task as an Integer Program. After that we court two algorithms, known as MapReduce scheduling algorithms and load scheduling algorithm, that ?nd the assignments of map and reduce tasks to the machines plenty in order to reduce the energy consumed when carrying out the application. The energy aware con?guration and scheduling will improve the energy e?ciency of MapReduce clusters thus help in reduction of the service costs of the data-centers

DReAM: An approach to estimate per-Task DRAM energy in multicore systems

Accurate per-task energy estimation in multicore systems would allow performing per-task energy-aware task scheduling and energy-aware billing in data centers, among other applications. Per-task energy estimation is challenged by the interaction between tasks in shared resources, which impacts tasks’ energy consumption in uncontrolled ways. Some accurate mechanisms have been devised recently to estimate per-task energy consumed on-chip in multicores, but there is a lack of such mechanisms for DRAM memories. This article makes the case for accurate per-task DRAM energy metering in multicores, which opens new paths to energy/performance optimizations. In particular, the contributions of this article are (i) an ideal per-task energy metering model for DRAM memories; (ii) DReAM, an accurate yet low cost implementation of the ideal model (less than 5% accuracy error when 16 tasks share memory); and (iii) a comparison with standard methods (even distribution and access-count based) proving that DReAM is much more accurate than these other methods.Peer ReviewedPostprint (author's final draft

Autonimic energy-aware task scheduling

International audienceThe increasing processing capability of data-centers increases considerably their energy consumption which leads to important losses for companies. Energy-aware task scheduling is a new challenge to optimize the use of the computation power provided by multiple resources. In the context of Cloud resources usage depends on users requests which are generally unpredictable. Autonomic computing paradigm provides systems with self-managing capabilities helping to react to unstable situation. This article proposes an autonomic approach to provide energy-aware scheduling tasks. The generic autonomic computing framework FrameSelf coupled with the CloudSim energy-aware simulator is presented. The proposed solution enables to detect critical schedule situations and simulate new placements for tasks on DVFS enabled hosts in order to improve the global energy efficiency

Energy-aware task scheduling in data centers using an application signature

Data centers are power hungry facilities. Energy-aware task scheduling approaches are of utmost importance to improve energy savings in data centers, although they need to know beforehand the energy consumption of the applications that will run in the servers. This is usually done through a full profiling of the applications, which is not feasible in long-running application scenarios due to the long execution times. In the present work we use an application signature that allows to estimate the energy without the need to execute the application completely. We use different scheduling approaches together with the information of the application signature to improve the makespan of the scheduling process and therefore improve the energy savings in data centers. We evaluate the accuracy of using the application signature by means of comparing against an oracle method obtaining an error below 1.5%, and Compression Ratios around 39.7 to 45.8

Designing Parametric Constraint Based Power Aware Scheduling System in a Virtualized Cloud Environment

The increasing rate of the demand for computational resources has led to the production of largescale data centers. They consume huge amounts of electrical power resulting in high operational costs and carbon dioxide emissions. Power-related costs have become one of the major economic factors in IT data-centers, and companies and the research community are currently working on new efficient power aware resource management strategies, also known as 201C;Green IT201D;. Here we propose a framework for autonomic scheduling of tasks based upon some parametric constraints. In this paper we propose an analysis of the critical factors affecting the energy consumption of cloud servers in cloud computing and consideration to make performance very fast by using Sigar API to solve speed problems. In PCBPAS we impose some parametric constraints during task allocation to the server that can be adjusted dynamically to balance the server2019;s workloads in an efficient way so that CPU consumption can be improved and energy saving be achieved

Energy and performance-optimized scheduling of tasks in distributed cloud and edge computing systems

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival and heterogeneity of tasks. This dissertation proposes a class of energy and performance-optimized scheduling algorithms built on top of several intelligent optimization algorithms. This dissertation includes two parts, including background work, i.e., Chapters 3–6, and new contributions, i.e., Chapters 7–11. 1) Background work of this dissertation. Chapter 3 proposes a spatial task scheduling and resource optimization method to minimize the total cost of CDCs where bandwidth prices of Internet service providers, power grid prices, and renewable energy all vary with locations. Chapter 4 presents a geography-aware task scheduling approach by considering spatial variations in CDCs to maximize the profit of their providers by intelligently scheduling tasks. Chapter 5 presents a spatio-temporal task scheduling algorithm to minimize energy cost by scheduling heterogeneous tasks among CDCs while meeting their delay constraints. Chapter 6 gives a temporal scheduling algorithm considering temporal variations of revenue, electricity prices, green energy and prices of public clouds. 2) Contributions of this dissertation. Chapter 7 proposes a multi-objective optimization method for CDCs to maximize their profit, and minimize the average loss possibility of tasks by determining task allocation among Internet service providers, and task service rates of each CDC. A simulated annealing-based bi-objective differential evolution algorithm is proposed to obtain an approximate Pareto optimal set. A knee solution is selected to schedule tasks in a high-profit and high-quality-of-service way. Chapter 8 formulates a bi-objective constrained optimization problem, and designs a novel optimization method to cope with energy cost reduction and QoS improvement. It jointly minimizes both energy cost of CDCs, and average response time of all tasks by intelligently allocating tasks among CDCs and changing task service rate of each CDC. Chapter 9 formulates a constrained bi-objective optimization problem for joint optimization of revenue and energy cost of CDCs. It is solved with an improved multi-objective evolutionary algorithm based on decomposition. It determines a high-quality trade-off between revenue maximization and energy cost minimization by considering CDCs’ spatial differences in energy cost while meeting tasks’ delay constraints. Chapter 10 proposes a simulated annealing-based bees algorithm to find a close-to-optimal solution. Then, a fine-grained spatial task scheduling algorithm is designed to minimize energy cost of CDCs by allocating tasks among multiple green clouds, and specifies running speeds of their servers. Chapter 11 proposes a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are met in cloud-edge computing systems. A single-objective constrained optimization problem is solved by a proposed simulated annealing-based migrating birds optimization. This dissertation evaluates these algorithms, models and software with real-life data and proves that they improve scheduling precision and cost-effectiveness of distributed cloud and edge computing systems

Power Management Techniques for Data Centers: A Survey

With growing use of internet and exponential growth in amount of data to be stored and processed (known as 'big data'), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy Efficiency, Green Computing, DVFS, Server Consolidatio