Mehdi Sheikhalishahi

Applications of neural-based spot market prediction for cloud computing

Advances in service-oriented architectures (SOA), virtualization, high-speed networks, and cloud computing has resulted in attractive pay-as-you-go services. Job scheduling on these systems results in commodity bidding for computing time. This bidding is institutionalized by Amazon for its Elastic Cloud Computing (EC2) environment and bidding methods exist for other cloud-computing vendors as well as multi-cloud and cluster computing brokers such as SpotCloud. Commodity bidding for computing has resulted in complex spot price models that have ad-hoc strategies to provide demand for excess capacity. In this paper we will discuss vendors who provide spot pricing and bidding and present a predictive model for future spot prices based on neural networking giving users a high confidence on future prices aiding bidding on commodity computing.

An approximate ϵ-constraint method for a multi-objective job scheduling in the cloud

Cloud computing is a hybrid model that provides both hardware and software resources through computer networks. Data services (hardware) together with their functionalities (software) are hosted on web servers rather than on single computers connected by networks. Through a device (e.g., either a computer or a smartphone), a browser and an Internet connection, each user accesses a cloud platform and asks for specific services. For example, a user can ask for executing some applications (jobs) on the machines (hosts) of a cloud infrastructure. Therefore, it becomes significant to provide optimized job scheduling approaches suitable to balance the workload distribution among hosts of the platform. In this paper, a multi-objective mathematical formulation of the job scheduling problem in a homogeneous cloud computing platform is proposed in order to optimize the total average waiting time of the jobs, the average waiting time of the jobs in the longest working schedule (such as the makespan) and the required number of hosts. The proposed approach is based on an approximate @e-constraint method, tested on a set of instances and compared with the weighted sum (WS) method. The computational results highlight that our approach outperforms the WS method in terms of a number of non-dominated solutions.

A multi-dimensional job scheduling

With the advent of new computing technologies, such as cloud computing and contemporary parallel processing systems, the building blocks of computing systems have become multi-dimensional. Traditional scheduling systems based on a single-resource optimization, like processors, fail to provide near optimal solutions. The efficient use of new computing systems depends on the efficient use of several resource dimensions. Thus, the scheduling systems have to fully use all resources. In this paper, we address the problem of multi-resource scheduling via multi-capacity bin-packing. We propose the application of multi-capacity-aware resource scheduling at host selection layer and queuing mechanism layer of a scheduling system. The experimental results demonstrate performance improvements of scheduling in terms of waittime and slowdown metrics. A proposal for scheduling problem based on multi-capacity bin-packing algorithms.A proposal for host selection and queuing based on multi-resource scheduling.Getting better waittime and slowdown metrics than the state of the art scheduling.

Autonomic resource contention-aware scheduling

The complexity of computing systems introduces a few issues and challenges such as poor performance and high energy consumption. In this paper, we first define and model resource contention metric for high performance computing workloads as a performance metric in scheduling algorithms and systems at the highest level of resource management stack to address the main issues in computing systems. Second, we propose a novel autonomic resource contention‐aware scheduling approach architected on various layers of the resource management stack. We establish the relationship between distributed resource management layers in order to optimize resource contention metric. The simulation results confirm the novelty of our approach.Copyright

Electrical Grid and Supercomputing Centers: An Investigative Analysis of Emerging Opportunities and Challenges

Some of the largest supercomputing centers (SCs) in the United States are developing new relationships with their electricity service providers (ESPs). These relationships, similar to other commercial and industrial partnerships, are driven by a mutual interest to reduce energy costs and improve electrical grid reliability. While SCs are concerned about the quality, cost, environmental impact, and availability of electricity, ESPs are concerned about electrical grid reliability, particularly in terms of energy consumption, peak power demands, and power fluctuations. The power demand for SCs can be 20 MW or more – the theoretical peak power requirements are greater than 45 MW – and recurring intra-hour variability can exceed 8 MW. As a result of this, ESPs may request large SCs to engage in demand response and grid integration.This paper evaluates today’s relationships, potential partnerships, and possible integration between SCs and their ESPs. The paper uses feedback from a questionnaire submitted to supercomputing centers on the Top100 List in the United States to describe opportunities for overcoming the challenges of HPC-grid integration.

Hierarchical Approach for Efficient Workload Management in Geo-Distributed Data Centers

Geographically distributed data centers (DCs) offer promising business opportunities to both big companies that own several sites and multi-owner inter-cloud infrastructures. In these scenarios, workload management is a particularly challenging task, since the autonomy of single DCs should be preserved while global objectives, such as cost reduction and load balance, should be achieved. In this paper, a hierarchical approach for workload management in geographically distributed DCs is presented. The proposed solution is composed of two algorithms devoted to workload assignment and migration. Both algorithms are based on the computation of a simple function that represents the cost of running some workload in the different sites of the distributed DC. The framework requires a very limited exchange of state information among the sites and preserves the autonomy of single DCs and, at the same time, allows for an integrated management of heterogeneous platforms. Performance is analyzed for a specific infrastructure composed of four DCs, with two goals: 1) load balance and 2) energy cost reduction. Results show that the proposed approach smoothly adapts the workload distribution to variations of energy cost and load, while achieving the desired combination of management objectives.

Supercomputing Centers and Electricity Service Providers: A Geographically Distributed Perspective on Demand Management in Europe and the United States

Supercomputing Centers (SCs) have high and variable power demands, which increase the challenges of the Electricity Service Providers (ESPs) with regards to efficient electricity distribution and reliable grid operation. High penetration of renewable energy generation further exacerbates this problem. In order to develop a symbiotic relationship between the SCs and their ESPs and to support effective power management at all levels, it is critical to understand and analyze how the existing relationships were formed and how these are expected to evolve.

Hierarchical Approach for Green Workload Management in Distributed Data Centers

The efficient management of geographically distributed data centers has become an important issue not only for big companies that own several sites, but also due to the emerging of inter-Cloud infrastructures that allow heterogeneous data centers to cooperate. These environments open unprecedented avenues for the support of a huge amount of workload, but they need the definition of novel algorithms and procedures for their management, where scalability is a priority. The complexity derives by the size of the system and by the need for accomplishing several and sometimes conflicting goals, among which: load balancing among multiple sites, prevention of risks, workload consolidation, and reduction of costs, consumed energy and carbon emissions. In this paper a hierarchical approach is presented, which preserves the autonomy of single data centers and at the same time allows for an integrated management of heterogeneous platforms. The framework is purposely generic but can be tailored to the specific requirements of single environments. Performances are analyzed for a specific Cloud infrastructure composed of four data centers.

A Multi-capacity Queuing Mechanism in Multi-dimensional Resource Scheduling

With the advent of new computing technologies, such as cloud computing and contemporary parallel processing systems, the building blocks of computing systems have become multi-dimensional. Traditional scheduling algorithms based on a single-resource optimization like processor fail to provide near optimal solutions. The efficient use of new computing systems depends on the efficient use of all resource dimensions. Thus, the scheduling algorithms have to fully use all resources. In this paper, we propose a queuing mechanism based on a multi-resource scheduling technique. For that, we model multi-resource scheduling as a multi-capacity bin-packing scheduling algorithm at the queue level to reorder the queue in order to improve the packing and as a result improve scheduling metrics. The experimental results demonstrate performance improvements in terms of waittime and slowdown metrics.

Pervasive Cloud Computing Technologies: Future Outlooks and Interdisciplinary Perspectives

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