HPC in the Cloud Research Roundup
The top research stories of the week have been hand-selected from leading scientific centers, prominent journals and relevant conference proceedings. In this week’s assortment, researchers tackle the distinctions between HPC and research clouds, present a way to use fuzzy logic to make clouds more efficient, and provide a review of grid security best practices.
Research in the Cloud, Australian-style
A new paper came out this week detailing the activities of the NeCTAR Research Cloud, which has been running at the University of Melbourne since February 2012. During that time, the system has attracted more than 1,650 users and supported more than 110 projects.
In addition to offering a window into a successful “research cloud,” the authors make some interesting observations regarding the distinctions between HPC and cloud computing that are worth noting.
“HPC can be seen as the forerunner to cloud computing,” they write. “Rather than utilising local desktop computation resources, HPC allowed users to take advantage of available compute cycles on a massive remote resource. Cloud computing achieves a similar outcome. Both HPC systems and cloud computing are based on clusters of computers interconnected by some high-speed network, often managed by a dedicated additional (head) node.”
This isn’t the usual definition of (enterprise-leaning) cloud, which tends to run on general-purpose, vanilla infrastructure. Also, what makes it a cloud and not remote HPC or HPC as a Service?
Let’s go back to the document for the answer:
“Cloud computing and HPC differ in that HPC systems are predominantly task based whereas cloud computing is more often characterized as Infrastructure as a service (IaaS). On HPC systems, users submit tasks to a queuing system, which then allocates resources to the task as they become available. User tasks all run in the same software environment. Cloud computing on the other hand allows the users to develop VMs with their chosen software environment, which they then submit to an allocation system that allocates them the resources they need.”
The statement seems to be making reference to a heterogenous subset of resources which are provisioned on demand via the use of virtual machines. Fair enough. But there are still further distinctions to follow:
“The major differences are that on HPC systems, users are guaranteed exclusive access to the allocated resources for a limited time and sharing is accomplished by having tasks wait on a queue until resources become available, while in the Cloud resources are shared by being oversubscribed, but VMs are allowed to be persistent. This leads to the two systems having different best use situations.
“HPC, as the name implies, is most suited to well defined and bounded computational problems, whilst Cloud is most suited to ongoing continuous loads. Cloud systems also have the capability to add VMs in a dynamic fashion to cope with varying demand in a way that HPC systems find difficult, and this makes them suited to many collaborative activities where demand is hard to predict (Cohen et al. 2013; Suresh, Ezhilchelvan, and Watson 2013).”
The paper was written by Bernard Meade in collaboration with co-authors Steven Manos, Richard Sinnott, Andy Tseng and Dirk van der Knijff, all from the University of Melbourne, and Christopher Fluke from Swinburne University of Technology. It was presented this week at THETA Australasia: the Higher Education Technology Agenda in Hobart, Tasmania.
Using Fuzzy Logic to Improve Datacenter Efficiency
When you hear cloud computing, what is the first thing that comes to mind? Yes, there are many types of cloud and a fair amount of debate about the term, but the basic idea is a shared pool of resources. This means that management and monitoring software are necessary to ensure smooth operation. The importance of datacenter management to cloud computing was highlighted in a recent journal article by M. Jaiganesh and A. Vincent Antony Kumar.
These computer scientists with the Department of Information Technology, PSNA College of Engineering and Technology in India propose an innovative approach to optimizing the efficiency of the datacenter in cloud computing with a focus on three factors: bandwidth, memory, and central processing unit (CPU) cycle. What makes their work different from the other software out there is their reliance on so-called fuzzy logic.
We constructed a fuzzy expert system model to obtain maximum Data Center Load Efficiency (DCLE) in cloud computing environments. The advantage of the proposed system lies in DCLE computing. While computing, it allows regular evaluation of services to any number of clients.
The authors assert that cloud service providers will need to double-down on datacenter management if they are to continue to meet the needs of next generation computing.
Their working definition of cloud is “the art of managing tasks and applications by altering the software, platform, and infrastructure and by organizing third party datacenters known as Cloud Service Providers (CSP) such as Yahoo!, Amazon, Google, and VMware.”
After the researchers determine the datacenter load efficiency using fuzzy modeling, they discuss their results. The work is highly technical, but in the final analysis, they believe that DCLE proved to be a valuable method for determining overall system utilization and provides a useful assessment of the system efficiency.
Revisiting Grid Security
Grid computing may have fallen out of fashion as a marketing term, but the distributed computing technologies that helped set the stage for today’s cloud are very much alive and well. And as with cloud or any IT system, security is a top concern for the grid community. It’s also the subject of recent paper from Malaysian researchers Saiful Adli Ismail and Zailani Mohamed Sidek. The duo provide a comprehensive review of current security issues in the grid computing arena.
In addition to presenting an overview of grid computing security, the paper also details types of grid security and depicts a prototypical architecture for grid computing security. The computer scientists wrote the paper with an eye toward shaping “future research in encryption, access controls, and other security solutions for the grid computing environment.”
As with most types of cloud architectures, grid represents a shared environment and as such it is necessary for the various parties to work together to overcome any risks, gaps and vulnerabilities that could jeopardize grid security.
The authors highlight and describe six main areas of grid security requirements: authentication, authorization, confidentiality, integrity, no repudiation and management. They also emphasize three essential services – authentication, authorization and encryption – without which grids are left unsecured and open to man-in-the-middle attacks.
While this paper mainly serves as an overview of best practices for grid security, the authors are also hoping to inspire other researchers to make contributions that advance grid security.